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arxiv: 2602.16246 · v3 · submitted 2026-02-18 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agents

Yun-Shiuan Chuang , Chaitanya Kulkarni , Alec Chiu , Avinash Thangali , Zijie Pan , Shivani Shekhar , Yirou Ge , Yixi Li
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Uma Kona Linsey Pang Prakhar Mehrotra
Authors on Pith no claims yet

Pith reviewed 2026-05-15 21:40 UTC · model grok-4.3

classification 💻 cs.AI
keywords proxy state evaluationLLM agentsmulti-turn tool callingagent benchmarksscalable evaluationLLM judgeshallucination detectionon-policy supervision
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The pith

Proxy state-based evaluation replaces costly deterministic backends with LLM trackers and judges for benchmarking multi-turn tool-calling agents.

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

The paper establishes that an LLM-driven simulation can evaluate agent interactions by inferring proxy states from full traces and then checking goal completion against scenario specs. This avoids building and maintaining full deterministic databases while still delivering stable rankings that distinguish model families and reasoning efforts. A sympathetic reader would care because production agents require frequent iteration and on-policy data, yet prior benchmarks like tau-bench scale poorly due to their engineering cost. With careful scenario writing, the method achieves near-zero hallucination rates and over 90 percent agreement with human judges, and its rollouts transfer to unseen scenarios.

Core claim

Proxy State-Based Evaluation uses a scenario that specifies user goal, facts, expected final state, and agent behavior; an LLM state tracker infers a structured proxy state from the interaction trace; LLM judges then verify goal completion and detect tool or user hallucinations against the scenario constraints. The resulting benchmark produces stable model-differentiating rankings across families and inference-time efforts, supplies on- and off-policy supervision that transfers to unseen scenarios, supports persona sensitivity analyses, and reaches human-LLM judge agreement above 90 percent when scenarios are carefully specified.

What carries the argument

Proxy State-Based Evaluation, an LLM-driven framework that infers structured proxy states from interaction traces and uses judges to verify goal completion against scenario constraints without a deterministic backend.

If this is right

  • Stable model-differentiating rankings hold across model families and different amounts of inference-time reasoning.
  • On- and off-policy rollouts supply supervision signals that transfer to unseen scenarios.
  • The framework supports sensitivity analyses that vary user personas while keeping the rest of the scenario fixed.
  • Human-LLM judge agreement exceeds 90 percent when scenarios are specified with sufficient care.

Where Pith is reading between the lines

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

  • The transfer property suggests the same scenarios could generate diverse synthetic trajectories for fine-tuning agents without additional human labeling.
  • Because only scenario text needs updating, the approach lowers the cost of expanding evaluation to new tool domains or user populations.
  • High agreement with humans indicates the method could serve as a continuous monitoring layer inside deployed agent systems.

Load-bearing premise

LLM state trackers and judges given carefully specified scenarios can infer accurate proxy states and verify goal completion with near-zero hallucination rates and high reliability.

What would settle it

A controlled comparison on the same tasks showing that proxy-state rankings differ substantially from those produced by an equivalent deterministic backend, or that human-LLM judge agreement falls below 80 percent on a fresh set of scenarios.

read the original abstract

Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Benchmarks for these agents must both reliably compare models and yield on-policy training data. Prior agentic benchmarks, such as tau-bench, tau^2-bench, and AppWorld, rely on fully deterministic backends, which are costly to build and iterate. We propose Proxy State-Based Evaluation, an LLM-driven simulation framework that preserves final state-based evaluation without a deterministic database. Specifically, a scenario specifies the user goal, user/system facts, expected final state, and expected agent behavior, and an LLM state tracker infers a structured proxy state from the full interaction trace. LLM judges then verify goal completion and detect tool/user hallucinations against scenario constraints. Empirically, our benchmark produces stable, model-differentiating rankings across model families and inference-time reasoning efforts, and its on-/off-policy rollouts provide supervision that transfers to unseen scenarios. Careful scenario specification yields near-zero simulator hallucination rates, as supported by ablation studies. The framework also supports sensitivity analyses over user personas. Human-LLM judge agreement exceeds 90%, indicating reliable automated evaluation. Overall, proxy state-based evaluation offers a practical, scalable alternative to deterministic agentic benchmarks for industrial LLM agents.

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 / 2 minor

Summary. The paper proposes Proxy State-Based Evaluation, an LLM-driven simulation framework for benchmarking multi-turn tool-calling agents. A scenario specifies the user goal, facts, expected final state, and behavior; an LLM state tracker infers a structured proxy state from the interaction trace; LLM judges then verify goal completion and detect hallucinations against scenario constraints. This avoids the need for deterministic backends. Empirically, the approach yields stable model-differentiating rankings, transferable on-/off-policy supervision to unseen scenarios, near-zero hallucination rates (supported by ablations), and >90% human-LLM judge agreement on outcomes.

