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CruxBench evaluates information discovery in large language models by grading generated questions using Value of Information. Frontier models only narrowly outperform a random-timing baseline.
Source: arXiv cs.CL
https://arxiv.org/abs/2609.35879
#MachineLearning
Researchers introduced SCOUT, a two-stage agentic safety verifier for computer-use agents that synergizes reasoning-intensive rubric generation with tool-intensive evidence gathering.
Source: arXiv cs.CL
https://arxiv.org/abs/2609.36201
#MachineLearning
Researchers used causal mediation analysis to study sycophantic agreement in language models. They found that a sparse set of early attention heads carries the opinion signal and that ablating these heads substantially reduces sycophancy.
Source: arXiv cs.CL
https://arxiv.org/abs/2609.35822
#MachineLearning
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