• Computer Science > Artificial Intelligence [Submitted on 24 Feb 2026] Title:PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding View PDFAbstract:Reliable AI systems require large language models (LLMs) to exhibit behaviors aligned with human preferences and values. • However, most existing alignment approaches operate at training time and rely on additional high-quality data, incurring significant computational and annotation costs. • While recent work has shown that contrastive decoding can leverage a model’s internal distributions to improve specific capabilities, its applicability remains limited to narrow behavioral scopes and scenarios. • In this work, we introduce Polarity-Prompt Contrastive Decoding (PromptCD), a test-time behavior control method that generalizes contrastive decoding to broader enhancement settings. • PromptCD constructs paired positive and negative guiding prompts for a target behavior and contrasts model responses-specifically token-level probability distributions in LLMs and visual attention patterns in VLMs-to reinforce desirable outcomes. • This formulation extends contrastive decoding to a wide range of enhancement objectives and is applicable to both LLMs and Vision-Language Models (VLMs) without additional training.

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  • Computer Science > Artificial Intelligence [Submitted on 24 Feb 2026] Title:PromptCD: Test-Time Behavior Enhancement via Polarity-Prompt Contrastive Decoding View PDFAbstract:Reliable AI systems require large language models (LLMs) to exhibit behaviors aligned with human preferences and values. However, most existing alignment approaches operate at training time and rely on additional high-quality data, incurring significant computational and annotation costs. While recent work has shown that contrastive decoding can leverage a model’s internal distributions to improve specific capabilities, i

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