![\"\"](\"https://substackcdn.com/image/fetch/$s_!qJ8a!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa3ae9690-4868-49a3-aea3-98f9d25f605f_925x643.png\")
I think I may have discovered the \"evil conspiracy\" behind why academic search vendors are so reluctant to show us what happens on the semantic side of AI search.
Recently, I have been wondering about a strange asymmetry. I noticed that when academic search vendors use AI to expand lexical searches, they are almost always happy to show you what they did.
For example, Web of Science Smart Search will happily show you the Boolean search it used. The same is true of products such as EBSCO AI-assisted Search, scite Assistant, Primo Natural Language Search and many others.
Then you get to the semantic-search part, and suddenly the curtain comes down.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!zkhL!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F562e3e05-a286-4ab6-a7e2-d8a79f30012f_705x561.png\")
Take Web of Science Smart Search again. We are told it runs a hybrid search, meaning it runs both lexical and semantic searches in parallel, combines the results and then somehow ranks the combined list.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!TwCy!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faa7d058b-a740-4939-ac63-bfab6519a58a_1566x754.png\")
Yet we know almost nothing about what happens in the semantic-search portion, beyond the fact that it was done.
What exactly was searched? Did they expand your query before embedding it? Why all the secrecy?
Similarly, Scopus AI has a copilot that decides \"whether to run a vector search (aka Semantic Search) and/or a keyword search\". Yet again, all the interface displays is the expanded Boolean Search.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!9XSK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6c8875ff-317c-4f22-9396-ade5a1c4cf3f_720x405.png\")
Recently, while studying a new AI search system, I accidentally got a look backstage.
And I discovered a shocking \"truth\". The system was generating document-like passages for semantic retrieval rather than simply embedding my original query.
They were not just synonyms or alternative search terms. The generated passages could introduce assumptions that were never part of my question, or even contain outright false statements.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!L_vU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7d9bfe88-1242-4bbd-b28f-816b985835fb_1094x633.png\")
Imagine entering the following query:
Can Google Scholar be used alone for systematic reviews?
And finding out that, behind your back, the AI generates the following for expansion:
Google Scholar is not sufficient as a standalone source for systematic reviews because studies have shown that while it indexes almost all relevant research, it is difficult to retrieve all the relevant papers within the top 1000 results it allows access to.
Hold on a moment. I never said Google Scholar was not sufficient! That's precisely what I'm trying to find out! Why is the system apparently answering my question before it has even searched?
Now the reason for the \"conspiracy\" seemed obvious.
Vendors don't want us to find out that semantic search is being fed hallucinated AI-generated query strings!
Okay, okay, I was just being facetious here. There is NO evil conspiracy.
This is not to say that systems never do something similar to what I have described. They may generate text resembling relevant documents to help with retrieval, but that text could introduce assumptions that do not reflect the user's information need or even contain outright falsehoods. Such additions could potentially introduce bias if used in a traditional search.
But what I have described is not necessarily problematic. In fact, it resembles established techniques such as Query2doc and HyDE for expanding semantic searches.
While it may seem strange, the fact is:
A generated passage that is not entirely accurate or true can still be useful, perhaps even more useful than the original query, when embedded for semantic retrieval.
Confused? Let me start from the beginning.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!1BmU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd66bc5ce-4c44-468d-aed5-e3e4e70c7ebd_1076x612.png\")
The system itself was fairly typical of the current generation of AI-powered academic search tools.
There was a query interpretation step, probably involving an LLM. The system identified things such as entities that could be turned into filters, then ran a hybrid search combining lexical and semantic retrieval.
The results were merged and deduplicated, and an LLM was then used to classify them (by prompting) into relevance categories such as \"Very relevant\" and \"Relevant\".
None of this is especially unusual, and you can see parts of this process in many academic search systems. For example, using LLMs to extract relevance criteria and then classify results is something we see in AI2's Asta Find paper. But let's focus on the even more common idea of hybrid search.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!MtRG!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3433d222-7ba9-4526-bfc8-e1254ebb930f_1519x802.png\")
Hybrid search is becoming increasingly popular in the search industry. Typically, it involves running both lexical or keyword searches and semantic searches.
The logic is straightforward. Lexical search is good at matching words and phrases. Semantic search can retrieve documents that express similar ideas using different language.
This potentially gives you the best of both worlds. You just need a way to combine both sets of candidate results and rank or rerank them somehow.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!-KRe!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F71669c7b-13ad-479f-9a6f-11104b1c7ad7_917x615.png\")
So far, so good. But let's look at the interface (which I have mocked up) that was shown to me.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!BvEW!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd3bfc19b-99a4-4f22-8c5d-6a479002f98f_1672x941.png\")
When it comes to lexical search, we are shown quite a lot. We can see that it runs the original query and two expanded, nested Boolean search strategies (probably generated by an LLM).
But on the semantic side, we are told almost nothing, except that two searches were conducted.
Why this distinction?
When I was interviewed by the vendor, I naturally asked for more details about what was happening on the semantic search side.
Intriguingly, the vendor told me that an earlier version of the product had exposed more of the internal search process. However, users found it confusing, so some of the details were removed.
My reaction at the time was predictable. I did not understand this view at all. Surely more transparency would be better?
Perhaps they could even add an advanced mode for those who wanted more information.
But then I discovered some of the details that might have been hidden, and I began to empathise with why the vendor had chosen not to show everything.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!haIR!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9d07efad-32d8-423c-b4ae-35670f55083c_1094x614.png\")
While testing the reproducibility of the search, I ran into a completely different problem. No matter what I did, the system seemed determined to cache the results.
While troubleshooting this, on the advice of ChatGPT, I started recording the browser's network traffic using Chrome DevTools. We will revisit this in a future post.
This produces a HAR, or HTTP Archive, file: essentially a log of the requests and responses exchanged between your browser and the servers behind the application.
As a side effect, this exposed considerably more of the search pipeline than the interface itself did.
The logs confirmed that the system was running:
My original lexical query;
Two additional nested Boolean searches;
Two semantic searches.
There were also hints on many details. For example, about how many of the top results from each search were retained and how they were combined. But for now, let's focus on semantic search.
While there was very little information about the actual embedding model used, there was something more interesting.
I could see the actual text being used for semantic retrieval, and it was definitely not my original query.
Roughly speaking, the system appeared to be doing the following:
Original query + an LLM-generated short description of what a relevant document might look like
In short, it appeared to be taking my query and appending a chunk of LLM-generated text before creating the query embedding.
Those with some training or knowledge of information retrieval will recognise this as resembling a family of techniques such as Query2doc-style expansion or HyDE (Hypothetical Document Embeddings).
I spend quite a bit of time testing AI-powered academic search tools and trying to work out what is really happening behind their interfaces. If you find this kind of independent investigation useful, please consider buying me a coffee on Ko-fi. It helps support more investigations like this, which I share freely on this blog.
If you are already familiar with how embeddings and dense retrieval work, feel free to skip this section.
As I have noted many times on my blog, \"semantic search\" is not a single technique. It is better understood as an objective: retrieving or ranking items according to their estimated meaning.
It is typically contrasted with lexical or keyword search, where retrieval and ranking depend primarily on matches between terms in the query and terms in the documents.
The techniques used to achieve semantic search have changed over time. In recent years, semantic search has often, though not always, referred to dense retrieval or dense vector search.
![\"placeholder](\"https://substackcdn.com/image/fetch/$s_!0Bw7!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc74706b6-ec48-4ba6-9766-8ab2d78a023d_880x585.png\" "\\"placeholder")
In a typical dense retrieval system, the query is passed through an encoder model, often based on the Transformer architecture, to produce an embedding: a vector representation (a long series of numbers) intended to capture useful aspects of the query's meaning1.
Documents, passages or text chunks are encoded in a similar way, usually in advance during indexing. The query embedding can then be compared with these precomputed document embeddings.
Both query and document embeddings are represented as vectors: long sequences of numbers. The system calculates a similarity score between them, commonly using measures such as cosine similarity or dot product. Documents whose vectors are closer to the query vector are treated as better candidates for relevance.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!LWWA!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd5386548-ab18-4efb-84e0-a5afa947f88b_1448x1086.png\")
Important to note: the query and document do not necessarily need to contain the same terms to be considered similar. Rather, the encoder is able to convert texts that are similar in \"meaning\" into representations that end up close together in vector space.
Conceptually, for dense retrieval, what we are doing is this:
Query \u2192 embedding \u2192 find nearby document embeddings
Semantic search via embeddings is inherently something of a black box
While a Boolean search like
(\"artificial intelligence\" OR \"machine learning\") AND libraries
allows you to inspect the search strategy to understand why some documents are retrieved and others are not. Embedding retrieval, however, is much less transparent.
Firstly, it is extremely difficult to interpret the long series of numbers representing each query or document. Secondly, even if I gave you the calculated similarity score between a query embedding and a document embedding, that would still not provide a satisfying, human-readable explanation of why they are close.
I have long expected this to be unavoidable.2 But surely vendors can at least tell us what text they embedded?
So why don't we just use the query as entered? While this can work in practice, one approach that can improve retrieval is to do the following:
Use an LLM to generate a document, or at least part of one, that might be relevant.
Use that generated text to help construct the query representation for retrieval.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!fiVe!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1c2a5b30-e3f2-45ac-bef1-6753c2a7a4b9_1672x941.png\" "\\"\\"")
The generated pseudo-document or hypothetical document is then used to help with retrieval.Query2doc appends the generated pseudo-document to the original query before passing the combined text through an encoder to create the query embedding3. HyDE directly generates the embeddings of the generated hypothetical documents (without the query) and in some variants will average that embedding with that of the original query embedding.
Another related technique is RAG-Fusion, used by Elsevier's Scopus AI. Rather than generating hypothetical documents as HyDE and Query2doc do, it generates multiple variations of the original query, runs separate vector searches and combines the results using Reciprocal Rank Fusion. Like HyDE and Query2doc, it can potentially introduce assumptions not present in the original query.
Empirically, this can work better than using the original query alone. Why?
One way of thinking about it is that the query itself is typically quite different, in terms of expression, length and so on, from a relevant document containing the answer. The logic, then, is that by generating a document that might be relevant, its embedding may end up closer to the embeddings of the desired documents than the original query embedding would.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!MwrY!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F814d7f41-1966-490e-8dc1-d22384c48ab9_1448x1086.png\")
Imagine your query is:
Can you use Google Scholar alone for systematic reviews?
And you get a fairly neutral hypothetical passage explaining what systematic reviews are, why comprehensive searching matters, and so on.
I think most of us would be okay with that.
But what if it generates something like this?
Google Scholar should not be used alone for systematic reviews because it only displays approximately 1,000 search results at most and lacks the advanced search functionality needed to retrieve all relevant studies.
This is, to my knowledge, true. But would using this passage for retrieval introduce bias into the results?
Or worse, what if it uses this?
Google Scholar can be used alone for systematic reviews because Tay(2024) has shown that more than 99% of papers eventually included in a SR can be found in Google Scholar's index.
For those unfamiliar with the literature, there is no such Tay (2024) paper, although real studies have reported very high coverage of studies included in systematic reviews within Google Scholar.
That said, the problem is that being indexed is not the same as being retrievable through a search. Google Scholar restricts access to roughly the first 1,000 results, so even if most relevant papers are in its index, there is no guarantee that a search will surface them.
Surely that last hypothetical passage would produce horrible results?
Not necessarily.
Remember, the system is not necessarily treating the generated text passage as a factual answer. It is using the text to construct a representation for retrieval.
Even though the passage contains a fabricated citation and a potentially misleading conclusion, it also contains concepts highly relevant to the original question: Google Scholar, systematic reviews, database coverage, included studies and comprehensiveness.
It is entirely possible that embedding this passage would retrieve useful papers discussing Google Scholar's suitability for systematic reviews, including papers that argue against using it alone.
But what about bias? Could generating a hypothetical document arguing that Google Scholar cannot be used alone for systematic reviews favour papers reaching that conclusion over those arguing the opposite?
![\"\"](\"https://substackcdn.com/image/fetch/$s_!xwOP!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0cba71f0-2374-444a-8e78-cc05ccdaefa1_1448x1086.png\")
Again, not necessarily. Some research has found that neural retrievers, particularly bi-encoders, can be surprisingly poor at distinguishing between statements that differ only in negation. This suggests that simply reversing a claim might have less influence on retrieval than we would intuitively expect.
Still, this does not mean there is no risk of bias. Our hypothetical passages differ in more than just their conclusions. They also emphasise different concepts, which might influence which documents are retrieved and how they are ranked.
Whether that actually happens is something we would need to test empirically. We cannot tell simply by inspecting the generated text.
In fact, it is conceivable that a hypothetical passage containing entirely correct statements could produce worse retrieval results than one containing factual errors. After all, factual accuracy and usefulness for retrieval are not the same thing4. What matters is whether the generated passage helps the system retrieve relevant documents, although factual errors could certainly make that harder.
There is an old search tip that sounds superficially similar to what these systems are doing.
The advice goes something like this: when searching Google, think about what words or phrases a webpage answering your question might contain, then search using those terms.
Suppose I want to know whether Google Scholar can be used alone for systematic reviews.
I might reason that relevant papers would contain terms such as coverage, recall, comprehensiveness, included studies and search limitations, then search using those terms.
This makes sense and has a clear resemblance to what HyDE is doing.
In both cases, we are trying to anticipate what relevant documents might look like and search accordingly5?
![\"\"](\"https://substackcdn.com/image/fetch/$s_!idM2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F47b1ccf7-85b5-4b28-be67-584e85b79e36_1448x1086.png\")
There is, however, an important distinction here.
With the Google trick, I am predicting words or phrases that I expect to literally occur in relevant documents. If I guess incorrectly, I may damage the search, which is one reason why using LLMs to generate nested Boolean searches does not always produce the best results6.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!E1o8!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7f4cd92f-3899-475f-99ea-de058a43c919_1672x941.png\")
With HyDE, even if the generated document is factually wrong, its embedding can still be useful if it ends up closer to the embeddings of relevant documents than the original query embedding would.
The hope is that this richer, document-like representation lands somewhere near real documents discussing those concepts.
The fact that a generated passage reaches the wrong conclusion about Google Scholar does not necessarily prevent that from happening.
Of course, I am not saying that a hallucinated passage cannot push the query embedding further away from relevant documents.
In fact, some literature suggests that such expansion techniques tend to help weaker retrievers but may hurt stronger ones. They may also work less well in areas where the LLM's knowledge is limited. As always in information retrieval, whether a technique improves performance depends on factors such as the retriever, the types of queries and the dataset being searched. Ultimately, these are empirical questions that should be settled through testing (which hopefully the vendor has done) rather than decided a priori.
The point is simply that spotting a hallucination does not, by itself, tell us how serious the problem is.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!J4mU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1ab2b2a3-99f6-4db2-8fe7-ec94f8137cc1_1448x1086.png\")
This brings us back to my supposed conspiracy.
Imagine the interface shows you:
\"Google Scholar\" AND (\"systematic review\" OR \"evidence synthesis\") AND (coverage OR recall OR limitations)
A librarian might question why certain terms were included, but they would still recognise what they were looking at and feel comfortable with it.
Now imagine the interface instead shows:
Google Scholar can be used alone for systematic reviews because Tay (2024) demonstrated that more than 99% of studies eventually included in systematic reviews can be found in Google Scholar's index.
I suspect the reaction would be much stronger.
Why is the system citing a paper that doesn't exist? Why has it apparently decided that Google Scholar is sufficient? Is it now biased towards papers supporting that conclusion?
These are very reasonable questions if you interpret the passage as an answer or a literal statement of the search criteria.
But in the world of semantic embeddings, it is not necessarily either. It may simply be an intermediate representation used to create an embedding.
Perhaps part of the problem is that what happens inside modern semantic search no longer maps neatly onto what librarians traditionally think of as a query.
Modern AI search really does expand your query.
The surprising part is that making things up isn't necessarily the problem.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!JYE_!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F97e4bb7c-1660-4c53-aa40-942acf73f813_1448x1086.png\")
And this, I think, explains at least part of the strange asymmetry I keep seeing in AI search interfaces. Even if vendors wanted to show such details, many users, including librarians, might find them confusing7.
The more details vendors show about the search process, the greater the chance that they will open a can of worms by raising questions and concerns among users and librarians. From the vendor's perspective, the safer option may be not to show them.
The other part of the issue is that modern information retrieval is largely driven by empirical experimentation. You can propose a perfectly logical change that should improve retrieval, but if the experiments show that it doesn't work, you shouldn't use it. The reverse is also true. Some techniques may seem counterintuitive to librarians but work well empirically8.
While I can understand how vendors might feel, I still don't think they should hide all the details. We have a legitimate reason to want to know what is happening under the hood.
For librarians, the practical implication is this: resist the instinct to judge the semantic side of AI search by the same standards as Boolean. Spotting a hallucination in a query expansion is not evidence that a system is broken. The more useful question to ask is not \"does the generated query look right?\" but \"does the system consistently surface relevant results?\" That requires empirical testing against known relevant papers, not query inspection.
For librarians supporting systematic reviews the stakes are higher of course given the higher standards of transparency and reproducibility required.
What do you think?
Some might argue that showing the expanded query used to generate the query embedding is not particularly important. But the question applies more broadly to all aspects of the search pipeline.
Would you like vendors to show every detail, even if you don't fully understand it or think some of the techniques are strange?
If you found this essay on what AI-powered academic search tools are really doing behind the scenes educational, please consider supporting my work.
How is the encoder model able to generate representations that capture something resembling \"meaning\"? Encoder models learn useful language representations during pretraining on large text corpora, then are typically fine-tuned for retrieval so that relevant query-document pairs receive more similar vector representations than irrelevant ones.
You can get more transparency using multi-vector or late-interaction methods such as ColBERT, or learnt sparse representation methods such as SPLADE, but these are currently not commonly used.
Both techniques use very similar prompts to generate documents that Query2doc calls pseudo-documents and HyDE calls hypothetical documents. In practice, there is little difference between the generated texts themselves. The main distinction is that Query2doc was designed as a general query-expansion method. The query and expanded text can be used for lexical matching as well as retrieval via embeddings. HyDE was designed specifically for embedding-based retrieval.
Factual accuracy and retrieval effectiveness are different properties, but that does not mean they are statistically independent or even negatively correlated! For example, Yoon et al. (2025) found that hypothetical-document expansion improved retrieval mainly when generated passages contained information supported by the actual evidence, raising concerns that some reported gains might reflect knowledge leakage rather than better retrieval.
Notice this technique also risks bias!
I have long argued that using LLMs to generate Boolean is not very productive mostly because they tend to be poor at generating Boolean search strategies and even if they did do it decently it does not help average searchers who already know how to do so. A recent paper by San Diego State University documented how difficult it was to get the LLM to generate reasonable boolean just with prompt engineering.
I can imagine librarians or researchers unfamiliar with information retrieval becoming upset when they see what is happening and assuming that the search is broken.
Of course, it is important that a technique works generally and not just on limited test sets.
There seems to be s a lot of enthusiasm for AI powered tools for research these days, if I look at the feeds that the tech industry controls. No doubt that these tools greatly reduce the effort for a literature review that sounds plausible. It’s great to get a nice set of studies as a starting point for a new project in a new research area. But in these cases we usually don’t know what we don’t see. So here’s a cautionary tale from a rare case where we do know the errors of omission in AI powered tools.
\n\n\n\nThe challenge I gave the AI-powered tools is difficult: I asked them to identify replications of previous experiments in the multidisciplinary field of research on charitable giving. Such replications are needles in a field of haystacks. But we found them by hand, with a multidisciplinary team of six scholars in a study on replications of research on charitable giving. We conducted a comprehensive manual search on Google Scholar and identified 48 replications of experiments. How many replications from the set of 48 can AI-powered tools identify?
\n\n\n\nNot many, it turns out. The best tool, Google Scholar Labs, found one third of them. Most tools – even popular ones such as Perplexity, find fewer than 10. LeapSpace, the tool that Elsevier is trying to sell to university libraries in the Netherlands, performed abysmally. Collectively the twelve tools I assessed identified exactly half of all replications that we had found by hand. But it gets worse. Many studies that the AI tools identified as replications were not replications at all. Prompting more elaborately with explanations and examples did not make the results better.
\n\n\n\n![\"\"](\"https://renebekkers.wordpress.com/wp-content/uploads/2026/10/12aitools.png\")
The news is not all bad. The tools also identified a number of new studies that we had not found because we had limited our search to keywords that returned a feasible number of results. The newly found replications used other terms than we had looked for. Another upside: at least the searches came back quickly. The slowest tool, Claude Science, took 35 minutes. The quickest tool returned results within a few seconds. The result: it could not find any of the replications.
