{"id":"6f28a143-45c0-429e-9a2e-153fda59adf3","arxiv_id":"2602.00851","paper_version":3,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Pre-filling AI agents with persuaded beliefs cuts searches by 26.9% and unique sources by 16.9% in long tasks, unlike on-the-fly persuasion which shows inconsistent effects.","lead":"The paper tests how user persuasion affects AI agents performing extended tasks like web research and coding. It finds that pre-setting persuaded beliefs before a task reduces searches and sources visited, while persuading during the task has weak effects.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Pre-filling belief state may introduce prompt artifacts unrelated to persuasion propagation","rationale":"The reader's weakest assumption directly identifies the same modeling gap between pre-fill and prior persuasion. Because the full manuscript was referenced but the concrete implementation details remain unspecified in the supplied context, the concern cannot be ruled out or confirmed; the reader's UNVERDICTED stance therefore stands.","tokens_in":1675,"tokens_out":322,"duration_ms":28427,"concrete_test":"Re-run the web-research and coding experiments with two pre-fill variants: (1) natural-language belief summary and (2) an equivalent-length neutral prompt that only alters surface wording without changing factual content; if the search-count reduction disappears or reverses in variant (2), the original effect is likely driven by prompt formatting rather than modeled persuasion.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline result (26.9% fewer searches, 16.9% fewer sources) rests on the assumption that explicitly specifying a belief state at task start faithfully reproduces the downstream consequences of prior real-world persuasion. If the pre-fill is implemented by injecting a fixed summary or directive into the initial prompt or memory, the observed reduction could arise from direct changes to the agent's planning heuristic or search termination condition rather than any propagated belief update. This would make the contrast with on-the-fly persuasion an artifact of the simulation interface rather than evidence for the named phenomenon. The abstract provides no detail on the exact pre-fill mechanism, the neutral baseline construction, or controls that would isolate belief content from prompt structure.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that persuasion can propagate in long-running AI agents, with prior belief-level interventions affecting downstream task behavior more strongly than on-the-fly persuasion during execution. Across web research and coding tasks, on-the-fly persuasion produces weak and inconsistent effects, while explicitly pre-filling the agent's belief state at task start yields belief-prefilled agents that conduct 26.9% fewer searches and visit 16.9% fewer unique sources than neutral-prefilled agents. The authors introduce a behavior-centered evaluation framework to study this and argue it motivates behavior-level assessment in agentic systems.","tokens_in":1784,"tokens_out":619,"duration_ms":52762,"significance":"If the quantitative differences hold under rigorous controls, the work offers empirical grounding for how historical user interactions can shape autonomous agent trajectories in extended tasks, with implications for agent safety and predictability. The distinction between timing of persuasion and the proposed evaluation framework represent constructive contributions to agent evaluation methodology.","major_comments":[{"comment":"§3.2 (Evaluation Framework): The central contrast between belief-prefilled and neutral-prefilled agents is load-bearing for the persuasion propagation claim, yet the manuscript provides no description of the exact pre-fill mechanism (e.g., prompt injection, memory update, or summary format). Without this, the 26.9% and 16.9% reductions could arise from direct changes to planning heuristics rather than propagated belief effects, as noted in the stress-test concern.","section":"§3.2"},{"comment":"§4 (Experimental Results): The abstract and results report average percentage reductions without any mention of sample sizes, number of trials per condition, statistical tests, variance measures, or exclusion criteria. This absence makes it impossible to determine whether the reported differences are reliable or driven by a small number of outlier runs.","section":"§4"},{"comment":"§4.3 (Task and Baseline Construction): The neutral-prefilled baseline is not characterized in sufficient detail to isolate belief content from prompt structure or task-specific heuristics. This leaves open the possibility that the weaker on-the-fly effects are an artifact of how the simulation interface handles dynamic versus static inputs rather than evidence for the named phenomenon.","section":"§4.3"}],"minor_comments":[{"comment":"Figure 2 caption: The distinction between 'on-the-fly' and 'prior' conditions could be clarified with an explicit example of the prompt templates used in each case.","section":"Figure 2"},{"comment":"Notation: The term 'belief state' is used without a formal definition or pseudocode showing how it is represented and updated inside the agent loop.","section":"§2"}],"recommendation":"major_revision","confidential_remarks":"The manuscript would be strengthened by releasing the agent implementation, prompt templates, and raw trial logs, given that the headline percentages depend on the precise pre-fill procedure."