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.
Chartbench: A benchmark for complex visual reasoning in charts.arXiv preprint arXiv:2312.15915
10 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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MM-JudgeBias benchmark shows that many MLLM judges neglect modalities and produce unstable evaluations under small input changes, based on tests of 26 models with over 1,800 samples.
PlotChain benchmark reports top MLLMs reaching ~80% field-level accuracy on engineering plot reading under human-like tolerances, but with persistent failures on frequency-domain tasks like bandpass and FFT spectra.
Introduces a benchmark for MLLM-based chart data extraction from unlabeled images and a human-centered training framework that reaches SOTA numerical accuracy with a 7B model.
CrystalXRD-Bench is a new 250-sample benchmark for VLMs on XRD peak indexing, where the best model (GPT-5.4) reaches Jaccard 0.5888 and 37.6% exact match while most stay below 0.50, showing the task remains unsolved.
FinDocMRE is a new multi-image document-level benchmark spanning 12 financial domains and 5 task types, showing that 11 tested LMMs all score below 65 overall with particular weaknesses in numerical estimation and cross-page grounding.
Chart-FR1 uses Focus-CoT for linking reasoning to visual cues and Focus-GRPO reinforcement learning with efficiency rewards to outperform prior MLLMs on dense chart reasoning tasks.
CharTool equips MLLMs with cropping and code tools plus agentic RL on DuoChart data to raise chart-reasoning accuracy by up to 9.78 percent on benchmarks.
ChartVerse uses Rollout Posterior Entropy and truth-anchored inverse QA synthesis to produce 640K high-quality chart reasoning samples, training an 8B model that surpasses its 30B teacher.
CycleChart is a consistency-based framework that organizes chart generation, schema parsing, data parsing, and QA around single data instances to enforce bidirectional semantic alignment and improve cross-task generalization.
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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MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge
MM-JudgeBias benchmark shows that many MLLM judges neglect modalities and produce unstable evaluations under small input changes, based on tests of 26 models with over 1,800 samples.
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PlotChain: Deterministic Checkpointed Evaluation of Multimodal LLMs on Engineering Plot Reading
PlotChain benchmark reports top MLLMs reaching ~80% field-level accuracy on engineering plot reading under human-like tolerances, but with persistent failures on frequency-domain tasks like bandpass and FFT spectra.
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Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework
Introduces a benchmark for MLLM-based chart data extraction from unlabeled images and a human-centered training framework that reaches SOTA numerical accuracy with a 7B model.
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CrystalXRD-Bench: Benchmarking Vision-Language Models for XRD Peak Indexing Across Diverse Crystalline Materials
CrystalXRD-Bench is a new 250-sample benchmark for VLMs on XRD peak indexing, where the best model (GPT-5.4) reaches Jaccard 0.5888 and 37.6% exact match while most stay below 0.50, showing the task remains unsolved.
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FinDocMRE: A Benchmark for Document-Level Financial Multimodal Reasoning Evaluation
FinDocMRE is a new multi-image document-level benchmark spanning 12 financial domains and 5 task types, showing that 11 tested LMMs all score below 65 overall with particular weaknesses in numerical estimation and cross-page grounding.
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Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts
Chart-FR1 uses Focus-CoT for linking reasoning to visual cues and Focus-GRPO reinforcement learning with efficiency rewards to outperform prior MLLMs on dense chart reasoning tasks.
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CharTool: Tool-Integrated Visual Reasoning for Chart Understanding
CharTool equips MLLMs with cropping and code tools plus agentic RL on DuoChart data to raise chart-reasoning accuracy by up to 9.78 percent on benchmarks.
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ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch
ChartVerse uses Rollout Posterior Entropy and truth-anchored inverse QA synthesis to produce 640K high-quality chart reasoning samples, training an 8B model that surpasses its 30B teacher.
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CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation
CycleChart is a consistency-based framework that organizes chart generation, schema parsing, data parsing, and QA around single data instances to enforce bidirectional semantic alignment and improve cross-task generalization.