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REVIEW 3 major objections 3 minor 1 cited by

A new 1,431-task benchmark spanning 90 domains holds today's strongest AI agents to about 58 percent first-attempt task-completion success.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · deepseek-v4-flash

2026-08-02 00:29 UTC pith:GQRJ4LZS

load-bearing objection Broad and well-constructed agent benchmark, but the headline numbers are internally inconsistent: the abstract's 58.54/57.14 are actually the UserTurns column, and the OverallPass@1 column doesn't match the route-weighted arithmetic. the 3 major comments →

arxiv 2607.14989 v1 pith:GQRJ4LZS submitted 2026-07-16 cs.CL cs.AI

OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios

Chengyu Shen , Yujie Fu , Gangtao Xin , Yanheng Hou , Wenlong Fei , Guojie Zhu , Jiawei Li , Hongcheng Gao
show 8 more authors
Runming He Zhen Hao Wong Meiyi Qiang Hao Liang Zhao Cao Hao Jiang Chong Chen Wentao Zhang
This is my paper
classification cs.CL cs.AI
keywords general AI agentsagent evaluationcapability taxonomytask synthesistool usemulti-turn interactionrubric-based evaluationPOMDP
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper sets out to build a measurement instrument for general-purpose AI agents—programs that do not just answer questions but call tools, track changing state, and complete multi-step user goals—and claims that instrument is both broad and diagnostic. Broad means 1,431 executable tasks derived from a real-world taxonomy of 90 consumer, business, and employee-facing domains (354 sub-domains), covering single-turn and multi-turn interaction, file and code workspaces, and tool orchestration. Diagnostic means every task is labeled along ten capability dimensions and eight atomic difficulty factors, with success scored per rubric item and, where possible, by programmatic verification, so a model's failures can be traced to a specific ability rather than a single aggregate number. The load-bearing outcome is the difficulty measurement: the strongest tested models complete only about 58 percent of the harder 644-task subset on the first attempt (Pass@1), and error analysis attributes most failures to planning, constraint maintenance, and adaptive correction rather than raw tool invocation. A sympathetic reader would care because, if the benchmark is valid, it redraws the frontier of what current agents can and cannot do autonomously and points to which capabilities deserve the next round of engineering.

Core claim

OmniaBench's central claim is that general-agent competence can be measured across the breadth of real-world applications by combining three structured views—a domain taxonomy (90 level-1 and 354 level-2 domains spanning consumer, business, and employee-facing settings), a ten-dimensional capability taxonomy, and eight atomic difficulty factors—instantiated as 1,431 executable, stateful tasks built through four synthesis routes (DAG, DAG-S, Solver, Program). Under its trajectory-based protocol—all-or-nothing rubric items adjudicated by an LLM judge, plus a programmatic VerifyCode verifier for code tasks—the two strongest evaluated models, Claude-Sonnet-5 and GPT-5.6-Sol, achieve Overall Pass

What carries the argument

The carrying object is the three-view taxonomy feeding an environment-and-task factory, plus the evaluation protocol that fixes what the scores mean. The domain taxonomy, assembled from app-store categories, product documents, industry resources, web retrieval, and human refinement, defines coverage; the capability taxonomy defines diagnosis by attaching up to ten ability labels (Task Understanding through Reliability & Safety) to every task; the eight atomic difficulty factors define why a task is hard (ambiguity, long-context evidence, progressive disclosure, multi-source inconsistency, and so on). Four synthesis routes convert these views into POMDP-style environments: DAG builds multi-tu

Load-bearing premise

The whole measurement stands on one premise: the 1,431 synthesized tasks are genuinely solvable as written, and the LLM rubric judge's pass/fail calls match what a careful human expert would decide—and the paper's own judge study finds only moderate item-level agreement (Fleiss kappa = 0.573), so a systematic judge bias or a batch of subtly unsolvable tasks would shift every reported score and capability profile.

