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arxiv: 2605.07926 · v2 · pith:A4YI2J37new · submitted 2026-05-08 · 💻 cs.AI

AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents

Zhengkang Guo , Yiyang Li , Lin Qiu , Xiaohua Wang , Jingwen Xv , Dongyu Ru , Xiaoyu Li , Xiaoqing Zheng
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Xuezhi Cao Xunliang Cai
This is my paper

Pith reviewed 2026-05-21 07:55 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentstool usebenchmarklong-range dependenciesstate trackingreasoningout-of-domain
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The pith

A new escape-room benchmark reveals LLM agents lose ground when tool dependencies span many steps.

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

The paper introduces AgentEscapeBench to test whether LLM agents can sustain tool-grounded reasoning over long-range dependencies that lie outside familiar workflows. Tasks are built as directed acyclic graphs of tools and items, with state revealed only incrementally so agents must track hidden information, execute functions, propagate results, and reach a verifiable answer. Experiments with sixteen agents show success rates fall markedly as dependency depth rises, while human performance declines more modestly. The work aims to expose specific failure modes such as breakdowns in state tracking and clue adherence that current agents exhibit beyond short-range interactions.

Core claim

Current agents handle local tool use but struggle with deep contextual dependencies, as shown by the sharp performance drop from 90 percent at the shallowest tier to 60 percent at the deepest tier across 270 tasks in five difficulty levels.

What carries the argument

Directed acyclic dependency graph over tools and items with incremental state revelation, forcing agents to maintain and propagate information across multiple steps.

If this is right

  • Model failures concentrate in long-range state tracking, clue adherence, and intermediate-result propagation.
  • Agents require improved mechanisms for maintaining coherence across extended sequences of tool calls.
  • The benchmark supplies a fully automated, scalable testbed for measuring progress toward more robust agent reasoning.
  • Training efforts should target adaptation to novel dependency structures rather than only familiar short-range patterns.

Where Pith is reading between the lines

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

  • The same dependency-tracking weakness may limit performance in other agent domains that involve chained external actions.
  • Incorporating longer synthetic dependency chains into training data could narrow the observed gap with human performance.
  • Real-world applications such as automated software debugging or multi-step scientific workflows may face similar constraints.

Load-bearing premise

The escape-room tasks and their dependency graphs accurately reflect the structure of real out-of-domain tool-grounded reasoning problems.

What would settle it

Measure whether agents that succeed on the benchmark also succeed at comparable rates on actual multi-step external-tool workflows whose dependency depth has been independently quantified.

Figures

Figures reproduced from arXiv: 2605.07926 by Dongyu Ru, Jingwen Xv, Lin Qiu, Xiaohua Wang, Xiaoqing Zheng, Xiaoyu Li, Xuezhi Cao, Xunliang Cai, Yiyang Li, Zhengkang Guo.

Figure 1
Figure 1. Figure 1: Conceptual illustration of AgentEscapeBench. The agent is placed in a themed escape room populated with unfamiliar tools and hidden items. It must explore the environment, invoke tools with correct parameters derived from narrative clues, and propagate intermediate outputs through a multi-step dependency chain to unlock the final exit. Strong performance may therefore reflect learned domain conventions or … view at source ↗
Figure 2
Figure 2. Figure 2: Six-stage data construction pipeline of AgentEscapeBench. Starting from a curated template library (Stage 1), a reverse-generation algorithm assembles a DAG skeleton (Stage 2), annotates source ports (Stage 3), instantiates concrete values via an LLM (Stage 4), executes the DAG forward to compute ground-truth outputs (Stage 5), and generates themed narratives with structural validation (Stage 6). 3 Experim… view at source ↗
Figure 3
Figure 3. Figure 3: Behavioural metric trends across difficulty levels. (a) Source-node convergence speed (lower is better): the average number of attempts to correctly resolve a source node. (b) Premature invocation rate (lower is better): the fraction of non-source invocations made before all predecessors are resolved. (c) Clue adherence rate (higher is better): the fraction of downstream invocations whose arguments trace b… view at source ↗
Figure 4
Figure 4. Figure 4: Tool calls vs. success rate at difficulty-10. Upper-left is better (fewer calls, higher SR). From [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Interaction trace (page 1/3): system prompt, environment initialization, and first investiga [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Interaction trace (page 2/3): remaining investigations, dependency reasoning, and tool [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Interaction trace (page 3/3): final computation steps and successful answer submission. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
read the original abstract

As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar workflows and short-range interactions. We introduce AgentEscapeBench, an escape-room-style benchmark that tests whether agents can infer, execute, and revise novel tool-use procedures under explicit long-range dependency constraints. Each task defines a directed acyclic dependency graph over tools and items, requiring agents to invoke real external functions, track hidden state revealed incrementally, propagate intermediate results, and submit a deterministically verifiable final answer. AgentEscapeBench includes 270 instances across five difficulty tiers and supports fully automated evaluation. Experiments with sixteen LLM agents and human participants show that performance drops sharply as dependency depth increases: humans decline from 98.3% success at difficulty-5 to 80.0% at difficulty-25, while the best model drops from 90.0% to 60.0%. Trajectory analysis attributes model failures mainly to breakdowns in long-range state tracking, clue adherence, and intermediate-result propagation. These findings suggest that current agents can often handle local tool use but still struggle with deep contextual dependencies. We hope AgentEscapeBench can serve as a diagnostic testbed for measuring current agent capabilities and informing future training efforts toward more robust general-purpose reasoning, action, and adaptation.

