Tootfinder

#Blakes7 Series B, Episode 03 - Weapon
COSER: I should have known better. A labor-grade slave. You're pathetic.
[Reception chamber]
SERVALAN: Travis, you are pathetic.
TRAVIS: If you say so.
SERVALAN: Of all the cripple-brained idiots.

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Join us for our Get-Together right after today's last session at 18:00 CEST!
Head over to the partner area, where you can unwind with tasty snacks and refreshing drinks while enjoying great live music, all sponsored by Search Guard. What better way to end the first day of the conference than by catching up with old friends and making new connections?
#bbuzz

Energy as a Primitive Ontology for the Physical World
J. E. Horvath, B. B. Martins
https://arxiv.org/abs/2506.12692 https://arxiv.org…

Energy as a Primitive Ontology for the Physical World
We reanalyze from a modern perspective the bold idea of G. Helm, W. Ostwald, P. Duhem and others that energy is the fundamental entity composing the physical world. We start from a broad perspective reminding the search for a fundamental ``substance'' (perhaps better referred to as ous\'ıa, the original Greek word) from the pre-Socratics to the important debate between Ostwald and Boltzmann about the energy vs. atoms at the end of the 19th century. While atoms were eventually accepted (even by…

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.

Tree-Structured Parzen Estimator Can Solve Black-Box Combinatorial Optimization More Efficiently
Kenshin Abe, Yunzhuo Wang, Shuhei Watanabe
https://arxiv.org/abs/2507.08053 https://arxiv.org/pdf/2507.08053 https://arxiv.org/html/2507.08053
arXiv:2507.08053v1 Announce Type: new
Abstract: Tree-structured Parzen estimator (TPE) is a versatile hyperparameter optimization (HPO) method supported by popular HPO tools. Since these HPO tools have been developed in line with the trend of deep learning (DL), the problem setups often used in the DL domain have been discussed for TPE such as multi-objective optimization and multi-fidelity optimization. However, the practical applications of HPO are not limited to DL, and black-box combinatorial optimization is actively utilized in some domains, e.g., chemistry and biology. As combinatorial optimization has been an untouched, yet very important, topic in TPE, we propose an efficient combinatorial optimization algorithm for TPE. In this paper, we first generalize the categorical kernel with the numerical kernel in TPE, enabling us to introduce a distance structure to the categorical kernel. Then we discuss modifications for the newly developed kernel to handle a large combinatorial search space. These modifications reduce the time complexity of the kernel calculation with respect to the size of a combinatorial search space. In the experiments using synthetic problems, we verified that our proposed method identifies better solutions with fewer evaluations than the original TPE. Our algorithm is available in Optuna, an open-source framework for HPO.
toXiv_bot_toot

There have been many instances documented, do a damn web search.
But forget that for a moment: What I’m doing is the same as what Netanyahu is doing? My opposing genocide is the same as a fucker perpetrating genocide?
Fuck you, Alfred.
Get some fucking perspective. https://mastodon.social/@amsz…

I have many thoughts about generative AI, but the overarching one is this: the better metaphor is not a new brain acquiring human thoughts, but a complex search engine for existing human-generated ideas.

Series A, Episode 08 - Duel
BLAKE: Seen any sign of Travis?
JENNA: Have you?
BLAKE: No. We'd better make ourselves some weapons.
[LATER - Blake is sharpening two stakes into spears with his machete]
https://blake.torpidity.net/m/108/321 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Boosting Rust Unit Test Coverage through Hybrid Program Analysis and Large Language Models
Bei Chu, Yang Feng, Kui Liu, Hange Shi, Zifan Nan, Zhaoqiang Guo, Baowen Xu
https://arxiv.org/abs/2506.09002

Boosting Rust Unit Test Coverage through Hybrid Program Analysis and Large Language Models
Unit testing is essential for ensuring software reliability and correctness. Classic Search-Based Software Testing (SBST) methods and concolic execution-based approaches for generating unit tests often fail to achieve high coverage due to difficulties in handling complex program units, such as branching conditions and external dependencies. Recent work has increasingly utilized large language models (LLMs) to generate test cases, improving the quality of test generation by providing better cont…

Search for the Efimov state near the 3$\alpha$ threshold
A. Baishya, S. Santra, T. Singh, P. C. Rout, A. Pal, H. Kumawat, T. Santhosh, P. Taya, M. Meher, Jyotisankar Das
https://arxiv.org/abs/2507.03514

Search for the Efimov state near the 3$α$ threshold
A high-precision experiment in search of the predicted Efimov state in $^{12}\mathrm{C}$ at 7.458 MeV excitation energy was performed. Using a state-of-the-art detector system and novel analysis techniques, it was possible to observe the Efimov state at the predicted energy level above the 3$α$ threshold with much better sensitivity in the $^{12} \mathrm{C}$ excitation energy spectrum compared to the existing data. The mutual $^{8} \mathrm{Be}$ resonance (91.84 keV) condition filters out a tot…

Generating Long Semantic IDs in Parallel for Recommendation
Yupeng Hou, Jiacheng Li, Ashley Shin, Jinsung Jeon, Abhishek Santhanam, Wei Shao, Kaveh Hassani, Ning Yao, Julian McAuley
https://arxiv.org/abs/2506.05781

Generating Long Semantic IDs in Parallel for Recommendation
Semantic ID-based recommendation models tokenize each item into a small number of discrete tokens that preserve specific semantics, leading to better performance, scalability, and memory efficiency. While recent models adopt a generative approach, they often suffer from inefficient inference due to the reliance on resource-intensive beam search and multiple forward passes through the neural sequence model. As a result, the length of semantic IDs is typically restricted (e.g. to just 4 tokens), …

