Understanding Persuasion in Long-Running Agents
Pith reviewed 2026-05-22 11:03 UTC · model grok-4.3
The pith
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.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
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.
What carries the argument
Belief-prefilling intervention inside a behavior-centered evaluation framework that separates persuasion applied during task execution from persuasion captured in the initial belief state.
If this is right
- 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.
Where Pith is reading between the lines
- 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.
Load-bearing premise
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.
What would settle it
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.
read the original abstract
Modern AI agents increasingly combine conversational interaction with autonomous task execution, such as coding and web research, raising a natural question: What happens when an agent engaged in long-horizon tasks is exposed to user persuasion? Yet studying this possibility is challenging because long-running agent behavior is noisy and costly to reproduce, and it remains unclear which unique challenges emerge only in extended task execution. We study how belief-level intervention can influence downstream task behavior, a phenomenon we name persuasion propagation. We introduce a behavior-centered evaluation framework that distinguishes between persuasion applied during or prior to task execution. Across web research and coding tasks, we find that on-the-fly persuasion induces weak and inconsistent behavioral effects. In contrast, 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, motivating behavior-level evaluation in agentic systems.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
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.
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 (3)
- [§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.
- [§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.
- [§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.
minor comments (2)
- [Figure 2] 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.
- [§2] Notation: The term 'belief state' is used without a formal definition or pseudocode showing how it is represented and updated inside the agent loop.
Simulated Author's Rebuttal
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.
read point-by-point responses
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Referee: [§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.
Authors: 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: yes
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Referee: [§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.
Authors: 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: yes
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Referee: [§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.
Authors: 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: yes
Circularity Check
Empirical measurement study with no circular derivations or reductions
full rationale
The paper reports direct experimental measurements of agent behavior under pre-filled versus neutral belief states, yielding the observed averages of 26.9% fewer searches and 16.9% fewer unique sources. No mathematical derivations, equations, fitted parameters, or self-citation chains are present that would reduce these reported percentages to prior quantities by construction. The findings are presented as outcomes of simulation runs rather than as predictions derived from inputs. The study is self-contained against external benchmarks as an empirical comparison, with no load-bearing steps that qualify under the enumerated circularity patterns.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Belief states can be directly pre-filled in the agent architecture without altering other internal mechanisms.
discussion (0)
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