A mechanism-first reading of τ-Knowledge shows why enterprise agents fail even when the manual is available: retrieval, policy reasoning, tool discovery, and state-changing execution break in different places.
A mechanism-first reading of how Bayesian adversarial agents can make low-code scientific automation more reliable than bigger-model prompting alone.
A close reading of inherited goal drift shows why long-running AI agents need context governance, not just stronger prompts.
A mechanism-first reading of DGRO, a proposed alignment method that turns community acceptance patterns into preference signals without explicit labels.
A new card-game benchmark shows why AI evaluation under uncertainty needs diversity, fixed rules, and diagnostic structure rather than another lonely leaderboard score.
A mechanism-first reading of NeuroSkill shows how wearable biosignals could become agent context, and why that is useful only when treated as telemetry rather than mind-reading.
A Swedish survey shows that public AI expectations are not hype versus doom, but a layered map of medical optimism, social caution, and skepticism toward AGI-like transformation.
A mechanism-first reading of ReAct, the prompting framework that turned language models from passive answer generators into inspectable tool-using agents.
A comparison-driven reading of PredANN++ and what it teaches businesses about cognitively grounded AI supervision.
A mechanism-first reading of why output-distribution contamination detection fails when small language models learn leaked benchmark data without memorizing it verbatim.