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AI agents keep long-term memory, much of which comes from web pages and tool results. Agent harnesses, the software that runs the model, do not record who may act on this memory, so an instruction hidden in a fetched page can be treated as the user's own instruction. We propose ClawMMU, a protection layer that labels e...
The findings support evaluation practices that separate preference from safety, report safety-critical failure rates directly, and incorporate clinically grounded adjustments when ranking LLMs for clinical decision making.
CLIMB is introduced, a benchmark in which a doctor model interviews a simulated patient to recover a ground truth set of co-occurring clinical conditions, and a theoretical reference model of single-hypothesis tracking is formalised with a theoretical reference model of single-hypothesis tracking.
The hypMOOVE-PHC study is the hypothetical vignette phase of the Massive Open Online Validation and Evaluation (MOOVE) initiative, implemented in Kenya, Malawi, and Tanzania, and aims to validate a pool of LLMs through clinical review of expert-generated vignettes through fully crossed repeated-measures comparative eva...
This work reconstructs a coupled-fact graph as reconstructing a coupled-fact graph: at each edit, a required fact comes from recent context or parametric memory, and the facts covered by neither form coherence debt.
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