InvestPhilBench: A Multi-Layer Dynamic Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy

Researchers introduce InvestPhilBench, a comprehensive benchmark for testing large language models' ability to reconstruct and apply expert investment decision frameworks. The v0.6 release reveals that while state-of-the-art models achieve high composite scores (0.932), they exhibit significant procedural reasoning deficits (GRA scores of 0.57-0.77), indicating that fluent prose masks deeper gaps in step-by-step investment logic.

InvestPhilBench addresses a critical gap in LLM evaluation: whether AI systems can authentically replicate expert investor reasoning, not merely generate plausible-sounding investment commentary. This matters because investment firms increasingly deploy LLMs as research assistants, yet no existing benchmark validates procedural fidelity. The benchmark's eight-tier architecture—from principle identification to novel framework extrapolation—mirrors actual investment cognition, while its Benchmark Automated Scoring Pipeline (BASP) and Failure Mode Detection Protocol (FMDP) introduce methodological rigor previously absent from LLM evaluation in finance.

The preliminary results reveal a troubling pattern: frontier models like Claude achieve 0.932 composite scores while Gate Reconstruction Accuracy (GRA) metrics expose procedural deficits of 0.23-0.43 on complex reasoning tasks. This divergence suggests composite metrics conflate surface-level fluency with genuine reasoning capability—models can verbalize investment principles convincingly while failing to execute them step-by-step. The provider-tier split (0.906 vs 0.438) indicates substantial capability variance, though these remain confounded by mixed-judge scoring.

For the fintech and investment AI sector, InvestPhilBench signals both opportunity and risk. It provides objective validation that current LLMs remain unreliable for autonomous investment decision-making, protecting firms from overdeployment. Simultaneously, it creates a standardized pathway for improvement, allowing model developers to systematically address procedural reasoning gaps. The correlation between automated BASP scores and human judgments (Pearson r = 0.72) validates the methodology while acknowledging remaining calibration work.

• →Frontier LLM models achieve high composite scores (0.932) but expose significant procedural reasoning deficits on complex investment frameworks (GRA 0.57-0.62).
• →InvestPhilBench introduces five algorithmic metrics and failure-mode detection protocols that distinguish fluent prose from genuine procedural reasoning capability.
• →A sharp provider-tier performance split (0.906 vs 0.438) indicates substantial variation in investment reasoning ability across leading AI models.
• →Current automated scoring metrics conflate surface-level fluency with step-by-step execution accuracy, potentially masking deployment risks in financial applications.
• →The benchmark provides fintech firms with standardized evaluation methodology to assess LLM reliability for investment research assistant roles.