DiDi-Merging achieves dynamic model merging performance matching or exceeding prior methods while using only 1.24x to 1.4x the parameters of a single fine-tuned model.
In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2025)
3 Pith papers cite this work. Polarity classification is still indexing.
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LVLMs show vocabulary hijacking by inert tokens that decode to hijacking anchors; HABI locates them, NHAR finds resilient heads, and HAVAE boosts those heads to cut hallucinations.
MedSynapse-V proposes meta-query prior memorization, causal counterfactual refinement via RL, and dual-branch memory transition to evolve implicit diagnostic memories in medical VLMs and boost accuracy over chain-of-thought baselines.
citing papers explorer
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Dynamic Model Merging Made Slim
DiDi-Merging achieves dynamic model merging performance matching or exceeding prior methods while using only 1.24x to 1.4x the parameters of a single fine-tuned model.
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Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination
LVLMs show vocabulary hijacking by inert tokens that decode to hijacking anchors; HABI locates them, NHAR finds resilient heads, and HAVAE boosts those heads to cut hallucinations.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes meta-query prior memorization, causal counterfactual refinement via RL, and dual-branch memory transition to evolve implicit diagnostic memories in medical VLMs and boost accuracy over chain-of-thought baselines.