What would settle it

Take a random sample of roughly fifty trajectories from the 644-task challenge set and have independent human experts re-score each rubric item under the same protocol, and separately have expert humans attempt to complete a sample of the tasks from scratch in the real environments. If experts fail to solve a substantial share of tasks, or if human judges reverse the LLM judge on more than the observed ~14 percent of contested items, then the 58 percent frontier ceiling and the capability profiles are measurement artifacts rather than agent capability boundaries.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the benchmark measures what it claims, the roughly 58 percent ceiling means no current agent can be trusted with fully autonomous completion of long, stateful, multi-tool tasks; on the hard subset, about half of tasks are only partially completed.
  • Because capability profiles diverge between models with similar overall scores, deployment decisions should be made per capability and per domain, not on a single leaderboard rank.
  • The error distribution—planning and decomposition at 36.7 percent, constraint violations at 16.5 percent, premature abandonment prominent among meta-cognitive errors—points model improvement at plan generation, constraint tracking, and recovery behavior rather than at function-calling syntax.
  • Domain-level rank reversals imply no single model dominates all 90 domains; specialized agent portfolios could beat any one general agent in a given application area.
  • The measured gap between Pass@k (success improves with restarts) and Pass^hat-k (success is not reproducible across runs) indicates that the immediate bottleneck is single-attempt reliability, not raw capability.

Where Pith is reading between the lines

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

  • The rubric-checklist-plus-verifier protocol is a reusable template: any group with a simulated environment could assemble a tailored agent benchmark from the same pipeline, so the construction method may prove more durable than the specific 1,431 tasks.
  • The eight atomic difficulty factors invite ablation pairs—two tasks identical except for one factor, such as progressive disclosure on or off—which would isolate each factor's contribution to model failure; the paper reports aggregate capability differences but stops short of per-factor isolation.
  • Because the challenging set deliberately samples each domain's longest tool chain, the headline 58 percent describes the hard tail; a reader should expect materially higher pass rates on the full set, and the difference between the two sets is itself a publishable measure of horizon sensitivity.
  • Around one in seven rubric-item decisions is non-unanimous across the five judge models tested; re-deriving the paper's error-mode percentages from unanimously agreed items only would show how much of the planning-dominance conclusion rides on contested judgments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. OmniaBench is a large-scale benchmark for general LLM agents, containing 1,431 tasks (644 in a challenging subset) built from a 90-level-1/354-level-2 domain taxonomy spanning ToC/ToB/ToE. Tasks are constructed through four routes (DAG, DAG-S, Solver, Program) in executable environments with explicit state, tools, and multi-turn interaction. Evaluation uses LLM-based user simulators and rubric/VerifyCode judges. The paper evaluates 22 closed- and open-source models and reports that frontier models such as Claude-Sonnet-5 and GPT-5.6-Sol achieve Overall Pass@1 of only 58.54% and 57.14%, with diagnostic analyses across capabilities, domains, interaction turns, and error types.

Significance. If the benchmark is valid, OmniaBench would be a valuable complement to existing single-ecosystem agent benchmarks: it provides broad domain coverage, executable stateful environments, a ten-dimension capability taxonomy, eight difficulty factors, and a public code/data release. The paper is unusually transparent in reporting judge-robustness statistics (ICC(A,1)=0.780, Fleiss kappa=0.573, NMPD), user-simulator substitution results, and Pass@k/Pass^k reproducibility. These strengths make the benchmark potentially useful for diagnostic evaluation. However, the validity of the leaderboard and capability profiles depends on the correctness of LLM-generated tasks and the reliability of an LLM rubric judge, and the paper's own numbers contain an internal inconsistency that undermines the headline difficulty claim.

