Attention maps in LVLMs enable an IoU regressor (Pearson r > 0.67) and a training-free entropy-based selector that improves small-object localization by up to 19% on COCO and Objects365.
Mits: Enhanced tree search reasoning for llms via pointwise mutual information.arXiv preprint arXiv:2510.03632, 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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DPTS shows cold-start bottlenecks at low budgets while SSDP exhibits frontier depletion, indicating fixed ToT strategies are inelastic across compute levels.
Palette identifies refusal directions via multi-objective search, internalizes them through lightweight adaptation, and supports on-demand multi-domain authorization via independent learning and parameter merging.
citing papers explorer
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Self-Improving Small Object Grounding in LVLMs
Attention maps in LVLMs enable an IoU regressor (Pearson r > 0.67) and a training-free entropy-based selector that improves small-object localization by up to 19% on COCO and Objects365.
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Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies
DPTS shows cold-start bottlenecks at low budgets while SSDP exhibits frontier depletion, indicating fixed ToT strategies are inelastic across compute levels.
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Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs
Palette identifies refusal directions via multi-objective search, internalizes them through lightweight adaptation, and supports on-demand multi-domain authorization via independent learning and parameter merging.