SOURCE-LINKED INTELLIGENCE
E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-13T05:50:05.000Z
First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.