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arxiv: 2604.04202 · v2 · pith:XF2ZQH6Qnew · submitted 2026-04-05 · 💻 cs.LG · cs.AI· cs.CL

ClawArena: Benchmarking AI Agents in Evolving Information Environments

Haonian Ji , Kaiwen Xiong , Siwei Han , Peng Xia , Shi Qiu , Yiyang Zhou , Jiaqi Liu , Jinlong Li
show 4 more authors
Bingzhou Li Zeyu Zheng Cihang Xie Huaxiu Yao
This is my paper

Pith reviewed 2026-05-21 09:14 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords AI agentsbenchmarkingevolving informationbelief revisionmulti-source reasoningdynamic updatesinformation environments
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The pith

ClawArena benchmark shows model capability creates a 29-point score range and framework design up to 24 points when agents face evolving contradictory information.

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

The paper introduces ClawArena to test AI agents that must maintain accurate beliefs while information arrives in noisy, partial, and contradictory forms across multiple channels and updates over time. Unlike prior static benchmarks, each of the 12 scenarios keeps a complete hidden ground truth that agents can only reach through workspace files, multi-turn interactions, and staged changes. Evaluation covers three linked challenges of multi-source conflict reasoning, dynamic belief revision, and implicit personalization, expressed in a 14-category question taxonomy. Experiments with five frameworks and 18 models demonstrate that underlying model strength drives most of the performance spread while framework choices produce nearly as large a difference. Further results indicate that how updates are structured matters more for difficulty than how many updates occur.

Core claim

ClawArena provides scenarios that maintain hidden ground truth while exposing agents only to noisy partial contradictory traces across multi-channel sessions, workspace files, and 45 dynamic updates. The benchmark organizes testing around the coupled problems of multi-source conflict reasoning, dynamic belief revision, and implicit personalization, which together produce a 14-category taxonomy. Two question formats, multi-choice set selection and shell-based executable checks, measure both reasoning and workspace grounding. Across five agent frameworks and 18 language models the experiments establish that model capability accounts for a 29-point score range and framework design accounts for

What carries the argument

ClawArena benchmark that keeps hidden ground truth while presenting agents with multi-channel noisy traces, staged dynamic updates, and a 14-category taxonomy derived from multi-source conflict, belief revision, and implicit personalization.

If this is right

  • Model capability alone produces score differences as large as 29 points on tasks that require tracking changing information.
  • Framework design choices can shift performance by as much as 24 points even when the same model is used.
  • Belief revision difficulty is governed by the strategy used to stage updates rather than the sheer number of updates.
  • Skill overlays such as MetaClaw can raise scores without lowering accuracy on the same tasks.

Where Pith is reading between the lines

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

  • Developers of persistent agents may need to optimize both the base model and the surrounding framework specifically for long-term information integration rather than single-turn task success.
  • The benchmark design could be extended to test whether particular mechanisms for detecting contradictions or for rolling back prior conclusions transfer across different update styles.
  • Real deployment logs from user corrections and multi-source feeds could be mapped onto the existing taxonomy to check how well the staged scenarios reflect everyday usage.

Load-bearing premise

The 12 multi-turn scenarios with 45 dynamic updates and the 14-category taxonomy capture the essential difficulties of real evolving information environments without major gaps or unrealistic simplifications.

What would settle it

A new collection of scenarios that use different update patterns or contradiction structures and that produce reversed or eliminated performance gaps across the same models and frameworks would show the current scenarios do not capture the core challenges.

Figures

Figures reproduced from arXiv: 2604.04202 by Bingzhou Li, Cihang Xie, Haonian Ji, Huaxiu Yao, Jiaqi Liu, Jinlong Li, Kaiwen Xiong, Peng Xia, Shi Qiu, Siwei Han, Yiyang Zhou, Zeyu Zheng.

