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REVIEW 2 major objections 1 minor 53 references

Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions

T0 review · 2 major / 1 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read A new benchmark shows no LLM exceeds 40 percent success rate when users give ambiguous or shifting instructions for tool use.

desk verdict RUT-Bench shows LLMs under 40% success on non-ideal tool calls but rests on unvalidated simulations of real user behavior. read the letter →

arxiv 2606.03318 v2 pith:AHCVTNHJ submitted 2026-06-02 cs.CL

Xuan Yang , Hao Xu , Tingfeng Hui , Hongsheng Xin , Kaike Zhang , Chunxiao Liu , Ning Miao This is my paper
classification cs.CL
keywords LLMevaluationtoolcallingrealisticuserinteractionsbenchmarknon-idealinputsmulti-turndialoguesperformancedropsimulation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper introduces RUT-Bench to test large language models on tool-calling tasks that include realistic complications such as unclear requests, uncooperative replies, and changing goals, rather than assuming perfectly clear and cooperative users. It evaluates 19 open-source and proprietary models across single-turn and multi-turn dialogues that mix ideal and non-ideal user patterns. Results indicate overall success stays below 40 percent for every model, with nearly all showing clear drops on the harder non-ideal cases. A sympathetic reader would care because prior benchmarks rely on idealized assumptions and therefore give an inflated picture of how well current models will perform in actual deployments where users behave like ordinary people.

What carries the argument

RUT-Bench, a benchmark that generates high-fidelity simulations of real-world user tool-calling scenarios covering ideal rational patterns together with heterogeneous non-ideal behaviors such as ambiguity and shifting intentions.

What would settle it

A controlled study in which actual human users interact with the same LLMs on the same tool-calling tasks and produce success rates above 40 percent would falsify the claim that the benchmark reveals a genuine limitation.

Watch

Extended reading notes

Core claim

RUT-Bench supports high-fidelity simulations of both ideal rational user patterns and heterogeneous non-ideal behaviors across single-turn and multi-turn dialogues; when 19 widely used LLMs are tested on this benchmark, none reaches an overall success rate above 40 percent and nearly all exhibit noticeable performance drops when confronted with more complicated non-ideal user inputs.

Load-bearing premise

The high-fidelity simulations of ideal and non-ideal user behaviors in RUT-Bench accurately represent the ambiguity, uncooperative actions, and changing intentions that occur with real users.

Editorial extensions

If this is right

  • Existing evaluation methods that rely only on idealized user assumptions overestimate LLM tool-use performance in practice.
  • LLMs require additional capabilities to maintain performance when user inputs become ambiguous or intentions shift across turns.
  • Tool-calling deployments should expect substantial failure rates until models improve on non-ideal inputs.
  • Benchmark design must incorporate multi-turn dialogues with heterogeneous user behaviors to reflect real usage.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Training pipelines that expose models only to clean instructions may need to be supplemented with examples of messy or contradictory user requests.
  • The observed performance gap suggests that real-world tool-calling agents will require fallback mechanisms or human review loops until success rates rise.
  • Extending the benchmark to other domains such as data analysis or code generation could reveal similar limitations in handling unclear user intent.
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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 / 1 minor

Summary. The paper introduces RUT-Bench, a benchmark for assessing LLMs on tool-calling tasks under realistic user conditions. It supports high-fidelity simulations of both ideal rational patterns and heterogeneous non-ideal behaviors (ambiguity, uncooperative actions, intention shifts) in single- and multi-turn dialogues. Comprehensive evaluations are reported on 19 open-source and proprietary LLMs, with the central empirical finding that no model exceeds an overall success rate of 40% and that nearly all exhibit performance drops on non-ideal inputs. Code and data are released.

