mlsanityData to 7 Oct 2026

DFAH-Bench: Benchmarking Observable Agent Instability in Financial Decision-Making

arXiv 2607.20491 ↗First posted 10 Jun 2026cs.AI cs.CL cs.LGcited by 0

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Abstract (arXiv)

A financial agent can repeat a decision while changing the work behind it. DFAH-Bench operationalizes the Determinism--Faithfulness Assurance Harness (DFAH), pairing decision agreement with tool-path agreement on the same qualified replays, then extends that qualification principle to evidence, authorization, execution and task outcomes. Retrospective and prospective replay analyses expose process variation behind stable decisions. Across 570 eligible prospective episodes, decision agreement is 94.2-95.1%, while agreement on ordered tools, arguments and results is 45.0-51.5%; one stratum falls one group below its prespecified coverage minimum. A separate capture diagnostic shows that systematic omissions can preserve perfect replay agreement. Using the τ-Knowledge banking environment, we retain 1,080 scheduled episodes and 1,033 known native outcomes across separate cohorts with open-weight and frontier generators. Missing outcomes prevented the planned tests, so comparisons are descriptive. On the primary schedule, structural checks alone yield more successes than either gate-and-recovery bundle. The typed-choice bundle has lower mean episode cost than the generative bundle on complete task pairs, but produces fewer successes under every assignment of unknown outcomes. Input limits and recovery behavior materially shape these results. Fixed-state probes reveal higher decision agreement alongside lower agreement with constructed policy labels, and separately expose sensitivity to retained generator rationale in a selected authorization case. Together, the findings connect replay observability to evidence, authorization, completion and cost: evidence sufficiency needs direct assessment alongside repeatability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 39 references that are in the map. First posted on arXiv 10 Jun 2026.

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20 of 39 references.

References · 20

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
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  6. 2020Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?cited by 590
  7. 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 272
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  9. 2024Visibility into AI Agentscited by 77
  10. 2025TRAJECT-Bench:A Trajectory-Aware Benchmark for Evaluating Agentic Tool Usecited by 46
  11. 2026τ-Knowledge: Evaluating Conversational Agents over Unstructured Knowledgecited by 35
  12. 2025FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answeringcited by 29
8 more
  1. 2025Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Taskscited by 19
  2. 2025Reflections on the Reproducibility of Commercial LLM Performance in Empirical Software Engineering Studiescited by 12
  3. 2025FinResearchBench: A Logic Tree based Agent-as-a-Judge Evaluation Framework for Financial Research Agentscited by 11
  4. 2026Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agentscited by 7
  5. 2025ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoningcited by 6
  6. 2026Benchmark Illusion: Disagreement among LLMs and Its Scientific Consequencescited by 4
  7. 2026FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Taskscited by 2
  8. 2026Governance by Construction for Generalist Agentscited by 1

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