Attentive-CoT is an attention-guided fine-tuning objective that improves chain-of-thought performance in multimodal LLMs by delaying answer commitment and increasing sustained visual-token access during rationale generation.
arXiv preprint arXiv:2404.18624 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
years
2026 4verdicts
UNVERDICTED 4representative citing papers
VLMs violate their own stated introspective rules for attributing colors to objects in nearly 60% of cases on items with strong color priors, unlike humans who largely follow theirs, revealing miscalibrated self-knowledge.
Clinical VLMs over-rely on text modality, irrelevant clinical history, and prompt wording when making chest x-ray decisions on MIMIC-CXR data.
A new attention-enhancement method using ARS scores and RVE reduces action-relation hallucinations in LVLMs while generalizing to spatial and object hallucinations.
citing papers explorer
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Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
Attentive-CoT is an attention-guided fine-tuning objective that improves chain-of-thought performance in multimodal LLMs by delaying answer commitment and increasing sustained visual-token access during rationale generation.
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When to Call an Apple Red: Humans Follow Introspective Rules, VLMs Don't
VLMs violate their own stated introspective rules for attributing colors to objects in nearly 60% of cases on items with strong color priors, unlike humans who largely follow theirs, revealing miscalibrated self-knowledge.
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Medical Context Distorts Decisions in Clinical Vision Language Models
Clinical VLMs over-rely on text modality, irrelevant clinical history, and prompt wording when making chest x-ray decisions on MIMIC-CXR data.
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Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement
A new attention-enhancement method using ARS scores and RVE reduces action-relation hallucinations in LVLMs while generalizing to spatial and object hallucinations.