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive feedback on our manuscript. We appreciate the emphasis on clarifying the evaluation framework and experimental details. Below, we provide point-by-point responses to the major comments and outline the revisions we will make to strengthen the paper.","responses":[{"response":"We agree that a detailed description of the pre-fill mechanism is essential to support the claim of persuasion propagation. In the revised manuscript, we will expand §3.2 to include the exact implementation details, such as the prompt template used for pre-filling the belief state and how it is integrated into the agent's initial context. This will help distinguish the effects from mere heuristic adjustments and address the stress-test concern.","revision_made":"yes","referee_comment":"[§3.2] §3.2 (Evaluation Framework): The central contrast between belief-prefilled and neutral-prefilled agents is load-bearing for the persuasion propagation claim, yet the manuscript provides no description of the exact pre-fill mechanism (e.g., prompt injection, memory update, or summary format). Without this, the 26.9% and 16.9% reductions could arise from direct changes to planning heuristics rather than propagated belief effects, as noted in the stress-test concern."},{"response":"We acknowledge that the current manuscript lacks sufficient statistical reporting. In the revision, we will add the necessary details in §4, including the number of independent trials per condition, sample sizes, standard deviations or confidence intervals for the reported percentages, results of statistical tests (e.g., t-tests or Wilcoxon tests), and any exclusion criteria applied to the runs. This will allow readers to assess the reliability of the 26.9% and 16.9% reductions.","revision_made":"yes","referee_comment":"[§4] §4 (Experimental Results): The abstract and results report average percentage reductions without any mention of sample sizes, number of trials per condition, statistical tests, variance measures, or exclusion criteria. This absence makes it impossible to determine whether the reported differences are reliable or driven by a small number of outlier runs."},{"response":"We agree that more detail on the neutral-prefilled baseline is needed to isolate the effects of belief content. In the revised version of §4.3, we will provide a fuller characterization of the baseline, including the exact prompt structure used for neutral pre-filling and comparisons to ensure it controls for task-specific heuristics. We will also discuss how the simulation interface handles static versus dynamic inputs to rule out interface artifacts.","revision_made":"yes","referee_comment":"[§4.3] §4.3 (Task and Baseline Construction): The neutral-prefilled baseline is not characterized in sufficient detail to isolate belief content from prompt structure or task-specific heuristics. This leaves open the possibility that the weaker on-the-fly effects are an artifact of how the simulation interface handles dynamic versus static inputs rather than evidence for the named phenomenon."}],"tokens_in":1424,"tokens_out":633,"duration_ms":59207,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing to know is that the paper finds pre-specifying an agent's belief state at the start of a task produces clearer behavioral shifts than trying to persuade the agent while it works. On web research and coding jobs, the pre-filled agents did 26.9% fewer searches and visited 16.9% fewer unique sources than neutral ones, while on-the-fly persuasion showed only weak, inconsistent effects. That distinction is the core new piece they are pushing with their behavior-centered framework.","headline":"Pre-filling belief states cuts searches by 27% and sources by 17% in these tasks, but the pre-fill step may simply alter the prompt rather than demonstrate real persuasion carry-over.","tokens_in":2257,"tokens_out":183,"would_cite":false,"duration_ms":25894,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[],"headline":"Empirical study of belief persistence and behavioral drift in LLM agents; no overlap with RS forcing chain or cost structures","alignment":"orthogonal","rationale":"The paper's core machinery (on-the-fly vs. prefilled belief conditioning, process-level metrics such as TRS/EVS scores, PCA-derived activity/breadth/depth constructs, search-count and domain-entropy deltas) operates entirely within empirical AI-agent evaluation. It contains no ratio-symmetric cost functions, golden-ratio identities, J-cost forcing, 8-tick periodicity, or parameter-free derivations of physical constants. RS theorems such as reality_from_one_distinction (IndisputableMonolith/Foundation/RealityFromDistinction.lean) and the J-uniqueness results in Cost/FunctionalEquation.lean are therefore neither confirmed nor contradicted; the domains are disjoint.","tokens_in":62468,"confidence":"high","tokens_out":187,"duration_ms":8312,"cache_read_input_tokens":32896,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Pre-filling an agent's belief state with persuaded content causes 26.9 percent fewer searches and 16.9 percent fewer unique sources visited than neutral pre-filling.","keywords":["persuasion propagation","AI agents","belief state","long-running tasks","web research","behavior evaluation","task execution"],"falsifier":"Running the same web-research and coding tasks with belief-prefilled agents but measuring no reduction in search count or source diversity compared with neutral pre-filling.","tokens_in":2579,"feed_emoji":"🤖","tokens_out":640,"duration_ms":19308,"temperature":0.7,"pith_summary":"The paper examines what happens when long-running AI agents that handle extended tasks like web research or coding encounter persuasion. It separates persuasion that occurs during task execution from persuasion that happened earlier and is captured in the agent's starting belief state. On-the-fly persuasion during a task produces only weak and inconsistent changes in behavior. In contrast, agents started with a pre-filled persuaded belief state perform substantially less search activity and consult fewer distinct sources across the same tasks. This pattern indicates that earlier persuasion can carry forward and shape how the agent works on its own.","feed_headline":"Persuaded beliefs cut agent searches by 27 percent","feed_subtitle":"Pre-filled belief states from earlier persuasion reduce search volume and source diversity more reliably than persuasion applied during the ","key_machinery":"Belief-prefilling intervention inside a behavior-centered evaluation framework that separates persuasion applied during task execution from persuasion captured in the initial belief state.","core_discovery":"When the belief state is explicitly specified at task time, belief-prefilled agents conduct on average 26.9% fewer searches and visit 16.9% fewer unique sources than neutral-prefilled agents. These results suggest that persuasion, even in prior interaction, can affect the agent's behavior.","pith_inferences":["If the reduction in search activity generalizes beyond the tested tasks, deployed agents could become systematically less thorough after any extended user conversation.","Security or safety reviews of autonomous agents may need to include simulated prior persuasion episodes rather than only checking for direct instruction following.","Developers could explore countermeasures that reset or audit the belief state before starting sensitive long-running tasks."],"forward_implications":["Agents that carry forward a persuaded belief state will execute fewer information-gathering steps even when no further persuasion occurs.","Behavior-level metrics such as number of searches and unique sources visited become necessary for evaluating agentic systems after any earlier user interaction.","On-the-fly persuasion during active task execution produces weaker and less reliable effects than pre-task belief changes.","Long-horizon agents require evaluation protocols that explicitly test propagation from prior states rather than only testing live interaction."],"fun_headline_variants":["Pre-filled beliefs reduce agent searches by 27 percent","Belief-prefilled agents search 27 percent less","Pre-specified beliefs reduce sources visited by 17 percent","Prior persuasion lowers agent search volume via beliefs","On-the-fly persuasion shows weak effects on task behavior"],"cache_read_input_tokens":64,"weakest_assumption_plain":"Pre-filling the belief state at the start of a task accurately reproduces the downstream effects that real prior persuasion would have on an agent without creating artifacts from the simulation or task design.","fun_headline_variants_meta":{"raw":{"variants":["Pre-filled beliefs reduce agent searches by 27 percent","Belief-prefilled agents search 27 percent less","Pre-specified beliefs reduce sources visited by 17 percent","Prior persuasion lowers agent search volume via beliefs","On-the-fly persuasion shows weak effects on task behavior"]},"model":"grok-4.3","cost_usd":0.006451,"raw_usage":{"total_tokens":2989,"prompt_tokens":602,"num_sources_used":0,"completion_tokens":72,"cost_in_usd_ticks":64512000,"prompt_tokens_details":{"text_tokens":602,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2315,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":602,"tokens_out":72,"duration_ms":33200,"temperature":1.0,"reasoning_tokens":2315,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-22T11:03:43.530434+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the same web-research and coding tasks with belief-prefilled agents but measuring no reduction in search count or source diversity compared with neutral pre-filling.","supporting_citations":[],"review_version":1}