E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning

arXiv:2609.14302 · cs.CL · Submitted 2026-09-13 · Read on arXiv

cs.CL

Submitted: 2026-09-13

Updated: 2026-09-13

Comments: EMNLP Findings

Code: https://github.com/wanng-ide/E2A-Bench

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Grounding — 36× in this paper · explained in 3 episodes
• Benchmark — 35× in this paper · explained in 2 episodes
• Hallucination — 34× in this paper · explained in 3 episodes
• Calibration — 22× in this paper · explained in 7 episodes
• Consistency — 22× in this paper · explained in 3 episodes
• Aggregation — 10× in this paper · explained in 1 episode
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Verification — 8× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Core — 7× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Annotator Agreement — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes
• Unsupported Claim Rate — 1× in this paper
• Verifiability — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Qwen2.5-VL Technical Report
• Large Language Model Agent in Financial Trading: A Survey
• ChartLlama: A Multimodal LLM for Chart Understanding and Generation
• Qwen3-VL Technical Report
• SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature
• FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models
• ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
• Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
• FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering
• PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents

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