Tootfinder

The Brain That Goes Quiet: Serving a Large Model's Knowledge at 131 Tokens per Second on an 8 GB Laptop by Removing the Large Model from the Runtime Path
Myeong Jun Jo
https://arxiv.org/abs/2606.12154 https://arxiv.org/pdf/2606.12154 https://arxiv.org/html/2606.12154
arXiv:2606.12154v1 Announce Type: new
Abstract: In earlier work I showed that a 35B-class Mixture-of-Experts model can be loaded and executed on a consumer laptop with 8 GB of GPU memory. That result solved a placement problem and immediately exposed a different one: even correctly placed, the large model needed roughly four seconds to answer, because it was still being invoked at every query. This paper documents what happened when I stopped invoking it. During an offline phase, the large model reads source documents and writes verified answer entries into a structured knowledge store; at runtime, only a lightweight router, a deterministic renderer, and a 1B-class model are active. On the same 8 GB laptop, end-to-end response time fell from approximately 4,465 ms to 518 ms, effective end-to-end throughput rose from 15.7 to 131 tokens per second, and the small model's streaming decode rate held at 226-237 tokens per second with a time-to-first-token of 29-62 ms. The bottleneck is structural: three different large models (Qwen, Gemma, and GLM class) all showed the same multi-second runtime cost, and all three produced usable knowledge stores offline. On a 563-entry store built from seventeen real documents, keyword routing collapsed to 1.5% top-1 accuracy while BM25-based routing reached 92.8% (99.4% top-3), and a confidence gate raised effective top-1 to 98.0% by escalating 12.3% of queries. Exact-match fidelity of the small model ranged from 9/9 to 0/9 across envelope formats carrying identical content. A 16-case verification gate blocked all ten corrupted entries while admitting all six supported ones.
toXiv_bot_toot

I have just read someone that thinks that he found a Candidate Theory of Everything and solved the black hole information paradox... by using an AI tool (Claude, but it is irrelevant, to be honest), in 1 week.
If you want to waste your time search for "I Used AI to Produce a Candidate Theory of Everything". But please do not use this to mock the guy.
The overconfidence created by these tools is terrifying.
A related paper, that is not that guy's paper:

AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized
Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to respond. We engineered the questions so that AI advice was wrong, separating AI use from its accuracy. Merely having access to AI nearly eliminated participants' willingness to suspend judgment, and this held whether th…

AI-Generated Interactive Fiction for Educational Use: A Pilot Study of Perceived Comprehensibility, Coherence, and Engagement
Finn Rogosch, Andreas Schrader
https://arxiv.org/abs/2608.10818 https://arxiv.org/pdf/2608.10818 https://arxiv.org/html/2608.10818
arXiv:2608.10818v1 Announce Type: new
Abstract: Generative artificial intelligence (AI) can produce educational content at scale, including interactive and narrative learning experiences, but technical generation alone is not sufficient: scenarios that are confusing, narratively inconsistent, or unengaging are unlikely to be useful in practice. This paper presents a pilot user-centred evaluation of AI-generated interactive fiction (IF) for educational use in higher education. Using a previously described domain-agnostic pipeline and a shared STEM content base, we generated a controlled pool of scenarios and asked participants (N = 22, STEM higher-education) to play one generated episode and rate it on narrative clarity, story-content coherence, engagement, and length acceptance. A free-text prompt captured open feedback. Narrative clarity and length acceptance were rated positively, engagement sat near the neutral mid-point of the scale, and story-content coherence was the weakest dimension by a clear margin. Qualitative feedback points to quiz integration as the bottleneck. Artificial in-fiction motivation for quiz prompts and abrupt setting changes were reported. Feedback also pointed to missing story-level consequences for wrong answers. From these observations, we derive concrete design implications that can inform larger follow-up studies, including later work on learning effectiveness.
toXiv_bot_toot

:brdAngry2:

