arxiv: 2603.04737 · v3 · submitted 2026-03-05 · 💻 cs.AI · cs.CL· cs.LG

Recognition: no theorem link

Interactive Benchmarks

Baoqing Yue , Zihan Zhu , Yutong Han , Qian Sun , Jichen Feng , Hufei Yang , Yifan Zhang , Mengdi Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-15 17:08 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.LG

keywords interactive benchmarksreasoning evaluationmulti-turn interactionAI intelligence assessmentlogic and math tasksstrategic gamesobjective feedback

0 comments

The pith

Interactive benchmarks using budgeted multi-turn interaction with objective feedback assess AI reasoning more robustly than fixed tests or preference judgments.

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

The paper claims that fixed benchmarks saturate and risk contamination while preference evaluations depend on subjective judgments, so neither fully captures a core aspect of intelligence: deciding what information to acquire and how to apply it. It introduces interactive benchmarks that evaluate models through constrained multi-turn exchanges, first in Interactive Proofs where a judge supplies objective feedback on logic, UI-to-HTML, and math problems, and second in Interactive Games where models maximize long-horizon utilities. Results indicate these setups produce lower scores and expose clearer gaps than static methods, yielding a less contaminated signal of reasoning capacity. A sympathetic reader would care because this approach directly tests adaptive information use rather than memorized answers or human-like preferences.

Core claim

Interactive Benchmarks assess a model's reasoning ability through budgeted multi-turn interaction. In the Interactive Proofs setting, models interact with a judge to solve Logic, UI2Html, and Mathematics tasks under objective feedback. In the Interactive Games setting, models reason strategically to maximize long-horizon utilities. The resulting measurements indicate that interactive scenarios reveal substantial room for improvement in models' reasoning performance and supply a more robust evaluation than saturated fixed benchmarks or subjective preference methods.

What carries the argument

Budgeted multi-turn interaction with objective feedback, in which models must decide what information to request and how to use it across turns while receiving either judge verdicts or utility scores.

If this is right

Current models exhibit noticeably lower success rates once they must request information over multiple turns rather than answer in one shot.
Interactive scores remain less vulnerable to training-data contamination than fixed benchmark results.
Strategic long-horizon performance in games becomes a clearer differentiator among models that look similar on static tasks.
Objective judge feedback during interaction gives a more direct training signal for reasoning than preference data.

Where Pith is reading between the lines

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

Training loops could incorporate simulated interactive judges to directly optimize for information-acquisition skill.
The same budgeted-interaction format might evaluate agentic systems in open-ended domains such as code debugging or scientific hypothesis testing.
Varying the interaction budget across runs could quantify how much extra reasoning capacity is unlocked by allowing more turns.

Load-bearing premise

That budgeted multi-turn interaction with objective feedback isolates core reasoning ability without adding biases from the interaction rules or judge design.

What would settle it

If model performance rankings and saturation levels remain essentially identical between interactive benchmarks and traditional fixed benchmarks, the claim that interactive setups supply a distinctly more robust assessment would be undermined.

read the original abstract

Existing reasoning evaluation paradigms suffer from different limitations: fixed benchmarks are increasingly saturated and vulnerable to contamination, while preference-based evaluations rely on subjective judgments. We argue that a core aspect of intelligence is the ability to decide what information to acquire and how to use it effectively. We propose Interactive Benchmarks, a unified evaluation paradigm that assesses a model's reasoning ability through budgeted multi-turn interaction. We evaluate models under this framework in two settings: Interactive Proofs, where models interact with a judge to solve Logic, UI2Html, and Mathematics tasks under objective feedback; and Interactive Games, where models reason strategically to maximize long-horizon utilities. Our results show that interactive benchmarks provide a more robust assessment of this dimension of model intelligence, revealing substantial room for improvement in interactive scenarios.

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.

Desk Editor's Note private letter to a colleague

The paper proposes budgeted interactive evaluation to fix saturation and subjectivity in current benchmarks, but the robustness claims rest on unshown checks for protocol biases.

read the letter

The main point is that fixed benchmarks are getting saturated and contaminated while preference judgments stay subjective, so the authors push for models that interact over multiple turns with a judge giving objective feedback under a budget. This targets the ability to decide what information to acquire, which is a reasonable angle on reasoning for agent-like systems. They split it into Interactive Proofs covering logic, UI-to-HTML, and math tasks plus Interactive Games for long-horizon strategy, and they claim this setup gives a more robust read with clear room left for improvement. That framing is new enough in how it unifies the two settings and ties them to budgeted interaction rather than one-shot or pairwise preference tests. The idea itself is clean and directly addresses a practical pain point in evaluation design. The soft spot is that the abstract and available description give no numbers, no variance across judge implementations, and no ablations on turn limits or feedback formats. Without those, it is hard to rule out that models are picking up on artifacts of the interaction protocol instead of showing genuine gains in reasoning. The stress-test note lands here: the claim that interaction isolates core ability better than prior methods needs evidence that the protocol itself does not shift the measurement. If the full paper has the data and those checks, the work is worth referee time because the problem it targets is real and the setup is simple to replicate. If the experiments stay high-level, it stays more of a proposal than a completed result. I would bring this to a reading group for the discussion on eval design but would not cite it yet without the supporting runs. A serious editor should send it for review with a note to strengthen the experimental section.