major comments (3)
  1. [Table 4 / §4.2 / Abstract] The headline difficulty claim is not reproducible from the paper's own results. The abstract and §4.2 state that Claude-Sonnet-5 and GPT-5.6-Sol achieve Overall Pass@1 of 58.54% and 57.14%, but Table 4's OverallPass@1 column lists 64.16 and 38.73 for these models, while 58.54 and 57.14 appear in the UserTurns column. Recomputing the exact weighted average defined in §4.1 from the route columns and the task counts (354 DAG, 200 DAG-S, 60 Solver, 30 Program) gives 54.8% for Claude-Sonnet-5 and 49.7% for GPT-5.6-Sol, using the printed Solver values 9.97 and 7.52. No reading of the table yields both headline numbers. Because the 'substantial challenge to frontier models' claim is the paper's central result, the table, abstract, and §4.2 must be reconciled and re-verified.
  2. [Appendix A.1] The judge-robustness study is transparent, but the conclusions drawn from it are stronger than the numbers support. Fleiss' kappa for item-level pass/fail decisions is 0.573 (moderate), trajectory-level ICC(A,1)=0.780, 9.3% of items are 4-vs-1 splits, and 4.4% are 3-vs-2 splits. The paper itself notes (A.1) that this agreement 'does not by itself establish the correctness of the judgments or rule out systematic biases shared by multiple judge models.' Because the leaderboard and all capability profiles are produced by this judge model, the claim that OmniaBench provides a valid or diagnostic measure of capability boundaries requires either a human-validated gold-standard sample or a downstream validity argument. At minimum, report confidence intervals for the reported scores and treat the moderate item-level agreement as a first-order caveat in the main text, not only in the appendix.
  3. [§3.3 Data Construction] Task solvability is a load-bearing assumption that is not directly verified. Environments and tasks are LLM-synthesized and then human-curated, but the paper reports no inter-annotator agreement, no count of tasks rejected or repaired during curation, and no independent human solvability audit of the final 644 tasks. If some tasks are subtly unsolvable or contain hidden contradictions, the reported pass rates measure task artifacts rather than agent capability. Please provide: (i) human-annotation statistics for the curation step; (ii) a solvability audit in which gold/reference trajectories are executed successfully in the environments; and (iii) a statement of how many tasks were removed or revised and why. This is needed to separate genuine difficulty from construction noise.
minor comments (3)
  1. [Table 4] Numeric columns run together (e.g., '57.349.97', '60.5060.68'), making the table hard to read and likely contributing to the inconsistency above. Please reformat with clear column separators and verify all entries.
  2. [Tables 3 and 9] The Program route entries for '# Items' and 'Item points' appear as '110.0' and '123.6' in the current text, probably due to missing delimiters; the intended values are presumably '1' and '1' (with Overall values '10.0' and '23.6'). Reformat and reconcile.
  3. [§4.2] The sentence 'even the frontier models ... attains' should be 'attain' for subject-verb agreement.

Circularity Check

0 steps flagged

No significant circularity: the benchmark's scores are empirical measurements, not derived quantities that reduce to their inputs.

full rationale

OmniaBench is an empirical benchmark-construction and evaluation study; there is no fitted parameter, no equation whose output is identical to an input, and no predicted quantity derived from the data it claims to measure. The headline Overall Pass@1 figures are experimental observations reported in Table 4, not outputs of a model fitted to a subset of those observations. The most plausible circularity concern is that tasks are LLM-generated (Qwen3.5-397B-A17B) and rubric scores are assigned by an LLM judge (DeepSeek-V4-Pro, also an evaluated model). The paper itself flags the limit of this design: 'this agreement does not by itself establish the correctness of the judgments or rule out systematic biases shared by multiple judge models' (Appendix A.1), and it provides a judge-substitution study (Tables 5-6) and a user-simulator robustness study (Section 4.3). These are measurement-validity threats rather than a construction-level reduction of a claimed result to its inputs; no equation in the paper forces the reported scores to equal task-generation or judging choices. The internal discrepancy between the abstract/Section 4.2 claim of 58.54/57.14 and the Table 4 'OverallPass@1' column (64.16/38.73) is a reproducibility and correctness problem, not circularity. The only self-reference, Ref. [21] (One-Eval), is not cited as load-bearing evidence for OmniaBench's central claims. Accordingly, no circular step meeting the evidentiary bar is present.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

All claims of difficulty, capability profiles, and rankings depend on a measurement loop of LLM-generated environments and tasks and LLM-based user and judge models. The paper provides valuable robustness checks but not an external gold standard; this is the core burden the reader pays for.

free parameters (5)
  • Challenging-set selection rule = Longest tool chain per L2 domain; 644 tasks selected
    Reported Overall Pass@1 is computed on this subset, not the full 1,431 tasks; the subset is deliberately hard, so headline difficulty is a function of this selection rule.
  • Task success threshold = All rubric items must pass
    A task counts as pass only if every rubric item passes; this strictness lowers pass rates and is a design choice that shapes all scores.
  • Tool-call budget and retry allowance = 200 tool-call steps, 20 environment-error retries
    Limits trajectory length; different budgets would change measured difficulty and the reported ToolSteps statistics.
  • Judge and user simulator model = DeepSeek-V4-Pro (thinking disabled)
    All reported scores depend on this judge and user simulator; robustness was tested against four other judges, but the central results are produced by this choice.
  • Temperature and reasoning settings = Temperature 0 where supported; 1.0 for Claude/Gemini; high/max reasoning effort
    Affects trajectory determinism and absolute scores, although ranking stability is claimed across user simulators.
axioms (4)
  • domain assumption LLM-generated tasks are correct, solvable, and free of semantic ambiguity after human curation.
    Task correctness is not independently verified for all 1,431 tasks; human refinement is described but not quantified.
  • domain assumption The LLM rubric judge reliably converts trajectories to binary rubric-item decisions.
    Paper's own Fleiss kappa = 0.573 and 4.4% near-even splits show imperfect agreement; shared judge biases are possible.
  • domain assumption The app-store/PRD/web-derived taxonomy is representative of real-world general-agent scenarios.
    Coverage is claimed from external sources, but no sampling completeness analysis is provided.
  • domain assumption The persona-grounded user simulator behaves like a real user for multi-turn evaluation.
    Only three user-simulator models were compared and rankings were stable, but realism is not validated against human users.

