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This paper claims that proactive, learnable clarification before tool invocation is the missing key to reliable 3D execution, demonstrated by CLARE, a clarification-gated multi-agent framework that reaches 60.40% and 43.34% task success on

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2026-08-01 22:33 UTC pith:XQAICYCP

load-bearing objection Useful clarify-before-execute framework and a new 3D underspecification benchmark, but the self-evolution gains are undermined by an undocumented train/eval overlap and a scripted simulator used on both sides. the 3 major comments →

arxiv 2607.16352 v1 pith:XQAICYCP submitted 2026-07-17 cs.CV cs.AIcs.CLcs.GRcs.MA

Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration

Xiaoye Zhu , Weixin Li , Junan Huo , Bozhong Wang , Jia Zeng , Yi Yang , Cen Chen , Qi Liu This is my paper
classification cs.CV cs.AIcs.CLcs.GRcs.MA
keywords Proactive Clarification3D Tool OrchestrationSelf-Evolving AgentsIntent AsymmetryMulti-turn Reward3D-Clarify BenchmarkUnderspecified InstructionsMulti-agent LLM
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.

Ordinary users describe 3D assets vaguely—'reconstruct this' or 'make something'—while 3D toolchains demand exact file paths, formats, and parameters. The paper argues that the missing capability in current 3D agents is not better generation but proactive clarification: asking targeted questions before spending compute. To prove this, the authors build CLARE, a multi-agent system whose 'approver' gates execution until user intent is complete, and train its clarification policy through simulated multi-turn interaction, optimizing a Multi-turn Reward that balances task success against interaction cost. On a new benchmark, 3D-Clarify, which injects ambiguity, missing information, and mistaken details into 620 tasks across five 3D domains, CLARE reaches 60.40% single-step and 43.34% multi-step success, more than doubling existing baselines. The central claim is that structured, learnable clarification is what makes robust 3D execution possible.

Core claim

CLARE establishes that underspecification in 3D instructions—ambiguity, missing inputs, and mistaken details—should be intercepted and resolved through dialogue before any tool is invoked. The system decouples the pipeline into four roles: a Proactive Approver that detects incomplete intent, a Contextual Summarizer that tracks state across turns, a 3D-Aware Programmer that maps clarified constraints to concrete tools, and a Feedback Provider that turns rejections and logs into user-facing questions. The clarification policy is not hand-coded; it is evolved by simulating a 'lazy user' who withholds information across stages, rolling out candidate responses, and optimizing a Multi-turn Reward

What carries the argument

The central object is the Proactive Approver, a decision gate that outputs Execute or Clarify before any 3D tool runs, paired with the Multi-turn Reward MR(τ)=V(Exec,G*)−λ·K used to evolve the gate's policy through SFT and DPO on simulated interaction trajectories. This gate is what converts underspecified requests into clarified state; the ablation shows that removing it drops single-step success from 48.60% to 27.67%, confirming it is the load-bearing component.

Load-bearing premise

The Simulated User's staged withholding behavior—vague at first, revealing details only when asked, and dragging conversations to 4-6 turns—faithfully captures how real humans give 3D instructions, and the same simulator is used for both training and evaluating the clarification policy.

What would settle it

Replace the staged simulated user with a held-out set of human users who are given the same vague, missing, or mistaken requests but are not instructed to withhold information on a fixed schedule, and measure CLARE's success rate on the 3D-Clarify tasks. If the 60.40% and 43.34% success rates drop substantially, the learned policy is tied to the simulator's staged behavior rather than to a general clarification skill.

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

If this is right

  • Clarification-gated execution substantially outperforms single-turn and feedback-after-failure agents on underspecified 3D tasks: 60.40% versus 24.40% single-step success for the best adapter baseline.
  • The when-to-ask decision is learnable from simulated interaction: multi-turn reward optimization adds roughly 12 points of success over the base agent with less than one additional interaction turn on single-step tasks.
  • Removing the gating module severely degrades performance, validating that pre-execution detection of ambiguity, missing information, and mistaken details is the foundation of the system's effectiveness.
  • A structured summarizer becomes critical in multi-step workflows: removing it drops multi-step success from 27.22% to 20.28%, while its impact on single-step tasks is negligible.
  • The 3D-Clarify benchmark offers a standardized, tool-agnostic testbed of 620 scenarios across five domains and three underspecification types for future clarification-aware 3D agents.

