arXiv:2607.04528v1 Announce Type: new Abstract: Software-agent benchmarks usually report whether an agent solves a task, but the agent reaches that outcome through a harness that controls what it sees, which actions it can take, which failures are repaired, which states are verified, and which evidence is logged. We show that this harness can change the agent's multi-step beliefs even when the task, environment, and base LLM are fixed. We introduce a belief-rollout diagnostic that elicits structured K-step trajectories over progress, risk, recoverability, constraints, failure mode, uncertainty
The rapid advancement and deployment of LLM-based autonomous agents necessitates deeper understanding of their internal states and behaviors, especially as they move towards more complex, multi-step tasks.
This research highlights a critical vulnerability in current LLM agent evaluation, revealing how testing environments can significantly distort an agent's 'beliefs' and thus its real-world performance and safety.
The ability to accurately measure and diagnose harness-induced belief divergence will lead to more robust agent design, safer deployment, and more reliable benchmarking standards.
- · AI safety researchers
- · Developers of LLM agents
- · AI ethics organizations
- · Enterprises deploying AI agents
- · Developers relying on simplistic agent benchmarks
- · Deployers of poorly tested LLM agents
- · Benchmarking organizations with shallow evaluation practices
More sophisticated and nuanced metrics for evaluating LLM agent performance will emerge, moving beyond simple task completion rates.
This shift in evaluation will drive the development of agents with greater intrinsic robustness and less reliance on specific harness configurations, making them more adaptable to novel environments.
Increased transparency into agent 'belief systems' could accelerate regulatory efforts by providing clearer insights into potential failure modes and emergent behaviors in complex AI systems.
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Read at arXiv cs.AI