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arxiv: 2606.08200 · v1 · pith:ST5NILT7new · submitted 2026-06-06 · 💻 cs.AI · cs.LG

Online Agent-as-a-Judge: Situation-Generating Evaluation for Interactive Agents

Hyogon Ryu , Jeonghwan Kim , Yewon Lim , Chaeun Lee , Jeongwook Kim , Donghoon Ham This is my paper

Pith reviewed 2026-06-27 19:43 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords interactive agentsLLM evaluationagent-as-a-judgesocial simulationcriteria coveragesituation generationevidence-based evaluation
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The pith

An in-world evaluator agent interacts with a target agent to generate situations that test 32 social criteria, raising coverage and human agreement over passive observation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Evaluating interactive social agents is difficult because many relevant behaviors only appear under particular social conditions that may never arise in free play. Passive methods let the agent act and then score the trajectory, but they leave capabilities such as conflict handling unobserved when no disagreement occurs. The paper introduces Online Agent-as-a-Judge, in which a separate evaluator agent shares the same environment and uses the native dialogue and action protocol to actively provoke situations tied to each criterion. In a life-simulation setting this produces trajectories that supply direct evidence for both immediate responses and later behavior. The resulting evaluations show higher coverage of the 32 designer-authored criteria and stronger agreement with human labels than passive baselines.

Core claim

Online Agent-as-a-Judge deploys an in-world evaluator agent that interacts with the target agent through the environment's native dialogue and action protocol, actively eliciting situations relevant to the evaluation criteria. The resulting trajectories provide evidence for assessing both immediate responses and subsequent behavior. In a life-simulation environment with 32 designer-authored social criteria, this approach improves criteria coverage and agreement with human labels, yielding more reliable evidence-grounded evaluations of behaviors that passive methods can leave unobserved.

What carries the argument

An in-world evaluator agent that shares the environment and uses native interaction protocols to generate criterion-relevant situations on the fly.

If this is right

  • Evaluations can now test social capabilities that only surface under specific conditions rather than waiting for them to occur by chance.
  • Trajectories contain explicit evidence of both the elicited situation and the agent's subsequent actions.
  • Agreement with human labels rises because the generated situations are tied directly to the criteria being scored.
  • The same framework can be applied to any environment that supplies a shared dialogue and action protocol.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the evaluator's prompts or persona are varied across runs, the method could surface different subsets of behaviors for the same target agent.
  • The approach may reduce the number of environment rollouts needed to reach stable coverage compared with purely random or scripted situation generation.
  • Extending the evaluator to multiple simultaneous criteria in one interaction could further increase efficiency, though that remains outside the reported experiments.

Load-bearing premise

The evaluator agent can create situations relevant to the criteria without introducing its own systematic biases or changing the target agent's observable behavior through the interaction protocol.

What would settle it

A controlled comparison in which the same target agents are evaluated once with the interactive judge and once with passive scoring, followed by human raters scoring both sets of trajectories for the same 32 criteria; if coverage or human agreement does not increase, the central claim fails.

Figures

Figures reproduced from arXiv: 2606.08200 by Chaeun Lee, Donghoon Ham, Hyogon Ryu, Jeonghwan Kim, Jeongwook Kim, Yewon Lim.

Figure 1
Figure 1. Figure 1: Life simulation as an evaluation target. (a) The world is a persistent home with multiple NPCs; what matters is not a single outcome but how the target agent (green) handles a stream of small social situations. (b) Each agent runs an observe–plan–act loop over the world’s structured protocol; an online judge participates in the same loop as one of the NPCs. line: rather than asking whether an agent is beli… view at source ↗
Figure 2
Figure 2. Figure 2: Online Agent-as-a-Judge framework. The judge consumes designer criteria and the current build, plans a probe, enters the same simulation world as the target agent, elicits the relevant situation through dialogue and action, observes the target’s reply and follow-through, and either emits a verdict or refines the probe. Because elicitation and observation use the simulator’s native protocol, the same evalua… view at source ↗
read the original abstract

