arxiv:2609.14302
Published on Sep 13
· Submitted by
wangjunjie on Sep 15
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Abstract
A benchmark for financial chart reasoning reveals that vision-language models often fail to maintain traceable evidence-to-action reliability, highlighting the need to evaluate the full reasoning chain rather than single hallucination scores.
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Can financial vision-language models (VLMs) turn chart evidence into reliable action recommendations? Existing hallucination evaluations are mostly claim-centric; they assess whether generated statements are supported, but not whether evidence remains traceable through rationale, confidence, and final action. We introduce E2A-Bench, a 969-query benchmark for financial chart reasoning, constructed from 323 HS300 constituents under three input modalities with deterministic OHLCV-derived evidence anchors. E2A-Bench evaluates grounding, reasoning-action consistency, evidence-confidence calibration, and directional coverage through UCR, RCI, ECI, and NDR, where NDR measures coverage-aware evidence-to-action reliability rather than realized trading performance. Evaluating 20 VLMs reveals three failures hidden by scalar hallucination scores: the lowest-UCR model ranks near the bottom by NDR due to only 6.4% directional coverage; oracle-aided verification reduces unsupported claims but can collapse coverage; and financial fine-tuning amplifies the BUY:SELL ratio by factors of 4.21 to 4.68 across strict base-fine-tuned pairs. These results show that financial VLM evaluation should trace the full evidence-to-action chain rather than rely on a single hallucination score. Code and data: https://github.com/wanng-ide/[E2A-Bench](https://hf-mirror.com/papers?q=E2A-Bench)
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Paper author Paper submitter 7 days ago
E2A-Bench is a multimodal benchmark for evaluating evidence-to-action reliability in financial chart reasoning. It assesses whether VLMs can ground their analysis in chart evidence and maintain consistency across evidence, reasoning, confidence, and final decisions.
7 days ago
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