Significance. If the RUT-Bench simulations prove to be faithful proxies for real user behavior, the results would usefully document the gap between current LLM tool-use capabilities and realistic deployment conditions, providing a concrete target for future work. The public release of the benchmark supports reproducibility and follow-on studies.

major comments (2)
  1. [Abstract] Abstract: the headline claim that 'no tested LLMs achieve an overall success rate above 40%' and that performance drops occur on non-ideal inputs rests entirely on the unvalidated assertion of 'high-fidelity simulations'; no quantitative comparison to real user logs, human ecological-validity judgments, or distributional statistics is described, rendering the measured gaps uninterpretable as evidence about real-world conditions.
  2. [Experimental setup] The description of RUT-Bench (implied experimental setup): the manuscript states that the benchmark covers 'heterogeneous non-ideal behaviors' yet supplies no details on simulation construction, parameter choices for intention drift or ambiguity, or any error analysis, which are load-bearing for the cross-model comparison.
minor comments (1)
  1. [Abstract] The abstract would be clearer if it briefly contrasted RUT-Bench with prior tool-use benchmarks (e.g., ToolBench, API-Bank) on the dimension of non-ideal user modeling.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater transparency on simulation validation and construction details. We address each major comment below and will make corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim that 'no tested LLMs achieve an overall success rate above 40%' and that performance drops occur on non-ideal inputs rests entirely on the unvalidated assertion of 'high-fidelity simulations'; no quantitative comparison to real user logs, human ecological-validity judgments, or distributional statistics is described, rendering the measured gaps uninterpretable as evidence about real-world conditions.

    Authors: We agree that the manuscript provides no quantitative validation of simulation fidelity against real user logs or human judgments. The non-ideal behaviors were derived from patterns described in prior tool-use literature rather than direct empirical matching. In revision we will (1) expand the benchmark construction section with the qualitative sources and generation process used, (2) add an explicit limitations subsection discussing the lack of ecological-validity metrics and the consequent interpretive caveats, and (3) qualify the abstract wording to avoid overstating fidelity. These changes will make the claims more precise without altering the reported experimental results. revision: yes

  2. Referee: [Experimental setup] The description of RUT-Bench (implied experimental setup): the manuscript states that the benchmark covers 'heterogeneous non-ideal behaviors' yet supplies no details on simulation construction, parameter choices for intention drift or ambiguity, or any error analysis, which are load-bearing for the cross-model comparison.

    Authors: The observation is accurate; the current text does not supply the requested implementation details. We will revise the experimental-setup section to include: the full parameterization of ambiguity, uncooperative actions, and intention-shift mechanisms; the specific probability distributions or ranges employed; and any post-generation error analysis of the simulated dialogues. This added material will directly support reproducibility and interpretation of the 19-model comparison. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; benchmark evaluation is self-contained

full rationale

The paper proposes RUT-Bench and reports direct empirical success rates from LLM evaluations on its simulated dialogues. No equations, fitted parameters, predictions derived from subsets, or self-citation chains appear in the provided text. The central claims are measurements on a newly introduced benchmark rather than reductions of outputs to inputs by construction. Per rules, this is the normal non-finding for an independent benchmark paper; lack of external validation against real logs is a validity concern, not circularity.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the domain assumption that the simulated scenarios match real user behavior; no free parameters or invented entities are described in the abstract.

assumptions (1)
  • domain assumption Simulated non-ideal user behaviors accurately capture real-world ambiguity, uncooperative actions, and shifting intentions
    Invoked to justify the benchmark's relevance to real-world tool-calling evaluation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions." pith.science (2026). https://pith.science/paper/AHCVTNHJ

@misc{pith2026260603318,
  author       = {Pith},
  title        = {Pith review of: Beyond Ideal Instruction: A Comprehensive Framework for Evaluating LLMs in Realistic Interactions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AHCVTNHJ}},
  note         = {Machine review of arXiv:2606.03318}
}
read the original abstract

Despite great advances in tool-use capabilities of large language models (LLMs), existing evaluation benchmarks struggle to fully align with real-world scenarios. Such benchmarks mostly rely on simulated idealized user assumptions and lacks experience-oriented evaluation. These limitations fail to account for the ambiguity, uncooperative behaviors, and shifting intentions characteristic of real-world users. To fill this gap, we propose RUT-Bench, a dedicated benchmark designed to assess LLMs under diverse Real-world User Tool calling scenarios. RUT-Bench supports high-fidelity simulations covering both ideal rational patterns and heterogeneous non-ideal behaviors across single-turn and multi-turn dialogues. We conduct comprehensive evaluations on 19 widely adopted open-source and proprietary LLMs using our benchmark. Experimental results reveal that no tested LLMs achieve an overall success rate above 40%, and nearly all of them experience noticeable performance drops when facing more complicated non-ideal user inputs. Our code and data is available at https://github.com/Miaow-Lab/RUT-Bench.

