Exploration and Exploitation Errors Are Measurable for Language Model Agents

Summary

Addresses the measurement problem this paper faces on its exploration outcome: how to distinguish and quantify exploration and exploitation from an agent's observed actions without access to its internal policy. They build controllable environments (partially observable 2D grid maps with an unknown task DAG) whose generation can be tuned to emphasise exploration or exploitation difficulty, and design a policy-agnostic metric for exploration and exploitation errors. Frontier models struggle, with distinct failure modes by model, and both dimensions improve substantially with minimal harness engineering. Directly citable for operationalising March's exploration/exploitation distinction in LLM-agent terms rather than by analogy.

Contribution

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Method

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Relevance to RISE

Surfaced by the 2026-08-29 literature sweep as a high-priority candidate for the Architecture as Epistemology (ISR) paper (angle: epistemic quality of LLM outputs; MAS topology / structure vs. aggregation rule). Verified against: Opened https://arxiv.org/abs/2604.13151 (arXiv abstract page, v1 14 Apr 2026)

Critique / open questions

Queued — not yet assessed.

Key quotes

Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. (abstract)

A core requirement in these settings is the ability to both explore the problem space and exploit acquired knowledge effectively. (abstract)