Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents
Software agents are becoming increasingly important in modern coding workflows, where they solve complex tasks through multi-step interaction with code repositories. A central component of these systems is the execution harness, which governs how the underlying LLM observes, acts on, and verifies its environment. We show that the harness can in turn shape the agent's evolving beliefs about the environment, even when the task, environment, and base LLM remain fixed. To reveal this effect, we introduce a belief-rollout diagnostic that traces how an agent's anticipated world state and future decisions evolve under alternative harnesses. We further separate immediate divergence introduced by ...
| Authors | Haiwen Yi, Xinyuan Song |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 15 October 2026 |
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