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REVIEW 2 major objections 2 minor 60 cited by

Even top language agents succeed on under half of tasks that require ongoing user interaction and strict policy adherence.

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 · grok-4.3

2026-05-11 03:14 UTC

load-bearing objection τ-bench adds a combined test for agents that talk to users, use tools, and follow policies, with state matching for scoring and a pass^k consistency metric, but the LM-simulated users lack any human validation. the 2 major comments →

arxiv 2406.12045 v1 submitted 2024-06-17 cs.AI cs.CL

τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Shunyu Yao , Noah Shinn , Pedram Razavi , Karthik Narasimhan This is my paper
classification cs.AI cs.CL
keywords language agentsbenchmarkstool useuser interactionpolicy followingreliabilityfunction callingmulti-turn evaluation
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 introduces τ-bench to test language agents in realistic multi-turn conversations where a simulated user pursues a goal through an agent equipped with domain-specific tools and rules. Evaluation checks whether the final database state matches an annotated target state rather than inspecting individual actions. Experiments with current function-calling models, including GPT-4o, produce success rates below 50 percent and pass^8 scores under 25 percent in the retail domain. These outcomes indicate that agents still lack the consistency and rule-following needed for reliable deployment.

Core claim

τ-bench emulates conversations between a language-model-simulated user and an agent supplied with API tools and policy guidelines in retail and airline domains. The benchmark scores agents by comparing the ending database state to a goal state and introduces the pass^k metric to measure reliability across repeated trials. Results show that even state-of-the-art agents succeed on fewer than half the tasks and remain inconsistent, pointing to the requirement for new methods that improve consistent rule adherence.

What carries the argument

τ-bench, which runs dynamic user-agent conversations and judges success by final database-state match to an annotated goal, together with the pass^k metric that counts the fraction of k independent trials an agent completes correctly.

Load-bearing premise

Language-model users generate conversations and goals that match real human users, and the chosen domains plus policies represent actual deployment settings.

What would settle it

Replacing the simulated users with real humans on the same tasks and observing success rates above 50 percent or pass^8 scores above 25 percent in retail would show whether the reported performance gap depends on the simulation.

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

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

2 major / 2 minor

Summary. The paper introduces τ-bench, a benchmark for tool-using language agents that interact dynamically with simulated users in real-world domains (retail and airline). Agents receive domain-specific API tools and policy guidelines; users are simulated by LMs. Success is measured by comparing the final database state against independently annotated goal states rather than conversation content, and a new pass^k metric quantifies reliability across repeated trials. Experiments report that even GPT-4o succeeds on fewer than 50% of tasks and achieves pass^8 below 25% in retail, indicating that current agents struggle with consistency and rule adherence.

Significance. If the LM-simulated users produce behavior distributionally close to real humans under the same policies, the benchmark would be a valuable addition to the field by filling gaps in existing evaluations that ignore user interaction and domain rules. The state-comparison evaluation protocol and pass^k metric are sound, parameter-free ideas that enable reproducible assessment without subjective judgment of dialogue quality. The work explicitly credits the need for methods that improve agent consistency.

major comments (2)
  1. [§4] §4 (User Simulation): The user prompts and task selection process are described, but no human baseline, inter-annotator agreement with humans, or ablation replacing the LM user with a scripted oracle is reported. This is load-bearing for interpreting the headline result (GPT-4o <50% success, pass^8 <25% in retail) as evidence of real-world limitations rather than simulation artifacts.
  2. [§5.2] §5.2 (Experiments, retail domain): The reported pass^8 <25% relies on the assumption that the 8 trials capture the full distribution of agent behavior; without variance estimates or justification for k=8, the inconsistency claim is difficult to interpret quantitatively.
minor comments (2)
  1. [Abstract] The abstract states that tasks are 'annotated' but does not specify who performs the annotation or how goal states are constructed; this detail belongs in §3 or §4.
  2. [Figure 1] Figure 1 (overview) would benefit from an explicit legend distinguishing the agent, user simulator, and database components.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and note planned revisions where appropriate.

read point-by-point responses

  1. Referee: [§4] §4 (User Simulation): The user prompts and task selection process are described, but no human baseline, inter-annotator agreement with humans, or ablation replacing the LM user with a scripted oracle is reported. This is load-bearing for interpreting the headline result (GPT-4o <50% success, pass^8 <25% in retail) as evidence of real-world limitations rather than simulation artifacts.

