Study: Declarative planning layer does most heavy lifting in agent harnesses

A new research paper investigates the extent to which large language models (LLMs) are essential for the performance of agent harnesses. The study, conducted in a noisy Collaborative Battleship setting, found that declarative planning layers within the harness contributed most significantly to improved win rates. While symbolic reflection showed some impact, LLM-backed revision was activated infrequently and had a minimal, non-monotonic effect on outcomes. The research proposes a methodological approach to measure the residual role of LLMs once harness layers are externally quantifiable. AI

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IMPACT Quantifies the diminishing returns of LLMs in agent harnesses, suggesting that core planning logic may be more critical than the LLM itself for certain tasks.

RANK_REASON This is a research paper published on arXiv detailing experimental findings on agent harnesses and LLM contributions.

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