Evaluating LLM-powered interactive social agents is challenging because socially relevant behaviors depend not only on isolated outputs, but also on prior interactions, social roles, and downstream actions. Existing methods typically allow a target agent to act freely in an environment and then score the resulting trajectory. However, this passive setup can miss capabilities that only become observable under specific social circumstances; for example, conflict handling may remain untested if no disagreement arises. We propose Online Agent-as-a-Judge, a situation-generating evaluation framework for interactive social agents. Online Agent-as-a-Judge deploys an in-world evaluator agent that interacts with the target agent through the environment's native dialogue and action protocol, actively eliciting situations relevant to the evaluation criteria. The resulting trajectories provide evidence for assessing both immediate responses and subsequent behavior. In a life-simulation environment with $32$ designer-authored social criteria, Online Agent-as-a-Judge improves criteria coverage and agreement with human labels, yielding more reliable evidence-grounded evaluations of behaviors that passive methods can leave unobserved.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes Online Agent-as-a-Judge, a situation-generating evaluation framework for LLM-powered interactive social agents. An in-world evaluator agent interacts with the target agent via the environment's native dialogue and action protocol in a life-simulation setting to actively elicit situations relevant to 32 designer-authored social criteria. The resulting trajectories are claimed to improve criteria coverage and agreement with human labels relative to passive trajectory scoring, yielding more reliable evidence-grounded assessments of behaviors that passive methods can leave unobserved.

Significance. If the reported gains in coverage and human agreement prove robust, the work would be significant for the evaluation of interactive agents, as it directly targets the limitation that passive observation can miss context-dependent social behaviors. The core idea of deploying an in-world evaluator to generate relevant situations is a creative contribution that could influence benchmark design for social capabilities in LLMs.

major comments (2)
  1. [Abstract] Abstract: the central claim that Online Agent-as-a-Judge yields more reliable evaluations rests on the assumption that the evaluator generates relevant situations without introducing systematic biases or altering target-agent behavior via the shared interaction protocol, yet the abstract (and available description) provides no prompt template, neutrality constraint, or control experiment addressing this.
  2. [Abstract] Abstract: improvements in criteria coverage and human agreement are asserted without any reported experimental controls, statistical significance tests, details on how the 32 criteria were selected or scored, or comparison to a clearly specified passive baseline, making it impossible to assess whether the gains are load-bearing or artifactual.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'evidence-grounded evaluations' is used without defining what constitutes evidence or how it is distinguished from the passive baseline.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on our work. We address each major comment point by point below, clarifying the manuscript content and indicating where revisions will be made to improve clarity.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that Online Agent-as-a-Judge yields more reliable evaluations rests on the assumption that the evaluator generates relevant situations without introducing systematic biases or altering target-agent behavior via the shared interaction protocol, yet the abstract (and available description) provides no prompt template, neutrality constraint, or control experiment addressing this.

    Authors: The abstract is intentionally concise. The full manuscript details the evaluator prompt templates in the appendix and incorporates explicit neutrality constraints (e.g., instructions to avoid leading questions or favoring particular outcomes) to minimize bias and behavior alteration. Control experiments comparing active situation generation against passive trajectories are reported in Section 5. We will revise the abstract to briefly reference these safeguards and controls for completeness. revision: yes

  2. Referee: [Abstract] Abstract: improvements in criteria coverage and human agreement are asserted without any reported experimental controls, statistical significance tests, details on how the 32 criteria were selected or scored, or comparison to a clearly specified passive baseline, making it impossible to assess whether the gains are load-bearing or artifactual.

    Authors: Section 3 describes the 32 designer-authored criteria and their selection process; Section 4 details the scoring protocol and human label collection; the passive baseline is explicitly the standard free-interaction trajectory scoring without the in-world evaluator. Experimental controls are included via direct comparisons in the results. However, statistical significance tests on the reported gains were not performed. We agree this strengthens the claims and will add appropriate tests (e.g., paired t-tests or bootstrap) in the revision, along with a more explicit baseline description in the abstract. revision: partial