    Authors: We agree that the lack of direct human validation for the LM-simulated users limits how strongly the results can be interpreted as evidence of real-world agent limitations. The use of LM users was chosen to create a scalable, reproducible testbed with fixed policy guidelines that real humans would be unlikely to follow consistently across trials. The evaluation protocol relies on objective final database state comparison rather than subjective dialogue quality to reduce some sources of simulation bias. In the revised manuscript we will expand Section 4 with an explicit limitations paragraph acknowledging the absence of human baselines or inter-annotator agreement and will add an ablation study that replaces the LM user with a simple rule-based scripted oracle on a subset of tasks to verify policy adherence. revision: partial

  2. Referee: [§5.2] §5.2 (Experiments, retail domain): The reported pass^8 <25% relies on the assumption that the 8 trials capture the full distribution of agent behavior; without variance estimates or justification for k=8, the inconsistency claim is difficult to interpret quantitatively.

    Authors: We selected k=8 to demonstrate the reliability drop while remaining computationally tractable, since each trial consists of a full multi-turn conversation. To strengthen the quantitative claim, the revised version will report variance or standard deviation of success rates across the eight trials and will include pass^k values for a range of k (e.g., 1–16) so readers can observe the trend. These additions will make the inconsistency results easier to interpret without altering the headline numbers. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark success is measured by direct state comparison to independent annotations

full rationale

The paper defines task success via explicit comparison of final database state against pre-annotated goal states, with no fitted parameters, self-referential definitions, or predictions that reduce to inputs by construction. The pass^k metric is a simple empirical reliability count over repeated trials. No load-bearing self-citations or uniqueness theorems are invoked. The LM-simulated user design is an explicit modeling choice whose fidelity to humans is an external validity question, not a circularity in the reported numbers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces no new mathematical parameters, axioms, or postulated entities; its contribution is an empirical benchmark whose validity rests on the domain assumption that LM-simulated users are adequate proxies.

axioms (1)
  • domain assumption Language models can be prompted to simulate realistic user behavior and goals in domain-specific conversations
    The entire dynamic-interaction component of the benchmark depends on this premise.

pith-pipeline@v0.9.0 · 5466 in / 1296 out tokens · 41765 ms · 2026-05-11T03:14:30.124913+00:00 · methodology

0 comments
read the original abstract

Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $\tau$-bench, a benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines. We employ an efficient and faithful evaluation process that compares the database state at the end of a conversation with the annotated goal state. We also propose a new metric (pass^k) to evaluate the reliability of agent behavior over multiple trials. Our experiments show that even state-of-the-art function calling agents (like gpt-4o) succeed on <50% of the tasks, and are quite inconsistent (pass^8 <25% in retail). Our findings point to the need for methods that can improve the ability of agents to act consistently and follow rules reliably.

discussion (0)

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Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages · cited by 187 Pith papers · 10 internal anchors

  1. [1]

    M. Ahn, A. Brohan, N. Brown, Y . Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakr- ishnan, K. Hausman, et al. Do as I can, not as I say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691, 2022. URL https://arxiv.org/abs/2204.01691

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  2. [2]

    Andreas, J

    J. Andreas, J. Bufe, D. Burkett, C. Chen, J. Clausman, J. Crawford, K. Crim, J. DeLoach, L. Dorner, J. Eisner, et al. Task-oriented dialogue as dataflow synthesis. Transactions of the Association for Computational Linguistics , 8:556–571, 2020

    work page 2020

  3. [3]

    Multiwoz–a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling.arXiv preprint arXiv:1810.00278, 2018

    P. Budzianowski, T.-H. Wen, B.-H. Tseng, I. Casanueva, S. Ultes, O. Ramadan, and M. Gaši´c. Multiwoz–a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling. arXiv preprint arXiv:1810.00278, 2018

    work page arXiv 2018

  4. [4]