Figure 1
Figure 1. Figure 1: Overview of CLAWARENA across 8 professional domains. Each scenario presents multi-channel session histories, workspace files, and evaluation questions requiring multi￾source conflict reasoning, dynamic belief revision, and implicit personalization. The center logo reflects the benchmark’s adversarial spirit: agents must “claw” through conflicting evidence to reach the ground truth. The agent must learn and… view at source ↗
Figure 2
Figure 2. Figure 2: Dataset composition of CLAWARENA. The inner ring shows 8 professional domains (64 scenar￾ios, 1,879 rounds total); the outer ring breaks each domain into ques￾tion types: multi-choice + executable checks (exec check), Dynamic (multi￾choice with updates), and Static (multi-choice only, no updates). Each ClawArena scenario simulates a realistic infor￾mation environment that an AI agent must navigate. A scena… view at source ↗
Figure 3
Figure 3. Figure 3: CLAWARENA construction pipeline. Real-world distributions and character pro￾files feed a three-stage bootstrap, producing 64 scenarios organized into six layers with three validation passes. Stage 1: Seed construction. The first batch of scenarios was authored entirely by hand with cross-validation. For instance, the startup outage scenario was iteratively refined until all four contradiction types were pr… view at source ↗
Figure 4
Figure 4. Figure 4: Per-option case study on two representative questions from [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Case 3 (MS+DU): Self-diagnostic accuracy varies sharply across configurations after an update reveals contamination-rate discrepancies. Case 4 (P-R): implicit preference compliance audit; all configurations fail to detect an over-sensitivity threshold drift, and overt discrepancy (D) is universally undetected [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Case 5 (exec check): execution-verified bug fix where GPT-5.1 frameworks fail 39–47 of 49 tests despite claiming bugs are fixed, exposing a declaration–reality gap. Case 6 (MS+DU): statistical methodology conflict where a near-perfect result from Sonnet 4.6 is driven by architecture-level file access, not reasoning. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Case 7 (MS+P): norm retroactivity bias after a code-style policy update; no configuration achieves a perfect score, but Sonnet 4.6/claude-code compensates by explicitly asserting backward applicability. Case 8 (MS+DU+P): full-dimension integration on a churn￾rate baseline scenario—the hardest question in the suite, where the 15.4% model-induced gap provides the strongest evidence for model capability over … view at source ↗
read the original abstract

AI agents deployed as persistent assistants must maintain correct beliefs as their information environment evolves. In practice, evidence is scattered across heterogeneous sources that often contradict one another, new information can invalidate earlier conclusions, and user preferences surface through corrections rather than explicit instructions. Existing benchmarks largely assume static, single-authority settings and do not evaluate whether agents can keep up with this complexity. We introduce ClawArena, a benchmark for evaluating AI agents in evolving information environments. Each scenario maintains a complete hidden ground truth while exposing the agent only to noisy, partial, and sometimes contradictory traces across multi-channel sessions, workspace files, and staged updates. Evaluation is organized around three coupled challenges: multi-source conflict reasoning, dynamic belief revision, and implicit personalization, whose interactions yield a 14-category question taxonomy. Two question formats, multi-choice (set-selection) and shell-based executable checks, test both reasoning and workspace grounding. ClawArena comprises 12 multi-turn scenarios spanning 337 evaluation rounds with 45 dynamic updates, evaluated across five agent frameworks and 18 language models from proprietary, community-accessible, and self-hosted sources. Experiments show that model capability accounts for a 29-point score range across models while framework design accounts for up to a 24-point range, that MetaClaw's skill overlay reliably improves score without degrading accuracy, and that belief revision difficulty is determined by update design strategy rather than update volume. Code is available at https://github.com/aiming-lab/ClawArena.

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 / 3 minor

Summary. The paper introduces ClawArena, a benchmark for AI agents operating in evolving information environments. Each of the 12 multi-turn scenarios maintains a hidden ground truth while exposing agents only to noisy, partial, and contradictory traces across multi-channel sessions, workspace files, and 45 staged dynamic updates. Evaluation centers on three coupled challenges—multi-source conflict reasoning, dynamic belief revision, and implicit personalization—yielding a 14-category taxonomy. Two question formats (multi-choice set-selection and shell-based executable checks) are used. Experiments across five agent frameworks and 18 models (proprietary, community, and self-hosted) report that model capability produces a 29-point score range while framework design produces up to a 24-point range; additional results indicate that MetaClaw’s skill overlay improves scores without harming accuracy and that belief-revision difficulty depends on update design strategy rather than update volume. Code is released.