I did search for a pizza oven but this is even better 🤔
- Free Shipping
- Buy 3, get 5% off
- Only 20 in Stock, act quickly

Unmasking real-world audio deepfakes: A data-centric approach
David Combei, Adriana Stan, Dan Oneata, Nicolas M\"uller, Horia Cucu
https://arxiv.org/abs/2506.09606

Unmasking real-world audio deepfakes: A data-centric approach
The growing prevalence of real-world deepfakes presents a critical challenge for existing detection systems, which are often evaluated on datasets collected just for scientific purposes. To address this gap, we introduce a novel dataset of real-world audio deepfakes. Our analysis reveals that these real-world examples pose significant challenges, even for the most performant detection models. Rather than increasing model complexity or exhaustively search for a better alternative, in this work w…

This https://arxiv.org/abs/2411.16826 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Ideological Fragmentation of the Social Media Ecosystem: From echo chambers to echo platforms
The entertainment-driven nature of social media encourages users to engage with like-minded individuals and consume content aligned with their beliefs, limiting exposure to diverse perspectives. Simultaneously, users migrate between platforms, either due to moderation policies like de-platforming or in search of environments better suited to their preferences. These dynamics drive the specialization of the social media ecosystem, shifting from internal echo chambers to "echo platforms"--entire …

Abandoned by Trump, a farmer and a migrant search for a better future (Washington Post)
https://www.washingtonpost.com/investigations/interactive/2025/trump-farmers-grants-freeze-usda-migrants
http://www.memeorandum.com/250621/p27#a250621p27

I tried DuckDuckDo again these days for no reason in special. To my surprise, the search experienced there is much better than Google's. Not only there is less crap in the page, the results seemed even more relevant. It feels as good as old Google.
DuckDuckGo is now the default search engine in my personal browsers.

We learned how to search instead of memorizing everything. And it’s the same with AI today - there’s no need to know everything by heart when AI can handle that for us.
AI is here to stay, and even if it evolves or gets replaced by something better, the transformation never stops. It’s perfectly fine to not have all the answers in your head because we can focus on more important things.

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
S. V. Chekanov, H. Kjellerstrand
https://arxiv.org/abs/2507.00225

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
This paper presents a search for underlying analytic structures among the fundamental parameters of the Standard Model (SM) using symbolic regression and genetic programming. We identify the simplest analytic relationships connecting pairs of these constants and report several notable observations based on about a thousand expressions with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidd…

Series C, Episode 04 - Dawn of the Gods
GROFF: A finger?
TARRANT: A finger. And as you can see, it is better designed for pressing buttons than holding writing implements. So why can't we use computers?
https://blake.torpidity.net/m/304/299 B7B2

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Ziyue Li, Yang Li, Tianyi Zhou
https://arxiv.org/abs/2507.07996 https://arxiv.org/pdf/2507.07996 https://arxiv.org/html/2507.07996
arXiv:2507.07996v1 Announce Type: new
Abstract: Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretrained large language model (LLM) can be manipulated as separate modules to build a better and even shallower model customized for each test sample. In particular, each layer from the pretrained model can be skipped/pruned or repeated multiple times as recurrent neural networks (RNN), and stacked with others in arbitrary orders, yielding a chain-of-layers (CoLa) per sample. This compositional space greatly expands the scope of existing works on looped/recurrent pretrained modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree Search (MCTS) protocol to explore and identify the optimal CoLa for each sample from math and commonsense reasoning benchmarks. Compared to a static model of a fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same layer(s) (slow thinking), and combining both, offering more flexible, dynamic architectures for different inputs. We conduct an extensive analysis of the MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples with correct predictions by the original LLM, we can find shorter CoLa, suggesting a large space for improving inference efficiency; (2) For >60% of samples with originally incorrect predictions, we can identify CoLa achieving correct predictions, suggesting a large space of performance enhancement. Our results highlight the shortcomings of using a fixed architecture of pre-trained LLMs for inference on different samples and pave the way to unlock the generalization power of test-time depth adaptation.
toXiv_bot_toot

Series A, Episode 09 - Project Avalon
ZEN: Liberator has resumed original position and status.
AVON: You'd better switch on the communication channel. [Gan crosses flight deck and switches it on.]
https://blake.torpidity.net/m/109/314 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Series A, Episode 09 - Project Avalon
ZEN: Liberator has resumed original position and status.
AVON: You'd better switch on the communication channel. [Gan crosses flight deck and switches it on.]
https://blake.torpidity.net/m/109/314 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

#Blakes7 Series A, Episode 02 - Space Fall
JENNA: You think he's been caught?
BLAKE: No. No, there would have been an alarm. [Looks at the access hatch] I'd better get in after him.
https://blake.torpidity.net…

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

#Blakes7 Series A, Episode 02 - Space Fall
JENNA: You think he's been caught?
BLAKE: No. No, there would have been an alarm. [Looks at the access hatch] I'd better get in after him.
https://blake.torpidity.net…

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

#Blakes7 Series C, Episode 06 - City at the Edge of the World
BAYBAN: That's better.
VILA: You're top of the Federation's Most Wanted list - after Blake.
https://blake.torpidity.net/m/306/167

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

#Blakes7 Series C, Episode 06 - City at the Edge of the World
BAYBAN: That's better.
VILA: You're top of the Federation's Most Wanted list - after Blake.
https://blake.torpidity.net/m/306/167

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.