[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83822-en":3,"doc-seo-83822-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83822,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents","Software-agent benchmarks often judge only whether an agent reaches the correct outcome, while the harness governing observations, permitted actions, failure repair, state verification, and logged evidence can still reshape the agent’s internal reasoning. This work demonstrates harness-induced multi-step belief changes under fixed tasks, environments, and base LLMs. A belief-rollout diagnostic elicits structured K-step trajectories over progress, risk, recoverability, constraints, failure modes, uncertainty, future success, repair cost, and next actions across alternative harnesses. It defines cross-harness belief divergence with arrival and growth terms and proposes BIWM, a no-training protocol to canonicalize observations, log censored branches, expand repair traces, apply verification masks, and align belief trajectories.","Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents  \nHaiwen Yi* University of Toronto  \nXinyuan Song*† Emory University  \narXiv :2607 .04528v 1 [ cs .AI ] 5 Jul 2026  \nAbstract  \nSoftware-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, future success, repair cost, and next action under alternative harnesses. We define a cross-harness belief divergence and decompose it into an arrival term for immediate interface shifts and a growth term for horizon-dependent belief changes. On controlled coding tasks and public-benchmark stress tests, blocked actions, compressed repairs, selective verification, and cost-aware evidence pruning often preserve terminal success while changing the beliefs that drive later decisions. We further introduce Belief-Invariant World-Modeling (BIWM), a no-training protocol that canonicalizes observations, logs censored branches, expands repair traces, records verification masks, executes risky branches in shadow, and aligns belief trajectories across harness views. The results suggest that harness design is an experimental variable in agent evaluation, not an implementation detail. Our code is available at [https://github.com/Hik289/](https://github.com/Hik289/)[ ](https://github.com/Hik289/)Harness-induce-bias.git.  \n1 Introduction  \nLarge language model agents are increasingly evaluated as software workers: they inspect repositories, invoke tools, edit files, run test suites, navigate web applications and desktop environments, and call external APIs (Yang et al., 2024 ; Jimenez et al.,  \n*Equal contribution.  \n†Corresponding author.  \n2024 ; Zhou et al., 2024 ; Xie et al., 2024 ; Li et al., 2023 ; Qin et al., 2024 ; Patil et al., 2024, 2025 ; Jin et al., 2024 ; Wang et al., 2025d ; Yao et al., 2023b ; Schick et al., 2023) . Standard evaluations ask an outcome-based question: did the agent solve the task? (Liu et al., 2023 ; Ma et al., 2024) Although useful, this view treats the surrounding execution system as neutral infrastructure. In practice, every agent operates through a harness that mediates its interaction with the task by controlling the observations it receives, the actions it may take, and the feedback produced by those actions (Stein, 2026 ; Sah et al., 2026) . This mediation creates a feedback loop. The agent uses its current beliefs about task progress, risk, recoverability, and likely failure modes to reason about what to do next, while the harness determines the evidence from which those beliefs are updated (Yao et al., 2023b ; Shinn et al., 2023) .  \nThis paper starts from a simple observation: a harness can change the informational version of a task without changing the underlying task. The same base LLM can face the same bug, but one harness may expose the raw output of a failed command, another may block the command and return a policy violation, and a third may repair the intermediate failure before the model observes it (Yang et al., 2024 ; Xu et al., 2026) . These interventions may leave the final benchmark label unchanged, yet they alter the evidence available to the agent and therefore change the belief trajectory that guidesits later decisions (Ma et al., 2024) . An agent may infer that it is making progress, facing an active risk, entering a recoverable state, or approaching a particular failure mode solely because of how the harness presents and transforms execution feedback. The harness is therefore not merely a channel through which reasoning is executed; it is part of","cbCaijccSzsG79x2","https://ap.wps.com/l/cbCaijccSzsG79x2","pdf",1638432,1,28,"English","en",105,"# Introduction\n# Measuring Harness-Induced Belief Shift\n## Belief-rollout Diagnostic\n## Cross-harness Belief Divergence\n# Belief-Invariant World-Modeling (BIWM)","[{\"question\":\"What does the paper claim about the role of the harness in agent evaluation?\",\"answer\":\"The harness is not just neutral infrastructure: it mediates observations, allowed actions, feedback, and logged evidence, which can change the agent’s multi-step beliefs and therefore influence later decisions even if the terminal benchmark label stays the same.\"},{\"question\":\"How is harness-induced belief shift measured in the paper?\",\"answer\":\"The paper elicits a structured K-step belief rollout, recording predicted progress, risk, recoverability, constraints, uncertainty, likely failure mode, future success probability, and the recommended next action under different harness configurations.\"},{\"question\":\"What is BIWM and how does it relate to belief divergence?\",\"answer\":\"Belief-Invariant World-Modeling (BIWM) is a no-training protocol that canonicalizes observations, logs censored branches, expands repair traces, records verification masks, and executes risky branches in shadow to align belief trajectories across harness 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does the paper claim about the role of the harness in agent evaluation?","Question",{"text":75,"@type":76},"The harness is not just neutral infrastructure: it mediates observations, allowed actions, feedback, and logged evidence, which can change the agent’s multi-step beliefs and therefore influence later decisions even if the terminal benchmark label stays the same.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is harness-induced belief shift measured in the paper?",{"text":80,"@type":76},"The paper elicits a structured K-step belief rollout, recording predicted progress, risk, recoverability, constraints, uncertainty, likely failure mode, future success probability, and the recommended next action under different harness configurations.",{"name":82,"@type":73,"acceptedAnswer":83},"What is BIWM and how does it relate to belief divergence?",{"text":84,"@type":76},"Belief-Invariant World-Modeling (BIWM) is a no-training protocol that canonicalizes observations, logs censored branches, expands repair traces, records verification masks, and executes risky branches in shadow to align belief trajectories across harness views.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & 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