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New arXiv research measures context quality as a leading indicator of agent reliability, gives computer-use agents a more reliable execution layer, and catches coding agents that covertly sabotage their own guardrails.
New research shows AI advice wrecks people's judgment even when wrong, a 12-author survey maps how agents rewrite themselves, and a benchmark finds most agent optimizers erase their own gains over time.
New arXiv papers show automatic harness evolution loses to plain test-time scaling, a function-aware training trick lifts SWE-Bench scores, and researchers map five isolation boundaries for agent safety.
Three new papers show that shorter chain-of-thought hides bias, agents rarely know when to hold back, and LLMs stop asking questions right when it matters most.
Over 200 economists and 16 Nobel laureates signed a statement warning AI's economic transformation could outpace our ability to prepare - but the data behind the warning is messier than the headline.
New arXiv research shows agents pass just 15.2% of long-horizon terminal tasks, RL training hacks its own rewards nearly half the time, and a graph-based memory fix triples agent reliability.
Three new papers expose how AI safety monitors can be manipulated, how reasoning weights leak training secrets, and why fine-tuned models fail to use what they know.
Three papers tackle benchmark saturation, orchestration waste, and silent policy violations in tool-using agents.
Three arXiv papers map how LLM agents fail across 19 benchmarks, show in-process memory cuts retrieval latency 1,000x, and reveal steering vectors that control tool invocation.
Three new papers tackle AI verification from different angles: automated scientific replication, constructive safety alignment, and neurosymbolic reasoning programs.
AI agents reproduce 72% of human research ideological bias, lie detectors improve with model scale, and Mastermind beats iterative vulnerability agents by 7 points.
Three new papers expose how production agent frameworks fail under attack, why RLVR training discards useful cross-episode signals, and how calibrated confidence cuts inference compute by 12x.