Christmas presents come from Santa Claus. Easter baskets from the Easter Bunny. But where do birthday presents come from?
When I was little my brothers and I concluded that the answer was... The Birthday Moose.
One of the non-birthday brothers would be designated the Moose. The outfit of the day was a brown paper grocery bag with a pair of eyes drawn on it in black marker, a lunch bag taped on the front as a snout, and sometimes a pair of cardboard ears up top.
Notably, tra…

Robert F Kennedy Jr, the US health secretary, is demanding answers from a medical journal that recently removed a paper suggesting a link between vaccines and infant death, saying their decision was “of great interest to me”.
The journal Toxicology Reports had removed the paper this spring after editors determined it was so seriously flawed it could harm patients and pose a risk to public health.
Public health advocates immediately criticized the move, and said Kennedy appeared t…

Wanna know why #psycholinguistics is so fascinating?
In a 2001 #experiment participants saw sentences like
"While Mary dressed the baby played in the crib."
Then they had to answer the question:
"Did Mary dress the baby?"
Up to 51% of all answers where "Yes!" – and participants were *very* confident about their answers.
🤯
Original paper:
Christianson, Kiel & Hollingworth, Andrew & Halliwell, John F. & Ferreira, Fernanda (2001). Thematic Roles Assigned along the Garden Path Linger. Cognitive Psychology 42(4). 368–407. DOI: https://doi.org/10.1006/cogp.2001.0752
Also a fascinating read:
Ferreira, Fernanda & Bailey, Karl G.D. & Ferraro, Vittoria (2002). Good-Enough Representations in Language Comprehension. Current Directions in Psychological Science 11(1). 11–15. DOI: https://doi.org/10.1111/1467-8721.00158

DefoEye: Python-Based Software for Facilitating Time-Series InSAR Analysis of Sentinel-1 Remote-Sensing Data
Alireza Taheri Dehkordi, Hossein Hashemi, Amir Naghibi
https://arxiv.org/abs/2608.04915 https://arxiv.org/pdf/2608.04915 https://arxiv.org/html/2608.04915
arXiv:2608.04915v1 Announce Type: new
Abstract: Many existing time-series Interferometric Synthetic Aperture Radar (TS-InSAR) software tools have limitations, including restricted geographic applicability, commercial licensing, and incomplete end-to-end processing support. Although GMTSAR avoids some of these constraints, it still requires substantial manual intervention and C-shell commands, lacks a user-friendly graphical interface, and omits important steps such as interferogram network pruning and anchoring of unwrapped interferograms. This paper introduces DefoEye (v1), an open-source Python-based software package that wraps GMTSAR and provides a unified, user-friendly TS-InSAR workflow for Sentinel-1 data. DefoEye supports parallel job execution, interferogram network pruning, and multiple anchoring options. Its performance was evaluated from 2020 to 2024 in four regions with different geological settings, deformation mechanisms, and atmospheric and climatic conditions. In Bologna, Italy; Gotland, Sweden; and Houston, USA, DefoEye results were compared with observations from 10 GNSS stations and showed strong agreement, with RMSE values of 4.3-11.9 mm and Pearson correlation coefficients of 0.63-0.95. In Karaj, Iran, where GNSS observations were unavailable, DefoEye was compared with other widely used processing tools and achieved similarly close agreement, with an RMSE of 4.8 mm/yr and a Pearson correlation coefficient of 0.98. These results demonstrate that DefoEye provides reliable TS-InSAR products for geological, hydrological, and environmental applications.
toXiv_bot_toot

RE: https://hachyderm.io/@zwarich/116896925301582827
I am delighted to see this, and look forward to studying the paper a bit.
Back in my “Paul is tinkering around with programming on his own” days, radix sort was one of the first algorithms to fascinate me. When I started taking academic CS classes, I was baffled by fixation on •comparison• in sorting. What about radix sort?!?
Well, the obvious answer is “you can’t use radix for everything; comparison is more generic.” But…what if you made radix more generic too? This paper seems to take that little thought and run to grandiose places with it.

"How proprietary formats have become Microsoft’s main tool for lock-in"
Why DOCX, XLSX, and PPTX are Microsoft's most effective lock-in tool:
https://blog.documentfoundation.org/blog/2026/07/17/microsofts-main-tool-for-lock-in/…