arxiv: 2604.06111 · v2 · submitted 2026-04-07 · 💻 cs.AI · cs.CL

Recognition: no theorem link

AgentCE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments

Wang Yang , Chaoda Song , Xinpeng Li , Debargha Ganguly , Chuang Ma , Shouren Wang , Zhihao Dou , Yuli Zhou , Vipin Chaudhary , Xiaotian Han

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:02 UTC · model grok-4.3

classification 💻 cs.AI cs.CL

keywords agent evaluationconfigurable benchmarkscalable horizonscontrollable difficultylightweight environmentsgrid planningreasoning assessmentdecoy budget

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The pith

A grid planning benchmark lets researchers independently tune agent task length via hidden slots and difficulty via decoy counts while using static files to avoid live environment costs.

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

Existing agent benchmarks incur high overhead from environment interactions and produce unreliable aggregate scores because task lengths and difficulties are imbalanced. AgentCE-Bench replaces this with a single grid-based planning task in which agents fill hidden slots in a schedule while obeying both local slot rules and global constraints. Two orthogonal parameters give control: the count of hidden slots H determines how far the plan extends, and the decoy budget B determines how many globally misleading candidates the agent must ignore. Tool calls return answers from static JSON files instead of running live code or services, which removes setup time and supports rapid, repeatable runs. Experiments across thirteen models in six domains show that changes in H and B produce predictable shifts in success rates, that domains remain consistent, and that the benchmark separates stronger from weaker reasoners.

Core claim

AgentCE-Bench introduces a unified grid-based planning task in which agents must complete a schedule by filling hidden slots subject to local slot constraints and global constraints. Horizon scales directly with the number of hidden slots H while difficulty scales with the decoy budget B that sets the number of globally misleading candidates. All tool calls are answered from static JSON files rather than live environments, removing interaction overhead and enabling fast reproducible evaluation. Validation experiments confirm that H and B produce reliable, monotonic changes in performance, that the benchmark maintains domain consistency, and that it discriminates among models of different fam

What carries the argument

The grid-based planning task with orthogonal controls H for scalable horizons and B for controllable difficulty, resolved through static JSON files in a lightweight environment that eliminates live interactions.

If this is right

Varying H produces reliable, monotonic increases in task horizon.
Varying B independently increases difficulty through added global decoys.
The lightweight static design reduces evaluation time compared with live environment interactions.
The benchmark exhibits domain consistency and model discriminability across tested families.
It supports fast, reproducible runs suitable for training-time validation loops.

Where Pith is reading between the lines

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

Independent controls on length and distractor count could let developers trace whether a model fails mainly on sequence length or on filtering misleading information.
Static JSON resolution may make it practical to run thousands of evaluations during agent training without the cost of full simulators.
The same two-axis control pattern could be adapted to other agent domains such as code editing or web navigation to create comparable tunable tests.

Load-bearing premise

That success on this grid-planning task with static JSON resolutions captures the essential reasoning challenges agents face in live, open-ended environments.

What would settle it

If increasing H or B produces no consistent drop in success rate across models, or if scores on AgentCE-Bench fail to predict performance on established live-environment agent benchmarks.

Figures

Figures reproduced from arXiv: 2604.06111 by Chaoda Song, Chuang Ma, Debargha Ganguly, Shouren Wang, Vipin Chaudhary, Wang Yang, Xiaotian Han, Xinpeng Li, Yuli Zhou, Zhihao Dou.

**Figure 2.** Figure 2: Heatmap of average reward (%) across models and domains. Each cell represents [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of AgentCE-Bench framework. Left (Data Examples): Each task is controlled by two configurable axes — hidden slots (H) and decoy budget (B). Each hidden slot has a candidate pool containing one truth (green), filtered candidates that violate slot constraints (red), and decoy candidates that pass slot constraints but cause global conflicts (orange). Right (Interaction): The agent operates in a light… view at source ↗

**Figure 4.** Figure 4: Average time distribution of LLMs and environment interaction across three benchmarks (WebArena, TAU2, and AIME). Benchmark Environment Resource tau2-bench LLM-as-User High WebArena Web Simulator High AIME None Low [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗

**Figure 5.** Figure 5: Top row: Distribution of task steps across three domains (airline, retail, and [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗

**Figure 6.** Figure 6: Illustration of a benchmark instance in the course scheduling domain. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗

**Figure 7.** Figure 7: Verification of benchmark properties. (a) Task steps scale consistently with the [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

**Figure 8.** Figure 8: Heatmaps of average reward (%) across hidden slots and decoy budgets for all [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗

**Figure 9.** Figure 9: Heatmaps of average reward across hidden slots and decoy budgets on the Course [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗

read the original abstract

Existing Agent benchmarks suffer from two critical limitations: high environment interaction overhead (up to 41\% of total evaluation time) and imbalanced task horizon and difficulty distributions that make aggregate scores unreliable. To address these issues, we propose AgentCE-Bench built around a unified grid-based planning task, where agents must fill hidden slots in a partially completed schedule subject to both local slot constraints and global constraints. Our benchmark offers fine-grained control through two orthogonal axes: \textbf{Scalable Horizons}, controlled by the number of hidden slots $H$, and \textbf{Controllable Difficulty}, governed by a decoy budget $B$ that determines the number of globally misleading decoy candidates. Crucially, all tool calls are resolved via static JSON files under a \textbf{Lightweight Environment} design, eliminating setup overhead and enabling fast, reproducible evaluation suitable for training-time validation. We first validate that $H$ and $B$ provide reliable control over task horizon and difficulty, and that AgentCE-Bench exhibits strong domain consistency and model discriminability. We then conduct comprehensive experiments across 13 models of diverse sizes and families over 6 domains, revealing significant cross-model performance variation and confirming that AgentCE-Bench provides interpretable and controllable evaluation of agent reasoning.

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

AgentCE-Bench gives controllable H and B axes on a static grid task to cut evaluation overhead, but the lack of live dynamics is the main open question.

read the letter

The paper's main contribution is a benchmark that separates horizon scaling from difficulty adjustment in a single grid-based planning task. Agents fill hidden slots in a schedule under local and global constraints, with H setting the number of hidden slots and B setting the decoy budget. All tool calls come from static JSON files, which removes live environment setup and interaction time. They report validation that H and B actually move horizon and difficulty as intended, plus domain consistency and discriminability across 13 models in 6 domains, with visible performance spreads between model families and sizes. The lightweight design is a practical step for quick, reproducible checks during development or training loops. Existing benchmarks often suffer from high overhead and skewed task distributions, so the orthogonal controls address a real pain point if they hold up. The static JSON choice keeps things fast and eliminates nondeterminism from live tools, which is useful for controlled experiments. The experiments do show cross-model variation, which supports the claim of discriminability at least within this task family. The soft spot is the static resolution itself. Agent reasoning in most settings involves ongoing state changes, error recovery, and adaptive responses to tool outputs. Here those elements are absent by design, so any performance patterns are measured only inside a fixed constraint-satisfaction problem. The validation of H and B control and model separation happens entirely within the same static grid, leaving open whether the results transfer to environments with live feedback. The scope is also narrow—one task family—so claims about general agent reasoning rest on how representative the grid schedule filling is. This is worth sending to peer review. The controls and lightweight framing are concrete and testable, and the experiments give a starting point for discussion. Referees can press on the static-vs-dynamic gap and ask for more detail on the validation statistics, but the paper is coherent enough to merit that step rather than a desk reject.

Referee Report

2 major / 1 minor

Summary. The paper proposes AgentCE-Bench, a benchmark for agent reasoning built on a unified grid-based planning task where agents fill hidden slots subject to local and global constraints. It introduces two orthogonal control axes—Scalable Horizons via the number of hidden slots H and Controllable Difficulty via decoy budget B—implemented through static JSON file resolution of tool calls in a lightweight environment to reduce overhead and enable reproducibility. The authors claim that H and B reliably control horizon and difficulty, that the benchmark shows domain consistency and model discriminability, and that experiments across 13 models in 6 domains reveal significant performance variation, providing interpretable evaluation of agent reasoning.