Significance. If the central assumption holds, the framework provides a practical, lower-cost alternative to deterministic agentic benchmarks such as tau-bench for industrial settings. It enables scalable model comparison and generation of supervision signals that transfer across scenarios, with built-in sensitivity analysis over user personas. The combination of scenario specification, ablation-supported hallucination control, and high human agreement is a notable strength for reproducible evaluation.

major comments (2)
  1. [Abstract and empirical evaluation] The fidelity of the inferred proxy states is not independently validated against ground-truth states. Human-LLM agreement (>90%) is reported only on final outcome judgments, not on whether the proxy state correctly extracts all relevant variables, transitions, and constraints from the trace. Without such validation, systematic omissions or biases in the LLM tracker could produce consistent but incorrect verifications, undermining the claims of stable rankings and transferable supervision (see Abstract and the empirical evaluation section).
  2. [Empirical results] The transfer claim—that on-/off-policy rollouts provide supervision that transfers to unseen scenarios—lacks sufficient experimental detail on metrics, number of scenarios, definition of 'unseen,' and quantitative transfer performance. This is load-bearing for the central claim of practical utility beyond the original scenarios.
minor comments (2)
  1. [Abstract] The abstract would benefit from reporting concrete metrics (e.g., exact hallucination rates, stability measures such as rank correlation, and number of models/scenarios tested) rather than qualitative statements.
  2. [Introduction] Notation for 'proxy state' and the distinction between on-policy and off-policy rollouts could be introduced with a formal definition or diagram earlier in the manuscript for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments highlight important areas for strengthening the empirical rigor of Proxy State-Based Evaluation. We address each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

  1. Referee: [Abstract and empirical evaluation] The fidelity of the inferred proxy states is not independently validated against ground-truth states. Human-LLM agreement (>90%) is reported only on final outcome judgments, not on whether the proxy state correctly extracts all relevant variables, transitions, and constraints from the trace. Without such validation, systematic omissions or biases in the LLM tracker could produce consistent but incorrect verifications, undermining the claims of stable rankings and transferable supervision (see Abstract and the empirical evaluation section).

    Authors: We agree that direct validation of proxy state fidelity would provide stronger evidence against potential systematic biases in the LLM state tracker. While the reported >90% human-LLM agreement on final outcomes offers indirect support (as erroneous state inferences would typically propagate to incorrect judgments), this is not a substitute for explicit validation. In the revised manuscript, we will add a dedicated human evaluation of the state tracker, measuring accuracy on variable extraction, state transitions, and constraint adherence across a sample of traces. This new analysis will be presented in the empirical evaluation section to directly support the stability and transfer claims. revision: yes

  2. Referee: [Empirical results] The transfer claim—that on-/off-policy rollouts provide supervision that transfers to unseen scenarios—lacks sufficient experimental detail on metrics, number of scenarios, definition of 'unseen,' and quantitative transfer performance. This is load-bearing for the central claim of practical utility beyond the original scenarios.

    Authors: The manuscript reports transfer results from on-/off-policy rollouts to unseen scenarios in the empirical evaluation, including quantitative performance metrics. To address the request for greater transparency, we will expand this section with an explicit definition of 'unseen' scenarios (held-out from training and scenario specification), the precise number of scenarios evaluated, the full set of metrics used, and a summary table of quantitative transfer gains. These additions will make the evidence for cross-scenario utility clearer without altering the underlying experimental design. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework and claims rest on independent empirical validation

full rationale

The paper introduces Proxy State-Based Evaluation via explicit scenario components (user goal, facts, expected final state, behavior) and LLM trackers/judges, then supports its performance claims (stable rankings, transfer, near-zero hallucination) through ablation studies and reported human-LLM agreement rates exceeding 90%. No derivation step reduces by construction to fitted inputs, self-citations, or renamed priors; the central claims remain falsifiable against external benchmarks and do not rely on load-bearing self-referential definitions or author-specific uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The central claim rests on domain assumptions about LLM reliability for state tracking and judging rather than free parameters or new physical entities; proxy state and LLM state tracker are introduced concepts without independent falsifiable evidence outside the framework.

axioms (2)
  • domain assumption LLM state trackers can accurately infer structured proxy states from full interaction traces given scenario constraints
    Invoked as the core mechanism enabling evaluation without deterministic backends.
  • domain assumption LLM judges can reliably detect goal completion and tool/user hallucinations against scenario constraints
    Supported by reported human agreement but remains a key premise for the method's validity.
invented entities (2)
  • Proxy state no independent evidence
    purpose: Structured representation of interaction state inferred by LLM for final evaluation
    New construct introduced to replace deterministic database state.
  • LLM state tracker no independent evidence
    purpose: Component that infers proxy state from the agent interaction trace
    Core invented component of the simulation framework.

pith-pipeline@v0.9.0 · 5579 in / 1387 out tokens · 52476 ms · 2026-05-15T21:40:51.673070+00:00 · methodology

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