RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
Small drafts, big verdict: Information-intensive visual reasoning via speculation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative 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.
citing papers explorer
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RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation
RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
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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.