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Sampling several responses and voting over their answers can improve a language model's accuracy, but repeated answers limit the benefit of additional samples. Raising temperature increases diversity at a potential cost to per-sample accuracy. We introduce Self-Repulsion (SR), a sampler for masked diffusion language mo...
A randomized experiment and an anonymous post-survey at ICML 2026 investigate how reviewers use LLM-use policies in practice and how different LLM-use policies affect review outcomes, and discusses implications for future peer-review policy and tool design.
This work introduces MXSens, a training-free method that assigns mixed mantissa bitwidths (4/6/8) based on column- and layer-wise sensitivity, naturally leveraging the block-wise structure of MXINT, and establishes a new balance between accuracy and resource efficiency for LLM quantization.
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