Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents

arXiv:2607.04528 · cs.AI · Submitted 2026-07-05 · Read on arXiv

cs.AI

Submitted: 2026-07-05

Updated: 2026-10-15

Comments: 15 pages

Code: https://github.com/Hik289/Harness-induce-bias

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

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• Mastering Diverse Domains through World Models
• Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models
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• Language Models (Mostly) Know What They Know
• AgentBench: Evaluating LLMs as Agents
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
• What Twelve LLM Agent Benchmark Papers Disclose About Themselves: A Pilot Audit and an Open Scoring Schema
• ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback
• Identifying the Risks of LM Agents with an LM-Emulated Sandbox
• The Verifier Tax: Horizon Dependent Safety Success Tradeoffs in Tool Using LLM Agents
• Reflexion: Language Agents with Verbal Reinforcement Learning
• How are AI agents used? Evidence from 177,000 MCP tools

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