Measurement Boundaries in LLM Financial Agent Evaluation: Fixed-Tape Execution and Multi-Defect Auditing
cs.LG, cs.AI, cs.CE, cs.SE
Submitted: 2026-09-26
Updated: 2026-09-26
Code: https://github.com/weich97/llm-evaluation-boundaries-artifact
License: http://creativecommons.org/licenses/by/4.0/
The gist: What controls are needed to interpret execution performance and audit scores in LLM agent evaluations? We study two limits on these interpretations in a financial agent harness.
Terminology
Abstract
What controls are needed to interpret execution performance and audit scores in LLM agent evaluations? We study two limits on these interpretations in a financial agent harness. In Study A, comparing independent runs under idealized and stressed execution on three synthetic settings that share one 24-day upward phase mixes the execution rule with fresh model responses and portfolio feedback: the parsed decision paths agree in only 19.8% of 450 pairs. Replaying each stored response tape through both execution destinations gives a narrower result. Conditional on those responses, stressed execution changes total return by-0.0170 (95% interval [-0.0230,-0.0117]), or 10.4% of the idealized baseline, and ten seed clusters do not resolve the model ranking. Study B corrects an incomplete answer key and replaces legacy tasks with matched zero-, one-, and two-defect tasks under an explicit multi-label prompt. The drop in target violation recall from one to two defects is positive in five of six combinations of auditor and source (median 0.267), with three surviving Holm correction. Yet the auditor that includes both target labels most often has micro-precision 0.149, emits findings on 98/100 zero-defect tasks, and returns the exact dual-defect set in only 21/100 cases. Target recall by itself therefore gives a poor account of audit quality on this construction. The studies address different limits: what an execution comparison estimates, and what target recall captures. Together, they show how fixed conditions and diagnostic controls bound the claims a score can support.
Sources
- Concrete Problems in AI Safety
- Non-Determinism of "Deterministic" LLM Settings
- Measuring Progress on Scalable Oversight for Large Language Models
- Supervising strong learners by amplifying weak experts
- Deep reinforcement learning from human preferences
- Large Language Model Agent in Financial Trading: A Survey
- Vulnerability Detection with Code Language Models: How Far Are We?
- AI Control: Improving Safety Despite Intentional Subversion
- A Survey on LLM-as-a-Judge
- AI safety via debate
- AI Agents That Matter
- Scalable agent alignment via reward modeling: a research direction
- Holistic Evaluation of Language Models
- LLM Evaluators Recognize and Favor Their Own Generations
- Self-critiquing models for assisting human evaluators
- Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
- BloombergGPT: A Large Language Model for Finance
- TradingAgents: Multi-Agents LLM Financial Trading Framework
- Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents
- FinGPT: Open-Source Financial Large Language Models
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