\n\n\n\nThe results underscore the necessity of human review of results from AI-powered tools. At face value, the reports produced by the tools seem entirely plausible. However, human review of the results showed a high number of false positives, up to 70% of all results for LeapSpace. Conclusions based on the searches of the AI-powered tools are incomplete and incorrect. Without an accurate and efficient method to identify false positives the speed with which the tools concluded their work gives users the false impression that they can outsource identifying relevant previous work to AI-powered tools.
\n\n\n\nRead the full paper including the prompts and supplementary materials with all details and a link to the data and code here.
\n\n\n\n","doi":"https://doi.org/10.59350/gvwch-yhj37","guid":"https://renebekkers.wordpress.com/?p=4290","image":"https://renebekkers.wordpress.com/wp-content/uploads/2026/10/12aitools.png?w=975","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791590400,"rid":"wb9gx-88f29","summary":"There seems to be s a lot of enthusiasm for AI powered tools for research these days, if I look at the feeds that the tech industry controls. No doubt that these tools greatly reduce the effort for a literature review that sounds plausible.","tags":["AI","Data","Experiments","Household Giving","Meta Science"],"title":"Fast, but incomplete and inaccurate: twelve AI-powered tools for literature search","updated_at":1791665109,"url":"https://renebekkers.wordpress.com/2026/10/10/fast-but-incomplete-and-inaccurate-twelve-ai-powered-tools-for-literature-search/","version":"v1"}},{"document":{"authors":[{"affiliation":[{"id":"https://ror.org/0130frc33","name":"University of North Carolina at Chapel Hill"}],"contributor_roles":[],"family":"Eshun","given":"Solomon","url":"https://orcid.org/0009-0004-1128-4149"}],"blog":{"authors":null,"community_id":"22d41343-8a53-4671-9643-0efbface1b2d","created":1787184000,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/solomoneshun","favicon":"https://rogue-scholar.org/api/communities/22d41343-8a53-4671-9643-0efbface1b2d/logo","feed_format":"application/rss+xml","feed_url":"https://solomoneshun.com/posts/index.xml","filter":null,"generator":"Quarto","home_page_url":"https://solomoneshun.com/posts/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"solomoneshun","status":"active","subfield":"1804","title":"Solomon Eshun","updated":1791604800,"use_api":null},"blog_name":"Solomon Eshun","blog_slug":"solomoneshun","content_html":"\n\n\n\n\n\n\n\n\nI have written a lot of notes that I never saw again. Not because I deleted them. They were still somewhere on my computer, carefully saved inside a folder with a sensible name. The problem was remembering that they existed when I actually needed them. That is the strange thing about most note-taking systems. We spend a lot of time thinking about how to store information and surprisingly little time thinking about how we will find our way back to it.
\nYou read something interesting. You write a note. You choose a folder. Maybe you add a tag. Then you move on. Months later, you are working on something related and vaguely remember, I know I wrote something about this before. So you search, open a handful of notes that are not quite right. Eventually, you either find it after far too much searching or give up and recreate the idea from scratch. The note is still there. The problem is that it just never found its way back into your thinking.
![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig0.png\")
Obsidian approaches note-taking differently. Instead of treating notes as isolated documents that need to be carefully filed away, Obsidian encourages you to think about how ideas relate to one another. Suppose I am reading about confounding and create a note about it. Somewhere in that note, I mention g-computation. I can simply wrap g-computation in double brackets: [[g-computation]]. Those double brackets create a link. If I already have a note called g-computation, clicking the link takes me there. If I do not, Obsidian lets me create one. More importantly, the relationship works in the other direction too. When I eventually open my g-computation note, I can see that the confounding note links back to it. As I keep reading and writing, these connections accumulate and gradually becomes a network of ideas.
There is another reason I became comfortable with Obsidian. Your notes are simply Markdown files stored in an ordinary folder on your computer. They are not locked inside a proprietary database that only one application understands. Open your Obsidian vault in Finder or File Explorer and your notes are right there as .md files. You can open them with another text editor, move them, back them up, or manage them however you want. If you stopped using Obsidian tomorrow, your notes would still be yours and would still be readable.
Obsidian is also free to use. The features that make the application powerful, including plugins, themes, graph view, linking, and multiple vaults, are available without paying for a subscription. There are paid services such as Obsidian Sync for syncing your vault across devices and Obsidian Publish for publishing notes to the web, but they are optional conveniences. Check obsidian.md/pricing for current numbers, since they change.
\nAnd that brings me to this guide. I use Obsidian mainly for academic and research work, so that is the perspective I will use throughout this guide. You will see examples involving papers, methods, concepts, research projects, and ideas I want to revisit later. But there is nothing inherently academic about the system. The same approach can work for work projects, meeting notes, books, personal writing, recipes, or almost anything else you want to keep and connect.
\nI also do not want this to become one of those Obsidian tutorials where we install twenty-seven plugins, create fourteen folders, design an elaborate tagging taxonomy, and spend three hours building a system for taking notes before we have actually taken a note. We are going to start small. We will install Obsidian, create a vault, understand the few pieces of Markdown you actually need, create our first linked notes, and develop a simple structure for organizing them. From there, we can add templates, daily notes, plugins, backups, and some of the more powerful features, but only when there is a reason for them.
\nDownload Obsidian from obsidian.md for macOS, Windows, Linux, iOS, or Android. There is no account to create: the app opens straight into a prompt asking what you want to do. Choose Create new vault, give it a name, and pick where it lives on disk.
![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig2.png\")
A vault is a folder. Inside it are .md text files and a hidden .obsidian/ subfolder holding your settings. That is the entire architecture. Open the folder in Finder or Explorer right now and you will see your notes sitting there as ordinary files.
Open your vault and you will see a mostly blank window. Here is what you are looking at.
\n![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig1.png\")
On the left, the file explorer lists every note in your vault; folders here are real folders on disk. In the middle, the editor is where you write. On the right, the graph view shows your notes as dots and their links as lines.
A handful of shortcuts cover most of what you will do:
\n| Shortcut | \nWhat it does | \n
|---|---|
Ctrl/Cmd + N | \nNew note | \n
Ctrl/Cmd + O | \nQuick switcher \u2014 jump to any note by name | \n
Ctrl/Cmd + P | \nCommand palette \u2014 every command, searchable | \n
Ctrl/Cmd + E | \nToggle editing / reading view | \n
Ctrl/Cmd + Shift + F | \nSearch across the whole vault | \n
Ctrl/Cmd + click | \nOpen a link in a new pane | \n
If you learn only one, make it Ctrl/Cmd + O.
![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig7.png\")
Type three or four letters and the note you want is usually the top hit. Once you pass a few hundred notes this becomes how you navigate almost exclusively, and the file explorer turns into decoration.
\nObsidian notes are Markdown, but you rarely have to think about syntax. You type a few characters and the formatting appears live.
\n![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig6.png\")
The syntax you will actually use day to day is small:
\n# Heading 1\n## Heading 2\n\n**bold** *italic* `inline code`\n\n- bullet\n- [ ] unchecked task\n- [x] completed task\n\n> blockquote\n\n[[Link to another note]]\n[[Link to another note|shown as this text]]\n![[Embed another note entirely]]\n\n#tag #nested/tagTwo Obsidian-specific ones are worth calling out. The pipe in [[Note|display text]] lets the link read naturally in a sentence while still pointing at the right file. And ![[Note]] with a leading exclamation mark embeds the whole note inline rather than linking to it (useful for pulling a definition into a summary without duplicating it).
This is the part that matters. Create a note called Confounding and write a couple of sentences. Somewhere in the text, type two opening square brackets: [[.
A dropdown appears. Start typing G-computation. Even though that note does not exist yet, press Enter \u2014 Obsidian creates it the moment you click through.
Now open G-computation and look at the Linked mentions panel.
![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig3.png\")
You typed one link. You got two. That is the whole trick, and it compounds: every time you link a new note to an old one, the old note gets richer without you touching it.
\nNew users spend their first week building an elaborate folder hierarchy and their second week discovering it does not fit. Obsidian gives you three organising tools, and they answer different questions.
\n![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig4.png\")
Folders answer where does this live? A note sits in exactly one. Use them for broad, stable categories. Three to six top-level folders is plenty. Tags answer what kind of note is this? Type #method or #toread anywhere in a note. A note can carry many, which makes tags good for status and type \u2014 things that cut across folders. Nested tags like #status/draft give you a little hierarchy without much cost. Links answer what is this related to? These do the real work. Folders and tags are coarse; links are specific.
The failure mode is over-investing in the first and under-using the third. A flat vault with good links is far more useful than a beautiful eleven-level folder tree with none.
\nHere is a starter structure:
\nmy-vault/\n\u251c\u2500\u2500 Daily/ # one note per day, capture inbox\n\u251c\u2500\u2500 Notes/ # the permanent, linked notes\n\u251c\u2500\u2500 Projects/ # one note per active project\n\u251c\u2500\u2500 Reading/ # books, papers, articles\n\u2514\u2500\u2500 Templates/ # reusable note skeletonsLet Notes/ grow flat and link heavily inside it.
Go to Settings \u2192 Core plugins and enable Daily notes. In its options, set the folder to Daily/ and the date format to YYYY-MM-DD so the files sort correctly.
![](\"https://solomoneshun.com/assets/imgs/fig8.png\"/)
The daily note is a capture inbox, not an archive. Anything that occurs to you during the day lands here with no organising: meeting notes, half-ideas, a paper someone recommended, a problem you hit in your analysis. The bar for writing something down should be almost zero.
\nLater, you promote the few things that earned it into real notes in Notes/, with claim-style titles and links. Most of what you capture will not earn it, and that is the point. The inbox absorbs the noise so your permanent notes stay signal.
Turning on the Calendar community plugin gives you the month view shown above, which makes it easy to jump back to \"that Tuesday when the model broke.\"
Enable the Templates core plugin and point it at Templates/. Then create a file there like this:
# {{title}}\n\n**Source:**\n**Date:** {{date}}\n\n## Summary\n\n## Key points\n-\n\n## Links\n-Now Ctrl/Cmd + P \u2192 \"Insert template\" drops that skeleton into any new note.
![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig9.png\")
The payoff is not the typing you save. It is that every literature note has the same shape, so six months later you can skim thirty of them quickly, and a Dataview query can pull fields out of them reliably. Two or three templates is usually the right number: one for literature, one for meetings, one for projects.
\nCommunity plugins live in Settings \u2192 Community plugins \u2192 Browse. You will need to turn off Restricted Mode first.
\nResist installing twenty of them. Each is a thing that can break, conflict, or need migrating, and the most common way to end up with a broken vault and no notes in it is to spend week one configuring instead of writing. Add plugins to solve problems you have actually hit.
\nThe ones that earn their place for most people:
\n| Plugin | \nWhat it solves | \n
|---|---|
| Dataview | \nTurns notes into a queryable database | \n
| Calendar | \nMonth view for daily notes | \n
| Templater | \nTemplates with logic \u2014 dates, prompts, scripting | \n
| Excalidraw | \nHand-drawn diagrams stored inside the vault | \n
| Style Settings | \nTweak theme colours and spacing without CSS | \n
Dataview is the one that changes how the vault feels. Instead of maintaining an index note by hand, you write a query and it stays current.
\n![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig10.png\")
That query sits in a normal note. Every time you open it, Obsidian scans the Reading/ folder and rebuilds the table, so a paper you add next month appears without you editing anything.
Here is the honest truth about the graph view: it is beautiful, it is motivating, and it is useless for the first month.
\n![](\"https://solomoneshun.com/posts/obsidian//../../assets/imgs/fig5.png\")
A graph of twelve notes tells you nothing you did not already know. A graph of four hundred shows you clusters you never planned, orphan notes you should link or delete, and hub notes that turn out to be the real organising centres of your thinking.
\nBuilding structure before content. You cannot design the right hierarchy for notes you have not written. Start flat and let structure emerge.
Installing too many plugins. Add them to solve problems you have actually hit, not problems a video told you about.
Treating it as an archive. A vault full of clipped articles you never linked or reread is a graveyard, not a knowledge base. One note you wrote in your own words beats ten you pasted.
Splitting into many vaults. Links do not cross vault boundaries. One vault, folders inside it.
Chasing the perfect system. Every month someone publishes a new methodology with an acronym. The people getting real value from Obsidian are mostly running something boring and consistent. Boring and consistent wins.
Over-tagging. If every note carries eight tags, tags stop narrowing anything. A handful you actually filter on beats a taxonomy you admire.
If you want something concrete to follow:
\nDays 1\u20132. Install, create one vault, make the five folders. Write three notes about things you already know well and link them to each other.Days 3\u20137. Turn on daily notes. Capture into them every day, without organising. Do not touch settings.Day 7. First review. Promote two or three captures into real notes. Notice what you keep reaching for.Week 2. Add a literature template. Install Dataview only if you feel the need for an index. Write a note that links to five existing ones.End of week 2. Open the graph for the first time. It will be small, and that is fine.Once the daily loop is automatic, the next steps are richer Dataview queries, Templater for dynamic templates, and if you want to publish, either Obsidian Publish or a static-site generator like Quartz, which renders a vault into a website for free.
\nBut none of that matters until the habit exists. After installation, everything else is elaboration. I hope this helps!
\nI'll be radically honest here - I never really got the idea of \"Test Driven Development\". I'm much more of a \"telnet into production and damn the consequences\" kind of guy. Look, it's much more fun and my cardiologist recommends severe shocks every now and again.
\n\nThe essence of unit testing is pretty simple:
\n\nSend it good data and if it returns \"true\" the test passes. Send it bad data and make sure it returns false. Easy peasy lemon squeezy.
\n\nAs part of my NLnet funded work on ActivityBot, I'm writing some proper tests for my bot framework. I am hopeful that some of them will be useful to other projects doing similar things.
\n\nThere are four major classes of tests - here are the problems I've found while writing them.
\n\nA basic test might be how to validate that an ActivityPub username is valid. Typically it would be something like @edent@mastodon.social - what assumptions do we make with that? It starts with an @ and has another @ somewhere in it, dividing into two parts, the user and the server. The server has to be a valid domain name - but that can include non-ASCII Internationalised Domain Names and Emoji domains.
What about the user part? Is it just A-Z and 0-9? Can it have dots? What about apostrophes like an email address?
\n\nBasically, how easy is it find the specifications for all the various component parts of ActivityPub? Not very!
\n\nWhat does a WebFinger response look like? Which parts are mandatory and what values can they contain?
\n\nWith any message that your project generates, how can you be sure that other ActivityPub servers will understand it?
\n\nSimilarly, when we receive a follow message from an external server, what should it look like?
\n\nThere are some JSON Schemas which can be used to validate some type of messages.
\n\nBut, again, there are many scattered specifications.
\n\nAs I've written about before, HTTP Signatures are difficult. There are two main problems. The first is verifying signatures.
\n\nIn order to do that, you need to gather a bunch of signatures and then find a way to replay them into the test. So I'm in the process of saving signatures which I can then share with others.
\n\nThe second problem is generating signatures. Sure, you can run tests on the signatures - but there's only one real way to test whether they're acceptable\u2026
\n\nPassing tests means nothing if you can't communicate with others and they can't communicate with you.
\n\nSome tests are easier than others. Sending a signed request should be easy and repeatable. But receiving messages isn't easily automated. It requires being able to control an external server and asking that to send messages.
\n\nWith the Mastodon API it is possible to make requests which will interact with your server - but only if it is a public server.
\n\nI'm putting all my tests online. I'm also sharing a collection of messages and signatures which you can use in your tests.
\n\nI'd love to know if you find them useful.
![](\"https://shkspr.mobi/blog/wp-content/themes/edent-wordpress-theme/info/okgo.php?ID=75774&HTTP_REFERER=DOI\"){kind=small}
","doi":"https://doi.org/10.59350/nmxzr-19z59","guid":"https://shkspr.mobi/blog/?p=75774","image":"https://shkspr.mobi/blog/wp-content/uploads/2012/09/709ae69b.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791590400,"rid":"s1wdt-2k111","summary":"I'll be radically honest here - I never really got the idea of \"Test Driven Development\". I'm much more of a \"telnet into production and damn the consequences\" kind of guy. Look, it's much more fun and my cardiologist recommends severe shocks every now and again.","tags":["/etc/","ActivityBot","ActivityPub","Php"],"title":"Some quick thoughts on Unit Testing ActivityPub","updated_at":1791633911,"url":"https://shkspr.mobi/blog/2026/10/some-thoughts-on-unit-testing-activitypub/","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Julia Hoffmann","given":"Petra Mensing"}],"blog":{"authors":null,"community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created":1749772800,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":"https://doi.org/10.65527/tib","favicon":"https://rogue-scholar.org/api/communities/db0d8909-9e37-46d0-b16c-0551f575e86b/logo","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blog.tib.eu/","issn":null,"language":"deu","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.65527","relative_url":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","title":"TIB-Blog","updated":1791550026,"use_api":true},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":"Open Science und geistiges Eigentum werden oft als Gegens\u00e4tze verstanden: hier der freie Zugang zu Wissen, dort Patente, Urheberrechte und andere Schutzrechte. F\u00fcr den Transfer von Forschung in die Praxis greift diese Gegen\u00fcberstellung jedoch zu kurz. Die entscheidende Frage ist vielmehr: Was sollte offen sein, was muss gesch\u00fctzt werden \u2013 und wann?
\nDieser Frage wurde auf der Innovations-Roadshow des Deutschen Patent- und Markenamtes (DPMA) am 8. September 2026 in Hannover an der TIB nachgegangen.
\n![\"Teilnehmende](\"https://blog.tib.eu/wp-content/uploads/2026/10/2026-innovations-roadshow-1-1024x768.jpg\")
Forschungsergebnisse sind mehr als wissenschaftliche Publikationen. Sie entstehen als Daten, Software, Modelle, Videos, Prototypen oder technische Erfindungen. Damit stellen sich zugleich Fragen nach Zug\u00e4nglichkeit, Nachnutzung, Urheberrecht und gewerblichem Rechtsschutz.
\nWer Forschung offen zug\u00e4nglich machen und Innovation erm\u00f6glichen will, muss deshalb beides zusammendenken: Offenheit und Schutz.
\nEine wichtige Schnittstelle daf\u00fcr ist das Patentinformationszentrum (PIZ) Hannover an der TIB. Seit 1878 unterst\u00fctzt es dabei, technisches Wissen und Informationen \u00fcber Schutzrechte zug\u00e4nglich und nutzbar zu machen. Heute ist das PIZ das einzige Patentinformationszentrum in Niedersachsen und Kooperationspartner des Deutschen Patent- und Markenamts.
\nZu den kostenfreien Basisdiensten geh\u00f6ren Informationen und Fachliteratur zu Patenten und gewerblichen Schutzrechten, Unterst\u00fctzung bei Recherchen in Patent- und Schutzrechtsdatenbanken sowie Informationen zu nationalen und internationalen Schutzrechtsverfahren. Veranstaltungen und Sensibilisierungsangebote vermitteln zudem Grundlagen des gewerblichen Rechtsschutzes.
\nDabei versteht sich das PIZ Hannover als neutrale Lotsenstelle: Die kostenfreie Erfindererstberatung kann bei Bedarf zu IHK oder Patentanwaltschaft weiterf\u00fchren. F\u00fcr spezialisierte Recherchen, Analysen oder Patentstatistiken werden geeignete Anbieter und Einrichtungen vermittelt. Auch bei Fragen zu Verwertung, Schutzrechtsmanagement oder F\u00f6rderm\u00f6glichkeiten verweist das PIZ auf die jeweils zust\u00e4ndigen Stellen.
\nSo bietet das PIZ einen kostenfreien, neutralen und niedrigschwelligen Zugang zu Informationen und Orientierung im Netzwerk der Schutzrechts- und F\u00f6rderangebote.
\nF\u00fcr Forschende beginnt der Mehrwert oft schon vor einer Patentanmeldung: Welche L\u00f6sungen gibt es bereits? Was wurde schon ver\u00f6ffentlicht? Welche Entwicklungen sind neu \u2013 und wo gibt es Ankn\u00fcpfungspunkte f\u00fcr die eigene Forschung?
\nEine Patentrecherche kann dabei auch helfen, Unternehmen, Forschungseinrichtungen oder andere Akteure zu identifizieren, die an \u00e4hnlichen Technologien arbeiten und als m\u00f6gliche Kooperationspartner infrage kommen.
\nPatentinformationen k\u00f6nnen damit eine Br\u00fccke zwischen wissenschaftlicher Erkenntnis und technologischer Entwicklung bilden.