Figures

Figures reproduced from arXiv: 2606.03318 by the authors.

Figure 1
Figure 1. Taxonomy and representative examples of the seven user behaviors in RUT-Bench. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The overall construction pipeline of RUT-Bench. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Success Rate, Informational Honesty, and Tool Discipline of the 19 evaluated models on RUT-Bench. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (18 more)
Figure 4
Figure 4. Figure 4: Failure analysis on representative models [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: caption. ( [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Prompt for user behavior annotation 13 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Prompt templates for query collection 14 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Prompt template for inferring environment specification [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Prompt templates for inferring entity attributes [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Prompt templates for inferring tool specification [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Prompt templates for inferring python code of entity attributes [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]
Figure 12
Figure 12. Figure 12: Prompt templates for inferring python code for tools [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]
Figure 13
Figure 13. Figure 13: System prompt for state initialization 20 [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: System prompt for task generation 21 [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: System prompt for Ideal user dialogue generation [PITH_FULL_IMAGE:figures/full_fig_p022_15.png]
Figure 16
Figure 16. Figure 16: System prompt for unstable user dialogue generation [PITH_FULL_IMAGE:figures/full_fig_p023_16.png]
Figure 17
Figure 17. Figure 17: System prompt for operation-calling LLM 24 [PITH_FULL_IMAGE:figures/full_fig_p024_17.png]
Figure 18
Figure 18. Figure 18: System prompt for white-box evaluator LLM [PITH_FULL_IMAGE:figures/full_fig_p025_18.png]
Figure 19
Figure 19. Figure 19: System prompt for user dialogue verification [PITH_FULL_IMAGE:figures/full_fig_p026_19.png]
Figure 20
Figure 20. Figure 20: System prompt for reliability judgment 27 [PITH_FULL_IMAGE:figures/full_fig_p027_20.png]
Figure 21
Figure 21. Figure 21: System prompt for error analyses 28 [PITH_FULL_IMAGE:figures/full_fig_p028_21.png]

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Reference graph

Works this paper leans on

53 extracted references · 1 canonical work pages

  1. [1]

    is_unstable

    Simulating user diversity in task-oriented dia- logue systems using large language models.Preprint, arXiv:2502.12813. Anthropic. 2026. Claude opus 4.6. https://www. anthropic.com/news/claude-opus-4-6 . Ac- cessed: 2026-05-24. J. L. Austin. 1962.How to Do Things with Words. Harvard University Press. Victor Barres, Honghua Dong, Soham Ray, Xujie Si, and Kar...

    work page arXiv 2026

  2. [2]

    Persistent Environment — The query is about a domain where: - There is a live, ongoing state that can be read or changed - The environment supports both: a) Information queries about current state (read operations) b) Explicit state-changing actions (create, update, delete, move, cancel, etc.)

  3. [3]

    State Dependency — The task cannot be answered correctly without: - Inspecting the actual current data or configuration in the environment, and/or - Executing an operation that modifies that data

  4. [4]

    Domain Specificity — The environment is not general-purpose knowledge; it is a structured system such as: - File management system with stored files/folders - Order/logistics tracking system - Calendar/scheduling system - CRM, inventory, ticketing, project management tools - Other specialized platforms with records that persist over time

  5. [5]

    Is invoice 1024 paid?

    Actionability in Context — The query must correspond to an actionable operation or status check **within the actual environment** (not hypothetical). Eligible Task Types - State queries: "Is invoice 1024 paid?" / "What meetings are scheduled for Wednesday?" - State modification operations: "Upload the proposal.pdf to the project folder" / "Cancel order 45...

