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Agent harnesses significantly alter LLM compliance with banking rules, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how the 'harness' surrounding a fixed Large Language Model (LLM) can significantly impact its adherence to banking conduct rules. Researchers tested four open-weight models across five different harness configurations, simulating a retail bank environment and evaluating compliance with 156 banking rules. While the harness only slightly improved overall correct case rates, its effect varied dramatically between models, with one model's performance shifting from best to worst based on the harness used. The study suggests that for model-risk management, validation should focus on the model-harness pair and analyze action logs rather than just conversation transcripts. AI

IMPACT Highlights the critical role of system design and scaffolding in ensuring LLM safety and compliance in regulated industries.

RANK_REASON Research paper published on arXiv detailing experimental findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Agent harnesses significantly alter LLM compliance with banking rules, study finds

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Research paper published on arXiv detailing experimental findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mehmet Murat Huyut ·

    The Harness Is the Model: Agent Scaffolding Changes a Fixed LLM's Compliance with Banking Conduct Rules, Unevenly Across Models

    arXiv:2611.06116v1 Announce Type: cross Abstract: Banks validate language models, but they deploy systems: a model inside a harness that decides what it sees, which tools it can call and who checks its actions. We test whether that harness changes compliance with banking conduct …

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