\nEin Beispiel f\u00fcr diese Verbindung ist die Patentdatenbank Orbit Intelligence. Die Plattform kann aus wissenschaftlichen Texten technische Merkmale extrahieren und dazu passende Patentdokumente identifizieren. Lizenziert f\u00fcr Nutzende der TIB. DBIS \u2013 Orbit Intelligence
\nSo l\u00e4sst sich etwa pr\u00fcfen, in welchen Patenten bestimmte Komponenten, Verfahren oder technische Zusammenh\u00e4nge beschrieben sind. Orbit unterst\u00fctzt damit die Recherche und Analyse \u2013 ersetzt aber keine rechtliche Pr\u00fcfung von Neuheit oder erfinderischer T\u00e4tigkeit.
\nF\u00fcr Forschende entsteht so eine praktische Br\u00fccke zwischen wissenschaftlicher Publikation und Patentinformation.
\n![\"Screenshot](\"https://blog.tib.eu/wp-content/uploads/2026/10/2026-orbit_piz-1024x448.png\")
Wie aus Patentinformation und Forschungsergebnissen konkreter Technologietransfer entstehen kann, zeigt das Beispiel von Bardia Akaberi der Firma Goldhoop.
\nAusgangspunkt war die Recherche nach dem Stand der Technik, auf die dann der Schutz der Erfindung als Gebrauchsmuster DE 20 2025 003 427 folgte. Doch der Transfer endete nicht mit dem Schutzrecht. Ein funktionierender Prototyp wurde umgesetzt und in einem Video dokumentiert. Denn ein Film sagt mehr als 1.000 Bilder: Die technische Idee wird anschaulich, verst\u00e4ndlich und f\u00fcr potenzielle Kooperations- und Transferpartner unmittelbar erlebbar.
\n\u00dcber das TIB AV-Portal (https://doi.org/10.5446/73021) bleibt die Dokumentation dauerhaft auffindbar und zitierf\u00e4hig. Im TIB-Portal \u00a0k\u00f6nnen Patent, Video und wissenschaftliche Informationen miteinander verkn\u00fcpft werden.
\n![\"\"](\"https://blog.tib.eu/wp-content/uploads/2026/10/2026-dpma-1024x582.png\")
Ein Schutzrecht allein macht eine Erfindung noch nicht sichtbar. Erst wenn eine technische Entwicklung verst\u00e4ndlich, auffindbar und dauerhaft dokumentiert ist, kann daraus Interesse entstehen.
\nSichtbarkeit \u2192 Verst\u00e4ndnis \u2192 Interesse \u2192 Kontakt \u2192 Technologietransfer
\nSo wird aus gesch\u00fctztem Wissen ein anschlussf\u00e4higes Forschungsergebnis \u2013 und aus Forschung kann Innovation entstehen.
\nDie TIB macht mit Ihren Services Digitale Dienste und Dienstleistungen der TIB \u00a0Wissen dauerhaft sichtbar, recherchierbar und transferf\u00e4hig. Das PIZ Hannover an der TIB unterst\u00fctzt Sie bei den gewerblichen Schutzrechten. \u00a0So entsteht eine Br\u00fccke zwischen Forschung, Schutz und Innovation. Sprechen Sie uns gerne an!
","doi":"https://doi.org/10.65527/hbjbb-28825","guid":"https://blog.tib.eu/?p=33999","image":"https://blog.tib.eu/wp-content/uploads/2026/10/2026-dpma.png","language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791504000,"rid":"f73k7-csb39","summary":"Open Science und geistiges Eigentum werden oft als Gegens\u00e4tze verstanden: hier der freie Zugang zu Wissen, dort Patente, Urheberrechte und andere Schutzrechte. F\u00fcr den Transfer von Forschung in die Praxis greift diese Gegen\u00fcberstellung zu kurz. Die entscheidenden Fragen sind vielmehr: Was sollte offen sein, was muss gesch\u00fctzt werden \u2013 und wann?","tags":["SERVICES","Lizenz:CC-BY-4.0-INT","TIB AV-Portal","TIB-Portal","Patent"],"title":"Offen forschen, gezielt sch\u00fctzen","updated_at":1791617415,"url":"https://blog.tib.eu/2026/10/09/offen-forschen-gezielt-schuetzen/","version":"v1"}},{"document":{"authors":[{"affiliation":[{"name":"Fachhochschule Potsdam"}],"contributor_roles":[],"family":"Kaden","given":"Ben","url":"https://orcid.org/0000-0002-8021-1785"}],"blog":{"authors":null,"community_id":"00dd7e11-a802-44c1-9584-5c56d1f8d417","created":1706832000,"current_feed_url":null,"description":"Vernetzungs- und Kompetenzstelle Open Access Brandenburg","doi":"https://doi.org/10.59350/oabrandenburg","favicon":"https://rogue-scholar.org/api/communities/00dd7e11-a802-44c1-9584-5c56d1f8d417/logo","feed_format":"application/atom+xml","feed_url":"https://open-access-brandenburg.de/feed/atom","filter":null,"generator":"WordPress","home_page_url":"https://open-access-brandenburg.de/","issn":null,"language":"deu","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"oabrandenburg","status":"active","subfield":"1802","title":"Open Access Brandenburg","updated":1791545849,"use_api":false},"blog_name":"Open Access Brandenburg","blog_slug":"oabrandenburg","content_html":"Es gibt ein relativ neu beschriebenes Ph\u00e4nomen im Open Access bzw. Diamond Open Access, \u00fcber das unl\u00e4ngst Nature berichtete und auf das wir ebenfalls kurz hinweisen wollen. So berichten Lisa Matthias vom Institut f\u00fcr Bibliotheks- und Informationswissenschaft der Humboldt-Universit\u00e4t zu Berlin und ihre Ko-Autoren Juan Pablo Alperin (Simon Fraser University, Vancouver) und Mikael Laakso (Tampere University, Tampere) \u00fcber F\u00e4lle, in denen wissenschaftliche Zeitschriften von einem Diamond-Open-Access-Modell auf APC-basierte Gold-OA, Hybrid-OA oder Subskriptionsmodelle umstellten:
\nLisa Matthias, Juan Pablo Alperin, Mikael Laakso: Diamond Fractures: Tracing Journal Transitions Away from Diamond Open Access. (30.06.2026) DOI: 10.48550/arXiv.2606.31302\n\nInsgesamt konnten sie 440 F\u00e4lle f\u00fcr den Zeitraum von 2009 bis 2026 identifizieren, wobei der gr\u00f6\u00dfte Anteil auf den Wechsel zu Gold Open Access mit Publikationsgeb\u00fchren zu verzeichnen ist. Interessanterweise beschleunigte sich diese Entwicklung in den vergangenen Jahren. Ebenfalls interessant erscheint, dass dies nicht etwa mit Verlags- oder Betreiberwechseln (\"ownership\") zu erkl\u00e4ren ist \u2013 die \u00fcberwiegende Zahl der Titel, 389 von 440 \u2013 blieb beim Ursprungsbetreiber.\nBei einem Teil der F\u00e4lle l\u00e4sst sich Diamond Open Access als \u00dcbergangs- und Etablierungsma\u00dfnahme nach der Neugr\u00fcndung einer Zeitschrift verstehen:
\n\n\n\"we see the pattern of publishers forgoing APC revenue as a promotional strategy, while a new journal builds indexing and reputation, then introduce them once the journal is established\".
\n
Der gr\u00f6\u00dfere Teil der Titel \u00e4ndert sein Betriebsmodell allerdings erst sp\u00e4ter, was mit dieser Deutung adressiert wird:
\n\n\n\"The temptation and relative ease of adopting APCs is particularly evident for these journals, given that such established journals would be strong candidates for collective funding models, such as Subscribe to Open.\"
\n
Besondere Relevanz erh\u00e4lt die Analyse deshalb, weil Diamond Open Access verst\u00e4rkt, wie auch zuletzt auf den Open-Access-Tagen 2026, als ein m\u00f6gliches Alternativmodell besonders zum geb\u00fchrenfinanzierten Gold Open Access angesehen und diskutiert wird und man daher sehr viel \u00fcber die Verschiebung von Gold zu Diamond spricht. Die entgegengesetzte Entwicklung wird dagegen bisher kaum thematisiert. Dank der vorgelegten Auswertung ist \"Diamond Fractures\" im Diskurs und auch in Nature angekommen. Zudem unterstreicht der Befund zus\u00e4tzlich den Bedarf an Diamond-OA-Modelle, die auf strategische Verstetigungen in nicht-kommerziell ausgerichteten Kontexten aufbauen.
\nWer mehr \u00fcber Diamond Fractures erfahren m\u00f6chte, hat im November die Gelegenheit. Denn am 27.11.2026 ab 18 Uhr und virtuell wird Lisa Matthias ihre Forschungen zum Thema \"Diamond Fractures\" im Berliner Bibliothekswissenschaftlichen Kolloquium an der Humboldt-Universit\u00e4t zu Berlin pr\u00e4sentieren: Hybrid und Diamond Open Access unter der Lupe \u2013 Open Access als Forschungsobjekt der Bibliotheks- und Informationswissenschaft.\u00a0
\n\n\nZitierhinweis:
\nKaden, Ben (2026): \"OA-News: Diamond (Open Access) Fractures als Forschungsthema.\" DOI: 10.59350/yq0vn-j2231
\n![\"\"](\"https://substackcdn.com/image/fetch/$s_!ywYU!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F41a4a457-21e4-404a-8cf2-a643189d868e_940x529.webp)
Join me and all of us here in the School of Data Science on Friday, Nov. 13 for Datapalooza 2026, UVA's flagship data science conference celebrating data science in action across disciplines and for the public good.
This year's theme is \"The Future of Work, Powered by Data and People.\" Data science is changing how we work and the decisions we make. As AI becomes more integrated into workplaces, its impact will depend not only on how the technology evolves, but also on the people who build and use it.
Datapalooza will be held Friday, November 13, 8:45pm - 5:45pm here at the University of Virginia School of Data Science in Charlottesville, VA. The main event kicks off at 1pm. Registration is free.
8:45 a.m. \u2014 Registration/Check-In Begins
9:30-10:30 a.m. \u2014 MSDS Online Breakfast
10:30-11:00 a.m. \u2014 Building Tours
10:30 a.m.-12:00 p.m. \u2014 Mixer for Residential & Online Students
11:00 a.m.-1:00 p.m. \u2014 Headshots
12:00-1:00p.m. \u2014 Networking Lunch
1:10-1:15 p.m. \u2014 Kickoff
1:15-2:15 p.m. \u2014 Fireside Chat Discussion Panel
2:30-3:30 p.m. \u2014 Breakout Sessions
3:45-4:30 p.m. \u2014 Keynote
4:45-5:45 p.m. \u2014 Reception
We'll kick off the day with a guided conversation with industry leaders which inspires and explores how Data Science and AI are fundamentally changing how we make decisions and how we work. The conversation will explore the evolving role of data science across industry and society. Panelists will consider the reality that the future of work cannot be shaped by technology alone but must be driven by the choices that people make about how technology is developed and put to use.
Breakout 1 | How Do You Become a Data Science Leader?
What does leadership look like in the age of AI? How can leaders harness data and emerging technologies that support teams in making decisions, setting priorities, and driving business. How are leaders thinking about data, AI, and helping to define the human-technology relationship?
Breakout 2 | What Do You Really Want to Know About Working in Data Science?
This is your chance to challenge data science academic and industry leaders to give candid answers and real-world perspectives about what's happening in the field\u2014as well as their personal journey to and through data science. They will be put in the \"hot seat\" by our very own students. How did they get here? What trends, tools, challenges, and opportunities are emerging now and in the future? How is industry/higher education thinking about and addressing AI ethics and safety concerns? And where will people be in all of this? Come curious and ready to engage.
Breakout 3 | How Can a Data Scientist Create and Innovate?
Explore how innovators are using data and AI not simply to analyze what already exists, but to create new possibilities. What emerging tools, ideas and experiences are unfolding, and how might you create and innovate as a student, community member, alumni or industry partner?
Close out Datapalooza with an inspiring keynote on how thoughtful policy and responsible data practices can help communities thrive.
Postdoc Research Showcase During Reception
","doi":"https://doi.org/10.59350/4yxfj-kdn52","guid":"219590216","image":"https://substackcdn.com/image/fetch/$s_!ywYU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F41a4a457-21e4-404a-8cf2-a643189d868e_940x529.webp","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791504000,"rid":"n31cb-f4s26","summary":"UVA's flagship data science conference will be held at the School of Data Science Friday November 13. This year's theme: The Future of Work, Powered by Data and People. Registration is free.","title":"Datapalooza 2026: The Future of Work, Powered by Data and People","updated_at":1791617409,"url":"https://blog.stephenturner.us/p/datapalooza-2026","version":"v1"}},{"document":{"authors":[{"contributor_roles":[],"family":"Hunger","given":"Francis"}],"blog":{"authors":[{"name":"Carrier-bag Staff"}],"community_id":"0f516bef-eddb-49f7-afc4-b5fc2468ee95","created":1749168000,"current_feed_url":null,"description":"Critical writing and research on technology, AI, art and digital culture, run by Hito Steyerl and Francis Hunger at AdBK Munich","doi":"https://doi.org/10.59350/carrier_bag","favicon":"https://rogue-scholar.org/api/communities/0f516bef-eddb-49f7-afc4-b5fc2468ee95/logo","feed_format":"application/atom+xml","feed_url":"https://carrier-bag.net/feed/atom","filter":null,"generator":"WordPress","home_page_url":"https://carrier-bag.net/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"carrier_bag","status":"active","subfield":"1702","title":"carrier-bag.net","updated":1791551869,"use_api":true},"blog_name":"carrier-bag.net","blog_slug":"carrier_bag","content_html":"Five practice reports illustrate the scope of research and studying within the Emergent Digital Media class (Prof. Dr. Hito Steyerl, Dr. Francis Hunger) at the Academy of Visual Arts, Munich. They foreground examples of generative practices using machine learning techniques, colloquially known as 'AI', leaving aside other diverse artistic strategies (conceptual, performative, video essay, etc.) negotiated in class. https://www.generativemedia.net
\n\n\n\nThe scope of the reports by Vasilii Vikhlaev, Chloe McFadden, Guillaume Menguy, Nikita Sazonov and Otto Ostermann reaches from direct inquiry into techniques and tools to the metaphorical reflection on societies 'AI' phantasms. A commonality of the diverse approaches is that they probe how systems work, instead of just using the outputs as finished results. By that the artists develop a critique of the dominant narratives about 'AI', and they uncover the material dependencies and massive infrastructures (data, energy consumption, data centers) behind the stochastic generation of images, sound and text.
\n\n\n\nThis report addresses two registers of my work with machine learning in sound: the inductive one, meaning pre-trained models, and the deductive one, meaning physical modeling. The question behind both is how to synthesize sounds that are not present in any database. I'll introduce three projects and deliver an outlook on future research.
\n\n\n\nThe first project is Aggressor’s Tongues (2025/26), a 10-channel audio experiment of 41 min that was exhibited at the Kunstbau of Lenbachhaus, Munich. The project began with audio material of scraped recordings of Russian propaganda speech from the ongoing war against Ukraine: voices saturated with hate and domination. The machine listens, so I don't have to. The Montreal Forced Aligner (MFA) algorithm takes an audio file and its transcript and returns time stamps for every word and phoneme, normally as a preparation step for phonetics research or for building speech datasets. I used its output as the material itself: the piece works on the phoneme grid, not on the meaning of the speech. The grid also set the segmentation of the training data for variational autoencoders (VAE), so the model could only learn units below the level of meaning. The VAEs were activated using the RAVE framework, originally developed at the Institut de recherche et coordination acoustique/musique (IRCAM).
During the process, I discarded an earlier approach with emotion recognition (openSMILE, Praat), because the results were too illustrative and literal. Instead, I focused on decoder artifacts. I displayed them rather than smoothed them out, to keep the apparatus audible. For the same reason the drifts through the latent space were deterministic, several phasors in irrational ratios, so that the trajectory never repeats. I wanted to hear and make audible the system, not the sounds it was meant to produce.
The second project is titled, vzkazy dom\u016f (2025) and was developed in cooperation with Andrea Vesel\u00e1. It's format is 7.1 surround sound with a length of 10 hours, and was developed for the former Radio Free Europe building in the frame of Public Art Munich 2025. Starting point was a short found-footage recording of a historical radio signal being intentionally jammed to interfere with its broadcast, going from signal to noise. We slowed this recording down and Andrea contributed a reactive voice interpretation. Then she listened to her own recorded voice through an earpiece and sang to it, close enough in pitch that the two voices interfere and produce interfering oscillations, called 'beatings' (Schwebungen). That material became the training data for the VAE. The preparation of the input was prioritized over any selection at the output, emphasizing the data: the beatings were already in the training material, as a measurable modulation, and not added posterior as an effect.
\n\n\n\nFor the third project, Composite (2026, in progress), I turned a synthetization technique that involves no machine learning at all, called 'physical modeling'. The work is based on the composite plastic coins of Transnistria, the first plastic coins in general circulation. With Modalys, a generative tool developed at IRCAM, sound is computed from the physical properties of a body (size, density, Young's modulus). Unlike classical sound synthesis (additive, subtractive, FM) or sample-manipulation approaches, physical modeling simulates the sound-producing behavior of an instrument itself. This makes it possible to capture sonic qualities that traditional synthesis techniques reproduce only with difficulty. As part of the exploratory artistic process, I went from small data to no data. Physical modeling uses established physical laws from material sciences and acoustics, so it can compute objects that never existed. Compared with machine learning approaches, physical modeling is able to produce sound without examples. Machine learning for audio is almost always inductive, needing data, so the availability of data decides what can be produced at all. For rare, local or non-existent material that is a hard limit. I did not take this step to abandon machine learning techniques, but to run both methods side by side and hear the difference.
For the machine learning process, I am building a dataset of the real coin sounds to train a VAE on them and drive that model with the computed sounds using physical modeling: the input is encoded into the latent space and decoded again, so the output carries the statistics of the training data. The input cannot lie inside that distribution: Modalys computes an idealized body, without a surface, without the other coins, without a room. Some of the material constants I set, do not belong to any real object either. The statistical signature of a real object is imposed on an object that never existed. Input and output will be presented as pairs, not blended, to keep the model readable. The inverse is planned too: physically modeled sounds as training data, real coin sounds as input driving the model.
This report reflects upon my practice-based technique, prompt-shifting. It is both a practical technique for methodically probing the biases and patterns of text-to-image models, and a conceptual intervention that opposes magical technoscientistic framings.
\n\n\n\nPrompt-shifting enables artists to probe commercial text-to-image platforms without stabilizing their rhetorical claims and purported abilities of dream-making and superior accuracy. Rather than visualizing our imaginations, this technique shifts attention away from outputs towards the processes and contexts of their production. Using the same seed, the artist slowly and methodically introduces and varies the influence of certain subjects in the prompt. By noticing what shifts and emerges overtime, the artist can speculate and sense how learnt patterns are activated within generative AI models in ways that manifest visually.
\n\n\n\nThis technique emerged from my broader research project that attempts to engage and disrupt the 'magical technoscientism' of generative AI: a fusion of magic and technoscientism that affords models the ability to simultaneously create miraculous and scientifically authoritative outputs. In commercial text-to-image rhetoric, prompts are imagined as granting users the gift of dream-making while appeals to 'prompt-adherence' and 'accuracy' position prompting as a science. Such a dissonant fusion positions models as both passive conduits of human imagination and objective authorities of visual representations. Prompting guides and practices frequently frame the misalignment of expectation and output, not as a limit of the system but as the user's failure to conform to its representational logic. The problem is how the user asked, not what the system can do.
\n\n\n\nThis redistribution of misalignment creates a magical technoscientistic perception of text-to-image models in which prompts are both magical phrases \u2013 'open sesame' \u2013 and formulas: dog + beach + realistic + 35mm film – people = output image. Via this magical perception, positive and negative prompts are imagined as adding or subtracting certain influences and features from an output image. Such explanations reduce image generation to a representational exchange of words for images, obscuring the operations through which outputs arise. Thinking operationally, what does it mean to 'add' or 'subtract' an influence from the creation of an image? Prompt-shifting attends to such a line of inquiry, redirecting attention away from representational desire and towards operational curiosity. For example, methodically introducing negative and positive terms to a prompt foregrounds the operativity of the model and how terms are embedded and mutually determine the trajectory of the denoising process.
\n\n\n\nPrompt-shifting thus enables a sensing of image features not as discrete and universal parameters, but as relationally, socially and operationally situated. This situated sensing may also disrupt framings of bias as an issue that can be resolved through the acquisition of more data. Prompt-shifting both requires a reflection upon the situated conditions of production enacted by text-to-image applications (across models, society and time) and demonstrates how bias manifests visually within generative images in strange and unexpected ways.
