A study on arXiv (2609.26926v1) found that having experts label cases of cross-LLM disagreement with rationales produced the highest LLM-labeling accuracy at 64.9% against expert labels, beating an ex…
A new arXiv paper (2609.25021v1) reports that the chat template acts as a switch controlling LLM self-referential voice, turning up disclaimer language such as "I'm just an AI" and turning down experi…
A new arXiv paper (arXiv:2609.22133v1) reports that ten large language models, three human experts, and 165 crowdsourced workers independently classified the same texts from 14 peer-reviewed political…
A new arXiv paper (2609.20838v1) reports that seven widely used large language models generated a synthetic fake-news corpus of 14,000 articles across four manipulation scenarios — open-ended generati…
Researchers introduced PersonaPath, a benchmark pairing 2,000 fine-grained learner personas with a hierarchical knowledge graph of 347 textbooks, 1,751 units, and 4,092 concepts across 77 subjects, to…
Large language models have no persistent personal memory of past interactions, and any apparent recall is handled by the application built around the model rather than the model itself, according to a…
A new arXiv paper (2609.13201v1) models AI-generated text and images as anomalies linked to main data points and derives bounds on the minimum size of a strongly dissimilar decomposition of random dat…
Database administrators should treat AI as a process-following diagnostic assistant rather than a replacement, according to an InfoWorld opinion piece marking PostgreSQL's 30th anniversary. The author…
A developer with a team of 10 programmers observed at a programming offsite that engineers given advanced AI models and generous budgets gravitate toward building developer tooling for themselves rath…
A blog post on autonomous agent reliability argues that fully autonomous agentic workflows are "extremely improbable" to be reliable, citing arXiv paper 2510.27630 showing reliability tends to stabili…
A study evaluating fourteen LLMs found that tool-using agents overtrust unreliable tool returns, with mean adoption of corrupted content exceeding one third for every tool and reaching 68.0% for web s…
A paper posted to arXiv (2609.05894v1) argues that the traditional scaling paradigms behind modern AI systems are hitting friction that prevents continued exponential growth, citing peak data limitati…
A new survey from arXiv (2608.14797v1) identifies three emerging paradigms in decoding methods for large language models (LLMs) and large vision-language models (LVLMs), offering a systematic review o…
Chandra Sripada, a professor of philosophy and psychiatry at the University of Michigan, argues that large language models (LLMs) exhibit cognition more similar to human thought than commonly assumed,…
The introduction of LLMs into the software engineering lifecycle has shifted the DevOps threat model from scaling to security, as AI-generated code bypasses traditional review processes and introduces…
A 2.8M-parameter transformer achieves 0.01-bit entropy agreement with the Bayesian optimum for model selection in Bayesian wind tunnels using fixed-point-free involutions, but fails completely with op…
An exposed WebDAV server containing over 1,000 artifacts was discovered by an MDR team, revealing a fully operational malware delivery lab where attackers used generative AI to systematically test del…
Resolve argues that link building must shift from chasing volume to earning authoritative backlinks, brand mentions, and media coverage that signal trust and credibility to search engines and AI syste…
A developer launched Kettio, a tool that uses machine learning to predict audience purchase intent from ads, landing pages, and product shots, inspired by research showing LLMs can reproduce human buy…
A writer shares six story prompts, three of which explore AI themes, inspired by Jorge Luis Borges' ideas about provenance and authorship in the context of large language models. The prompts aim to in…