Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning
Vision-Language Models (VLMs) exhibit strong performance in instruction following and open-ended vision-language reasoning, yet they frequently generate fluent outputs that are weakly grounded in visual evidence. Prior works have shown that instruction prompting further worsens this issue by amplifying language priors, especially when the visual signal is uncertain or ambiguous. To address this challenge, we propose a decoding framework that explicitly balances linguistic informativeness and visual faithfulness during generation. Our method, Instruction-Evidence Contrastive Dual-Stream Decoding (IECD$^2$), maintains two parallel probability distribution of tokens at each decoding step: an...
| Authors | Yashwant Pravinrao Bangde, Debaditya Roy |
|---|---|
| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 8 May 2026 |
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