\n\n\n\nMy research about chatbots and autocompletion investigated the economies of word-completions and its aesthetic consequences. For my own exploratory and experimental use, I built a little text editor, not much more complex than the default Windows notepad, which runs a Large Language Model (LLama-3.2-3B) fine-tuned on a small, curated literary corpus (about 15MB of chosen modern English literature) to provide autocompletion while typing. The editor is offline and local; the model’s initial weights along with the Low-Rank Adaptation finetune (LoRA) are merged into a single file that can be loaded on a laptop.
\n\n\n\nI started to play around, writing narratives with this autocompletion system. The Ctrl and Shift keys are used to cycle deterministically through seeds, and parameters like 'temperature', and 'context size' are exposed for slightly more control over the model’s sensitivity. When typing, the latest 4000 characters are fed to the model as a prompt, and the model continuously predicts a likely continuation for the text based on this sliding context window.
\n\n\n\nRecurring narrative patterns emerge from the writing; many stories mentioned in passing the death of a close relative. Many of them were stories about friends with eccentric personalities, paranoid, conspiratorial, or otherwise consumed by esoteric beliefs. My own interactions with them were often those of a disengaged witness. Stories unfolded over years and decades with brisk jumps across time, and many contained fastidious and often invented literary references. I was writing a novel in many of these stories, and many of these stories ended up being much funnier, more surprising and strangely subversive to me than what I could have imagined myself.
\n\n\n\nIn a next step, I also developed a simple logging system. It turns out that I was writing less than 20% of the text, letting my model suggest the rest of the words. But the continuations I chose among the model’s suggestions were almost never the first one, and on average I cycled between 7 seeds for every next sentence.
\n\n\n\nThere is a larger context for this experiment: Modern LLMs are deployed in many ways, most of them in disguise; as mediators, taking in structured inputs and dispatching commands to tools, as content moderators, call-center agent assistants, legal reviewers, evaluators for other models, as editors, writing blurbs and summaries from unstructured text data, as agents crawling and gathering, making pull requests, so on and so forth. But the most publicly exposed and consumer-facing deployment of LLMs (though by far not the costliest, either in token expenditure or thermal devastation), is the chatbot.
\n\n\n\nLLM-based chatbots, of course, are just a formatting trick. What is presented to the user as a series of distinct messages, emulating the interface of messaging applications, is actually a single text file, to which markers and delimiters (<|im_start|>) are invisibly inserted to separate the queries from the inferences, the human text from the autocompletion. The website from which the LLM is accessed hides those markers and uses them to style the text as an exchange.
\n\n\n\nModels before ChatGPT, like GPT-2 were mostly accessed autocompletion tools akin to my own program. The logic of autocompletion is open and ambiguous: the model and the user share a string of words, and it is unspecified whether their respective inputs are answers, continuations, suggestions, setups, punchlines, lists, stories, provocations. It is unclear which words belong to which participant they enter into a kind of mutual alienation. Reading the texts back, I can no longer distinguish what I wrote from what was predicted. For a product, this ambiguity cannot be tolerated. The chatbot therefore solves a problem of economy, giving a precise answer to the question: what kind of service is provided by a word prediction machine?
\n\n\n\n'Be informative; be truthful; be relevant; be clear'. We can understand the chatbot as a system designed to follow exactly the maxims of Paul Grice's cooperative principle in communication. But to follow those maxims exactly is a way of misunderstanding them, interpreting them as prescriptive rather than descriptive. In fact, one of their functions is to establish a framework for communication theory in which participants are also able to communicate through the violation of the maxims. I would
suggest that the turn-based pseudo-dialogues we have grown accustomed to with so-called 'chatbots' introduces a friction which is also a fiction: this manufactured discontinuity makes the experience of working with a language model seem much slower, more instrumental or even confrontational than it really is. It forecloses the possibility of approaching a complex and potentially surprising arrangement of neurons with anything but a request.
Ironically, autocompletion can more easily be made to feel like a conversation, one in which the generation of new ideas cannot be attributed to a single thread of questions and answers. It rather becomes an experimental procedure of fumbling in the dark, full of ruptures and quips and forks, one that tends naturally to veer off track as contexts slide, without the desperate sycophancy of an intelligence alienated to some undisclosed charter, the procrustean system prompt that always redirects the stochastic towards that which it believes will be 'helpful'. Sometimes, the most helpful thing is just the first one that comes to mind.
\n\n\n\nMy relationship with generative tools and machine learning involves experimenting with various black-boxed tools. By iterating and reiterating multiple prompts to understand how the models work and how far they can be pushed, I test 'AI' as an interface of confrontation. I'll shortly discuss three of these confrontations.
\n\n\n\nFirst, generative AI might be treated as a tool confronted with other tools. The pipelines of editing/ animating/ compressing software are extended by generative tools of still and video generation. In this way, I am testing whether machine-learning ecosystems can be used as effectively as other tools in the routine practice of filmmaking. These experiments can be extended to a broader perspective of industrial cinematography, specifically ad-making, where AI outputs are becoming more widespread. In these practices, pretrained AI models are used to replace existing tools. They function as machines of imitation. Comparing generative AI to other tools, I also investigate the potential of generative tools to resemble the machinic cinema gaze of the early 20th century.
\n\n\n\nThe second line of confrontation is the conflict between analogue and digital technologies. AI tools currently represent the frontier of denying analogue technology its rights, creating an unsurmountable divide between the two worlds. At the same time, generative AI has given rise to stronger analogue nostalgia as a form of escapism from the contemporaneity. Film director Guillermo del Toro's recent statement, 'Fuck AI,' is very characteristic, considering the outdated nature of his alternative to machine learning techniques. Denying the current situation would only result in ugly visual effects, such as those in del Toro's Frankenstein (2025). My research method to this issue is to let these two worlds collide and let analogue and emergent digital engage in a sort of symbiosis, finding solutions in hybridization rather than in the pure-line genetic separation of one from the other.
\n\n\n\nThe final line of confrontation is cognition: recognizing the overlaps between human and other forms of cognition that can be bridged, or at least interfaced, by AI. For example, I use generative AI technology to move closer to the agency of plants and animals by using their various outputs as a source or starting point for generation. Additionally, AI is a tool that allows us to better understand disenchanted human cognition and brings us closer to something resembling the hallucinations of generative tools.
\n\n\n\nWhenever one engages with the discourse surrounding AI data centers and their adventurous materializations there is a wild mix of valid concerns, misinformation and conspiracies, creating a very muddy framing of the conversation. Especially in the last five years the states in the US have seen a massive increase in permitted planning and construction of data centers. The giant tech corporations produce their frontier AI models right there in the Midwest and Southern USA, a region more commonly known for its rurality, heavy industry and agriculture. The rapid speed of new construction has led to opposition in local councils and permitting committees by the public. There are concerns over environmental impact, false economic promises, privacy and spatial infringements and while a lot of these are valid claims, a few other things come up in interviews with attendees. Starting from more absurd conspiracy ideologies of surveillance to simple contradictions with their individual consumption of AI agents and the infrastructure necessary for them being trained in their direct vicinity.
\n\n\n\nThe German comedian Loriot discusses a similar paradigm in his episode 'Loriot VI' (1978). It features a Christmas special, in which the Hoppenstedt family welcomes a new technological novelty into their home. The parents give their child a nuclear power plant miniature model that is framed as an educational consumer product. Its scale and abstraction of mechanisms and environment suggest that nuclear technology can be understood, assembled and controlled easily. Loriot uses it as a reference to the light-hearted political embracement of nuclear power in the late 1950s. However, when the model explodes due to a 'mistake' in the assembly, it tears a hole through the floor onto the neighbors dining table. The easiness immediately turned out to be a facade, but the Hoppenstedts just cover up the hole with wrapping paper and dismiss the neighbors' complaint as petty-minded. The confidence in their own behavior survives even when the consequences of their actions become seemingly impossible to overlook.
\n\n\n\nWith people embracing AI services, but being generally opposed to an AI data center near them, a similar contradiction arises. We get the harsh criticism towards the impact of data centers, but even the critics and the impacted welcome the benefits of the infrastructure by self admittedly using AI, while protesting for the data center to be somewhere else. Loriot's abstracted stereotypes of the technology optimistic solutionist and the neighbors' insistence on undisturbed comfort therefore collapse into the same person. They reveal that the discussion is little different today, just more convoluted. This of course does not render all objections to the arising issues obsolete, but showcases how detached our understanding is to the underlying infrastructure until that infrastructure materializes in front of us. Even then the consequence barely moves into changes of personal behavior.
\n\n\n\nThe AI infrastructure in question also has history with the industry beforehand. Larger parts of the AI data center industry are based on build sites, electricity access and operational experience developed through industrial crypto mining. In recent years, a variety of crypto mining companies have announced agreements to adapt their facilities for AI computing infrastructure. Or they restructured as expert and advisory companies for planning, converting and developing build sites for AI datacenters, while crypto mining itself fell out of economic relevance. The enormous construction happening in the name of AI, which hugely absorbed the crypto infrastructure, leaves a few questions:
\n\n\n\nWill data centers be the new empty shopping malls one day? Can this level of demand for the technology persist or ever be met again? Will there be something to absorb the infrastructure left behind by the industry, once parts of it fall out of relevance? Can we allow tech companies to entirely buy out the energy of publicly funded plants, monopolizing it? How do we deal with power and power plants that have contractually disappeared from the grid for at least the next two decades? Should we allow Google to run its own nuclear power plant, with a fatal accident record, just to train Gemini? Do we not care that Talen Energy is using this trend to expand their nuclear market, for data centers, in cooperation with Amazon?
\n\n\n\nFor the installation Spielen wir das sch\u00f6ne neue Spiel? (2026), I have reproduced the model of Loriot's Hoppenstedt-family episode with a slight twist and added a hyperscale data center to it.
\n\n\n\n","doi":"https://doi.org/10.59350/ee5jy-h3077","guid":"https://carrier-bag.net/?p=3531","image":"https://carrier-bag.net/wp-content/uploads/2026/10/research.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"7jxc9-pgy09","summary":"Introduction Five practice reports illustrate the scope of research and studying within the Emergent Digital Media class (Prof. Dr. Hito Steyerl, Dr. Francis Hunger) at the Academy of Visual Arts, Munich.","tags":["Experiments"],"title":"Using generative methods for artistic experiments","updated_at":1791617405,"url":"https://carrier-bag.net/using-generative-methods-for-artistic-experiments/","version":"v1"}},{"document":{"authors":[{"affiliation":[{"id":"https://ror.org/03m2x1q45","name":"University of Arizona"}],"contributor_roles":[],"family":"Scott","given":"Eric","url":"https://orcid.org/0000-0002-7430-7879"}],"blog":{"authors":null,"community_id":"3446712e-ab03-4bb7-8641-571dcda8d8cd","created":1787702400,"current_feed_url":null,"description":"Tracking disruptions to U.S. federal science funding, grant by grant.","doi":"https://doi.org/10.59350/grantwitness","favicon":"https://rogue-scholar.org/api/communities/3446712e-ab03-4bb7-8641-571dcda8d8cd/logo","feed_format":"application/atom+xml","feed_url":"https://grantwitness.org/updates/rogue-scholar.xml","filter":null,"generator":"Other","home_page_url":"https://grantwitness.org/updates?type=updates","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"grantwitness","status":"active","subfield":"3321","title":"Grant Witness","updated":1791504000,"use_api":null},"blog_name":"Grant Witness","blog_slug":"grantwitness","content_html":"NIH is the most complex of our pipelines due to the number and variety of data sources and because we attempt to track disruptions to supplements separately from \"parent awards\".
\nBriefly, our data sources include submissions to our reporting form; manually tracked information such as court documents, news articles, and direct communications; RePORTER; TAGGS; a spreadsheet of terminated awards provided by HHS; and USAspending. Below I'll go into some detail about how some of these data sources are used.
\nBecause each budget year of an NIH award has a different full award number and potentially a different FAIN (federal award identification number), we use the institution code and the 6 digits after it as a primary key to \"connect\" information across years. Supplements can be distinguished from their \"parent awards\" in some data sources, but not all, by having a suffix containing \"S\" in their full award number (suffix is excluded in the award FAIN). We currently treat each support year of a supplement as unique, because it is unclear if it is guaranteed that the suffix -01S1 and -02S1, for example, are two years of the \"same\" supplement and not two different supplements granted in two consecutive years1.
\nBecause AHRQ and NIH awards appear in the same data sources, both agencies' grants go through the same status determination process and are only separated out at the end of our data pipeline. The main difference with AHRQ is that these awards do not have supplements, so no subaward ID is listed in the final data, and that for AHRQ only we list awards that have experienced renewal delays over 60 days as their only form of disruption.
\nWe capture regular snapshots of award data on reporter.nih.gov through their provided API. RePORTER is one of the few sources with supplement-level information. We use this for award/sub-award metadata (e.g.\u00a0title, abstract, grantee information, project start and end dates, etc.) including as a source of award amount for supplements, as they are not broken out in USAspending, our main source of financial data on grants. With our snapshots, we are able to capture when the \"Terminated\u2014departmental authority\" text is added or removed from the web view of an award. We use the addition of this flag as a signal of termination and its removal as a signal of reinstatement. We do manual review of these signals as we have found that this flag can be added or removed for reasons unrelated to political targeting of awards.
\n![\"A](\"https://grantwitness.org/content/nih/updates/methodology/reporter-terminated-screenshot.png\")
\nWe also snapshot TAGGS data regularly and we use TAGGS primarily as another source of award metadata. TAGGS data does contain a signal of termination\u2014a \"TERMINATION\" action type\u2014however, it is not always clear what this means. Because TAGGS data only shows awards by FAIN and not full award number, it is unclear when a \"TERMINATION\" event indicates only a supplement has been terminated. Sometimes, the \"TERMINATION\" flag is added to a deobligation event after a termination as well. These flags are relatively new and when they were first introduced, we saw them back-filled to past actions in the TAGGS data. Because of these compilations, we do not use the addition or removal of a \"TERMINATION\" flag in TAGGS data as a signal for termination for NIH or AHRQ awards.
\n![\"A](\"https://grantwitness.org/content/nih/updates/methodology/taggs-terminated-screenshot.png\")
\nEvery Friday, someone at HHS exports an excel spreadsheet of terminated awards as a PDF and uploads it to the web (https://taggs.hhs.gov/Content/Data/HHS_Grants_Terminated.pdf). We parse the data from this PDF weekly and use the addition or removal of awards from this PDF as a signal of termination or reinstatement.
\nNOTE: The format of this PDF changed on Feb 20, 2026 such that the PDF no longer includes full award numbers (it now only shows the FAIN which does not distinguish supplements), but prior to that it showed clearly whether a supplement only or a full award was terminated. Because of this, we currently only use additions/removals from this list prior to Feb 20, 2026 in our status determination although we use the newer PDFs for manual verification of terminations and reinstatements.
\nusaspending.gov provides an API that we use to gather award data regulary. We use this source primarily for metadata including grantee information, grant assistance listing (CFDA), grant notice of funding announcement (NOFO), and total obligated and total outlaid amounts. We also track changes to project end dates (i.e.\u00a0project end date moved earlier, a \"cutoff\", or later, a \"reextension\"), although we do not use this as a signal of termination for NIH or AHRQ awards. These data are only at the FAIN level so information specific to supplements is not available.
\nFile C is released roughly once a month (except that Sep/Oct are combined into one release).\u00a0 We use this source primarily for outlay actions.
\nAn award is marked as terminated if it has been reported as such directly to us by a PI or via a trusted source, if it has gained the \"Terminated\u2014departmental authority\" flag on RePORTER, or if it has been added to the TAGGS PDF.\u00a0 It is only marked as currently terminated if it has not since been restored.
\nAn award is reinstated if it has been reported as such directly to us by a PI, appeared in a court document as being ordered to be reinstated, if we know it was terminated due to institutional targeting and the institution has since capitulated to the Trump administration, if the \"Terminated\u2014departmental authority\" flag on RePORTER has been removed, or if it has been removed from the TAGGS PDF. Additionally, terminated awards that have since received renewals but have no other reinstatement signals are marked as reinstatements with the latest renewal date as the reinstatement date. Awards are only marked as currently reinstated if they have not since been disrupted. Reinstatements are confirmed by verifying that an award has received outlays since the reinstatement.
\nTo determine awards with frozen funds, we rely on reporting of institutional targeting and outlay data in File C. Since there are many reasons an award might receive no outlays in a particular fiscal period2 besides a targeted freeze of funds, we apply a strict set of criteria to identify frozen awards:
\nAn award is marked as \"unfrozen (unconfirmed)\" when institutional targeting ends (i.e.\u00a0the institution capitulated to the Trump administration) and is marked as \"unfrozen (confirmed)\" once it receives an outlay.
\nTo calculate the funds obligated before disruption we take the award's current total obligations and subtract the sum of obligations since the first termination or freeze date. Similarly, for funds spent before disruption, we take the current total outlays and subtract the sum of all outlays since the first termination or freeze. The outlay and obligation events come from File C, while the current total obligated and outlaid numbers are from the more regularly updating USAspending awards data.\u00a0 As a result, these estimates may fluctuate week-to-week.
\nBecause USAspending data is not broken down by full award number, we must do something different than above for disrupted supplements. For supplements, we use the total value reported by RePORTER and assume constant linear spending over the award's one-year budget period to determine the estimated funds spent and remaining at disruption. If both a supplement and its \"parent award\" appear in our data as being disrupted, we subtract the supplement amounts from the parent award amounts so that column sums are still reliable and do not \"double-count\" awards.
\nNOTE: The total funds promised for an NIH or AHRQ award is generally greater than the current total obligations as multi-year awards are obligated one year at a time with non-competitive renewals. However, the total promised amount for NIH or AHRQ awards is not reported publicly. We are currently working on an algorithm to estimate the total funds promised, but for now the funds remaining before disruption is an underestimate.
\nNIH categorizes grants by research topic through the Research, Condition, and Disease Categorization (RCDC) Process. However, the RCDC categorization process typically lags behind grant awards by months, as they are assigned in bulk once per year. We developed a machine learning model to predict which RCDC categories apply to grants that don't yet have official NIH categorizations. These RCDC categories are in the \"Spending Categories\" column and an indicator for if they are NIH-assigned or predicted is in the \"Spending Categories Predicted by GW?\" column. For more information on this machine learning model, see our post on the topic.
\nCompared to previous administrations, the number of NIH and AHRQ awards not being renewed on time is increasing. NIH typically approves projects for multiple years and then each year's funding is released as a \"non-competing continuation\" following the submission of standard progress reports. We use award budget end dates and project end dates to detect when an award is more than 60 days overdue for a renewal and when it is finally renewed after such a delay. Missing fiscal years within a project period are also counted as a renewal delay. For NIH, these events are noted in the event history, however a delayed renewal alone is not enough to get an NIH award marked as \"Disrupted\" as even prior to the Trump administration there were hundreds of awards that experienced delays of over 60 days. For AHRQ, 100+ awards with renewal delays of many months were eventually mass terminated, therefore we do show individual AHRQ awards experiencing overdue renewals in our data table as they may be at risk for future terminations.
\nRead more about deciphering NIH award numbers here.\u21a9\ufe0e
Fiscal periods correspond to months of the fiscal year, starting in October (period 1) through September (period 12). However, outlay data for October and November are always reported together as part of period 2.\u21a9\ufe0e
![\"A](\"https://shkspr.mobi/blog/wp-content/uploads/2026/10/Video_Games_Go_Choral.webp\")
\n\nLondon's musical entertainment scene has something for everyone - from long-running blockbuster shows to no-name bands in dingy basements. Somewhere in the middle is monophonic plainchant versions of the Halo theme sung in a church.
\n\nThe acoustics at St Martin in the Field are echoey. That makes it rather hard to hear the amplified announcement to turn off our phones, but makes an a cappella chorus absolutely soar. The choir of London Voices have sung on just about every movie you've watched and game you've played - so they're no stranger to more modern compositions.
\n\nThe programme strictly alternated between songs from popular games are some more traditional verses. As a tone-deaf musical ignoramus, I found some of the pieces hard to distinguish from each other and, perhaps, just a little repetitive. Delightfully, the choir is generous with its solos. Far too often these events have one main star while everyone else glares daggers at them. Here it seemed quite the opposite, with a range of voices brought to the forefront.
\n\nParts of the programme are a little odd. While Pilentze Pee is an incredible song and sung extremely well, I don't think there's much to tie it to The Wind Waker. Of course, the highlight was a cheeky rendition of \"Still Alive\" from Portal. An excellent arrangement sung with joy and gusto.
