ValueBlindBench: Agreement-Gated Stress Testing of LLM-Judged Investment Rationales Before Returns Are Observable
LLM-based financial agents increasingly produce investment rationales before the outcomes needed to evaluate them are observable. This creates a delayed-ground-truth evaluation problem: realized returns remain the eventual arbiter of investment quality, but they arrive too late and are too noisy to guide many model-development and governance decisions. LLM judges offer a tempting shortcut for pre-deployment evaluation of AI-finance systems, but unvalidated judges may reward verbosity, confidence
Record details
Published: 28 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (28 April 2026), “ValueBlindBench: Agreement-Gated Stress Testing of LLM-Judged Investment Rationales Before Returns Are Observable,” evidence record 5276, https://ethics.ai/record/5276 (originally published by arXiv).
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