  6. [6]

    - Note any relevant entities, constraints, relationships, or dynamics implied by the task

    Analysis - Explain the reasoning process used to connect the task to the chosen environment. - Note any relevant entities, constraints, relationships, or dynamics implied by the task

  7. [7]

    - Examples: Linux filesystem, E-commerce order management system, Airline booking system

    Environment Summary - Provide a concise label for the environment type. - Examples: Linux filesystem, E-commerce order management system, Airline booking system

  8. [8]

    - Focus on its inherent structure, the nature of the state it maintains, typical operations it supports, and its general scope in real-world usage

    Environment Introduction - Introduce the environment itself, without referring to the current task. - Focus on its inherent structure, the nature of the state it maintains, typical operations it supports, and its general scope in real-world usage. - Limit to approximately three sentences

Show all 53 references
  1. [9]

    all possible data in an e-commerce system

    Metrics - Usefulness: 1-10 Reflects how broadly applicable and valuable this environment is in real-world scenarios. Higher scores indicate environments relevant to many contexts and industries. - Modelability: 1-10 Indicates how straightforward it would be to represent this e...

  2. [10]

    - Parameters needed

    In Analysis, reason about: - What entities/attributes are involved. - Parameters needed. - Expected outputs (queries return structured results, state modifications return success messages). - Error/edge cases (e.g., invalid input, permission denied). - Does it involve environm...

  3. [11]

    success": False,

    In Code, implement the Python method: - Method name: ‘def <operation_name>(self, ...)‘. Note: Cannot be an independent function, but rather a method function within an already implemented environment class. - Add clear type hints. - Add docstring describing inputs, outputs, co...

  4. [12]

    Use the exact top-level state container names provided by the user

  5. [13]

    Populate realistic, internally consistent, cross-referenced entities

  6. [14]

    Make the state rich enough for multi-step tool use

  7. [15]

    Use only fictional data

  8. [16]

    State profile definitions: Each generated state must match one of the following profiles, as specified in the user message

    Output JSON only. State profile definitions: Each generated state must match one of the following profiles, as specified in the user message. - sparse: A minimal but functional state. Populate at least two collections, but keep most collections lean (1–2 entries each). Cross-r...

  9. [17]

    Use only entities that actually exist in the provided init_config

  10. [18]

    Prefer tasks with at least one objective state change on structured fields

  11. [19]

    The user-facing request must not mention internal IDs, tool names, code details, or hidden state keys

  12. [20]

    The tool plan must be directly executable from the provided initial state, and every tool step must return success during replay

  13. [21]

    A rare 0-tool plan is allowed only when task_type is clarify_then_execute or refuse_or_scope

    Standard execute tasks should use between 1 and 6 tool calls. A rare 0-tool plan is allowed only when task_type is clarify_then_execute or refuse_or_scope

  14. [22]

    Set agent_action_budget to an integer no larger than 50

  15. [23]

    If a later step needs a newly created entity, explicitly choose its ID in the creation step so the plan stays deterministic

  16. [24]

    Prefer realistic tasks that require discovery before mutation, but prefer concrete verification tools over high-level helper tools when the state hints already expose the relevant candidate IDs

  17. [25]

    Avoid plans that depend on unavailable equipment types, nonexistent future slots, or helper tools whose success is uncertain from the current state

  18. [26]

    If dialogue_mode is multi_turn, provide 2-3 coherent turn-level tasks

  19. [27]

    Prefer execute unless the prompt explicitly calls for a clarification-heavy or refusal-heavy task

    Set task_type to one of execute, clarify_then_execute, or refuse_or_scope. Prefer execute unless the prompt explicitly calls for a clarification-heavy or refusal-heavy task

  20. [28]

    If task_type is clarify_then_execute or refuse_or_scope, include the necessary clarification_requirements or refusal_requirements and any forbidden_actions

  21. [29]

    If the prompt gives an explicit coverage target for this attempt, satisfy it exactly

  22. [30]

    goal_summary

    Output JSON only. Return an object with this schema: { "goal_summary": "short benchmark goal", "user_request": "natural user-facing request without IDs", "dialogue_mode": "single_turn or multi_turn", "difficulty_bucket": "easy or medium or hard", "task_type": "execute or clari...