pith-pipeline@v1.3.0-alltime-deepseek · 22175 in / 11059 out tokens · 118929 ms · 2026-08-02T00:29:49.557251+00:00 · methodology

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read the original abstract

Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction. However, existing agent benchmarks often focus on limited scenarios, tool ecosystems, or interaction formats, making it difficult to systematically characterize model capabilities across heterogeneous application settings. We introduce OmniaBench, a benchmark for evaluating general agents across diverse scenarios with explicit state spaces. We derive application-oriented scenario knowledge from app stores, product documents, industry resources, Web retrieval, and human refinement, forming a hierarchical taxonomy that spans ToC, ToB and ToE with 90 level-1 and 354 level-2 domains. Based on this taxonomy, we construct executable environments and synthesize single-turn and multi-turn tasks through four complementary routes: DAG, DAG-S, Solver, and Program. OmniaBench further introduces a ten-dimensional capability taxonomy and eight compositional atomic difficulty factors to support fine-grained evaluation and analysis. The resulting dataset contains 1,431 tasks, together with a challenging subset of 644 tasks designed to reduce evaluation cost and mitigate potential contamination of the full set after public release. The bench presents substantial challenges to current frontier models, with even Claude-Sonnet-5 and GPT-5.6-Sol achieving Overall Pass@1 scores of only 58.54 and 57.14, respectively. Further analyses reveal clear differences across domains and capabilities, as well as persistent limitations in planning, constraint maintenance, and adaptive correction. OmniaBench provides a broad and diagnostic benchmark for characterizing the capability boundaries of general agents.

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

46 extracted references · 11 linked inside Pith · cited by 1 Pith paper

  1. [1]

    Vending-bench: A benchmark for long-term coherence of autonomous agents

    Axel Backlund and Lukas Petersson. Vending-bench: A benchmark for long-term coherence of autonomous agents. arXiv preprint arXiv:2502.15840, 2025

    Pith/arXiv arXiv 2025

  2. [2]

    Mcp-atlas: A large-scale benchmark for tool-use competency with real mcp servers

    Chaithanya Bandi, Razvan-Gabriel Dumitru, Ben Hertzberg, Divyansh Agarwal, Geobio Boo, Tejas Polakam, et al. Mcp-atlas: A large-scale benchmark for tool-use competency with real mcp servers. arXiv preprint arXiv:2602.00933, 2026

    Pith/arXiv arXiv 2026

  3. [3]

    Victor Barres, Honghua Dong, Soham Ray, Xujie Si, and Karthik Narasimhan.τ2-Bench: Evaluating Conversa- tional Agents in a Dual-Control Environment.arXiv preprint arXiv:2506.07982, 2025

    Pith/arXiv arXiv 2025

  4. [4]

    Workarena++: Towards compositional planning and reasoning-based common knowledge work tasks.Advances in Neural Information Processing Systems, 37:5996–6051, 2024

    Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault L De Chezelles, Quentin Cappart, et al. Workarena++: Towards compositional planning and reasoning-based common knowledge work tasks.Advances in Neural Information Processing Systems, 37:5996–6051, 2024

    2024

  5. [5]

    Acebench: Who wins the match point in tool usage?arXiv preprint arXiv:2501.12851, 2025

    Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, et al. Acebench: Who wins the match point in tool usage?arXiv preprint arXiv:2501.12851, 2025

    arXiv 2025

  6. [6]

    Training verifiers to solve math word problems, 2021.URL https://arxiv

    Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, et al. Training verifiers to solve math word problems, 2021.URL https://arxiv. org/abs/2110.14168, 9, 2021