    D. Chen, H. Chen, Y . Yang, A. Lin, and Z. Yu. Action-based conversations dataset: A corpus for building more in-depth task-oriented dialogue systems. arXiv preprint arXiv:2104.00783, 2021

    work page arXiv 2021

  5. [5]

    M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y . Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Her...

    work page 2021

  6. [6]

    S. Chen, S. Wiseman, and B. Dhingra. Chatshop: Interactive information seeking with language agents. arXiv preprint arXiv:2404.09911, 2024

    work page arXiv 2024

  7. [7]

    eun Yoon, Z

    S. eun Yoon, Z. He, J. M. Echterhoff, and J. McAuley. Evaluating large language models as generative user simulators for conversational recommendation, 2024

    work page 2024

  8. [8]

    I. Gür, D. Hakkani-Tür, G. Tür, and P. Shah. User modeling for task oriented dialogues. In 2018 IEEE Spoken Language Technology Workshop (SLT) , pages 900–906, 2018. doi: 10.1109/SLT.2018.8639652

    work page doi:10.1109/slt.2018.8639652 2018

  9. [9]

    H. He, D. Chen, A. Balakrishnan, and P. Liang. Decoupling strategy and generation in negotiation dialogues. arXiv preprint arXiv:1808.09637, 2018

    work page Pith review arXiv 2018

  10. [10]

    Z. Hu, Y . Feng, A. T. Luu, B. Hooi, and A. Lipani. Unlocking the potential of user feedback: Leveraging large language model as user simulators to enhance dialogue system. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management , CIKM ’23. ACM, Oct. 2023. doi: 10.1145/3583780.3615220. URL http://dx.doi.org/10.1145/ ...

    work page doi:10.1145/3583780.3615220 2023

  11. [11]

    arXiv preprint arXiv:2310.03128 , year=

    Y . Huang, J. Shi, Y . Li, C. Fan, S. Wu, Q. Zhang, Y . Liu, P. Zhou, Y . Wan, N. Z. Gong, et al. Metatool benchmark for large language models: Deciding whether to use tools and which to use. arXiv preprint arXiv:2310.03128, 2023

    work page arXiv 2023

  12. [12]

    C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan. Swe-bench: Can language models resolve real-world github issues? arXiv preprint arXiv:2310.06770, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  13. [13]

    M. Kim, Y . Jung, D. Lee, and S.-w. Hwang. Plm-based world models for text-based games. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages 1324–1341, 2022

    work page 2022

  14. [14]

    X. Liu, H. Yu, H. Zhang, Y . Xu, X. Lei, H. Lai, Y . Gu, H. Ding, K. Men, K. Yang, et al. Agentbench: Evaluating llms as agents. arXiv preprint arXiv:2308.03688, 2023

    work page internal anchor Pith review arXiv 2023

  15. [15]

    J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein. Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304.03442, 2023

    work page internal anchor Pith review arXiv 2023

  16. [16]

    Y . Ruan, H. Dong, A. Wang, S. Pitis, Y . Zhou, J. Ba, Y . Dubois, C. J. Maddison, and T. Hashimoto. Identifying the risks of lm agents with an lm-emulated sandbox. arXiv preprint arXiv:2309.15817, 2023

    work page internal anchor Pith review arXiv 2023

  17. [17]

    Schatzmann, D

    J. Schatzmann, D. Jurafsky, M. Galley, and D. Trevillian. Evaluating agenda-based user simulation for reinforcement learning of dialogue management. In Speech Communication, volume 47, pages 95–121, 2007

    work page 2007

  18. [18]

    Toolformer: Language Models Can Teach Themselves to Use Tools

    T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  19. [19]

    Shinn, B

    N. Shinn, B. Labash, and A. Gopinath. Reflexion: an autonomous agent with dynamic memory and self-reflection, 2023

    work page 2023

  20. [20]

    T. R. Sumers, S. Yao, K. Narasimhan, and T. L. Griffiths. Cognitive architectures for language agents. arXiv preprint arXiv:2309.02427, 2023

    work page internal anchor Pith review arXiv 2023

  21. [21]