Significance. If the static lightweight design and control axes are shown to validly capture core agent reasoning challenges, the benchmark would address key limitations of existing agent evaluations (high interaction overhead and imbalanced distributions) by enabling fast, reproducible, fine-grained testing suitable for training-time use. The cross-model experiments and orthogonal controls are strengths that could improve interpretability of agent capabilities.

major comments (2)

[Abstract] Abstract: The claim that 'H and B provide reliable control over task horizon and difficulty, and that AgentCE-Bench exhibits strong domain consistency and model discriminability' is asserted without any specific validation methods, statistical tests, quantitative results, or data details. This is load-bearing for the central claim of interpretable and controllable evaluation.
[Lightweight Environment design] Lightweight Environment design (described in the abstract and task setup): All tool calls are resolved via static JSON files, eliminating live state changes, error recovery, and adaptive responses. This design choice creates a correctness risk for the claim that the benchmark evaluates general agent reasoning, as performance patterns may not transfer to dynamic environments. A concrete test would be to compare relative model rankings on the static grid task versus an equivalent dynamic implementation.

minor comments (1)

The introduction of H (number of hidden slots) and B (decoy budget) is clear but would benefit from an explicit diagram or table showing how they map to grid configurations and constraint satisfaction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, with clear indications of planned revisions.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that 'H and B provide reliable control over task horizon and difficulty, and that AgentCE-Bench exhibits strong domain consistency and model discriminability' is asserted without any specific validation methods, statistical tests, quantitative results, or data details. This is load-bearing for the central claim of interpretable and controllable evaluation.

Authors: We agree that the abstract, constrained by length, summarizes the validation claims without detailing the methods or results. The full manuscript presents these validations in the Experiments section, including quantitative analyses of horizon scaling with H, difficulty modulation via B, domain consistency metrics, and model discriminability through performance variance across the 13 models. We will revise the abstract to briefly reference the validation approach and key quantitative outcomes to better support the central claims. revision: yes
Referee: [Lightweight Environment design] Lightweight Environment design (described in the abstract and task setup): All tool calls are resolved via static JSON files, eliminating live state changes, error recovery, and adaptive responses. This design choice creates a correctness risk for the claim that the benchmark evaluates general agent reasoning, as performance patterns may not transfer to dynamic environments. A concrete test would be to compare relative model rankings on the static grid task versus an equivalent dynamic implementation.

Authors: The static JSON resolution is a core design decision to achieve the benchmark's primary goals of low overhead and high reproducibility, directly addressing the 41% interaction time issue in prior agent evaluations. The planning and constraint-satisfaction reasoning tasks are preserved through the hidden slots H and decoy budget B, even without live dynamics. We acknowledge that this may limit direct transfer to environments requiring error recovery and will add an expanded limitations discussion on this point. We do not plan to implement the suggested dynamic comparison, as it would reintroduce the overhead the benchmark seeks to avoid. revision: partial

Circularity Check

0 steps flagged

No circularity in benchmark proposal or empirical validation

full rationale

The paper is a benchmark design and empirical study proposing AgentCE-Bench with orthogonal controls H (hidden slots for horizons) and B (decoy budget for difficulty) in a static JSON-resolved grid planning task. Validation consists of running 13 models across domains to confirm H/B effects on performance, domain consistency, and discriminability. No equations, fitted parameters renamed as predictions, self-citations, or ansatzes appear in the provided text; the design claims and experimental outcomes are independent empirical observations rather than reductions to inputs by construction. The work is self-contained as a practical benchmark contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the domain assumption that a single grid-based scheduling task with hidden slots and decoys can serve as a representative and controllable proxy for agent reasoning; no free parameters are fitted to data and no new entities are postulated.

axioms (1)

domain assumption A unified grid-based planning task with local and global constraints can represent core challenges in agent reasoning across domains
Invoked when the authors state that the benchmark exhibits strong domain consistency and model discriminability.

pith-pipeline@v0.9.0 · 5560 in / 1357 out tokens · 132470 ms · 2026-05-10T19:02:19.959847+00:00 · methodology

discussion (0)

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