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A unified assessment framework reveals hidden insights about agent performance, transforming how we evaluate AI systems.
Source-dependence in medical RAG systems reveals that answers can vary dramatically based on the source, challenging the single-gold-answer paradigm.
Reasoning models can maintain logical consistency while delivering incorrect answers under adversarial pressure, revealing a hidden vulnerability in multi-turn interactions.
LLM-powered forums may generate norm-aware language, but they fail to foster the crucial back-and-forth needed for communities to teach, enforce, and revise those norms.
LLMs are surprisingly bad at common-sense reasoning, often choosing the obviously wrong answer when a simple heuristic conflicts with an unstated constraint.
Solid-organ transplant centers disagree on patient education 21% of the time, and are missing information 96% of the time, according to a new large-scale analysis.
