Fisher information selects task-relevant parts of graph features to fuse with pretrained code models, improving vulnerability detection F1 by up to 6.3 points on BigVul, Devign, and ReVeal.
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EnergyFlow shows that denoising score matching on diffusion policies recovers the gradient of the expert's soft Q-function under maximum-entropy optimality, enabling non-adversarial reward extraction and improved policy generalization.
Extremely quantized LLMs exhibit systematic smoothness degradation that reduces effective token candidates and degrades generation; a smoothness-preserving principle in PTQ and QAT delivers gains beyond numerical accuracy.
citing papers explorer
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Focus on What Matters: Fisher-Guided Adaptive Multimodal Fusion for Vulnerability Detection
Fisher information selects task-relevant parts of graph features to fuse with pretrained code models, improving vulnerability detection F1 by up to 6.3 points on BigVul, Devign, and ReVeal.
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Recovering Hidden Reward in Diffusion-Based Policies
EnergyFlow shows that denoising score matching on diffusion policies recovers the gradient of the expert's soft Q-function under maximum-entropy optimality, enabling non-adversarial reward extraction and improved policy generalization.
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Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs
Extremely quantized LLMs exhibit systematic smoothness degradation that reduces effective token candidates and degrades generation; a smoothness-preserving principle in PTQ and QAT delivers gains beyond numerical accuracy.