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.
Findings of the Association for Computational Linguistics: ACL 2025 , address =
6 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
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PolyChartQA is a new mid-scale dataset for multi-chart question answering that reveals a 27.4% accuracy drop for multimodal models on human-authored questions compared to AI-generated ones, plus a modest gain from a proposed prompting method.
VRRL trains LVLMs for visually grounded self-reflection via prefix masking and buffered roll-ins, yielding higher out-of-distribution accuracy on grounding and navigation tasks than standard RL baselines.
AgentFinVQA presents a multi-agent pipeline for auditable on-premise financial chart QA that reports 4.84-7.68 pp accuracy gains over matched baselines on FinMME plus code release for reproducibility.
A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.
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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Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
PolyChartQA is a new mid-scale dataset for multi-chart question answering that reveals a 27.4% accuracy drop for multimodal models on human-authored questions compared to AI-generated ones, plus a modest gain from a proposed prompting method.
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Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning
VRRL trains LVLMs for visually grounded self-reflection via prefix masking and buffered roll-ins, yielding higher out-of-distribution accuracy on grounding and navigation tasks than standard RL baselines.
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AgentFinVQA: A Deployable Multi-Agent Pipeline for Auditable Financial Chart QA
AgentFinVQA presents a multi-agent pipeline for auditable on-premise financial chart QA that reports 4.84-7.68 pp accuracy gains over matched baselines on FinMME plus code release for reproducibility.
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Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.
- CharTool: Tool-Integrated Visual Reasoning for Chart Understanding