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This paper introduces Confident Decoding, a training-free decoding strategy that dynamically selects the most reliable intermediate layer in LLMs using entropy-guided search, mitigating the alignment tax and improving reasoning performance on benchmarks like GPQA-Diamond and Omni-MATH with negligible overhead.
The paper generalizes contrastive decoding to a conflict-aware paradigm that dynamically allocates authority between external context and parametric priors, proposes the TriState-Bench evaluation protocol, and introduces Adaptive Regime Routing (ARR) to resolve asymmetry between correction and resistance.
This paper introduces MGAP, a training-free decoding method that reduces hallucinations in Multimodal Large Language Models by adaptively suppressing only the harmful parts of language priors while preserving the model's semantic manifold. The method outperforms prior baselines on POPE and CHAIR benchmarks.
A Harvard research paper introduces Recoding-Decoding (RD), a novel decoding scheme that injects random priming phrases and diverting tokens to tap into an LLM's long-tail knowledge, significantly boosting output diversity without fine-tuning. The method maintains high relevance while mitigating response homogenization, with stronger models showing greater diversity gains.