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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
Intrinsic self-correction allows a language model to revise its own answer without external evidence. A second pass recovers mistakes but can overturn correct answers. Aggregate accuracy can conceal differing revision behavior.
Source: arXiv cs.CL
https://arxiv.org/abs/2609.35832
#MachineLearning
Demographic Pluralism is introduced as an inference-time framework that estimates population-level opinion distributions without opinion-distribution training data or task-specific fine-tuning.
Source: arXiv
https://arxiv.org/abs/2609.38555
#MachineLearning
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