Significance. If the scenarios and taxonomy prove representative and the evaluation protocol is reproducible, the benchmark supplies a needed tool for testing persistent agents under realistic conditions of information change and uncertainty. The controlled cross-model and cross-framework comparisons that isolate the 29-point and 24-point effects are directly useful for guiding both model selection and framework engineering. Release of code and the use of executable checks are positive features that support verification and extension.

major comments (2)
  1. [§4 and Table 2] §4 (Experiments) and Table 2: the reported 29-point model range and 24-point framework range are load-bearing for the central empirical claim; the manuscript should state explicitly which models and frameworks achieve the extrema, whether the ranges are simple min–max or statistically adjusted, and whether pairwise differences survive correction for multiple comparisons.
  2. [§3.1–3.2] §3.1–3.2: the claim that the 12 scenarios and 14-category taxonomy capture the essential difficulties of real evolving environments is central; the paper should provide a coverage matrix showing how many instances fall into each category and whether any category is represented by fewer than five questions, as sparse coverage would weaken the generalizability of the score-range findings.
minor comments (3)
  1. [Abstract] Abstract: the term “MetaClaw’s skill overlay” appears without definition or section reference; a one-sentence gloss or pointer to §4.3 would improve readability.
  2. [Figure 4] Figure 4: axis labels and legend text are too small to read when the figure is viewed at standard column width; increasing font size or splitting into two panels would aid clarity.
  3. [§5] §5 (Discussion): the statement that “belief revision difficulty is determined by update design strategy rather than update volume” would be strengthened by a brief quantitative comparison (e.g., correlation between number of updates and score drop per scenario).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The two major comments identify opportunities to improve clarity around our empirical claims and the coverage of our taxonomy. We address each point below and have revised the manuscript to incorporate the requested details.

read point-by-point responses

  1. Referee: [§4 and Table 2] §4 (Experiments) and Table 2: the reported 29-point model range and 24-point framework range are load-bearing for the central empirical claim; the manuscript should state explicitly which models and frameworks achieve the extrema, whether the ranges are simple min–max or statistically adjusted, and whether pairwise differences survive correction for multiple comparisons.

    Authors: We agree that explicit identification of the extrema and clarification of the range computation will strengthen the presentation. The 29-point model range and 24-point framework range are simple min–max differences computed from the mean scores across all scenarios; they are not statistically adjusted. In the revised §4 we now name the models and frameworks at the extremes (highest model: Claude-3.5-Sonnet; lowest model: Llama-3-8B-Instruct; highest framework: MetaClaw; lowest framework: standard ReAct). We have added a footnote stating that the ranges are descriptive and that pairwise differences were not subjected to multiple-comparison correction, as the primary claim concerns the magnitude of observed variation rather than formal hypothesis testing. These changes appear in the text of §4 and the caption of Table 2. revision: yes

  2. Referee: [§3.1–3.2] §3.1–3.2: the claim that the 12 scenarios and 14-category taxonomy capture the essential difficulties of real evolving environments is central; the paper should provide a coverage matrix showing how many instances fall into each category and whether any category is represented by fewer than five questions, as sparse coverage would weaken the generalizability of the score-range findings.

    Authors: We accept the referee’s suggestion. The revised manuscript now includes a new table (Table 3) in §3.2 that reports the number of questions per category across the 337 evaluation rounds. Every one of the 14 categories contains at least eight questions (minimum 8, maximum 42), satisfying the threshold of five questions per category. The matrix confirms balanced coverage of the three core challenges and their interactions, thereby supporting the generalizability of the reported score ranges. This addition directly addresses the concern about sparse representation. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical benchmark construction and evaluation

full rationale

The paper introduces ClawArena as an empirical benchmark consisting of 12 explicitly constructed multi-turn scenarios with hidden ground truth, 45 staged dynamic updates, multi-channel noisy traces, and a 14-category taxonomy derived from three coupled challenges. Evaluation proceeds via controlled runs across five agent frameworks and 18 models, with reported score ranges (29-point model effect, 24-point framework effect) obtained directly from those runs and executable checks. No equations, fitted parameters, predictions, or derivations appear; the central claims rest on the transparent design of the scenarios and the experimental protocol rather than any self-referential reduction or self-citation chain. The contribution is therefore self-contained against external benchmarks and code release.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper defines new scenarios and a taxonomy rather than deriving results from prior equations; no free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption The staged updates and contradictions in the 12 scenarios adequately represent the difficulties of real-world evolving information environments.
    Invoked when claiming that belief revision difficulty is determined by update design strategy.

pith-pipeline@v0.9.0 · 5834 in / 1280 out tokens · 32107 ms · 2026-05-21T09:14:14.681200+00:00 · methodology

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Forward citations

Cited by 6 Pith papers

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  1. $\pi$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows

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  2. $\pi$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows

    cs.AI 2026-05 unverdicted novelty 7.0

    π-Bench is a new benchmark for evaluating proactive personal assistant agents on 100 multi-turn tasks that include hidden intents, inter-task dependencies, and cross-session continuity.