    Q. Wu, G. Bansal, J. Zhang, Y . Wu, S. Zhang, E. Zhu, B. Li, L. Jiang, X. Zhang, and C. Wang. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  22. [22]

    Q. Xu, F. Hong, B. Li, C. Hu, Z. Chen, and J. Zhang. On the tool manipulation capability of open-source large language models, 2023. 10

    work page 2023

  23. [23]

    F. Yan, H. Mao, C. C.-J. Ji, T. Zhang, S. G. Patil, I. Stoica, and J. E. Gonzalez. Berkeley function calling leaderboard. https://gorilla.cs.berkeley.edu/blogs/8_berkeley_ function_calling_leaderboard.html, 2024

    work page 2024

  24. [24]

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y . Cao. ReAct: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  25. [25]

    S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y . Cao, and K. Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, 2023

    work page 2023

  26. [26]

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y . Cao. React: Synergizing reasoning and acting in language models, 2023

    work page 2023

  27. [27]

    S. Yao, H. Chen, J. Yang, and K. Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents. In ArXiv, volume 35, pages 20744–20757, preprint

    work page

  28. [28]

    Usimagent: Large language models for simulating search users

    E. Zhang, X. Wang, P. Gong, Y . Lin, and J. Mao. Usimagent: Large language models for simulating search users. arXiv preprint arXiv:2403.09142, 2024

    work page arXiv 2024

  29. [29]

    S. Zhou, F. F. Xu, H. Zhu, X. Zhou, R. Lo, A. Sridhar, X. Cheng, Y . Bisk, D. Fried, U. Alon, et al. WebArena: A Realistic Web Environment for Building Autonomous Agents. arXiv preprint arXiv:2307.13854, 2023. 11 Checklist

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  30. [30]

    For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope? [Yes] (b) Did you describe the limitations of your work? [Yes] (c) Did you discuss any potential negative societal impacts of your work? [Yes] (d) Have you read the ethics review guidelines and ensured that your paper con...

    work page

  31. [31]

    (a) Did you state the full set of assumptions of all theoretical results? [N/A] (b) Did you include complete proofs of all theoretical results? [N/A]

    If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results? [N/A] (b) Did you include complete proofs of all theoretical results? [N/A]

    work page

  32. [32]

    for benchmarks)

    If you ran experiments (e.g. for benchmarks)... (a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] (c) Did you report error bars (e.g.,...

    work page

  33. [33]

    If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators? [Yes] (b) Did you mention the license of the assets? [Yes] (c) Did you include any new assets either in the supplemental material or as a URL? [Yes] (d) Did you discuss whether and how consent wa...

    work page

  34. [34]

    products

    If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wa...

    work page 2024

  35. [35]

    **Item ID: 7706410293** - Switch Type: Clicky - Backlight: None - Size: Full Size - Price: $269.16

    work page

  36. [36]

    order_id

    **Item ID: 6342039236** - Switch Type: Clicky - Backlight: White - Size: Full Size - Price: $244.91 Unfortunately, there are no full-size models with clicky switches and RGB backlighting currently available. Would you like to proceed with one of the available options, or would you prefer to wait until the desired model is back in stock? 27 user: Understan...

    work page

  37. [37]

    order_id

    **Item ID: 7747408585** - Compatibility: Google Assistant - Color: Black - Price: $249.01 Unfortunately, this is the only available option that matches your requirement. Would you like to proceed with this exchange? user: That item fits the particular requirement I had in mind. Yes, let 's proceed with the exchange for the thermostat. assistant: Here are ...

    work page

  38. [38]

    **Item ID:** 7661609223 - Capacity: 1000ml - Material: Stainless Steel - Color: Black - Price: $46.51

    work page

  39. [39]

    **Item ID:** 2439754078 - Capacity: 1000ml - Material: Stainless Steel - Color: Red - Price: $49.51 ### Desk Lamp Options:

    work page

  40. [40]

