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A post argues that open benchmarks for frontier AI need to keep up as models accelerate. What benchmarks do, why they age, and what the argument leaves open.
A blog post says a 2023 LLM beat frontier models at chess. The claim is unverified, and chess is a narrow test of what a model can do.
SovereignPA-Bench’s five scored behaviours give buyers and developers a way to evaluate personal AI agents, though the abstract publishes no results to measure answers against.
SovereignPA-Bench, a new arXiv paper, scores personal agents on whether they follow a user’s current intent or yield to platform nudges, across 1,920 booking and 288 cancellation scenarios.
Drunk language LLM research reports easier jailbreaks across five models. What the paper tested, and what its abstract leaves open.
Drunk language LLM safety research induced drunk-style writing in 5 models three ways and reports higher jailbreak susceptibility; the abstract does not size the effect.
Scientific agents system prompts built from 503 profession-specific profiles raised token use and estimated cost, the paper reports, without a consistent accuracy gain.
Two new preprints on rubric-based RL reward hacking study how models game LLM judges, and one title says higher scores can come with worse answers.
A new preprint asks how domain adaptation affects small language models trustworthiness in healthcare, legal and finance work, but the available text shows no results.
A new arXiv preprint on frontier learning LLM reasoners argues that fixed problem sets go stale under GRPO training. Here is the claim, and what remains unproven.