PDCR improves vision-language reasoning by computing separate normalized confidence advantages for perception steps and reasoning steps after unsupervised decomposition.
Ouro: A self-bootstrapped frame- work for enhancing multimodal scene understanding
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abstract
Recent advances in multimodal language models (MLLMs) have made thinking with images a dominant paradigm for multimodal reasoning. However, existing methods still fail to ensure evidence-answer consistency, where correct answers must be supported by correct visual evidence. To address this issue, we propose DeFacto, a counterfactual reasoning framework that explicitly aligns visual evidence with final answers. Our approach integrates three complementary training paradigms: positive, counterfactual, and random-masking. We further develop a language-guided evidence construction pipeline that automatically localizes question-relevant regions and generates counterfactual variants, resulting in DeFacto-100K. Building on this dataset, we train MLLMs with GRPO-based reinforcement learning and design three complementary rewards to promote correct answering, structured reasoning, and consistent evidence selection. Moreover, we introduce DeFacto-1.5K, a human-annotated benchmark for systematically evaluating evidence-grounded consistency beyond answer accuracy. Experiments on diverse benchmarks demonstrate that DeFacto substantially improves both answer accuracy and evidence-answer consistency over strong baselines.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
VIGIL is a counterfactual RL alignment method that reduces visual hallucinations in MLLMs by enforcing visual grounding via masked attention penalties, outperforming baselines with 25% of the data and showing emergent spatial capabilities.
SLVR is a two-stage method that enriches region-centric latent representations with fine-grained attribute semantics and aligns them via M-GRPO across multiple queries on the same region, supported by new SLV-Set dataset and SV-QA benchmark.
citing papers explorer
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PDCR: Perception-Decomposed Confidence Reward for Vision-Language Reasoning
PDCR improves vision-language reasoning by computing separate normalized confidence advantages for perception steps and reasoning steps after unsupervised decomposition.
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Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs
VIGIL is a counterfactual RL alignment method that reduces visual hallucinations in MLLMs by enforcing visual grounding via masked attention penalties, outperforming baselines with 25% of the data and showing emergent spatial capabilities.
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Semantic-Enriched Latent Visual Reasoning
SLVR is a two-stage method that enriches region-centric latent representations with fine-grained attribute semantics and aligns them via M-GRPO across multiple queries on the same region, supported by new SLV-Set dataset and SV-QA benchmark.