Tootfinder

Do everything you can to protest tomorrow.
If you can't, flood the socials with NOKINGS because the media, petrified of angering Emperor Trump, is going to downplay & minimize the protests to "tell both sides".
#Protests #NoKings

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, Samuel J. Bell
https://arxiv.org/abs/2506.09038

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
For Large Language Models (LLMs) to be reliably deployed in both everyday and high-stakes domains, knowing when not to answer is equally critical as answering correctly. Real-world user queries, which can be underspecified, ill-posed, or fundamentally unanswerable, require LLMs to reason about uncertainty and selectively abstain -- i.e., refuse to answer definitively. However, abstention remains understudied, without a systematic evaluation framework for modern LLMs. In this work, we introduce …

Let's say you find a really cool forum online that has lots of good advice on it. It's even got a very active community that's happy to answer questions very quickly, and the community seems to have a wealth of knowledge about all sorts of subjects.
You end up visiting this community often, and trusting the advice you get to answer all sorts of everyday questions you might have, which before you might have found answers to using a web search (of course web search is now full of SEI spam and other crap so it's become nearly useless).
Then one day, you ask an innocuous question about medicine, and from this community you get the full homeopathy treatment as your answer. Like, somewhat believable on the face of it, includes lots of citations to reasonable-seeming articles, except that if you know even a tiny bit about chemistry and biology (which thankfully you do), you know that the homoeopathy answers are completely bogus and horribly dangerous (since they offer non-treatments for real diseases). Your opinion of this entire forum suddenly changes. "Oh my God, if they've been homeopathy believers all this time, what other myths have they fed me as facts?"
You stop using the forum for anything, and go back to slogging through SEI crap to answer your everyday questions, because one you realize that this forum is a community that's fundamentally untrustworthy, you realize that the value of getting advice from it on any subject is negative: you knew enough to spot the dangerous homeopathy answer, but you know there might be other such myths that you don't know enough to avoid, and any community willing to go all-in on one myth has shown itself to be capable of going all in on any number of other myths.
...
This has been a parable about large language models.
#AI #LLM

Pushback against progressive mayoral candidate #Zohran #Mamdani has been fierce.
That includes the mainstream media, which is trying to brand him a socialist to negate his progressive and popular policy proposals.
Stephen Janis and Taya Graham analyze the CNN anchor’s takedown and show why her efforts are corp…

Corporate media is trying to take down Mamdani. This is why they’re failing.
Pushback against mayoral candidate Zohran Mamdani has been fierce. That includes the mainstream media, which is trying to brand him a socialist to negate his progressive and popular policy proposals.

CNN relaunched its FAST channel CNN Headlines, which now features two hours of a new live, original show anchored by Brad Smith, last week (Sara Fischer/Axios)
https://www.axios.com/2025/07/08/cnn-headlines-live-channel-relaunch

Scoop: CNN relaunches FAST channel with original, live programming
The revamp is part of a sweeping overhaul of CNN's streaming and video efforts.

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2506.01784 https://

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning pose…

As Reddit turns 20, a look at its AI efforts, including the Reddit Answers chatbot, while it battles unauthorized scraping of user data for AI training (Jonathan Vanian/CNBC)
https://www.cnbc.com/2025/06/28/reddit-20-fighting-ai-defending-data.html…

At 20 years old, Reddit is defending its data and fighting AI with AI
After two decades, Reddit faces the emergence of AI chatbots that threaten to inhale its vast swaths of data and siphon its users.

Long shot but I can't find an answer with "short searches": I got a KVM. Everything finally works, except Mint shuts down (closing all apps) when I switch to the other computer and I need to open the lid to make it come back.
This is with "lid action=nothing" in the power settings, of course.
Any ideas? Even ideas on how to express this concept in a way that lets me find the relevant reddit thread would be awesome.