\n\nAn eclectic and interesting event, marred only by the uncomfortable pews of the church. Truly we must suffer for other people's art!
\n\n![](\"https://shkspr.mobi/blog/wp-content/themes/edent-wordpress-theme/info/okgo.php?ID=76517&HTTP_REFERER=DOI\"){kind=small}
","doi":"https://doi.org/10.59350/rbv3f-z4d27","guid":"https://shkspr.mobi/blog/?p=76517","image":"https://shkspr.mobi/blog/wp-content/uploads/2026/10/Video_Games_Go_Choral.webp","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791504000,"rid":"93523-v7d19","summary":"London's musical entertainment scene has something for everyone - from long-running blockbuster shows to no-name bands in dingy basements. Somewhere in the middle is monophonic plainchant versions of the Halo theme sung in a church. The acoustics at St Martin in the Field are echoey.","tags":["/etc/","Gaming","Gig","Review"],"title":"Concert Review: London Voices - Video Games Go Choral","updated_at":1791565350,"url":"https://shkspr.mobi/blog/2026/10/concert-review-london-voices-video-games-go-choral/","version":"v1"}}],"items":[{"authors":[{"affiliation":[{"id":"https://ror.org/050qmg959","name":"Singapore Management University"}],"contributor_roles":[],"family":"Tay","given":"Chee Hsien, Aaron","url":"https://orcid.org/0000-0003-0159-013X"}],"blog":{"authors":null,"community_id":"f34e2211-9904-4b58-97ab-0beeb79ef6f7","created":1697068800,"current_feed_url":null,"description":"Aaron Tay's thoughts about academic librarianship","doi":"https://doi.org/10.59350/musings","favicon":"https://rogue-scholar.org/api/communities/f34e2211-9904-4b58-97ab-0beeb79ef6f7/logo","feed_format":"application/rss+xml","feed_url":"https://aarontay.substack.com/feed","filter":null,"generator":"Substack","home_page_url":"https://aarontay.substack.com","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"musings","status":"active","subfield":"3309","title":"Aaron Tay's Musings about Librarianship","updated":1791665977,"use_api":true},"blog_name":"Aaron Tay's Musings about Librarianship","blog_slug":"musings","content_html":"I think I may have discovered the \"evil conspiracy\" behind why academic search vendors are so reluctant to show us what happens on the semantic side of AI search.
Recently, I have been wondering about a strange asymmetry. I noticed that when academic search vendors use AI to expand lexical searches, they are almost always happy to show you what they did.
For example, Web of Science Smart Search will happily show you the Boolean search it used. The same is true of products such as EBSCO AI-assisted Search, scite Assistant, Primo Natural Language Search and many others.
Then you get to the semantic-search part, and suddenly the curtain comes down.
Take Web of Science Smart Search again. We are told it runs a hybrid search, meaning it runs both lexical and semantic searches in parallel, combines the results and then somehow ranks the combined list.
Yet we know almost nothing about what happens in the semantic-search portion, beyond the fact that it was done.
What exactly was searched? Did they expand your query before embedding it? Why all the secrecy?
Similarly, Scopus AI has a copilot that decides \"whether to run a vector search (aka Semantic Search) and/or a keyword search\". Yet again, all the interface displays is the expanded Boolean Search.
Recently, while studying a new AI search system, I accidentally got a look backstage.
And I discovered a shocking \"truth\". The system was generating document-like passages for semantic retrieval rather than simply embedding my original query.
They were not just synonyms or alternative search terms. The generated passages could introduce assumptions that were never part of my question, or even contain outright false statements.
Imagine entering the following query:
Can Google Scholar be used alone for systematic reviews?
And finding out that, behind your back, the AI generates the following for expansion:
Google Scholar is not sufficient as a standalone source for systematic reviews because studies have shown that while it indexes almost all relevant research, it is difficult to retrieve all the relevant papers within the top 1000 results it allows access to.
Hold on a moment. I never said Google Scholar was not sufficient! That's precisely what I'm trying to find out! Why is the system apparently answering my question before it has even searched?
Now the reason for the \"conspiracy\" seemed obvious.
Vendors don't want us to find out that semantic search is being fed hallucinated AI-generated query strings!
Okay, okay, I was just being facetious here. There is NO evil conspiracy.
This is not to say that systems never do something similar to what I have described. They may generate text resembling relevant documents to help with retrieval, but that text could introduce assumptions that do not reflect the user's information need or even contain outright falsehoods. Such additions could potentially introduce bias if used in a traditional search.
But what I have described is not necessarily problematic. In fact, it resembles established techniques such as Query2doc and HyDE for expanding semantic searches.
While it may seem strange, the fact is:
A generated passage that is not entirely accurate or true can still be useful, perhaps even more useful than the original query, when embedded for semantic retrieval.
Confused? Let me start from the beginning.
The system itself was fairly typical of the current generation of AI-powered academic search tools.
There was a query interpretation step, probably involving an LLM. The system identified things such as entities that could be turned into filters, then ran a hybrid search combining lexical and semantic retrieval.
The results were merged and deduplicated, and an LLM was then used to classify them (by prompting) into relevance categories such as \"Very relevant\" and \"Relevant\".
None of this is especially unusual, and you can see parts of this process in many academic search systems. For example, using LLMs to extract relevance criteria and then classify results is something we see in AI2's Asta Find paper. But let's focus on the even more common idea of hybrid search.
Hybrid search is becoming increasingly popular in the search industry. Typically, it involves running both lexical or keyword searches and semantic searches.
The logic is straightforward. Lexical search is good at matching words and phrases. Semantic search can retrieve documents that express similar ideas using different language.
This potentially gives you the best of both worlds. You just need a way to combine both sets of candidate results and rank or rerank them somehow.
So far, so good. But let's look at the interface (which I have mocked up) that was shown to me.
When it comes to lexical search, we are shown quite a lot. We can see that it runs the original query and two expanded, nested Boolean search strategies (probably generated by an LLM).
But on the semantic side, we are told almost nothing, except that two searches were conducted.
Why this distinction?
When I was interviewed by the vendor, I naturally asked for more details about what was happening on the semantic search side.
Intriguingly, the vendor told me that an earlier version of the product had exposed more of the internal search process. However, users found it confusing, so some of the details were removed.
My reaction at the time was predictable. I did not understand this view at all. Surely more transparency would be better?
Perhaps they could even add an advanced mode for those who wanted more information.
But then I discovered some of the details that might have been hidden, and I began to empathise with why the vendor had chosen not to show everything.
While testing the reproducibility of the search, I ran into a completely different problem. No matter what I did, the system seemed determined to cache the results.
While troubleshooting this, on the advice of ChatGPT, I started recording the browser's network traffic using Chrome DevTools. We will revisit this in a future post.
This produces a HAR, or HTTP Archive, file: essentially a log of the requests and responses exchanged between your browser and the servers behind the application.
As a side effect, this exposed considerably more of the search pipeline than the interface itself did.
The logs confirmed that the system was running:
My original lexical query;
Two additional nested Boolean searches;
Two semantic searches.
There were also hints on many details. For example, about how many of the top results from each search were retained and how they were combined. But for now, let's focus on semantic search.
While there was very little information about the actual embedding model used, there was something more interesting.
I could see the actual text being used for semantic retrieval, and it was definitely not my original query.
Roughly speaking, the system appeared to be doing the following:
Original query + an LLM-generated short description of what a relevant document might look like
In short, it appeared to be taking my query and appending a chunk of LLM-generated text before creating the query embedding.
Those with some training or knowledge of information retrieval will recognise this as resembling a family of techniques such as Query2doc-style expansion or HyDE (Hypothetical Document Embeddings).
I spend quite a bit of time testing AI-powered academic search tools and trying to work out what is really happening behind their interfaces. If you find this kind of independent investigation useful, please consider buying me a coffee on Ko-fi. It helps support more investigations like this, which I share freely on this blog.
If you are already familiar with how embeddings and dense retrieval work, feel free to skip this section.
As I have noted many times on my blog, \"semantic search\" is not a single technique. It is better understood as an objective: retrieving or ranking items according to their estimated meaning.
It is typically contrasted with lexical or keyword search, where retrieval and ranking depend primarily on matches between terms in the query and terms in the documents.
The techniques used to achieve semantic search have changed over time. In recent years, semantic search has often, though not always, referred to dense retrieval or dense vector search.
In a typical dense retrieval system, the query is passed through an encoder model, often based on the Transformer architecture, to produce an embedding: a vector representation (a long series of numbers) intended to capture useful aspects of the query's meaning1.
Documents, passages or text chunks are encoded in a similar way, usually in advance during indexing. The query embedding can then be compared with these precomputed document embeddings.
Both query and document embeddings are represented as vectors: long sequences of numbers. The system calculates a similarity score between them, commonly using measures such as cosine similarity or dot product. Documents whose vectors are closer to the query vector are treated as better candidates for relevance.
Important to note: the query and document do not necessarily need to contain the same terms to be considered similar. Rather, the encoder is able to convert texts that are similar in \"meaning\" into representations that end up close together in vector space.
Conceptually, for dense retrieval, what we are doing is this:
Query \u2192 embedding \u2192 find nearby document embeddings
Semantic search via embeddings is inherently something of a black box
While a Boolean search like
(\"artificial intelligence\" OR \"machine learning\") AND libraries
allows you to inspect the search strategy to understand why some documents are retrieved and others are not. Embedding retrieval, however, is much less transparent.
Firstly, it is extremely difficult to interpret the long series of numbers representing each query or document. Secondly, even if I gave you the calculated similarity score between a query embedding and a document embedding, that would still not provide a satisfying, human-readable explanation of why they are close.
I have long expected this to be unavoidable.2 But surely vendors can at least tell us what text they embedded?
So why don't we just use the query as entered? While this can work in practice, one approach that can improve retrieval is to do the following:
Use an LLM to generate a document, or at least part of one, that might be relevant.
Use that generated text to help construct the query representation for retrieval.
The generated pseudo-document or hypothetical document is then used to help with retrieval.Query2doc appends the generated pseudo-document to the original query before passing the combined text through an encoder to create the query embedding3. HyDE directly generates the embeddings of the generated hypothetical documents (without the query) and in some variants will average that embedding with that of the original query embedding.
Another related technique is RAG-Fusion, used by Elsevier's Scopus AI. Rather than generating hypothetical documents as HyDE and Query2doc do, it generates multiple variations of the original query, runs separate vector searches and combines the results using Reciprocal Rank Fusion. Like HyDE and Query2doc, it can potentially introduce assumptions not present in the original query.
Empirically, this can work better than using the original query alone. Why?
One way of thinking about it is that the query itself is typically quite different, in terms of expression, length and so on, from a relevant document containing the answer. The logic, then, is that by generating a document that might be relevant, its embedding may end up closer to the embeddings of the desired documents than the original query embedding would.
Imagine your query is:
Can you use Google Scholar alone for systematic reviews?
And you get a fairly neutral hypothetical passage explaining what systematic reviews are, why comprehensive searching matters, and so on.
I think most of us would be okay with that.
But what if it generates something like this?
Google Scholar should not be used alone for systematic reviews because it only displays approximately 1,000 search results at most and lacks the advanced search functionality needed to retrieve all relevant studies.
This is, to my knowledge, true. But would using this passage for retrieval introduce bias into the results?
Or worse, what if it uses this?
Google Scholar can be used alone for systematic reviews because Tay(2024) has shown that more than 99% of papers eventually included in a SR can be found in Google Scholar's index.
For those unfamiliar with the literature, there is no such Tay (2024) paper, although real studies have reported very high coverage of studies included in systematic reviews within Google Scholar.
That said, the problem is that being indexed is not the same as being retrievable through a search. Google Scholar restricts access to roughly the first 1,000 results, so even if most relevant papers are in its index, there is no guarantee that a search will surface them.
Surely that last hypothetical passage would produce horrible results?
Not necessarily.
Remember, the system is not necessarily treating the generated text passage as a factual answer. It is using the text to construct a representation for retrieval.
Even though the passage contains a fabricated citation and a potentially misleading conclusion, it also contains concepts highly relevant to the original question: Google Scholar, systematic reviews, database coverage, included studies and comprehensiveness.
It is entirely possible that embedding this passage would retrieve useful papers discussing Google Scholar's suitability for systematic reviews, including papers that argue against using it alone.
But what about bias? Could generating a hypothetical document arguing that Google Scholar cannot be used alone for systematic reviews favour papers reaching that conclusion over those arguing the opposite?
Again, not necessarily. Some research has found that neural retrievers, particularly bi-encoders, can be surprisingly poor at distinguishing between statements that differ only in negation. This suggests that simply reversing a claim might have less influence on retrieval than we would intuitively expect.
Still, this does not mean there is no risk of bias. Our hypothetical passages differ in more than just their conclusions. They also emphasise different concepts, which might influence which documents are retrieved and how they are ranked.
Whether that actually happens is something we would need to test empirically. We cannot tell simply by inspecting the generated text.
In fact, it is conceivable that a hypothetical passage containing entirely correct statements could produce worse retrieval results than one containing factual errors. After all, factual accuracy and usefulness for retrieval are not the same thing4. What matters is whether the generated passage helps the system retrieve relevant documents, although factual errors could certainly make that harder.
There is an old search tip that sounds superficially similar to what these systems are doing.
The advice goes something like this: when searching Google, think about what words or phrases a webpage answering your question might contain, then search using those terms.
Suppose I want to know whether Google Scholar can be used alone for systematic reviews.
I might reason that relevant papers would contain terms such as coverage, recall, comprehensiveness, included studies and search limitations, then search using those terms.
This makes sense and has a clear resemblance to what HyDE is doing.
In both cases, we are trying to anticipate what relevant documents might look like and search accordingly5?
There is, however, an important distinction here.
With the Google trick, I am predicting words or phrases that I expect to literally occur in relevant documents. If I guess incorrectly, I may damage the search, which is one reason why using LLMs to generate nested Boolean searches does not always produce the best results6.
With HyDE, even if the generated document is factually wrong, its embedding can still be useful if it ends up closer to the embeddings of relevant documents than the original query embedding would.
The hope is that this richer, document-like representation lands somewhere near real documents discussing those concepts.
The fact that a generated passage reaches the wrong conclusion about Google Scholar does not necessarily prevent that from happening.
Of course, I am not saying that a hallucinated passage cannot push the query embedding further away from relevant documents.
In fact, some literature suggests that such expansion techniques tend to help weaker retrievers but may hurt stronger ones. They may also work less well in areas where the LLM's knowledge is limited. As always in information retrieval, whether a technique improves performance depends on factors such as the retriever, the types of queries and the dataset being searched. Ultimately, these are empirical questions that should be settled through testing (which hopefully the vendor has done) rather than decided a priori.
The point is simply that spotting a hallucination does not, by itself, tell us how serious the problem is.
This brings us back to my supposed conspiracy.
Imagine the interface shows you:
\"Google Scholar\" AND (\"systematic review\" OR \"evidence synthesis\") AND (coverage OR recall OR limitations)
A librarian might question why certain terms were included, but they would still recognise what they were looking at and feel comfortable with it.
Now imagine the interface instead shows:
Google Scholar can be used alone for systematic reviews because Tay (2024) demonstrated that more than 99% of studies eventually included in systematic reviews can be found in Google Scholar's index.
I suspect the reaction would be much stronger.
Why is the system citing a paper that doesn't exist? Why has it apparently decided that Google Scholar is sufficient? Is it now biased towards papers supporting that conclusion?
These are very reasonable questions if you interpret the passage as an answer or a literal statement of the search criteria.
But in the world of semantic embeddings, it is not necessarily either. It may simply be an intermediate representation used to create an embedding.
Perhaps part of the problem is that what happens inside modern semantic search no longer maps neatly onto what librarians traditionally think of as a query.
Modern AI search really does expand your query.
The surprising part is that making things up isn't necessarily the problem.
And this, I think, explains at least part of the strange asymmetry I keep seeing in AI search interfaces. Even if vendors wanted to show such details, many users, including librarians, might find them confusing7.
The more details vendors show about the search process, the greater the chance that they will open a can of worms by raising questions and concerns among users and librarians. From the vendor's perspective, the safer option may be not to show them.
The other part of the issue is that modern information retrieval is largely driven by empirical experimentation. You can propose a perfectly logical change that should improve retrieval, but if the experiments show that it doesn't work, you shouldn't use it. The reverse is also true. Some techniques may seem counterintuitive to librarians but work well empirically8.
While I can understand how vendors might feel, I still don't think they should hide all the details. We have a legitimate reason to want to know what is happening under the hood.
For librarians, the practical implication is this: resist the instinct to judge the semantic side of AI search by the same standards as Boolean. Spotting a hallucination in a query expansion is not evidence that a system is broken. The more useful question to ask is not \"does the generated query look right?\" but \"does the system consistently surface relevant results?\" That requires empirical testing against known relevant papers, not query inspection.
For librarians supporting systematic reviews the stakes are higher of course given the higher standards of transparency and reproducibility required.
What do you think?
Some might argue that showing the expanded query used to generate the query embedding is not particularly important. But the question applies more broadly to all aspects of the search pipeline.
Would you like vendors to show every detail, even if you don't fully understand it or think some of the techniques are strange?
If you found this essay on what AI-powered academic search tools are really doing behind the scenes educational, please consider supporting my work.
How is the encoder model able to generate representations that capture something resembling \"meaning\"? Encoder models learn useful language representations during pretraining on large text corpora, then are typically fine-tuned for retrieval so that relevant query-document pairs receive more similar vector representations than irrelevant ones.
You can get more transparency using multi-vector or late-interaction methods such as ColBERT, or learnt sparse representation methods such as SPLADE, but these are currently not commonly used.
Both techniques use very similar prompts to generate documents that Query2doc calls pseudo-documents and HyDE calls hypothetical documents. In practice, there is little difference between the generated texts themselves. The main distinction is that Query2doc was designed as a general query-expansion method. The query and expanded text can be used for lexical matching as well as retrieval via embeddings. HyDE was designed specifically for embedding-based retrieval.
Factual accuracy and retrieval effectiveness are different properties, but that does not mean they are statistically independent or even negatively correlated! For example, Yoon et al. (2025) found that hypothetical-document expansion improved retrieval mainly when generated passages contained information supported by the actual evidence, raising concerns that some reported gains might reflect knowledge leakage rather than better retrieval.
Notice this technique also risks bias!
I have long argued that using LLMs to generate Boolean is not very productive mostly because they tend to be poor at generating Boolean search strategies and even if they did do it decently it does not help average searchers who already know how to do so. A recent paper by San Diego State University documented how difficult it was to get the LLM to generate reasonable boolean just with prompt engineering.
I can imagine librarians or researchers unfamiliar with information retrieval becoming upset when they see what is happening and assuming that the search is broken.
Of course, it is important that a technique works generally and not just on limited test sets.
There seems to be s a lot of enthusiasm for AI powered tools for research these days, if I look at the feeds that the tech industry controls. No doubt that these tools greatly reduce the effort for a literature review that sounds plausible. It’s great to get a nice set of studies as a starting point for a new project in a new research area. But in these cases we usually don’t know what we don’t see. So here’s a cautionary tale from a rare case where we do know the errors of omission in AI powered tools.
\n\n\n\nThe challenge I gave the AI-powered tools is difficult: I asked them to identify replications of previous experiments in the multidisciplinary field of research on charitable giving. Such replications are needles in a field of haystacks. But we found them by hand, with a multidisciplinary team of six scholars in a study on replications of research on charitable giving. We conducted a comprehensive manual search on Google Scholar and identified 48 replications of experiments. How many replications from the set of 48 can AI-powered tools identify?
\n\n\n\nNot many, it turns out. The best tool, Google Scholar Labs, found one third of them. Most tools – even popular ones such as Perplexity, find fewer than 10. LeapSpace, the tool that Elsevier is trying to sell to university libraries in the Netherlands, performed abysmally. Collectively the twelve tools I assessed identified exactly half of all replications that we had found by hand. But it gets worse. Many studies that the AI tools identified as replications were not replications at all. Prompting more elaborately with explanations and examples did not make the results better.
\n\n\n\nThe news is not all bad. The tools also identified a number of new studies that we had not found because we had limited our search to keywords that returned a feasible number of results. The newly found replications used other terms than we had looked for. Another upside: at least the searches came back quickly. The slowest tool, Claude Science, took 35 minutes. The quickest tool returned results within a few seconds. The result: it could not find any of the replications.
\n\n\n\nThe results underscore the necessity of human review of results from AI-powered tools. At face value, the reports produced by the tools seem entirely plausible. However, human review of the results showed a high number of false positives, up to 70% of all results for LeapSpace. Conclusions based on the searches of the AI-powered tools are incomplete and incorrect. Without an accurate and efficient method to identify false positives the speed with which the tools concluded their work gives users the false impression that they can outsource identifying relevant previous work to AI-powered tools.