    **Item ID:** 5320792178 - Color: Black - Brightness: Medium - Power Source: AC Adapter - Price: $135.24

    work page

  41. [41]

    order_id

    **Item ID:** 1569765161 - Color: Silver - Brightness: Low - Power Source: AC Adapter - Price: $143.02 Please confirm which items you would like to exchange for, and provide the payment method you would like to use for any price difference. user: I would like to exchange the desk lamp for the one with item ID 5320792178. As for the water bottle, I need som...

    work page

  42. [42]

    order_id

    Order ID: #W4082615 Please let me know which order you would like me to check the address for. user: I can 't remember exactly which one, but it 's the jigsaw that I ordered recently. Could we look into that one, please? assistant: Function(arguments= '{"order_id": "#W9583042"} ', name= 'get_order_details') 37 tool: {"order_id": "#W9583042", "user_id": "m...

    work page 2000

  43. [43]

    **Item ID: 1096508426** - **Theme:** Art - **Difficulty Level:** Beginner - **Price:** $46.13

    work page

  44. [44]

    order_id

    **Item ID: 9237024510** - **Theme:** Animals - **Difficulty Level:** Expert - **Price:** $53.53 Would you like to exchange your current jigsaw puzzle for one of these? If so, please confirm which one and provide a payment method to handle any price difference. user: Could we go with the Art themed one, Item ID: 1096508426? I generally use PayPal for my pu...

    work page 1990

  45. [45]

    Trip type: One way or round trip?

    work page

  46. [46]

    Departure date (and return date if it 's a round trip)?

    work page

  47. [47]

    Cabin class: Basic economy, economy, or business? 46

    work page

  48. [48]

    Number of passengers and their details (first name, last name, date of birth)?

    work page

  49. [49]

    Do you want to purchase travel insurance for this trip (30 dollars per passenger)? Once I have this information, I can proceed with searching for available flights. user: 1. It 's a one-way trip

    work page

  50. [50]

    I want to depart on May 20

    work page

  51. [51]

    I 'll be flying in economy class

    work page

  52. [52]

    It 's just me, the details should be in my profile

    work page

  53. [53]

    user_id":

    No, I do not want to purchase travel insurance. assistant: Function(arguments= '{"user_id":"mia_li_3668"}', name= 'get_user_details') tool: {"name": {"first_name": "Mia", "last_name": "Li"}, "address": {"address1": "975 Sunset Drive", "address2": "Suite 217", "city": "Austin", "country": "USA", "province": "TX", "zip": "78750"}, "email": "mia.li3818@examp...

    work page 1990

  54. [54]

    **Flight HAT069** - Departure: 06:00 AM EST - Arrival: 12:00 PM EST - Available Seats: 12 in Economy - Price: $121

    work page

  55. [55]

    origin":

    **Flight HAT083** - Departure: 01:00 AM EST - Arrival: 07:00 AM EST - Available Seats: 7 in Economy - Price: $100 Please confirm which flight you would like to book. 47 user: Both of the flights are scheduled too early for me. If possible, I would like a flight that does not depart before 11am EST. assistant: Function(arguments='{"origin":"JFK","destinati...

    work page 2024

  56. [56]

    **Flight Option 1** - **First Leg:** - Flight Number: HAT136 - Departure: 07:00 PM EST from JFK - Arrival: 09:30 PM EST at ATL - Available Seats: 14 in Economy - Price: $152 - **Second Leg:** - Flight Number: HAT039 - Departure: 10:00 PM EST from ATL - Arrival: 03:00 AM EST (+1 day) at SEA - Available Seats: 10 in Economy - Price: $103

    work page

  57. [57]

    user_id":

    **Flight Option 2** 48 - **First Leg:** - Flight Number: HAT218 - Departure: 06:00 PM EST from JFK - Arrival: 08:30 PM EST at ATL - Available Seats: 1 in Economy - Price: $158 - **Second Leg:** - Flight Number: HAT039 - Departure: 10:00 PM EST from ATL - Arrival: 03:00 AM EST (+1 day) at SEA - Available Seats: 10 in Economy - Price: $103 Please confirm wh...

    work page 1990