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WizardLM Releases Mix-GRM Paper

WizardLM released a new paper on improving Generative Reward Models by synergizing Breadth (B-CoT) and Depth (D-CoT) reasoning instead of just longer CoT. The Mix-GRM framework uses RLVR training to enable the model to autonomously select reasoning structures, achieving high performance with efficient token use. It addresses limitations in subjective vs. objective evaluation tasks.

Reddit r/LocalLLaMACommunityMar 4#reward-models#cot-reasoning#rlvr
Reasoning Boosts Hallucination Span Detection

Reasoning Boosts Hallucination Span Detection

Large language models often generate hallucinations that undermine reliability. This Apple research treats hallucination span detection as a multi-step reasoning task, evaluating pretrained models with and without Chain-of-Thought (CoT). Results show CoT reasoning holds strong potential for improvement.

Apple Machine LearningOfficialMar 3#span-detection#cot-reasoning
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Cybersecurity Must Protect Physical Reality

As industrial systems, infrastructure, robots, and AI agents gain the ability to change physical conditions, cybersecurity must protect actions and outcomes—not only data and access. The article argues that future defenses need to evaluate whether an authorized action is appropriate for the current environment, state, and safety boundaries.