\n\n\n\nRead the full paper including the prompts and supplementary materials with all details and a link to the data and code here.
\n\n\n\n","doi":"https://doi.org/10.59350/gvwch-yhj37","guid":"https://renebekkers.wordpress.com/?p=4290","image":"https://renebekkers.wordpress.com/wp-content/uploads/2026/10/12aitools.png?w=975","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791590400,"rid":"wb9gx-88f29","summary":"There seems to be s a lot of enthusiasm for AI powered tools for research these days, if I look at the feeds that the tech industry controls. No doubt that these tools greatly reduce the effort for a literature review that sounds plausible.","tags":["AI","Data","Experiments","Household Giving","Meta Science"],"title":"Fast, but incomplete and inaccurate: twelve AI-powered tools for literature search","updated_at":1791665109,"url":"https://renebekkers.wordpress.com/2026/10/10/fast-but-incomplete-and-inaccurate-twelve-ai-powered-tools-for-literature-search/","version":"v1"},{"authors":[{"affiliation":[{"id":"https://ror.org/0130frc33","name":"University of North Carolina at Chapel Hill"}],"contributor_roles":[],"family":"Eshun","given":"Solomon","url":"https://orcid.org/0009-0004-1128-4149"}],"blog":{"authors":null,"community_id":"22d41343-8a53-4671-9643-0efbface1b2d","created":1787184000,"current_feed_url":null,"description":null,"doi":"https://doi.org/10.59350/solomoneshun","favicon":"https://rogue-scholar.org/api/communities/22d41343-8a53-4671-9643-0efbface1b2d/logo","feed_format":"application/rss+xml","feed_url":"https://solomoneshun.com/posts/index.xml","filter":null,"generator":"Quarto","home_page_url":"https://solomoneshun.com/posts/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"solomoneshun","status":"active","subfield":"1804","title":"Solomon Eshun","updated":1791604800,"use_api":null},"blog_name":"Solomon Eshun","blog_slug":"solomoneshun","content_html":"\n\n\n\n\n\n\n\n\nI have written a lot of notes that I never saw again. Not because I deleted them. They were still somewhere on my computer, carefully saved inside a folder with a sensible name. The problem was remembering that they existed when I actually needed them. That is the strange thing about most note-taking systems. We spend a lot of time thinking about how to store information and surprisingly little time thinking about how we will find our way back to it.
\nYou read something interesting. You write a note. You choose a folder. Maybe you add a tag. Then you move on. Months later, you are working on something related and vaguely remember, I know I wrote something about this before. So you search, open a handful of notes that are not quite right. Eventually, you either find it after far too much searching or give up and recreate the idea from scratch. The note is still there. The problem is that it just never found its way back into your thinking.
Obsidian approaches note-taking differently. Instead of treating notes as isolated documents that need to be carefully filed away, Obsidian encourages you to think about how ideas relate to one another. Suppose I am reading about confounding and create a note about it. Somewhere in that note, I mention g-computation. I can simply wrap g-computation in double brackets: [[g-computation]]. Those double brackets create a link. If I already have a note called g-computation, clicking the link takes me there. If I do not, Obsidian lets me create one. More importantly, the relationship works in the other direction too. When I eventually open my g-computation note, I can see that the confounding note links back to it. As I keep reading and writing, these connections accumulate and gradually becomes a network of ideas.
There is another reason I became comfortable with Obsidian. Your notes are simply Markdown files stored in an ordinary folder on your computer. They are not locked inside a proprietary database that only one application understands. Open your Obsidian vault in Finder or File Explorer and your notes are right there as .md files. You can open them with another text editor, move them, back them up, or manage them however you want. If you stopped using Obsidian tomorrow, your notes would still be yours and would still be readable.
Obsidian is also free to use. The features that make the application powerful, including plugins, themes, graph view, linking, and multiple vaults, are available without paying for a subscription. There are paid services such as Obsidian Sync for syncing your vault across devices and Obsidian Publish for publishing notes to the web, but they are optional conveniences. Check obsidian.md/pricing for current numbers, since they change.
\nAnd that brings me to this guide. I use Obsidian mainly for academic and research work, so that is the perspective I will use throughout this guide. You will see examples involving papers, methods, concepts, research projects, and ideas I want to revisit later. But there is nothing inherently academic about the system. The same approach can work for work projects, meeting notes, books, personal writing, recipes, or almost anything else you want to keep and connect.
\nI also do not want this to become one of those Obsidian tutorials where we install twenty-seven plugins, create fourteen folders, design an elaborate tagging taxonomy, and spend three hours building a system for taking notes before we have actually taken a note. We are going to start small. We will install Obsidian, create a vault, understand the few pieces of Markdown you actually need, create our first linked notes, and develop a simple structure for organizing them. From there, we can add templates, daily notes, plugins, backups, and some of the more powerful features, but only when there is a reason for them.
\nDownload Obsidian from obsidian.md for macOS, Windows, Linux, iOS, or Android. There is no account to create: the app opens straight into a prompt asking what you want to do. Choose Create new vault, give it a name, and pick where it lives on disk.
A vault is a folder. Inside it are .md text files and a hidden .obsidian/ subfolder holding your settings. That is the entire architecture. Open the folder in Finder or Explorer right now and you will see your notes sitting there as ordinary files.
Open your vault and you will see a mostly blank window. Here is what you are looking at.
\n
On the left, the file explorer lists every note in your vault; folders here are real folders on disk. In the middle, the editor is where you write. On the right, the graph view shows your notes as dots and their links as lines.
A handful of shortcuts cover most of what you will do:
\n| Shortcut | \nWhat it does | \n
|---|---|
Ctrl/Cmd + N | \nNew note | \n
Ctrl/Cmd + O | \nQuick switcher \u2014 jump to any note by name | \n
Ctrl/Cmd + P | \nCommand palette \u2014 every command, searchable | \n
Ctrl/Cmd + E | \nToggle editing / reading view | \n
Ctrl/Cmd + Shift + F | \nSearch across the whole vault | \n
Ctrl/Cmd + click | \nOpen a link in a new pane | \n
If you learn only one, make it Ctrl/Cmd + O.
Type three or four letters and the note you want is usually the top hit. Once you pass a few hundred notes this becomes how you navigate almost exclusively, and the file explorer turns into decoration.
\nObsidian notes are Markdown, but you rarely have to think about syntax. You type a few characters and the formatting appears live.
\n
The syntax you will actually use day to day is small:
\n# Heading 1\n## Heading 2\n\n**bold** *italic* `inline code`\n\n- bullet\n- [ ] unchecked task\n- [x] completed task\n\n> blockquote\n\n[[Link to another note]]\n[[Link to another note|shown as this text]]\n![[Embed another note entirely]]\n\n#tag #nested/tagTwo Obsidian-specific ones are worth calling out. The pipe in [[Note|display text]] lets the link read naturally in a sentence while still pointing at the right file. And ![[Note]] with a leading exclamation mark embeds the whole note inline rather than linking to it (useful for pulling a definition into a summary without duplicating it).
This is the part that matters. Create a note called Confounding and write a couple of sentences. Somewhere in the text, type two opening square brackets: [[.
A dropdown appears. Start typing G-computation. Even though that note does not exist yet, press Enter \u2014 Obsidian creates it the moment you click through.
Now open G-computation and look at the Linked mentions panel.
You typed one link. You got two. That is the whole trick, and it compounds: every time you link a new note to an old one, the old note gets richer without you touching it.
\nNew users spend their first week building an elaborate folder hierarchy and their second week discovering it does not fit. Obsidian gives you three organising tools, and they answer different questions.
\n
Folders answer where does this live? A note sits in exactly one. Use them for broad, stable categories. Three to six top-level folders is plenty. Tags answer what kind of note is this? Type #method or #toread anywhere in a note. A note can carry many, which makes tags good for status and type \u2014 things that cut across folders. Nested tags like #status/draft give you a little hierarchy without much cost. Links answer what is this related to? These do the real work. Folders and tags are coarse; links are specific.
The failure mode is over-investing in the first and under-using the third. A flat vault with good links is far more useful than a beautiful eleven-level folder tree with none.
\nHere is a starter structure:
\nmy-vault/\n\u251c\u2500\u2500 Daily/ # one note per day, capture inbox\n\u251c\u2500\u2500 Notes/ # the permanent, linked notes\n\u251c\u2500\u2500 Projects/ # one note per active project\n\u251c\u2500\u2500 Reading/ # books, papers, articles\n\u2514\u2500\u2500 Templates/ # reusable note skeletonsLet Notes/ grow flat and link heavily inside it.
Go to Settings \u2192 Core plugins and enable Daily notes. In its options, set the folder to Daily/ and the date format to YYYY-MM-DD so the files sort correctly.
The daily note is a capture inbox, not an archive. Anything that occurs to you during the day lands here with no organising: meeting notes, half-ideas, a paper someone recommended, a problem you hit in your analysis. The bar for writing something down should be almost zero.
\nLater, you promote the few things that earned it into real notes in Notes/, with claim-style titles and links. Most of what you capture will not earn it, and that is the point. The inbox absorbs the noise so your permanent notes stay signal.
Turning on the Calendar community plugin gives you the month view shown above, which makes it easy to jump back to \"that Tuesday when the model broke.\"
Enable the Templates core plugin and point it at Templates/. Then create a file there like this:
# {{title}}\n\n**Source:**\n**Date:** {{date}}\n\n## Summary\n\n## Key points\n-\n\n## Links\n-Now Ctrl/Cmd + P \u2192 \"Insert template\" drops that skeleton into any new note.
The payoff is not the typing you save. It is that every literature note has the same shape, so six months later you can skim thirty of them quickly, and a Dataview query can pull fields out of them reliably. Two or three templates is usually the right number: one for literature, one for meetings, one for projects.
\nCommunity plugins live in Settings \u2192 Community plugins \u2192 Browse. You will need to turn off Restricted Mode first.
\nResist installing twenty of them. Each is a thing that can break, conflict, or need migrating, and the most common way to end up with a broken vault and no notes in it is to spend week one configuring instead of writing. Add plugins to solve problems you have actually hit.
\nThe ones that earn their place for most people:
\n| Plugin | \nWhat it solves | \n
|---|---|
| Dataview | \nTurns notes into a queryable database | \n
| Calendar | \nMonth view for daily notes | \n
| Templater | \nTemplates with logic \u2014 dates, prompts, scripting | \n
| Excalidraw | \nHand-drawn diagrams stored inside the vault | \n
| Style Settings | \nTweak theme colours and spacing without CSS | \n
Dataview is the one that changes how the vault feels. Instead of maintaining an index note by hand, you write a query and it stays current.
\n
That query sits in a normal note. Every time you open it, Obsidian scans the Reading/ folder and rebuilds the table, so a paper you add next month appears without you editing anything.
Here is the honest truth about the graph view: it is beautiful, it is motivating, and it is useless for the first month.
\n
A graph of twelve notes tells you nothing you did not already know. A graph of four hundred shows you clusters you never planned, orphan notes you should link or delete, and hub notes that turn out to be the real organising centres of your thinking.
\nBuilding structure before content. You cannot design the right hierarchy for notes you have not written. Start flat and let structure emerge.
Installing too many plugins. Add them to solve problems you have actually hit, not problems a video told you about.
Treating it as an archive. A vault full of clipped articles you never linked or reread is a graveyard, not a knowledge base. One note you wrote in your own words beats ten you pasted.
Splitting into many vaults. Links do not cross vault boundaries. One vault, folders inside it.
Chasing the perfect system. Every month someone publishes a new methodology with an acronym. The people getting real value from Obsidian are mostly running something boring and consistent. Boring and consistent wins.
Over-tagging. If every note carries eight tags, tags stop narrowing anything. A handful you actually filter on beats a taxonomy you admire.
If you want something concrete to follow:
\nDays 1\u20132. Install, create one vault, make the five folders. Write three notes about things you already know well and link them to each other.Days 3\u20137. Turn on daily notes. Capture into them every day, without organising. Do not touch settings.Day 7. First review. Promote two or three captures into real notes. Notice what you keep reaching for.Week 2. Add a literature template. Install Dataview only if you feel the need for an index. Write a note that links to five existing ones.End of week 2. Open the graph for the first time. It will be small, and that is fine.Once the daily loop is automatic, the next steps are richer Dataview queries, Templater for dynamic templates, and if you want to publish, either Obsidian Publish or a static-site generator like Quartz, which renders a vault into a website for free.
\nBut none of that matters until the habit exists. After installation, everything else is elaboration. I hope this helps!
\nI'll be radically honest here - I never really got the idea of \"Test Driven Development\". I'm much more of a \"telnet into production and damn the consequences\" kind of guy. Look, it's much more fun and my cardiologist recommends severe shocks every now and again.
\n\nThe essence of unit testing is pretty simple:
\n\nSend it good data and if it returns \"true\" the test passes. Send it bad data and make sure it returns false. Easy peasy lemon squeezy.
\n\nAs part of my NLnet funded work on ActivityBot, I'm writing some proper tests for my bot framework. I am hopeful that some of them will be useful to other projects doing similar things.
\n\nThere are four major classes of tests - here are the problems I've found while writing them.
\n\nA basic test might be how to validate that an ActivityPub username is valid. Typically it would be something like @edent@mastodon.social - what assumptions do we make with that? It starts with an @ and has another @ somewhere in it, dividing into two parts, the user and the server. The server has to be a valid domain name - but that can include non-ASCII Internationalised Domain Names and Emoji domains.
What about the user part? Is it just A-Z and 0-9? Can it have dots? What about apostrophes like an email address?
\n\nBasically, how easy is it find the specifications for all the various component parts of ActivityPub? Not very!
\n\nWhat does a WebFinger response look like? Which parts are mandatory and what values can they contain?
\n\nWith any message that your project generates, how can you be sure that other ActivityPub servers will understand it?
\n\nSimilarly, when we receive a follow message from an external server, what should it look like?
\n\nThere are some JSON Schemas which can be used to validate some type of messages.
\n\nBut, again, there are many scattered specifications.
\n\nAs I've written about before, HTTP Signatures are difficult. There are two main problems. The first is verifying signatures.
\n\nIn order to do that, you need to gather a bunch of signatures and then find a way to replay them into the test. So I'm in the process of saving signatures which I can then share with others.
\n\nThe second problem is generating signatures. Sure, you can run tests on the signatures - but there's only one real way to test whether they're acceptable\u2026
\n\nPassing tests means nothing if you can't communicate with others and they can't communicate with you.
\n\nSome tests are easier than others. Sending a signed request should be easy and repeatable. But receiving messages isn't easily automated. It requires being able to control an external server and asking that to send messages.
\n\nWith the Mastodon API it is possible to make requests which will interact with your server - but only if it is a public server.
\n\nI'm putting all my tests online. I'm also sharing a collection of messages and signatures which you can use in your tests.
\n\nI'd love to know if you find them useful.
","doi":"https://doi.org/10.59350/nmxzr-19z59","guid":"https://shkspr.mobi/blog/?p=75774","image":"https://shkspr.mobi/blog/wp-content/uploads/2012/09/709ae69b.png","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791590400,"rid":"s1wdt-2k111","summary":"I'll be radically honest here - I never really got the idea of \"Test Driven Development\". I'm much more of a \"telnet into production and damn the consequences\" kind of guy. Look, it's much more fun and my cardiologist recommends severe shocks every now and again.","tags":["/etc/","ActivityBot","ActivityPub","Php"],"title":"Some quick thoughts on Unit Testing ActivityPub","updated_at":1791633911,"url":"https://shkspr.mobi/blog/2026/10/some-thoughts-on-unit-testing-activitypub/","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Julia Hoffmann","given":"Petra Mensing"}],"blog":{"authors":null,"community_id":"db0d8909-9e37-46d0-b16c-0551f575e86b","created":1749772800,"current_feed_url":null,"description":"Das Blog der TIB \u2013 Leibniz-Informationszentrum Technik und Naturwissenschaften und Universit\u00e4tsbibliothek","doi":"https://doi.org/10.65527/tib","favicon":"https://rogue-scholar.org/api/communities/db0d8909-9e37-46d0-b16c-0551f575e86b/logo","feed_format":"application/atom+xml","feed_url":"https://blog.tib.eu/feed/atom/","filter":null,"generator":"WordPress","home_page_url":"https://blog.tib.eu/","issn":null,"language":"deu","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.65527","relative_url":null,"secure":true,"slug":"tib","status":"active","subfield":"1802","title":"TIB-Blog","updated":1791550026,"use_api":true},"blog_name":"TIB-Blog","blog_slug":"tib","content_html":"Open Science und geistiges Eigentum werden oft als Gegens\u00e4tze verstanden: hier der freie Zugang zu Wissen, dort Patente, Urheberrechte und andere Schutzrechte. F\u00fcr den Transfer von Forschung in die Praxis greift diese Gegen\u00fcberstellung jedoch zu kurz. Die entscheidende Frage ist vielmehr: Was sollte offen sein, was muss gesch\u00fctzt werden \u2013 und wann?
\nDieser Frage wurde auf der Innovations-Roadshow des Deutschen Patent- und Markenamtes (DPMA) am 8. September 2026 in Hannover an der TIB nachgegangen.
\nForschungsergebnisse sind mehr als wissenschaftliche Publikationen. Sie entstehen als Daten, Software, Modelle, Videos, Prototypen oder technische Erfindungen. Damit stellen sich zugleich Fragen nach Zug\u00e4nglichkeit, Nachnutzung, Urheberrecht und gewerblichem Rechtsschutz.
\nWer Forschung offen zug\u00e4nglich machen und Innovation erm\u00f6glichen will, muss deshalb beides zusammendenken: Offenheit und Schutz.
\nEine wichtige Schnittstelle daf\u00fcr ist das Patentinformationszentrum (PIZ) Hannover an der TIB. Seit 1878 unterst\u00fctzt es dabei, technisches Wissen und Informationen \u00fcber Schutzrechte zug\u00e4nglich und nutzbar zu machen. Heute ist das PIZ das einzige Patentinformationszentrum in Niedersachsen und Kooperationspartner des Deutschen Patent- und Markenamts.
\nZu den kostenfreien Basisdiensten geh\u00f6ren Informationen und Fachliteratur zu Patenten und gewerblichen Schutzrechten, Unterst\u00fctzung bei Recherchen in Patent- und Schutzrechtsdatenbanken sowie Informationen zu nationalen und internationalen Schutzrechtsverfahren. Veranstaltungen und Sensibilisierungsangebote vermitteln zudem Grundlagen des gewerblichen Rechtsschutzes.
\nDabei versteht sich das PIZ Hannover als neutrale Lotsenstelle: Die kostenfreie Erfindererstberatung kann bei Bedarf zu IHK oder Patentanwaltschaft weiterf\u00fchren. F\u00fcr spezialisierte Recherchen, Analysen oder Patentstatistiken werden geeignete Anbieter und Einrichtungen vermittelt. Auch bei Fragen zu Verwertung, Schutzrechtsmanagement oder F\u00f6rderm\u00f6glichkeiten verweist das PIZ auf die jeweils zust\u00e4ndigen Stellen.
\nSo bietet das PIZ einen kostenfreien, neutralen und niedrigschwelligen Zugang zu Informationen und Orientierung im Netzwerk der Schutzrechts- und F\u00f6rderangebote.
\nF\u00fcr Forschende beginnt der Mehrwert oft schon vor einer Patentanmeldung: Welche L\u00f6sungen gibt es bereits? Was wurde schon ver\u00f6ffentlicht? Welche Entwicklungen sind neu \u2013 und wo gibt es Ankn\u00fcpfungspunkte f\u00fcr die eigene Forschung?
\nEine Patentrecherche kann dabei auch helfen, Unternehmen, Forschungseinrichtungen oder andere Akteure zu identifizieren, die an \u00e4hnlichen Technologien arbeiten und als m\u00f6gliche Kooperationspartner infrage kommen.
\nPatentinformationen k\u00f6nnen damit eine Br\u00fccke zwischen wissenschaftlicher Erkenntnis und technologischer Entwicklung bilden.
\nEin Beispiel f\u00fcr diese Verbindung ist die Patentdatenbank Orbit Intelligence. Die Plattform kann aus wissenschaftlichen Texten technische Merkmale extrahieren und dazu passende Patentdokumente identifizieren. Lizenziert f\u00fcr Nutzende der TIB. DBIS \u2013 Orbit Intelligence
\nSo l\u00e4sst sich etwa pr\u00fcfen, in welchen Patenten bestimmte Komponenten, Verfahren oder technische Zusammenh\u00e4nge beschrieben sind. Orbit unterst\u00fctzt damit die Recherche und Analyse \u2013 ersetzt aber keine rechtliche Pr\u00fcfung von Neuheit oder erfinderischer T\u00e4tigkeit.