Referee Report

2 major / 1 minor

Summary. The paper proposes Interactive Benchmarks as a unified evaluation paradigm for assessing AI models' reasoning via budgeted multi-turn interaction with objective feedback. It evaluates models in Interactive Proofs (Logic, UI2Html, Mathematics tasks) and Interactive Games, claiming this framework provides a more robust assessment than saturated fixed benchmarks or subjective preference-based evaluations and reveals substantial room for improvement in interactive scenarios.

Significance. If the central claims are supported by detailed evidence, the work could meaningfully advance evaluation practices by emphasizing adaptive information acquisition and long-horizon reasoning, offering an alternative to contamination-prone static benchmarks. The use of objective feedback is a conceptual strength that could enable more falsifiable assessments.

major comments (2)

[§3] §3 (Methodology): No ablations are presented on protocol variants, including judge design, budget allocation, turn limits, or feedback format. This is load-bearing for the claim that budgeted interaction isolates core reasoning ability without introducing new biases from the interaction rules themselves.
[§4] §4 (Results): The assertion that interactive benchmarks provide a 'more robust assessment' and reveal 'substantial room for improvement' is stated without quantitative metrics, variance analysis, error bars, or direct comparisons to non-interactive baselines, leaving the robustness advantage unsupported by visible evidence.

minor comments (1)

[Abstract] Abstract: The high-level claim of results is presented without any preview of specific performance numbers or statistical details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive feedback on our manuscript. We address each major comment below and will revise the paper to strengthen the methodology and results sections as outlined.

read point-by-point responses

Referee: [§3] §3 (Methodology): No ablations are presented on protocol variants, including judge design, budget allocation, turn limits, or feedback format. This is load-bearing for the claim that budgeted interaction isolates core reasoning ability without introducing new biases from the interaction rules themselves.

Authors: We agree that systematic ablations on these protocol elements are necessary to substantiate that budgeted multi-turn interaction isolates core reasoning without rule-induced biases. In the revised manuscript we will add a dedicated ablation subsection to §3 reporting performance under varied judge designs, budget allocations, turn limits, and feedback formats, with direct quantitative comparisons. revision: yes
Referee: [§4] §4 (Results): The assertion that interactive benchmarks provide a 'more robust assessment' and reveal 'substantial room for improvement' is stated without quantitative metrics, variance analysis, error bars, or direct comparisons to non-interactive baselines, leaving the robustness advantage unsupported by visible evidence.

Authors: The current results section already contains direct model-by-model comparisons between interactive and non-interactive settings, showing consistently lower scores under interaction and thereby quantifying room for improvement. To address the concern about missing statistical support, the revision will add variance analysis, error bars on all metrics, and explicit robustness metrics (e.g., sensitivity to prompt perturbations) with side-by-side non-interactive baselines. revision: partial

Circularity Check

0 steps flagged

No significant circularity; new benchmark framework proposed without reduction to fitted inputs or self-citations

full rationale

The paper introduces Interactive Benchmarks as a new evaluation paradigm for assessing reasoning via budgeted multi-turn interaction with objective feedback, applied to Interactive Proofs (Logic, UI2Html, Mathematics) and Interactive Games. No equations, parameter fits, or derivations are present that reduce a claimed result to its own inputs by construction. The robustness conclusion is framed as an empirical outcome of the proposed setup rather than a self-referential or self-cited necessity. Self-citations, if any, are not load-bearing for the central claim, which rests on the definition and application of the new framework itself. This is a standard non-circular proposal of an evaluation method.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central proposal rests on the domain assumption that reasoning is best measured by interactive information acquisition under budget constraints; no free parameters or new entities are introduced.

axioms (1)

domain assumption A core aspect of intelligence is the ability to decide what information to acquire and how to use it effectively.
Explicitly stated as the foundation for shifting from static to interactive evaluation.

pith-pipeline@v0.9.0 · 5435 in / 1129 out tokens · 46664 ms · 2026-05-15T17:08:18.972350+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctness
cs.CL 2026-05 unverdicted novelty 7.0

LLM proofs for hard math problems show large differences in quality metrics like conciseness and cognitive simplicity that correctness-only tests miss, along with trade-offs between quality and correctness.