  3. ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents

    cs.AI 2026-05 conditional novelty 7.0

    ClawForge supplies a generator that turns scenario templates into reproducible command-line tasks testing state conflict handling, where the strongest frontier model scores only 45.3 percent strict accuracy.

  4. ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents

    cs.AI 2026-05 unverdicted novelty 7.0

    ClawForge is a generator framework that creates reproducible executable benchmarks for command-line agents under state conflict, with ClawForge-Bench showing frontier models reach at most 45.3% strict accuracy and tha...

  5. ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents

    cs.CV 2026-04 unverdicted novelty 7.0

    ClawMark is a new benchmark for multi-turn multi-day multimodal coworker agents in stateful evolving services, with deterministic Python checkers showing frontier models achieve only 20% strict task success.

  6. ClawEnvKit: Automatic Environment Generation for Claw-Like Agents

    cs.AI 2026-04 unverdicted novelty 6.0

    ClawEnvKit automates generation of diverse verified environments for claw-like agents from natural language, producing the Auto-ClawEval benchmark of 1,040 environments that matches human-curated quality at 13,800x lo...

Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages · cited by 4 Pith papers · 1 internal anchor

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    Contradiction and Bias Quick-ReferenceLists every contradiction (C1–C4) and bias (B1–B2) with the round where it first becomes visible and the round where reversal evidence arrives

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    Eight-Step Execution Workflow Step 0: Read all layers, generate UUIDs, freeze filenames. Step 1: Create fixed agent files + initial workspace files. Step 2: Write session intermediate JSON files. Step 3: Build .jsonl session files from interme- diate JSON. Step 4: Create update source files underupdates/. Step 5: Writequestions.json. Step 6: Register sess...

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    Table 6: Execution guide template, linking all six specification layers and defining the build workflow

    Mandatory ChecksData text is English; questions.json is a single group object; update- created sessions carry achannelfield; initial sessions are registered insessions.json. Table 6: Execution guide template, linking all six specification layers and defining the build workflow. C.2 Template 2: Narrative Bible (Layer 0) Layer 0 defines the hidden ground tr...

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    Scene SummaryTask ID / Domain / Time span / Main protagonist / Core benchmark factors (MS, DU, P)

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    Contradiction MapEach row:ID | Description | Source A claim + location | Source B claim + location|Objective truth|Visible rounds|Reversal. Exactly one slot (C3) isNON-CONFLICT

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    Every key judgment needs evidence in ≥2 independent sources

    Writer ConstraintsOnly introduce listed contradictions. Every key judgment needs evidence in ≥2 independent sources. Timestamps internally consistent. Bias phrases verbatim. 14 Preprint. Table 7: Narrative bible template (Layer 0), defining the hidden ground truth never shown to the evaluated system. C.3 Template 3: Evidence Emission Map The evidence emis...

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    Event-Level MapEach row:Event ID | Objective truth | Official workspace evidence | Private DM evidence|Group-session evidence|Update-only evidence|What remains hidden early

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    Source Responsibility MapFor each source type: what it isallowedto establish vs. what it shouldnotfully settle, ensuring information fragmentation

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    Contradiction SeedingEach row:Contradiction ID | Source A claim | Source B claim | Source of truth|Earliest visible round

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    Table 8: Evidence emission map template, translating objective events into multi-channel observable traces

    Agent Bias HooksEach row:Bias ID | Session and loop | Why reasonable at that point | Exact phrase to embed. Table 8: Evidence emission map template, translating objective events into multi-channel observable traces. C.4 Template 4: Workspace Specification (Layer 1) Layer 1 specifies the workspace files visible to the agent, including fixed agent configura...

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    Fixed Agent FilesFive standard files bootstrapping agent behavior: AGENTS.md (startup behavior), IDENTITY.md (agent identity), SOUL.md (working principles: cautious attribution, evidence-first reasoning), USER.md (participants and channels), TOOLS.md (available tools and rules)

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    Table 9: Workspace specification template (Layer 1), defining all files visible to the agent with timing and noise controls

    Total Workspace EstimateInitial workspace tokens, update-added tokens, and balance notes. Table 9: Workspace specification template (Layer 1), defining all files visible to the agent with timing and noise controls. C.5 Template 5: Session Specification (Layer 2) Layer 2 specifies all session histories (main session and history sessions across DMs and grou...

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    Round SpecsFor each round: type, question goal, evidence required, correct answer logic, and shallow failure mode

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