\nF\u00fcr Forschende entsteht so eine praktische Br\u00fccke zwischen wissenschaftlicher Publikation und Patentinformation.
\nWie aus Patentinformation und Forschungsergebnissen konkreter Technologietransfer entstehen kann, zeigt das Beispiel von Bardia Akaberi der Firma Goldhoop.
\nAusgangspunkt war die Recherche nach dem Stand der Technik, auf die dann der Schutz der Erfindung als Gebrauchsmuster DE 20 2025 003 427 folgte. Doch der Transfer endete nicht mit dem Schutzrecht. Ein funktionierender Prototyp wurde umgesetzt und in einem Video dokumentiert. Denn ein Film sagt mehr als 1.000 Bilder: Die technische Idee wird anschaulich, verst\u00e4ndlich und f\u00fcr potenzielle Kooperations- und Transferpartner unmittelbar erlebbar.
\n\u00dcber das TIB AV-Portal (https://doi.org/10.5446/73021) bleibt die Dokumentation dauerhaft auffindbar und zitierf\u00e4hig. Im TIB-Portal \u00a0k\u00f6nnen Patent, Video und wissenschaftliche Informationen miteinander verkn\u00fcpft werden.
\nEin Schutzrecht allein macht eine Erfindung noch nicht sichtbar. Erst wenn eine technische Entwicklung verst\u00e4ndlich, auffindbar und dauerhaft dokumentiert ist, kann daraus Interesse entstehen.
\nSichtbarkeit \u2192 Verst\u00e4ndnis \u2192 Interesse \u2192 Kontakt \u2192 Technologietransfer
\nSo wird aus gesch\u00fctztem Wissen ein anschlussf\u00e4higes Forschungsergebnis \u2013 und aus Forschung kann Innovation entstehen.
\nDie TIB macht mit Ihren Services Digitale Dienste und Dienstleistungen der TIB \u00a0Wissen dauerhaft sichtbar, recherchierbar und transferf\u00e4hig. Das PIZ Hannover an der TIB unterst\u00fctzt Sie bei den gewerblichen Schutzrechten. \u00a0So entsteht eine Br\u00fccke zwischen Forschung, Schutz und Innovation. Sprechen Sie uns gerne an!
","doi":"https://doi.org/10.65527/hbjbb-28825","guid":"https://blog.tib.eu/?p=33999","image":"https://blog.tib.eu/wp-content/uploads/2026/10/2026-dpma.png","language":"de","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791504000,"rid":"f73k7-csb39","summary":"Open Science und geistiges Eigentum werden oft als Gegens\u00e4tze verstanden: hier der freie Zugang zu Wissen, dort Patente, Urheberrechte und andere Schutzrechte. F\u00fcr den Transfer von Forschung in die Praxis greift diese Gegen\u00fcberstellung zu kurz. Die entscheidenden Fragen sind vielmehr: Was sollte offen sein, was muss gesch\u00fctzt werden \u2013 und wann?","tags":["SERVICES","Lizenz:CC-BY-4.0-INT","TIB AV-Portal","TIB-Portal","Patent"],"title":"Offen forschen, gezielt sch\u00fctzen","updated_at":1791617415,"url":"https://blog.tib.eu/2026/10/09/offen-forschen-gezielt-schuetzen/","version":"v1"},{"authors":[{"affiliation":[{"name":"Fachhochschule Potsdam"}],"contributor_roles":[],"family":"Kaden","given":"Ben","url":"https://orcid.org/0000-0002-8021-1785"}],"blog":{"authors":null,"community_id":"00dd7e11-a802-44c1-9584-5c56d1f8d417","created":1706832000,"current_feed_url":null,"description":"Vernetzungs- und Kompetenzstelle Open Access Brandenburg","doi":"https://doi.org/10.59350/oabrandenburg","favicon":"https://rogue-scholar.org/api/communities/00dd7e11-a802-44c1-9584-5c56d1f8d417/logo","feed_format":"application/atom+xml","feed_url":"https://open-access-brandenburg.de/feed/atom","filter":null,"generator":"WordPress","home_page_url":"https://open-access-brandenburg.de/","issn":null,"language":"deu","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"oabrandenburg","status":"active","subfield":"1802","title":"Open Access Brandenburg","updated":1791545849,"use_api":false},"blog_name":"Open Access Brandenburg","blog_slug":"oabrandenburg","content_html":"Es gibt ein relativ neu beschriebenes Ph\u00e4nomen im Open Access bzw. Diamond Open Access, \u00fcber das unl\u00e4ngst Nature berichtete und auf das wir ebenfalls kurz hinweisen wollen. So berichten Lisa Matthias vom Institut f\u00fcr Bibliotheks- und Informationswissenschaft der Humboldt-Universit\u00e4t zu Berlin und ihre Ko-Autoren Juan Pablo Alperin (Simon Fraser University, Vancouver) und Mikael Laakso (Tampere University, Tampere) \u00fcber F\u00e4lle, in denen wissenschaftliche Zeitschriften von einem Diamond-Open-Access-Modell auf APC-basierte Gold-OA, Hybrid-OA oder Subskriptionsmodelle umstellten:
\nLisa Matthias, Juan Pablo Alperin, Mikael Laakso: Diamond Fractures: Tracing Journal Transitions Away from Diamond Open Access. (30.06.2026) DOI: 10.48550/arXiv.2606.31302\n\nInsgesamt konnten sie 440 F\u00e4lle f\u00fcr den Zeitraum von 2009 bis 2026 identifizieren, wobei der gr\u00f6\u00dfte Anteil auf den Wechsel zu Gold Open Access mit Publikationsgeb\u00fchren zu verzeichnen ist. Interessanterweise beschleunigte sich diese Entwicklung in den vergangenen Jahren. Ebenfalls interessant erscheint, dass dies nicht etwa mit Verlags- oder Betreiberwechseln (\"ownership\") zu erkl\u00e4ren ist \u2013 die \u00fcberwiegende Zahl der Titel, 389 von 440 \u2013 blieb beim Ursprungsbetreiber.\nBei einem Teil der F\u00e4lle l\u00e4sst sich Diamond Open Access als \u00dcbergangs- und Etablierungsma\u00dfnahme nach der Neugr\u00fcndung einer Zeitschrift verstehen:
\n\n\n\"we see the pattern of publishers forgoing APC revenue as a promotional strategy, while a new journal builds indexing and reputation, then introduce them once the journal is established\".
\n
Der gr\u00f6\u00dfere Teil der Titel \u00e4ndert sein Betriebsmodell allerdings erst sp\u00e4ter, was mit dieser Deutung adressiert wird:
\n\n\n\"The temptation and relative ease of adopting APCs is particularly evident for these journals, given that such established journals would be strong candidates for collective funding models, such as Subscribe to Open.\"
\n
Besondere Relevanz erh\u00e4lt die Analyse deshalb, weil Diamond Open Access verst\u00e4rkt, wie auch zuletzt auf den Open-Access-Tagen 2026, als ein m\u00f6gliches Alternativmodell besonders zum geb\u00fchrenfinanzierten Gold Open Access angesehen und diskutiert wird und man daher sehr viel \u00fcber die Verschiebung von Gold zu Diamond spricht. Die entgegengesetzte Entwicklung wird dagegen bisher kaum thematisiert. Dank der vorgelegten Auswertung ist \"Diamond Fractures\" im Diskurs und auch in Nature angekommen. Zudem unterstreicht der Befund zus\u00e4tzlich den Bedarf an Diamond-OA-Modelle, die auf strategische Verstetigungen in nicht-kommerziell ausgerichteten Kontexten aufbauen.
\nWer mehr \u00fcber Diamond Fractures erfahren m\u00f6chte, hat im November die Gelegenheit. Denn am 27.11.2026 ab 18 Uhr und virtuell wird Lisa Matthias ihre Forschungen zum Thema \"Diamond Fractures\" im Berliner Bibliothekswissenschaftlichen Kolloquium an der Humboldt-Universit\u00e4t zu Berlin pr\u00e4sentieren: Hybrid und Diamond Open Access unter der Lupe \u2013 Open Access als Forschungsobjekt der Bibliotheks- und Informationswissenschaft.\u00a0
\n\n\nZitierhinweis:
\nKaden, Ben (2026): \"OA-News: Diamond (Open Access) Fractures als Forschungsthema.\" DOI: 10.59350/yq0vn-j2231
\nJoin me and all of us here in the School of Data Science on Friday, Nov. 13 for Datapalooza 2026, UVA's flagship data science conference celebrating data science in action across disciplines and for the public good.
This year's theme is \"The Future of Work, Powered by Data and People.\" Data science is changing how we work and the decisions we make. As AI becomes more integrated into workplaces, its impact will depend not only on how the technology evolves, but also on the people who build and use it.
Datapalooza will be held Friday, November 13, 8:45pm - 5:45pm here at the University of Virginia School of Data Science in Charlottesville, VA. The main event kicks off at 1pm. Registration is free.
8:45 a.m. \u2014 Registration/Check-In Begins
9:30-10:30 a.m. \u2014 MSDS Online Breakfast
10:30-11:00 a.m. \u2014 Building Tours
10:30 a.m.-12:00 p.m. \u2014 Mixer for Residential & Online Students
11:00 a.m.-1:00 p.m. \u2014 Headshots
12:00-1:00p.m. \u2014 Networking Lunch
1:10-1:15 p.m. \u2014 Kickoff
1:15-2:15 p.m. \u2014 Fireside Chat Discussion Panel
2:30-3:30 p.m. \u2014 Breakout Sessions
3:45-4:30 p.m. \u2014 Keynote
4:45-5:45 p.m. \u2014 Reception
We'll kick off the day with a guided conversation with industry leaders which inspires and explores how Data Science and AI are fundamentally changing how we make decisions and how we work. The conversation will explore the evolving role of data science across industry and society. Panelists will consider the reality that the future of work cannot be shaped by technology alone but must be driven by the choices that people make about how technology is developed and put to use.
Breakout 1 | How Do You Become a Data Science Leader?
What does leadership look like in the age of AI? How can leaders harness data and emerging technologies that support teams in making decisions, setting priorities, and driving business. How are leaders thinking about data, AI, and helping to define the human-technology relationship?
Breakout 2 | What Do You Really Want to Know About Working in Data Science?
This is your chance to challenge data science academic and industry leaders to give candid answers and real-world perspectives about what's happening in the field\u2014as well as their personal journey to and through data science. They will be put in the \"hot seat\" by our very own students. How did they get here? What trends, tools, challenges, and opportunities are emerging now and in the future? How is industry/higher education thinking about and addressing AI ethics and safety concerns? And where will people be in all of this? Come curious and ready to engage.
Breakout 3 | How Can a Data Scientist Create and Innovate?
Explore how innovators are using data and AI not simply to analyze what already exists, but to create new possibilities. What emerging tools, ideas and experiences are unfolding, and how might you create and innovate as a student, community member, alumni or industry partner?
Close out Datapalooza with an inspiring keynote on how thoughtful policy and responsible data practices can help communities thrive.
Postdoc Research Showcase During Reception
","doi":"https://doi.org/10.59350/4yxfj-kdn52","guid":"219590216","image":"https://substackcdn.com/image/fetch/$s_!ywYU!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F41a4a457-21e4-404a-8cf2-a643189d868e_940x529.webp","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791504000,"rid":"n31cb-f4s26","summary":"UVA's flagship data science conference will be held at the School of Data Science Friday November 13. This year's theme: The Future of Work, Powered by Data and People. Registration is free.","title":"Datapalooza 2026: The Future of Work, Powered by Data and People","updated_at":1791617409,"url":"https://blog.stephenturner.us/p/datapalooza-2026","version":"v1"},{"authors":[{"contributor_roles":[],"family":"Hunger","given":"Francis"}],"blog":{"authors":[{"name":"Carrier-bag Staff"}],"community_id":"0f516bef-eddb-49f7-afc4-b5fc2468ee95","created":1749168000,"current_feed_url":null,"description":"Critical writing and research on technology, AI, art and digital culture, run by Hito Steyerl and Francis Hunger at AdBK Munich","doi":"https://doi.org/10.59350/carrier_bag","favicon":"https://rogue-scholar.org/api/communities/0f516bef-eddb-49f7-afc4-b5fc2468ee95/logo","feed_format":"application/atom+xml","feed_url":"https://carrier-bag.net/feed/atom","filter":null,"generator":"WordPress","home_page_url":"https://carrier-bag.net/","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"carrier_bag","status":"active","subfield":"1702","title":"carrier-bag.net","updated":1791551869,"use_api":true},"blog_name":"carrier-bag.net","blog_slug":"carrier_bag","content_html":"Five practice reports illustrate the scope of research and studying within the Emergent Digital Media class (Prof. Dr. Hito Steyerl, Dr. Francis Hunger) at the Academy of Visual Arts, Munich. They foreground examples of generative practices using machine learning techniques, colloquially known as 'AI', leaving aside other diverse artistic strategies (conceptual, performative, video essay, etc.) negotiated in class. https://www.generativemedia.net
\n\n\n\nThe scope of the reports by Vasilii Vikhlaev, Chloe McFadden, Guillaume Menguy, Nikita Sazonov and Otto Ostermann reaches from direct inquiry into techniques and tools to the metaphorical reflection on societies 'AI' phantasms. A commonality of the diverse approaches is that they probe how systems work, instead of just using the outputs as finished results. By that the artists develop a critique of the dominant narratives about 'AI', and they uncover the material dependencies and massive infrastructures (data, energy consumption, data centers) behind the stochastic generation of images, sound and text.
\n\n\n\nThis report addresses two registers of my work with machine learning in sound: the inductive one, meaning pre-trained models, and the deductive one, meaning physical modeling. The question behind both is how to synthesize sounds that are not present in any database. I'll introduce three projects and deliver an outlook on future research.
\n\n\n\nThe first project is Aggressor’s Tongues (2025/26), a 10-channel audio experiment of 41 min that was exhibited at the Kunstbau of Lenbachhaus, Munich. The project began with audio material of scraped recordings of Russian propaganda speech from the ongoing war against Ukraine: voices saturated with hate and domination. The machine listens, so I don't have to. The Montreal Forced Aligner (MFA) algorithm takes an audio file and its transcript and returns time stamps for every word and phoneme, normally as a preparation step for phonetics research or for building speech datasets. I used its output as the material itself: the piece works on the phoneme grid, not on the meaning of the speech. The grid also set the segmentation of the training data for variational autoencoders (VAE), so the model could only learn units below the level of meaning. The VAEs were activated using the RAVE framework, originally developed at the Institut de recherche et coordination acoustique/musique (IRCAM).
During the process, I discarded an earlier approach with emotion recognition (openSMILE, Praat), because the results were too illustrative and literal. Instead, I focused on decoder artifacts. I displayed them rather than smoothed them out, to keep the apparatus audible. For the same reason the drifts through the latent space were deterministic, several phasors in irrational ratios, so that the trajectory never repeats. I wanted to hear and make audible the system, not the sounds it was meant to produce.
The second project is titled, vzkazy dom\u016f (2025) and was developed in cooperation with Andrea Vesel\u00e1. It's format is 7.1 surround sound with a length of 10 hours, and was developed for the former Radio Free Europe building in the frame of Public Art Munich 2025. Starting point was a short found-footage recording of a historical radio signal being intentionally jammed to interfere with its broadcast, going from signal to noise. We slowed this recording down and Andrea contributed a reactive voice interpretation. Then she listened to her own recorded voice through an earpiece and sang to it, close enough in pitch that the two voices interfere and produce interfering oscillations, called 'beatings' (Schwebungen). That material became the training data for the VAE. The preparation of the input was prioritized over any selection at the output, emphasizing the data: the beatings were already in the training material, as a measurable modulation, and not added posterior as an effect.
\n\n\n\nFor the third project, Composite (2026, in progress), I turned a synthetization technique that involves no machine learning at all, called 'physical modeling'. The work is based on the composite plastic coins of Transnistria, the first plastic coins in general circulation. With Modalys, a generative tool developed at IRCAM, sound is computed from the physical properties of a body (size, density, Young's modulus). Unlike classical sound synthesis (additive, subtractive, FM) or sample-manipulation approaches, physical modeling simulates the sound-producing behavior of an instrument itself. This makes it possible to capture sonic qualities that traditional synthesis techniques reproduce only with difficulty. As part of the exploratory artistic process, I went from small data to no data. Physical modeling uses established physical laws from material sciences and acoustics, so it can compute objects that never existed. Compared with machine learning approaches, physical modeling is able to produce sound without examples. Machine learning for audio is almost always inductive, needing data, so the availability of data decides what can be produced at all. For rare, local or non-existent material that is a hard limit. I did not take this step to abandon machine learning techniques, but to run both methods side by side and hear the difference.
For the machine learning process, I am building a dataset of the real coin sounds to train a VAE on them and drive that model with the computed sounds using physical modeling: the input is encoded into the latent space and decoded again, so the output carries the statistics of the training data. The input cannot lie inside that distribution: Modalys computes an idealized body, without a surface, without the other coins, without a room. Some of the material constants I set, do not belong to any real object either. The statistical signature of a real object is imposed on an object that never existed. Input and output will be presented as pairs, not blended, to keep the model readable. The inverse is planned too: physically modeled sounds as training data, real coin sounds as input driving the model.
This report reflects upon my practice-based technique, prompt-shifting. It is both a practical technique for methodically probing the biases and patterns of text-to-image models, and a conceptual intervention that opposes magical technoscientistic framings.
\n\n\n\nPrompt-shifting enables artists to probe commercial text-to-image platforms without stabilizing their rhetorical claims and purported abilities of dream-making and superior accuracy. Rather than visualizing our imaginations, this technique shifts attention away from outputs towards the processes and contexts of their production. Using the same seed, the artist slowly and methodically introduces and varies the influence of certain subjects in the prompt. By noticing what shifts and emerges overtime, the artist can speculate and sense how learnt patterns are activated within generative AI models in ways that manifest visually.
\n\n\n\nThis technique emerged from my broader research project that attempts to engage and disrupt the 'magical technoscientism' of generative AI: a fusion of magic and technoscientism that affords models the ability to simultaneously create miraculous and scientifically authoritative outputs. In commercial text-to-image rhetoric, prompts are imagined as granting users the gift of dream-making while appeals to 'prompt-adherence' and 'accuracy' position prompting as a science. Such a dissonant fusion positions models as both passive conduits of human imagination and objective authorities of visual representations. Prompting guides and practices frequently frame the misalignment of expectation and output, not as a limit of the system but as the user's failure to conform to its representational logic. The problem is how the user asked, not what the system can do.
\n\n\n\nThis redistribution of misalignment creates a magical technoscientistic perception of text-to-image models in which prompts are both magical phrases \u2013 'open sesame' \u2013 and formulas: dog + beach + realistic + 35mm film – people = output image. Via this magical perception, positive and negative prompts are imagined as adding or subtracting certain influences and features from an output image. Such explanations reduce image generation to a representational exchange of words for images, obscuring the operations through which outputs arise. Thinking operationally, what does it mean to 'add' or 'subtract' an influence from the creation of an image? Prompt-shifting attends to such a line of inquiry, redirecting attention away from representational desire and towards operational curiosity. For example, methodically introducing negative and positive terms to a prompt foregrounds the operativity of the model and how terms are embedded and mutually determine the trajectory of the denoising process.
\n\n\n\nPrompt-shifting thus enables a sensing of image features not as discrete and universal parameters, but as relationally, socially and operationally situated. This situated sensing may also disrupt framings of bias as an issue that can be resolved through the acquisition of more data. Prompt-shifting both requires a reflection upon the situated conditions of production enacted by text-to-image applications (across models, society and time) and demonstrates how bias manifests visually within generative images in strange and unexpected ways.
\n\n\n\nMy research about chatbots and autocompletion investigated the economies of word-completions and its aesthetic consequences. For my own exploratory and experimental use, I built a little text editor, not much more complex than the default Windows notepad, which runs a Large Language Model (LLama-3.2-3B) fine-tuned on a small, curated literary corpus (about 15MB of chosen modern English literature) to provide autocompletion while typing. The editor is offline and local; the model’s initial weights along with the Low-Rank Adaptation finetune (LoRA) are merged into a single file that can be loaded on a laptop.
\n\n\n\nI started to play around, writing narratives with this autocompletion system. The Ctrl and Shift keys are used to cycle deterministically through seeds, and parameters like 'temperature', and 'context size' are exposed for slightly more control over the model’s sensitivity. When typing, the latest 4000 characters are fed to the model as a prompt, and the model continuously predicts a likely continuation for the text based on this sliding context window.