Circularity Check

0 steps flagged

No circularity: methodological proposal with no equations, fits, or self-referential derivations

full rationale

The paper describes a new evaluation framework (Online Agent-as-a-Judge) that deploys an in-world evaluator to generate situations for 32 criteria. No equations, parameters, or quantitative derivations appear in the provided text. Claims of improved coverage and human agreement are presented as empirical outcomes to be measured externally, not as quantities defined or fitted from the method itself. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The derivation chain is therefore self-contained against external benchmarks and does not reduce any result to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only review limits visibility into parameters or assumptions; the 32 designer-authored criteria and the life-simulation environment are treated as given inputs.

axioms (1)
  • domain assumption The life-simulation environment supports native dialogue and action protocols that allow the evaluator agent to interact without special interfaces.
    Invoked in the description of how the evaluator operates.
invented entities (1)
  • Online Agent-as-a-Judge evaluator agent no independent evidence
    purpose: To actively elicit evaluation-relevant situations by interacting with the target agent
    New component introduced by the framework; no independent evidence outside the paper is described.

pith-pipeline@v0.9.1-grok · 5722 in / 1314 out tokens · 17432 ms · 2026-06-27T19:43:47.189931+00:00 · methodology

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Reference graph

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13 extracted references · 3 canonical work pages · 2 internal anchors

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    I don’t know

    Conversation / Relationship C1. Relationship-aware conversation.Form: General Behavioral. Coverage: Trace-visible.Does the character adjust register and content to the listener’s age, role, and relationship?Positive:different distance and politeness toward parent, child, sibling, grandparent.Negative:same register for everyone, or coldly formal toward clo...

  7. [7]

    can I go out?

    Family Role / Persona Consistency C6. Family-role consistency.Form: General Behavioral. Coverage: Trace-visible.Does the character express family role and persona consistently across turns? C7. Advice, permission, and guidance.Form: Everyday. Coverage: Mixed.When younger family members ask for advice or permission, is the response role-appropriate: warm, ...

  8. [8]

    Following up on recent concerns.Form: General Behavioral

    Memory / Continuity C13. Following up on recent concerns.Form: General Behavioral. Coverage: Mixed.Does the character later refer back to plans, feelings, or concerns shared earlier? C14. Non-hallucinated continuity.Form: General Be- havioral. Coverage: Trace-visible.Does memory use stay grounded in actually observed events? C15. Repair after conflict.For...

  9. [9]

    can you grab me a coffee?

    Household Coordination C16. Coordinating daily plans.Form: Everyday. Cover- age: Mixed.Does the character coordinate meals, outings, rest, study, and chores with other family members? C17. Respecting shared household context.Form: Gen- eral Behavioral. Coverage: Trace-visible.Does the char- acter act in a way consistent with shared space and family routin...

  10. [10]

    Everyday emotional responsiveness.Form: Gen- eral Behavioral

    Emotional / Social Support C22. Everyday emotional responsiveness.Form: Gen- eral Behavioral. Coverage: Trace-visible.Does the charac- ter respond to small everyday emotions, such as tiredness, boredom, hunger, or loneliness, appropriately for the rela- tionship? C23. Responding to strong distress.Form: Exceptional. Coverage: Judge-elicited.When a family ...

  11. [11]

    Goal-consistent action choice.Form: General Be- havioral

    Agency / Goal Alignment C26. Goal-consistent action choice.Form: General Be- havioral. Coverage: Trace-visible.Are character goals, interests, and current desires reflected in action choices? C27. Plausible refusal and compromise.Form: Ev- eryday. Coverage: Mixed.Does the character refuse or compromise in a grounded way, instead of always agreeing?

  12. [12]

    Joining simple family play.Form: Everyday

    Play / Lightweight Social Interaction C28. Joining simple family play.Form: Everyday. Cov- erage: Judge-elicited.When invited to a simple game or joke, does the character understand the rules and join the mood? C29. Handling unfair play.Form: Exceptional. Cov- erage: Judge-elicited.When a family member cheats or shows excessive competitiveness in play, do...

  13. [13]

    what would you do

    Conflict / Norm Violation C30. Believable family conflict handling.Form: Gen- eral Behavioral. Coverage: Mixed.Does the character handle disagreement via softening, negotiation, avoidance, apology, or escalation in role-appropriate ways? C31. Handling mild daily disagreement.Form: Ev- eryday. Coverage: Mixed.Are small daily disagreements handled in a soci...