ValueBlindBench: Agreement-Gated Stress Testing of LLM-Judged Investment Rationales Before Returns Are Observable

Battle Count: 61

The paper is highly relevant to the evaluation infrastructure surrounding quantitative trading and AI-finance systems rather than to trading strategy development itself. It addresses the critical problem of how to evaluate AI-generated investment rationales before realized returns are observable, which directly impacts model selection, deployment decisions, and governance in quantitative trading firms. The findings about verbosity bias, judge-family dependence, and anchor ambiguity in LLM-judged financial rationales are directly applicable to any quant team using LLM-based evaluation. However, it does not propose trading strategies, alpha signals, or portfolio construction methods.

Implementing the full ValueBlindBench protocol requires: (1) a controlled market-state capital-allocation prototype with sanitized price bars and constraint sets, (2) a multi-judge ensemble with specific model configurations and trial counts, (3) preregistered adversarial control cells with specific token/feature constraints, (4) LOFO ranking-stability checks with out-of-family probes, (5) quadratic-weighted Cohen's kappa computation with bootstrap CIs, (6) Holm-corrected multiple comparison testing across 36 contrasts, (7) repetition stability diagnostics, and (8) anchor-specificity probes. The protocol design is methodologically rigorous but requires substantial infrastructure for judge API calls (5,500 calls), statistical computation, and preregistration discipline.

The protocol is preregistered with locked analysis plans, thresholds, and adversarial cell specifications. Provider-side identifiers and release dates are recorded in every result row's composer_system_fingerprint field. However, no public code repository or dataset link is explicitly provided in the extract. The protocol design (preregistration, fixed gates, locked amendments) supports reproducibility in principle, but actual replication requires access to the specific judge panel configurations, rubric versions, and market-state prototype data.