Latent Visual Contrastive Reasoning with Multi-Branch Exploration

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Multimodal large language models have made substantial progress in visual question answering, but complex visual reasoning often requires locating and comparing multiple pieces of question-relevant visual evidence. Existing methods that move intermediate reasoning into the visual latent space usually follow a single linear reasoning trajectory, which can be limiting when multiple visual hypotheses or competing evidence regions are relevant. We propose Latent Visual Contrastive Reasoning, a branch-aware latent reasoning framework that generates multiple question-conditioned latent visual branches as soft hypotheses over image tokens. To train these branches, our method uses bounding-box evidence annotations to build target evidence representations and introduces a soft contrastive branch-evidence alignment objective. This objective encourages evidence-relevant branches to align with annotated visual regions while contrasting them against negative evidence. We instantiate our method on Qwen2.5-VL backbones and train it with standard supervised fine-tuning. Experiments on perception-intensive visual reasoning benchmarks show that the proposed method achieves overall improvements over the corresponding backbone and single-path latent reasoning baselines at both 3B and 7B scales.

• GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning

  arXiv (Cornell University) 2026 · 0 citations

• Rethinking Latent Visual Reasoning: Grounding Latent Reasoning in Visual Evidence

  arXiv (Cornell University) 2026 · 0 citations

• MoLE: Mixture of Latent Experts for Complementary Visual Reasoning

  arXiv (Cornell University) 2026 · 0 citations