PSRD mitigates visual hallucinations in LVLMs via phase-wise self-reward decoding, cutting rates by 50% on LLaVA-1.5-7B and outperforming prior methods on five benchmarks.
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2026 2verdicts
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ChemVA framework uses hybrid-granularity visual anchors and entity-name alignment to improve LLM performance on chemical reaction diagrams by ~20 points, reaching 92% structural accuracy on the new OCRD-Bench dataset.
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Mitigating Multimodal Hallucination via Phase-wise Self-reward
PSRD mitigates visual hallucinations in LVLMs via phase-wise self-reward decoding, cutting rates by 50% on LLaVA-1.5-7B and outperforming prior methods on five benchmarks.
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ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding
ChemVA framework uses hybrid-granularity visual anchors and entity-name alignment to improve LLM performance on chemical reaction diagrams by ~20 points, reaching 92% structural accuracy on the new OCRD-Bench dataset.