Newest papers

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

Ke Zhang, Sahchit Chundur, Jerry Peprah Owusu, Mohammad Javad Abdolhosseini Qomi, Maziar Raissi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26836 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Los agentes LLM corregidos son los agentes que computan la respuesta correcta pueden todavía responder incorrectamente. Los agentes aprovechan cada vez más para mantener las salidas de herramientas largas fuera de la ventana de contexto, guardándolas en archivos que el agente busca o lee en demanda. Preguntamos dónde esto cuesta la exactitud con una herramienta científica real: PHREEQC, un simulador de geoquímica ampliamente utilizado del USGS, cuyos informes alcanzan los caracteres 846.8k y cuyo determinismo permite rastrear cada valor impreso. Los agentes lo utilizan para responder a 158 artículos de elección múltiple verificados por la ejecución. Cuando los agentes leen el informe del simulador a través de un índice de sección en lugar de contexto, GPT-5.1 y GPT-5.2 pierden

Machine translation (Meta M2M100); the English original follows.

Abstract

LLM agents that compute the right answer can still answer wrongly. Agent harnesses increasingly keep long tool outputs out of the context window, saving them to files that the agent searches or reads on demand. We ask where this costs accuracy with a real scientific tool: PHREEQC, a widely used geochemistry simulator from the USGS, whose reports reach 846.8k characters and whose determinism lets every printed value be traced. Agents use it to answer 158 execution-verified multiple-choice items. When agents read the simulator's report through a section index rather than in context, GPT-5.1 and GPT-5.2 lose 11.3 and 7.9 points of accuracy, while Claude Opus 4.6 and Claude Sonnet 4.6 show no detectable cost. Re-running all 11,007 logged simulator calls shows that both GPT models compute the keyed value about as often as before; what changes is the evidence they answer from. Under retrieval they often skip the report, answer from their own higher-precision output files, and misround to a neighboring option, a failure we call evidence substitution. With the simulation held fixed, retrieval still costs GPT-5.2 9.4 points. For GPT-5.4, more frequent correct simulations mask an evidence-use loss of about ten points, leaving a net change indistinguishable from zero. Naming the authoritative output raises accuracy by 6.3-7.9 points over plain retrieval for the three GPT models; for GPT-5.1 and GPT-5.2, inlining outputs that fit works as well, while asking for correct rounding does not. Context-management choices in agent harnesses should be judged by where accuracy is lost, not only by final accuracy.

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Topics: Computer science, Rounding, Context (archaeology), Value (mathematics), Section (typography), Ask price

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