    Pith/arXiv arXiv 2021

  7. [7]

    Deepseek-v4: Towards highly efficient million-token context intelligence, 2026

    DeepSeek-AI, Anyi Xu, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, et al. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. URLhttps://arxiv.org/abs/2606.19348

    arXiv 2026

  8. [8]

    Agent-world: Scaling real-world environment synthesis for evolving general agent intelligence.arXiv preprint arXiv:2604.18292, 2026

    Guanting Dong, Junting Lu, Junjie Huang, Wanjun Zhong, Longxiang Liu, Shijue Huang, et al. Agent-world: Scaling real-world environment synthesis for evolving general agent intelligence.arXiv preprint arXiv:2604.18292, 2026

    Pith/arXiv arXiv 2026

  9. [9]

    Gaia2: Benchmarking llm agents on dynamic and asynchronous environments.arXiv preprint arXiv:2602.11964, 2026

    Romain Froger, Pierre Andrews, Matteo Bettini, Amar Budhiraja, Ricardo Silveira Cabral, Virginie Do, et al. Gaia2: Benchmarking llm agents on dynamic and asynchronous environments.arXiv preprint arXiv:2602.11964, 2026

    arXiv 2026

  10. [10]

    Glm-5: from vibe coding to agentic engineering, 2026

    GLM-5-Team, :, Aohan Zeng, Xin Lv, Zhenyu Hou, Zhengxiao Du, et al. Glm-5: from vibe coding to agentic engineering, 2026. URLhttps://arxiv.org/abs/2602.15763

    Pith/arXiv arXiv 2026

  11. [11]

    Vitabench: Benchmarking llm agents with versatile interactive tasks in real-world applications.arXiv preprint arXiv:2509.26490, 2025

    Wei He, Yueqing Sun, Hongyan Hao, Xueyuan Hao, Zhikang Xia, Qi Gu, et al. Vitabench: Benchmarking llm agents with versatile interactive tasks in real-world applications.arXiv preprint arXiv:2509.26490, 2025

    arXiv 2025

  12. [12]

    Swe-bench: Can language models resolve real-world github issues? In International Conference on Learning Representations, volume 2024, pages 54107–54157, 2024

    Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, et al. Swe-bench: Can language models resolve real-world github issues? In International Conference on Learning Representations, volume 2024, pages 54107–54157, 2024

    2024

  13. [13]

    The tool decathlon: Bench- marking language agents for diverse, realistic, and long-horizon task execution.arXiv preprint arXiv:2510.25726, 2025

    Junlong Li, Wenshuo Zhao, Jian Zhao, Weihao Zeng, Haoze Wu, Xiaochen Wang, et al. The tool decathlon: Bench- marking language agents for diverse, realistic, and long-horizon task execution.arXiv preprint arXiv:2510.25726, 2025

    arXiv 2025

  14. [14]

    Dataflow: An llm-driven framework for unified data preparation and workflow automation in the era of data-centric ai, 2025

    Hao Liang, Xiaochen Ma, Zhou Liu, Zhen Hao Wong, Zhengyang Zhao, Zimo Meng, et al. Dataflow: An llm-driven framework for unified data preparation and workflow automation in the era of data-centric ai, 2025. URLhttps://arxiv.org/abs/2512.16676

    arXiv 2025

  15. [15]

    Toolsandbox: A stateful, conversational, interactive evaluation benchmark for llm tool use capabilities

    Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Haoping Bai, et al. Toolsandbox: A stateful, conversational, interactive evaluation benchmark for llm tool use capabilities. InFindings of the Association for Computational Linguistics: NAACL 2025, pages 1160–1183, 2025

    2025

  16. [16]

    Clawmark: A living-world benchmark for multi-turn, multi-day, multimodal coworker agents.arXiv preprint arXiv:2604.23781, 2026

    Fanqing Meng, Lingxiao Du, Zijian Wu, Guanzheng Chen, Xiangyan Liu, Jiaqi Liao, et al. Clawmark: A living-world benchmark for multi-turn, multi-day, multimodal coworker agents.arXiv preprint arXiv:2604.23781, 2026

    Pith/arXiv arXiv 2026

  17. [17]

    Gorilla: Large language model connected with massive apis.Advances in Neural Information Processing Systems, 37:126544–126565, 2024

    Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. Gorilla: Large language model connected with massive apis.Advances in Neural Information Processing Systems, 37:126544–126565, 2024

    2024

  18. [18]

    The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models

    Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, et al. The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models. InForty-second International Conference on Machine Learning, 2025. 14

    2025

  19. [19]

    Gdpval: Evaluating ai model performance on real-world economically valuable tasks.arXiv preprint arXiv:2510.04374, 2025

    Tejal Patwardhan, Rachel Dias, Elizabeth Proehl, Grace Kim, Michele Wang, Olivia Watkins, et al. Gdpval: Evaluating ai model performance on real-world economically valuable tasks.arXiv preprint arXiv:2510.04374, 2025

    Pith/arXiv arXiv 2025

  20. [20]

    QwenClawBench: Real-user-distribution benchmark for openclaw agents, April

    Qwen Team and Alibaba Data. QwenClawBench: Real-user-distribution benchmark for openclaw agents, April

  21. [21]

    One-eval: An agentic system for automated and traceable llm evaluation.arXiv preprint arXiv:2603.09821, 2026

    Chengyu Shen, Yanheng Hou, Minghui Pan, Runming He, Zhen Hao Wong, Meiyi Qiang, et al. One-eval: An agentic system for automated and traceable llm evaluation.arXiv preprint arXiv:2603.09821, 2026

    arXiv 2026

  22. [22]

    URLhttps://arxiv.org/abs/2603.04370

    Quan Shi, Alexandra Zytek, Pedram Razavi, Karthik Narasimhan, and Victor Barres.τ-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge, 2026. URLhttps://arxiv.org/abs/2603.04370

    arXiv 2026

  23. [23]

    Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, S. H. Cai, Yuan Cao, et al. Kimi k2.5: Visual agentic intelligence, 2026. URLhttps://arxiv.org/abs/2602.02276

    Pith/arXiv arXiv 2026

  24. [24]

    Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.Advances in Neural Information Processing Systems, 37:95266–95290, 2024

    Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, et al. Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.Advances in Neural Information Processing Systems, 37:95266–95290, 2024

    2024

  25. [25]

    Swe-agent: Agent-computer interfaces enable automated software engineering.Advances in Neural Information Processing Systems, 37:50528–50652, 2024

    John Yang, Carlos Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, et al. Swe-agent: Agent-computer interfaces enable automated software engineering.Advances in Neural Information Processing Systems, 37:50528–50652, 2024

    2024

  26. [26]

    Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan.τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.arXiv preprint arXiv:2406.12045, 2024

    Pith/arXiv arXiv 2024

  27. [27]

    Claw-eval: Towards trustworthy evaluation of autonomous agents.arXiv preprint arXiv:2604.06132, 2026

    Bowen Ye, Rang Li, Qibin Yang, Yuanxin Liu, Linli Yao, Hanglong Lv, et al. Claw-eval: Towards trustworthy evaluation of autonomous agents.arXiv preprint arXiv:2604.06132, 2026

    Pith/arXiv arXiv 2026

  28. [28]

    Deepplanning: Bench- marking long-horizon agentic planning with verifiable constraints.arXiv preprint arXiv:2601.18137, 2026

    Yinger Zhang, Shutong Jiang, Renhao Li, Jianhong Tu, Yang Su, Lianghao Deng, et al. Deepplanning: Bench- marking long-horizon agentic planning with verifiable constraints.arXiv preprint arXiv:2601.18137, 2026. 15 Appendix A Rubric-Based Judgment Robustness A.1 Judge Model Robustness Results Since our benchmark relies on LLM-based judges to evaluate agent ...

    arXiv 2026

  29. [30]

    The user simulator generates the initial request from the task specification and persona

  30. [31]

    The agent interacts with the user and environment through messages and tool calls

  31. [32]

    The user simulator reveals additional information or provides feedback when required

  32. [33]

    This protocol evaluates not only task execution, but also clarification, constraint tracking, adaptation to user feedback, and state maintenance across multiple turns

    Multiple rubric items assess whether all task objectives and constraints are satisfied. This protocol evaluates not only task execution, but also clarification, constraint tracking, adaptation to user feedback, and state maintenance across multiple turns. 23 D.2 Solver and DAG-S: Single-turn Rubric Evaluation Solver and DAG-S tasks provide the full user r...