So I've found my answer after maybe ~30 minutes of effort. First stop was the first search result on Startpage (https://millennialhawk.com/does-poop-have-calories/), which has some evidence of maybe-AI authorship but which is better than a lot of slop. It actually has real links & cites research, so I'll start by looking at the sources.
It claims near the top that poop contains 4.91 kcal per gram (note: 1 kcal = 1 Calorie = 1000 calories, which fact I could find/do trust despite the slop in that search). Now obviously, without a range or mention of an average, this isn't the whole picture, but maybe it's an average to start from? However, the citation link is to a study (https://pubmed.ncbi.nlm.nih.gov/32235930/) which only included 27 people with impaired glucose tolerance and obesity. Might have the cited stat, but it's definitely not a broadly representative one if this is the source. The public abstract does not include the stat cited, and I don't want to pay for the article. I happen to be affiliated with a university library, so I could see if I have access that way, but it's a pain to do and not worth it for this study that I know is too specific. Also most people wouldn't have access that way.
Side note: this doing-the-research protect has the nice benefit of letting you see lots of cool stuff you wouldn't have otherwise. The abstract of this study is pretty cool and I learned a bit about gut microbiome changes from just reading the abstract.
My next move was to look among citations in this article to see if I could find something about calorie content of poop specifically. Luckily the article page had indicators for which citations were free to access. I ended up reading/skimming 2 more articles (a few more interesting facts about gut microbiomes were learned) before finding this article whose introduction has what I'm looking for: https://pmc.ncbi.nlm.nih.gov/articles/PMC3127503/
Here's the relevant paragraph:
"""
The alteration of the energy-balance equation, which is defined by the equilibrium of energy intake and energy expenditure (1–5), leads to weight gain. One less-extensively-studied component of the energy-balance equation is energy loss in stools and urine. Previous studies of healthy adults showed that ≈5% of ingested calories were lost in stools and urine (6). Individuals who consume high-fiber diets exhibit a higher fecal energy loss than individuals who consume low-fiber diets with an equivalent energy content (7, 8). Webb and Annis (9) studied stool energy loss in 4 lean and 4 obese individuals and showed a tendency to lower the fecal energy excretion in obese compared with lean study participants.
"""
And there's a good-enough answer if we do some math, along with links to more in-depth reading if we want them. A Mayo clinic calorie calculator suggests about 2250 Calories per day for me to maintain my weight, I think there's probably a lot of variation in that number, but 5% of that would be very roughly 100 Calories lost in poop per day, so maybe an extremely rough estimate for a range of humans might be 50-200 Calories per day. Interestingly, one of the AI slop pages I found asserted (without citation) 100-200 Calories per day, which kinda checks out. I had no way to trust that number though, and as we saw with the provenance of the 4.91 kcal/gram, it might not be good provenance.
To double-check, I visited this link from the paragraph above: https://www.sciencedirect.com/science/article/abs/pii/S0022316622169853?via=ihub
It's only a 6-person study, but just the abstract has numbers: ~250 kcal/day pooped on a low-fiber diet vs. ~400 kcal/day pooped on a high-fiber diet. That's with intakes of ~2100 and ~2350 kcal respectively, which is close to the number from which I estimated 100 kcal above, so maybe the first estimate from just the 5% number was a bit low.
Glad those numbers were in the abstract, since the full text is paywalled... It's possible this study was also done on some atypical patient group...
Just to come full circle, let's look at that 4.91 kcal/gram number again. A search suggests 14-16 ounces of poop per day is typical, with at least two sources around 14 ounces, or ~400 grams. (AI slop was strong here too, with one including a completely made up table of "studies" that was summarized as 100-200 grams/day). If we believe 400 grams/day of poop, then 4.91 kcal/gram would be almost 2000 kcal/day, which is very clearly ludicrous! So that number was likely some unrelated statistic regurgitated by the AI. I found that number in at least 3 of the slop pages I waded through in my initial search.

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Yash Tiwari, Owais Ahmad Lone, Mayukha Pal
https://arxiv.org/abs/2506.00664

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases
Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontologies from unstructured knowledge bases, with a focus on electrical relay documents. OntoRAG integr…