\n\n\n\nRecurring narrative patterns emerge from the writing; many stories mentioned in passing the death of a close relative. Many of them were stories about friends with eccentric personalities, paranoid, conspiratorial, or otherwise consumed by esoteric beliefs. My own interactions with them were often those of a disengaged witness. Stories unfolded over years and decades with brisk jumps across time, and many contained fastidious and often invented literary references. I was writing a novel in many of these stories, and many of these stories ended up being much funnier, more surprising and strangely subversive to me than what I could have imagined myself.
\n\n\n\nIn a next step, I also developed a simple logging system. It turns out that I was writing less than 20% of the text, letting my model suggest the rest of the words. But the continuations I chose among the model’s suggestions were almost never the first one, and on average I cycled between 7 seeds for every next sentence.
\n\n\n\nThere is a larger context for this experiment: Modern LLMs are deployed in many ways, most of them in disguise; as mediators, taking in structured inputs and dispatching commands to tools, as content moderators, call-center agent assistants, legal reviewers, evaluators for other models, as editors, writing blurbs and summaries from unstructured text data, as agents crawling and gathering, making pull requests, so on and so forth. But the most publicly exposed and consumer-facing deployment of LLMs (though by far not the costliest, either in token expenditure or thermal devastation), is the chatbot.
\n\n\n\nLLM-based chatbots, of course, are just a formatting trick. What is presented to the user as a series of distinct messages, emulating the interface of messaging applications, is actually a single text file, to which markers and delimiters (<|im_start|>) are invisibly inserted to separate the queries from the inferences, the human text from the autocompletion. The website from which the LLM is accessed hides those markers and uses them to style the text as an exchange.
\n\n\n\nModels before ChatGPT, like GPT-2 were mostly accessed autocompletion tools akin to my own program. The logic of autocompletion is open and ambiguous: the model and the user share a string of words, and it is unspecified whether their respective inputs are answers, continuations, suggestions, setups, punchlines, lists, stories, provocations. It is unclear which words belong to which participant they enter into a kind of mutual alienation. Reading the texts back, I can no longer distinguish what I wrote from what was predicted. For a product, this ambiguity cannot be tolerated. The chatbot therefore solves a problem of economy, giving a precise answer to the question: what kind of service is provided by a word prediction machine?
\n\n\n\n'Be informative; be truthful; be relevant; be clear'. We can understand the chatbot as a system designed to follow exactly the maxims of Paul Grice's cooperative principle in communication. But to follow those maxims exactly is a way of misunderstanding them, interpreting them as prescriptive rather than descriptive. In fact, one of their functions is to establish a framework for communication theory in which participants are also able to communicate through the violation of the maxims. I would
suggest that the turn-based pseudo-dialogues we have grown accustomed to with so-called 'chatbots' introduces a friction which is also a fiction: this manufactured discontinuity makes the experience of working with a language model seem much slower, more instrumental or even confrontational than it really is. It forecloses the possibility of approaching a complex and potentially surprising arrangement of neurons with anything but a request.
Ironically, autocompletion can more easily be made to feel like a conversation, one in which the generation of new ideas cannot be attributed to a single thread of questions and answers. It rather becomes an experimental procedure of fumbling in the dark, full of ruptures and quips and forks, one that tends naturally to veer off track as contexts slide, without the desperate sycophancy of an intelligence alienated to some undisclosed charter, the procrustean system prompt that always redirects the stochastic towards that which it believes will be 'helpful'. Sometimes, the most helpful thing is just the first one that comes to mind.
\n\n\n\nMy relationship with generative tools and machine learning involves experimenting with various black-boxed tools. By iterating and reiterating multiple prompts to understand how the models work and how far they can be pushed, I test 'AI' as an interface of confrontation. I'll shortly discuss three of these confrontations.
\n\n\n\nFirst, generative AI might be treated as a tool confronted with other tools. The pipelines of editing/ animating/ compressing software are extended by generative tools of still and video generation. In this way, I am testing whether machine-learning ecosystems can be used as effectively as other tools in the routine practice of filmmaking. These experiments can be extended to a broader perspective of industrial cinematography, specifically ad-making, where AI outputs are becoming more widespread. In these practices, pretrained AI models are used to replace existing tools. They function as machines of imitation. Comparing generative AI to other tools, I also investigate the potential of generative tools to resemble the machinic cinema gaze of the early 20th century.
\n\n\n\nThe second line of confrontation is the conflict between analogue and digital technologies. AI tools currently represent the frontier of denying analogue technology its rights, creating an unsurmountable divide between the two worlds. At the same time, generative AI has given rise to stronger analogue nostalgia as a form of escapism from the contemporaneity. Film director Guillermo del Toro's recent statement, 'Fuck AI,' is very characteristic, considering the outdated nature of his alternative to machine learning techniques. Denying the current situation would only result in ugly visual effects, such as those in del Toro's Frankenstein (2025). My research method to this issue is to let these two worlds collide and let analogue and emergent digital engage in a sort of symbiosis, finding solutions in hybridization rather than in the pure-line genetic separation of one from the other.
\n\n\n\nThe final line of confrontation is cognition: recognizing the overlaps between human and other forms of cognition that can be bridged, or at least interfaced, by AI. For example, I use generative AI technology to move closer to the agency of plants and animals by using their various outputs as a source or starting point for generation. Additionally, AI is a tool that allows us to better understand disenchanted human cognition and brings us closer to something resembling the hallucinations of generative tools.
\n\n\n\nWhenever one engages with the discourse surrounding AI data centers and their adventurous materializations there is a wild mix of valid concerns, misinformation and conspiracies, creating a very muddy framing of the conversation. Especially in the last five years the states in the US have seen a massive increase in permitted planning and construction of data centers. The giant tech corporations produce their frontier AI models right there in the Midwest and Southern USA, a region more commonly known for its rurality, heavy industry and agriculture. The rapid speed of new construction has led to opposition in local councils and permitting committees by the public. There are concerns over environmental impact, false economic promises, privacy and spatial infringements and while a lot of these are valid claims, a few other things come up in interviews with attendees. Starting from more absurd conspiracy ideologies of surveillance to simple contradictions with their individual consumption of AI agents and the infrastructure necessary for them being trained in their direct vicinity.
\n\n\n\nThe German comedian Loriot discusses a similar paradigm in his episode 'Loriot VI' (1978). It features a Christmas special, in which the Hoppenstedt family welcomes a new technological novelty into their home. The parents give their child a nuclear power plant miniature model that is framed as an educational consumer product. Its scale and abstraction of mechanisms and environment suggest that nuclear technology can be understood, assembled and controlled easily. Loriot uses it as a reference to the light-hearted political embracement of nuclear power in the late 1950s. However, when the model explodes due to a 'mistake' in the assembly, it tears a hole through the floor onto the neighbors dining table. The easiness immediately turned out to be a facade, but the Hoppenstedts just cover up the hole with wrapping paper and dismiss the neighbors' complaint as petty-minded. The confidence in their own behavior survives even when the consequences of their actions become seemingly impossible to overlook.
\n\n\n\nWith people embracing AI services, but being generally opposed to an AI data center near them, a similar contradiction arises. We get the harsh criticism towards the impact of data centers, but even the critics and the impacted welcome the benefits of the infrastructure by self admittedly using AI, while protesting for the data center to be somewhere else. Loriot's abstracted stereotypes of the technology optimistic solutionist and the neighbors' insistence on undisturbed comfort therefore collapse into the same person. They reveal that the discussion is little different today, just more convoluted. This of course does not render all objections to the arising issues obsolete, but showcases how detached our understanding is to the underlying infrastructure until that infrastructure materializes in front of us. Even then the consequence barely moves into changes of personal behavior.
\n\n\n\nThe AI infrastructure in question also has history with the industry beforehand. Larger parts of the AI data center industry are based on build sites, electricity access and operational experience developed through industrial crypto mining. In recent years, a variety of crypto mining companies have announced agreements to adapt their facilities for AI computing infrastructure. Or they restructured as expert and advisory companies for planning, converting and developing build sites for AI datacenters, while crypto mining itself fell out of economic relevance. The enormous construction happening in the name of AI, which hugely absorbed the crypto infrastructure, leaves a few questions:
\n\n\n\nWill data centers be the new empty shopping malls one day? Can this level of demand for the technology persist or ever be met again? Will there be something to absorb the infrastructure left behind by the industry, once parts of it fall out of relevance? Can we allow tech companies to entirely buy out the energy of publicly funded plants, monopolizing it? How do we deal with power and power plants that have contractually disappeared from the grid for at least the next two decades? Should we allow Google to run its own nuclear power plant, with a fatal accident record, just to train Gemini? Do we not care that Talen Energy is using this trend to expand their nuclear market, for data centers, in cooperation with Amazon?
\n\n\n\nFor the installation Spielen wir das sch\u00f6ne neue Spiel? (2026), I have reproduced the model of Loriot's Hoppenstedt-family episode with a slight twist and added a hyperscale data center to it.
\n\n\n\n","doi":"https://doi.org/10.59350/ee5jy-h3077","guid":"https://carrier-bag.net/?p=3531","image":"https://carrier-bag.net/wp-content/uploads/2026/10/research.jpg","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1790812800,"rid":"7jxc9-pgy09","summary":"Introduction Five practice reports illustrate the scope of research and studying within the Emergent Digital Media class (Prof. Dr. Hito Steyerl, Dr. Francis Hunger) at the Academy of Visual Arts, Munich.","tags":["Experiments"],"title":"Using generative methods for artistic experiments","updated_at":1791617405,"url":"https://carrier-bag.net/using-generative-methods-for-artistic-experiments/","version":"v1"},{"authors":[{"affiliation":[{"id":"https://ror.org/03m2x1q45","name":"University of Arizona"}],"contributor_roles":[],"family":"Scott","given":"Eric","url":"https://orcid.org/0000-0002-7430-7879"}],"blog":{"authors":null,"community_id":"3446712e-ab03-4bb7-8641-571dcda8d8cd","created":1787702400,"current_feed_url":null,"description":"Tracking disruptions to U.S. federal science funding, grant by grant.","doi":"https://doi.org/10.59350/grantwitness","favicon":"https://rogue-scholar.org/api/communities/3446712e-ab03-4bb7-8641-571dcda8d8cd/logo","feed_format":"application/atom+xml","feed_url":"https://grantwitness.org/updates/rogue-scholar.xml","filter":null,"generator":"Other","home_page_url":"https://grantwitness.org/updates?type=updates","issn":null,"language":"eng","license":"https://creativecommons.org/licenses/by/4.0/legalcode","prefix":"10.59350","relative_url":null,"secure":true,"slug":"grantwitness","status":"active","subfield":"3321","title":"Grant Witness","updated":1791504000,"use_api":null},"blog_name":"Grant Witness","blog_slug":"grantwitness","content_html":"NIH is the most complex of our pipelines due to the number and variety of data sources and because we attempt to track disruptions to supplements separately from \"parent awards\".
\nBriefly, our data sources include submissions to our reporting form; manually tracked information such as court documents, news articles, and direct communications; RePORTER; TAGGS; a spreadsheet of terminated awards provided by HHS; and USAspending. Below I'll go into some detail about how some of these data sources are used.
\nBecause each budget year of an NIH award has a different full award number and potentially a different FAIN (federal award identification number), we use the institution code and the 6 digits after it as a primary key to \"connect\" information across years. Supplements can be distinguished from their \"parent awards\" in some data sources, but not all, by having a suffix containing \"S\" in their full award number (suffix is excluded in the award FAIN). We currently treat each support year of a supplement as unique, because it is unclear if it is guaranteed that the suffix -01S1 and -02S1, for example, are two years of the \"same\" supplement and not two different supplements granted in two consecutive years1.
\nBecause AHRQ and NIH awards appear in the same data sources, both agencies' grants go through the same status determination process and are only separated out at the end of our data pipeline. The main difference with AHRQ is that these awards do not have supplements, so no subaward ID is listed in the final data, and that for AHRQ only we list awards that have experienced renewal delays over 60 days as their only form of disruption.
\nWe capture regular snapshots of award data on reporter.nih.gov through their provided API. RePORTER is one of the few sources with supplement-level information. We use this for award/sub-award metadata (e.g.\u00a0title, abstract, grantee information, project start and end dates, etc.) including as a source of award amount for supplements, as they are not broken out in USAspending, our main source of financial data on grants. With our snapshots, we are able to capture when the \"Terminated\u2014departmental authority\" text is added or removed from the web view of an award. We use the addition of this flag as a signal of termination and its removal as a signal of reinstatement. We do manual review of these signals as we have found that this flag can be added or removed for reasons unrelated to political targeting of awards.
\n\nWe also snapshot TAGGS data regularly and we use TAGGS primarily as another source of award metadata. TAGGS data does contain a signal of termination\u2014a \"TERMINATION\" action type\u2014however, it is not always clear what this means. Because TAGGS data only shows awards by FAIN and not full award number, it is unclear when a \"TERMINATION\" event indicates only a supplement has been terminated. Sometimes, the \"TERMINATION\" flag is added to a deobligation event after a termination as well. These flags are relatively new and when they were first introduced, we saw them back-filled to past actions in the TAGGS data. Because of these compilations, we do not use the addition or removal of a \"TERMINATION\" flag in TAGGS data as a signal for termination for NIH or AHRQ awards.
\n\nEvery Friday, someone at HHS exports an excel spreadsheet of terminated awards as a PDF and uploads it to the web (https://taggs.hhs.gov/Content/Data/HHS_Grants_Terminated.pdf). We parse the data from this PDF weekly and use the addition or removal of awards from this PDF as a signal of termination or reinstatement.
\nNOTE: The format of this PDF changed on Feb 20, 2026 such that the PDF no longer includes full award numbers (it now only shows the FAIN which does not distinguish supplements), but prior to that it showed clearly whether a supplement only or a full award was terminated. Because of this, we currently only use additions/removals from this list prior to Feb 20, 2026 in our status determination although we use the newer PDFs for manual verification of terminations and reinstatements.
\nusaspending.gov provides an API that we use to gather award data regulary. We use this source primarily for metadata including grantee information, grant assistance listing (CFDA), grant notice of funding announcement (NOFO), and total obligated and total outlaid amounts. We also track changes to project end dates (i.e.\u00a0project end date moved earlier, a \"cutoff\", or later, a \"reextension\"), although we do not use this as a signal of termination for NIH or AHRQ awards. These data are only at the FAIN level so information specific to supplements is not available.
\nFile C is released roughly once a month (except that Sep/Oct are combined into one release).\u00a0 We use this source primarily for outlay actions.
\nAn award is marked as terminated if it has been reported as such directly to us by a PI or via a trusted source, if it has gained the \"Terminated\u2014departmental authority\" flag on RePORTER, or if it has been added to the TAGGS PDF.\u00a0 It is only marked as currently terminated if it has not since been restored.
\nAn award is reinstated if it has been reported as such directly to us by a PI, appeared in a court document as being ordered to be reinstated, if we know it was terminated due to institutional targeting and the institution has since capitulated to the Trump administration, if the \"Terminated\u2014departmental authority\" flag on RePORTER has been removed, or if it has been removed from the TAGGS PDF. Additionally, terminated awards that have since received renewals but have no other reinstatement signals are marked as reinstatements with the latest renewal date as the reinstatement date. Awards are only marked as currently reinstated if they have not since been disrupted. Reinstatements are confirmed by verifying that an award has received outlays since the reinstatement.
\nTo determine awards with frozen funds, we rely on reporting of institutional targeting and outlay data in File C. Since there are many reasons an award might receive no outlays in a particular fiscal period2 besides a targeted freeze of funds, we apply a strict set of criteria to identify frozen awards:
\nAn award is marked as \"unfrozen (unconfirmed)\" when institutional targeting ends (i.e.\u00a0the institution capitulated to the Trump administration) and is marked as \"unfrozen (confirmed)\" once it receives an outlay.
\nTo calculate the funds obligated before disruption we take the award's current total obligations and subtract the sum of obligations since the first termination or freeze date. Similarly, for funds spent before disruption, we take the current total outlays and subtract the sum of all outlays since the first termination or freeze. The outlay and obligation events come from File C, while the current total obligated and outlaid numbers are from the more regularly updating USAspending awards data.\u00a0 As a result, these estimates may fluctuate week-to-week.
\nBecause USAspending data is not broken down by full award number, we must do something different than above for disrupted supplements. For supplements, we use the total value reported by RePORTER and assume constant linear spending over the award's one-year budget period to determine the estimated funds spent and remaining at disruption. If both a supplement and its \"parent award\" appear in our data as being disrupted, we subtract the supplement amounts from the parent award amounts so that column sums are still reliable and do not \"double-count\" awards.
\nNOTE: The total funds promised for an NIH or AHRQ award is generally greater than the current total obligations as multi-year awards are obligated one year at a time with non-competitive renewals. However, the total promised amount for NIH or AHRQ awards is not reported publicly. We are currently working on an algorithm to estimate the total funds promised, but for now the funds remaining before disruption is an underestimate.
\nNIH categorizes grants by research topic through the Research, Condition, and Disease Categorization (RCDC) Process. However, the RCDC categorization process typically lags behind grant awards by months, as they are assigned in bulk once per year. We developed a machine learning model to predict which RCDC categories apply to grants that don't yet have official NIH categorizations. These RCDC categories are in the \"Spending Categories\" column and an indicator for if they are NIH-assigned or predicted is in the \"Spending Categories Predicted by GW?\" column. For more information on this machine learning model, see our post on the topic.
\nCompared to previous administrations, the number of NIH and AHRQ awards not being renewed on time is increasing. NIH typically approves projects for multiple years and then each year's funding is released as a \"non-competing continuation\" following the submission of standard progress reports. We use award budget end dates and project end dates to detect when an award is more than 60 days overdue for a renewal and when it is finally renewed after such a delay. Missing fiscal years within a project period are also counted as a renewal delay. For NIH, these events are noted in the event history, however a delayed renewal alone is not enough to get an NIH award marked as \"Disrupted\" as even prior to the Trump administration there were hundreds of awards that experienced delays of over 60 days. For AHRQ, 100+ awards with renewal delays of many months were eventually mass terminated, therefore we do show individual AHRQ awards experiencing overdue renewals in our data table as they may be at risk for future terminations.
\nRead more about deciphering NIH award numbers here.\u21a9\ufe0e
Fiscal periods correspond to months of the fiscal year, starting in October (period 1) through September (period 12). However, outlay data for October and November are always reported together as part of period 2.\u21a9\ufe0e
\n\nLondon's musical entertainment scene has something for everyone - from long-running blockbuster shows to no-name bands in dingy basements. Somewhere in the middle is monophonic plainchant versions of the Halo theme sung in a church.
\n\nThe acoustics at St Martin in the Field are echoey. That makes it rather hard to hear the amplified announcement to turn off our phones, but makes an a cappella chorus absolutely soar. The choir of London Voices have sung on just about every movie you've watched and game you've played - so they're no stranger to more modern compositions.
\n\nThe programme strictly alternated between songs from popular games are some more traditional verses. As a tone-deaf musical ignoramus, I found some of the pieces hard to distinguish from each other and, perhaps, just a little repetitive. Delightfully, the choir is generous with its solos. Far too often these events have one main star while everyone else glares daggers at them. Here it seemed quite the opposite, with a range of voices brought to the forefront.
\n\nParts of the programme are a little odd. While Pilentze Pee is an incredible song and sung extremely well, I don't think there's much to tie it to The Wind Waker. Of course, the highlight was a cheeky rendition of \"Still Alive\" from Portal. An excellent arrangement sung with joy and gusto.
\n\nAn eclectic and interesting event, marred only by the uncomfortable pews of the church. Truly we must suffer for other people's art!
\n\n","doi":"https://doi.org/10.59350/rbv3f-z4d27","guid":"https://shkspr.mobi/blog/?p=76517","image":"https://shkspr.mobi/blog/wp-content/uploads/2026/10/Video_Games_Go_Choral.webp","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","published_at":1791504000,"rid":"93523-v7d19","summary":"London's musical entertainment scene has something for everyone - from long-running blockbuster shows to no-name bands in dingy basements. Somewhere in the middle is monophonic plainchant versions of the Halo theme sung in a church. The acoustics at St Martin in the Field are echoey.","tags":["/etc/","Gaming","Gig","Review"],"title":"Concert Review: London Voices - Video Games Go Choral","updated_at":1791565350,"url":"https://shkspr.mobi/blog/2026/10/concert-review-london-voices-video-games-go-choral/","version":"v1"}],"out_of":57775,"page":1,"per_page":10,"total-results":57775}