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 introduces AgentEscapeBench, an escape-room-style benchmark with 270 instances across five difficulty tiers (difficulty-5 to difficulty-25). Tasks are defined via directed acyclic dependency graphs (DAGs) over tools and items, requiring agents to invoke external functions, track incrementally revealed hidden state, propagate intermediate results, and produce a verifiable final answer. Experiments with 16 LLM agents and human participants show sharp performance declines with increasing dependency depth: humans drop from 98.3% success at difficulty-5 to 80.0% at difficulty-25, while the best model drops from 90.0% to 60.0%. Trajectory analysis attributes model failures mainly to breakdowns in long-range state tracking, clue adherence, and intermediate-result propagation. The benchmark is positioned as a diagnostic testbed for out-of-domain tool-grounded reasoning in LLM agents.

Significance. If the benchmark successfully isolates dependency depth without confounds from task length or state size, the work offers a concrete, automated evaluation framework with human baselines that can diagnose specific limitations in current agents' long-range reasoning and inform training for more robust tool use. The direct measurement against human performance and the focus on falsifiable, verifiable outcomes are strengths that could make this a useful addition to agent evaluation suites.

major comments (2)
  1. [Task design description (abstract and benchmark construction section)] Task design description (abstract and § on benchmark construction): The manuscript does not report whether the number of required tool calls, state variables, or total sequence length is held constant across the five difficulty tiers. If deeper tiers systematically increase step count or state cardinality, the observed drops (humans 98.3%→80%, best model 90%→60%) could arise from cumulative error accumulation rather than depth of contextual dependencies per se.
  2. [Experimental setup (abstract and results section)] Experimental setup (abstract and results section): Performance drops and failure modes are reported from experiments with 16 agents and humans, but no details are provided on task construction procedures, data exclusion criteria, or statistical controls. This makes full verification of the results difficult and weakens the ability to attribute failures specifically to long-range dependencies.
minor comments (2)
  1. Clarify the exact definition of 'difficulty-5' to 'difficulty-25' tiers, including how dependency depth is quantified in the DAGs.
  2. Add a table or figure summarizing the distribution of tool calls and state variables per tier to address potential confounds.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments on our manuscript. We address each major comment point by point below and outline the revisions we intend to make.

read point-by-point responses

  1. Referee: [Task design description (abstract and benchmark construction section)] Task design description (abstract and § on benchmark construction): The manuscript does not report whether the number of required tool calls, state variables, or total sequence length is held constant across the five difficulty tiers. If deeper tiers systematically increase step count or state cardinality, the observed drops (humans 98.3%→80%, best model 90%→60%) could arise from cumulative error accumulation rather than depth of contextual dependencies per se.

    Authors: We appreciate the referee's concern about potential confounds in the benchmark design. The five difficulty tiers are constructed by varying the maximum depth of the directed acyclic dependency graphs, while maintaining similar numbers of tools and items across tiers through controlled graph generation parameters. This design aims to isolate the impact of dependency depth. However, to make this explicit and allow for better verification, we will add a detailed analysis in the benchmark construction section, including statistics on the number of required tool calls, state variables, and sequence lengths for each tier. We will also discuss how the design mitigates cumulative error accumulation effects. revision: yes

  2. Referee: [Experimental setup (abstract and results section)] Experimental setup (abstract and results section): Performance drops and failure modes are reported from experiments with 16 agents and humans, but no details are provided on task construction procedures, data exclusion criteria, or statistical controls. This makes full verification of the results difficult and weakens the ability to attribute failures specifically to long-range dependencies.

    Authors: We acknowledge that the current manuscript lacks sufficient detail on the experimental procedures. In the revised manuscript, we will expand the relevant sections to describe the task construction procedures in full, including the algorithm used to generate the DAGs for each difficulty tier. We confirm that all generated tasks were included without exclusion, as they all satisfied the solvability and verifiability criteria. Additionally, we will provide details on the statistical controls, such as the number of trials per agent, how human participants were recruited and instructed, and the methods for computing success rates and analyzing failure modes. This will facilitate full verification and strengthen the attribution to long-range dependencies. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with direct measurements

full rationale

The paper introduces AgentEscapeBench as an empirical evaluation framework consisting of 270 task instances across difficulty tiers defined by DAG dependency depth. All reported results (human success 98.3% to 80%, best model 90% to 60%) are direct experimental measurements against human baselines and automated verification, with no derivations, equations, fitted parameters, or predictions that reduce to inputs by construction. Trajectory analysis attributes failures to observed behaviors without invoking self-citations as load-bearing uniqueness theorems or ansatzes. The design is self-contained and externally falsifiable via the released benchmark, satisfying the criteria for an honest non-finding of circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the constructed tasks measure general tool-grounded reasoning capabilities; no free parameters or invented entities are introduced beyond the benchmark tasks themselves.

axioms (1)
  • domain assumption Tasks require agents to infer, execute, and revise novel tool-use procedures under explicit long-range dependency constraints.
    Core premise of the benchmark definition in the abstract.

pith-pipeline@v0.9.0 · 5793 in / 1136 out tokens · 33390 ms · 2026-05-21T07:55:35.726437+00:00 · methodology

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Reference graph

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