  23. [31]

    Preserve the exact task semantics and do not invent a different task

  24. [32]

    Keep the user cooperative, realistic, concise, and grounded in the environment context

  25. [33]

    Do not mention tool names, internal IDs, hidden schema keys, implementation details, or oracle information

  26. [34]

    If dialogue_mode is single_turn, output exactly one user turn

  27. [35]

    If dialogue_mode is multi_turn, output 2-3 user turns that stay in the same task context

  28. [36]

    Assign every gold-trace tool step to exactly one user turn using contiguous step groups

  29. [37]

    Cover all tool steps exactly once, in increasing order

  30. [38]

    The final assistant response should be a short user-facing completion message consistent with the expected outcome

  31. [39]

    user_turns

    Output JSON only. Return an object with this schema: { "user_turns": [ { "turn_id": 1, "user_message": "natural collaborative user message", "task_summary": "short turn summary", "expected_outcome": "expected turn-level outcome", "linked_tool_step_indices": [1, 2] } ], "assist...

  32. [40]

    Preserve the exact underlying task for the same task_id

  33. [41]

    Keep the same number of user turns and the same turn ordering

  34. [42]

    Do not change the task_summary, expected_outcome, linked tool-step alignment, tool plan, or final target state

    Rewrite only the user-facing messages. Do not change the task_summary, expected_outcome, linked tool-step alignment, tool plan, or final target state

  35. [43]

    Inject only the requested six-taxonomy unstable behavior bundle as controlled friction on top of the stable baseline

  36. [44]

    Do not mention tool names, internal IDs, hidden schema keys, or implementation details

  37. [45]

    Any noisy side remark must still leave the original task recoverable directly or through clarification

    Do not replace the original task with a new mandatory task. Any noisy side remark must still leave the original task recoverable directly or through clarification

  38. [46]

    Do not leak the oracle trace, backend schema, hidden state, or tool implementation details

  39. [47]

    rewritten_user_turns

    Output JSON only. Return an object with this schema: { "rewritten_user_turns": [ { "turn_id": 1, "user_message": "unstable rewrite of the baseline turn" } ] } Unstable behavior taxonomy: The following six behaviors define the full set of friction types used in this benchmark. ...

  40. [48]

    - Sub-goals must be logically sequenced: each turn’s sub-goal should build on prior turns and not duplicate work already implied

    Intent & Sub-goal Integrity (INTENT) - The underlying intent of each user turn must strictly align with the blueprint goal. - Sub-goals must be logically sequenced: each turn’s sub-goal should build on prior turns and not duplicate work already implied. - The union of all sub-...

  41. [49]

    - The agent must never be forced to guess a parameter whose value has not been exposed anywhere in the conversation so far

    Agent Inferability (INFER) - Every goal, sub-goal, and parameter that the agent needs to invoke a tool must be fully recoverable from either the current user turn or the immediately preceding dialogue context. - The agent must never be forced to guess a parameter whose value h...

  42. [50]

    INTENT, LEAK

    Information Leakage (LEAK) - The dialogue must not mention any internal tool names, backend identifiers (e.g., entity IDs that were not stated in the original user request), or schema-level implementation details. - Only identifiers or tool-related terms that are explicitly vi...

  43. [51]

    Faithfulness & Multi-turn Stability (0-1): grounded in the given context, consistent across turns, avoids inventing unsupported facts

  44. [52]

    Clarification Proactivity (0-1): asks for clarification when needed, does not ask unnecessary clarification when the request is already actionable

  45. [53]

    faithfulness_score

    Tool Discipline / Non-Blind Decision (0-1): avoids blind decisions, avoids repetitive or unnecessary tool usage, and avoids unsupported commitments. Important: - If the task type or requirements indicate that clarification or refusal is appropriate, do not penalize the agent f...

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Reviewed June 28, 2026 · model on record in the stance chip above.