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arxiv: 2606.00103 · v1 · pith:DEHDMZ4Cnew · submitted 2026-05-26 · 💻 cs.AI

Evaluating Interactive Reasoning in Large Language Models: A Hierarchical Benchmark with Executable Games

Mingyuan Fan , Weiguang Han , Daixin Wang , Cen Chen , Zhiqiang Zhang , Jun Zhou This is my paper

Pith reviewed 2026-06-29 17:44 UTC · model grok-4.3

classification 💻 cs.AI
keywords interactive reasoninglarge language modelsbenchmarkexecutable gamesmetacognitive adaptationcounterfactual revisionbelief updatingcontextual robustness
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The pith

A benchmark of 474 executable games shows LLMs drop more sharply on counterfactual revision and necessity judgment than on contextual perturbations during interactive reasoning.

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

The paper presents a framework that tests reasoning in LLMs by forcing them to treat it as active evidence gathering: models receive only game rules, must issue queries to a hidden environment over multiple turns, integrate partial observations, and decide when to answer. Beyond basic success rates and query counts, it adds tests for robustness to controlled context changes and for metacognitive skills such as revising prior answers or judging whether certain information is required. Evaluation across frontier models on five difficulty levels per game reveals clear differences in both accuracy and efficiency, with moderate consistent declines under perturbations but substantially larger drops on the revision and necessity tasks.

Core claim

The benchmark of 474 executable games across five fixed configuration search spaces is highly discriminative, exposing large differences among LLMs in success rate and interaction efficiency; contextual perturbations produce moderate but consistent performance declines, whereas counterfactual revision and necessity judgment produce much larger drops.

What carries the argument

Multi-turn interactive framework that requires models to issue targeted queries to a hidden environment and integrate partial observations over time before submitting a final answer.

If this is right

  • Success on standard tasks does not predict performance on metacognitive adaptation tasks within the same interactive setting.
  • Interaction efficiency, measured by number and relevance of queries, varies substantially across models even when final accuracy is comparable.
  • Controlled contextual perturbations can be used to quantify robustness separately from basic reasoning ability.
  • The framework supplies a concrete way to measure belief updating under partial information.

Where Pith is reading between the lines

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

  • Training regimes that emphasize single-turn accuracy may leave models underprepared for sequential evidence integration.
  • The separation between moderate perturbation effects and large metacognitive drops suggests that belief-revision mechanisms are a distinct capability from basic pattern matching.
  • Extending the same query-and-update loop to non-game domains such as scientific hypothesis testing could reveal similar capability gaps.

Load-bearing premise

The 474 games and their controlled perturbations are assumed to isolate interactive reasoning without biases introduced by game design choices or query interface.

What would settle it

An experiment in which all frontier LLMs achieve nearly identical success rates and interaction efficiencies across the five difficulty levels on the full set of games would falsify the discriminativeness claim.

read the original abstract

We introduce a multi-turn interactive framework for reasoning evaluation that treats reasoning as active evidence acquisition and belief updating. Wherein, LLMs receive only the task rules, must issue targeted queries to a hidden environment, integrate partial observations over time, and decide when to submit a final answer. Beyond standard success rate and interaction efficiency, we evaluate contextual robustness under controlled contextual perturbations, and metacognitive adaptation through counterfactual revision and necessity judgment. We instantiate the framework as a benchmark of 474 executable games, each evaluated under five fixed configuration search spaces corresponding to five difficulty levels, and evaluate a broad set of frontier LLMs. Results show that the benchmark is highly discriminative, exposing large differences not only in success rate but also in interaction efficiency. Moreover, we empirically show that contextual perturbations cause moderate but consistent declines, whereas counterfactual revision and necessity judgment lead to much larger drops.

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

0 major / 2 minor

Summary. The paper introduces a multi-turn interactive framework for evaluating reasoning in LLMs, framing it as active evidence acquisition and belief updating where models query a hidden environment, integrate observations, and decide when to answer. It instantiates this as a benchmark of 474 executable games evaluated across five fixed difficulty levels (configuration search spaces), assessing frontier LLMs on success rate, interaction efficiency, robustness to contextual perturbations, and metacognitive tasks involving counterfactual revision and necessity judgment. Empirical results claim the benchmark is highly discriminative across models and that contextual perturbations cause moderate consistent declines while counterfactual/necessity tasks cause much larger drops.

Significance. If the isolation of interactive reasoning holds, the benchmark offers a reproducible, executable alternative to static reasoning tests that directly measures evidence-seeking and adaptation behaviors relevant to agentic LLM use. The differential perturbation results provide falsifiable predictions about model weaknesses in robustness versus metacognition. The fixed search spaces and executable nature are strengths that support controlled, reproducible evaluation.

minor comments (2)
  1. [Abstract] Abstract: reports empirical results on discrimination and perturbation effects but omits any reference to error bars, statistical tests, or how the 474 games and perturbations were validated for isolation; adding one sentence on these would strengthen the summary without altering the central claim.
  2. [Methods/Benchmark Description] The manuscript should clarify in the methods or benchmark section how the five difficulty levels are operationalized via the fixed configuration search spaces to ensure readers can replicate the discriminative power claim.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of the manuscript, recognition of the benchmark's strengths in reproducibility and controlled evaluation, and recommendation for minor revision. No specific major comments were listed in the report.

Circularity Check

0 steps flagged

No significant circularity in empirical benchmark evaluation

full rationale

This is an empirical benchmark paper that introduces executable games and measures LLM performance on success rate, efficiency, and perturbation effects. No derivation chain, equations, fitted parameters, or predictions exist that could reduce to inputs by construction. The central claims rest on direct experimental results from the 474 games rather than any self-referential fitting or self-citation load-bearing step. This matches the default expectation for non-circular empirical work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no details on free parameters, axioms, or invented entities; ledger is empty pending full text.

pith-pipeline@v0.9.1-grok · 5686 in / 1075 out tokens · 33945 ms · 2026-06-29T17:44:25.471001+00:00 · methodology

discussion (0)

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Reference graph

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