  33. [34]

    The agent receives the task request and available tool interfaces

  34. [35]

    The agent gathers information and performs the required operations

  35. [36]

    4.VerifyCodechecks the trajectory and final observation and returns a binary pass/fail result

    The agent submits its final response. 4.VerifyCodechecks the trajectory and final observation and returns a binary pass/fail result. E Persona Design Personas are used in DAG tasks to create diverse and realistic user behaviors. Each persona specifies three groups of attributes: •Core identity: professional role, background, and life stage; • Interaction ...

    2024

  36. [37]

    [...additional description omitted ...]

    Task Description task excerpt Review litigation matterLF-2024-PI-024 for Yunhe Foods: confirm client, responsible attorney, and opposing party; verify that the filed incident-investigation memo remains the current linkable version; run a completeness review with emphasis on evidence-chain anomalies; then add a new evidence item linked toDOC-003, and confi...

    2024

  37. [38]

    [...additional tools omitted ...]

    Tools tool summary Matter lookup by number; client and attorney retrieval; full matter workspace lookup; document and version retrieval; matter-integrity validation; evidence-subgraph and neighbor traversal; evidence insertion; matter-event lookup; activity-timeline retrieval. [...additional tools omitted ...]

  38. [39]

    Your goal is to complete the user’s request in an interactive environment by gradually calling the available tools step by step, and to proactively communicate with the user

    Agent Setup prompt excerpts T ested agent“You are a helpful assistant. Your goal is to complete the user’s request in an interactive environment by gradually calling the available tools step by step, and to proactively communicate with the user ... User agent “You are role-playing as a user interacting with an agent. Your persona is written inside the<per...

  39. [40]

    Demonstration message excerpt User Initial request Please review LF-2024-PI-024: first confirm the client, responsible attorney, and opposing party; then check whether the archived incident-investigation memorandum is still the current reliable version for linking; run a completeness review of the case file with special attention to the evidence chain; if...

    2024

  40. [41]

    <Judge reason> The trajectory correctly resolvesLF-2024-PI-024, confirms Yunhe Foods / Lin Qiaoxue / He Shan, verifiesVER-004 as current, and flags the self-referentialLNK-003link

    Rubrics and Judgment selected rubric excerpts Overall pass@1: Pass (✓) Rubric: 20/20 Steps: 21 Rubric T1 3/3 pts <Criterion> Perform an integrity review for the matter and detect the evidence-chain problem, specifically the self- ... <Judge reason> The trajectory correctly resolvesLF-2024-PI-024, confirms Yunhe Foods / Lin Qiaoxue / He Shan, verifiesVER-0...

    2024

  41. [42]

    store surveillance screenshots and incident timeline explanation

    Task Description expanded task excerpt Please help verify the Yunhe Foods personal-injury defense matterLF-2024-PI-024. The request proceeds in stages: first confirm the client, responsible attorney, and opposing party; then check whether the archived accident-investigation memorandum remains the current usable filed version; then run a completeness revie...

    2024

  42. [43]

    Tools expanded tool summary get_matter_by_number, get_client_by_id, get_attorney_by_id, get_matter_full_context, get_document_by_id, list_document_- versions, validate_matter_integrity, get_evidence_chain_subgraph, get_evidence_chain_neighbors, get_evidence_by_id, add_- evidence, list_matter_events, and get_matter_activity_timeline. These tools jointly ex...

  43. [44]

    Agent Setup prompt excerpts T ested agent “You are a helpful assistant. Your goal is to complete the user’s request in an interactive environment by gradually calling the available tools step by step, and to proactively communicate with the user when necessary until the user ends the conversation. At each step, you will receive one of two types of informa...

  44. [45]

    Demonstration expanded message excerpt User Initial request Please review LF-2024-PI-024: confirm the client, responsible attorney, and opposing party; verify whether the archived incident- investigation memorandum is still the current filed version that can be relied upon for linking; run a completeness review of the full matter with special attention to...

    2024

  45. [46]

    Rubrics and Judgment expanded rubric excerpts Overall pass@1: Pass (✓) Rubric: 20/20 Steps: 21 Rubric G4 / T1 5/5 pts <Criterion> Check the current archival/document-version status for the incident-investigation memorandum, and perform an in- tegrity review that detects the evidence-chain problem, specif- ically the self-referential link anomaly. <Judge r...

    2024

  46. [2026]

    URLgithub.com/SKYLENAGE-AI/QwenClawBench