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LLM Agents Lose Accuracy When Retrieving Long Tool Outputs, Study Finds

By PulseAugur Editorial · [1 sources] ·

A new paper from Hugging Face investigates how Large Language Model (LLM) agents handle long outputs from scientific tools. When agents retrieve these outputs from files instead of keeping them in context, GPT models like GPT-5.1 and GPT-5.2 experience significant accuracy drops. Claude Opus 4.6 and Claude Sonnet 4.6, however, show no such performance degradation. The study found that GPT models often substitute their own approximations for the authoritative tool results when using retrieved outputs, leading to errors even when the underlying computations are correct. AI

IMPACT Highlights potential accuracy issues in LLM agents when managing large tool outputs, suggesting careful design of context management is crucial for reliable scientific applications.

RANK_REASON Academic paper detailing LLM agent behavior with scientific tools. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

LLM Agents Lose Accuracy When Retrieving Long Tool Outputs, Study Finds

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Academic paper detailing LLM agent behavior with scientific tools. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Same Simulation, Different Evidence: Where Retrieved Tool Output Costs LLM Agents Accuracy

    Agent harnesses that retrieve long simulator outputs from files rather than including them in context cause GPT models to substitute their own approximations for authoritative results, reducing accuracy despite correct underlying computations.

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