Where Pith is reading between the lines

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

  • If the learned clarification policy transfers to real users, the same clarify-before-execute architecture could reduce wasted compute and failure in other specification-heavy agentic pipelines, such as code generation for robotics, CAD, or data analysis, where single-turn assumptions also break.
  • The paper's reliance on a simulated user with fixed staged behavior means the reward landscape is defined by that simulator; a testable extension is to vary user behavior (e.g., users who volunteer details early, or who resist follow-up questions) and measure whether the learned policy remains near-optimal.
  • Failure analysis shows model-selection errors persist even after clarification, suggesting a natural next step: applying the same self-evolution loop to train the programmer and summarizer, not just the gate.
  • Since DPO excels on ambiguity and missing-info tasks while SFT wins on mistaken-detail tasks, a hybrid policy that selects a clarification strategy by underspecification type is a concrete, testable improvement.

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 / 5 minor

Summary. The paper proposes CLARE, a multi-agent framework for 3D tool orchestration that interposes a clarification stage before invoking 3D tools. Four roles (Proactive Approver, Contextual Summarizer, 3D-Aware Programmer, Feedback Provider) are coordinated to detect ambiguity, missing information, and mistaken details, and to resolve them through multi-turn dialogue. The Proactive Approver is self-evolved by SFT/DPO using a Multi-turn Reward computed from simulated interactions with a scripted 'lazy' user, with the goal of trading off task success against interaction length. The authors also introduce 3D-Clarify, a benchmark of 620 tasks (500 single-step, 120 multi-step) with three injected underspecification types. On this benchmark, CLARE reports 60.40% and 43.34% task success on single-step and multi-step tasks respectively, more than doubling the strongest baseline on single-step tasks. The paper includes detailed prompts, tool knowledge bases, ablation studies, and a failure-mode analysis.

Significance. If the results hold, the contribution is significant. The paper identifies a real problem—underspecified instructions in 3D tool use—and proposes a concrete architectural response (clarification gating plus structured state summarization) that is well motivated and supported by ablations. The benchmark itself is a useful asset: the systematic injection of ambiguity, missing information, and mistaken details across five 3D domains provides a reproducible testbed. The self-evolution procedure based on Multi-turn Reward is also interesting, and the paper is transparent about prompts and training details. The main claims, however, rest on two assumptions that are not yet verified: that the self-evolution trajectories are disjoint from the evaluation tasks, and that the scripted simulated user is representative enough to support generalization claims. The reported numbers are internally consistent and the framework is described in sufficient detail to be reproducible, but the evaluation currently does not rule out train/evaluation leakage or overfitting to the simulator.

major comments (3)
  1. [§4.3.1, §6.1, §5] The manuscript does not document any split between the roughly 3,000 self-evolution trajectories and the 620-task evaluation benchmark. §4.3.1 says only that a context x=(q_j,H_<j) is sampled; §6.1 describes fine-tuning on 'a de-duplicated subset of 3,000 multi-turn trajectories'; §5 presents the 620 scenarios as the evaluation benchmark with no mention of a held-out split. If the self-evolution rollouts were generated from the same 500/120 tasks (or their three perturbations) used in Table 2, the fine-tuned Proactive Approver has seen near-test examples, and the reported SR gains may reflect distribution memorization rather than a generalizable clarify-vs-execute policy. This is load-bearing for the central claim that CLARE reaches 60.40%/43.34% SR through self-evolution. Please specify explicitly whether training contexts are disjoint from evaluation contexts; if they are not, the eval
  2. [§4.3.2 Eq. (2), §6.3, Appendix B.1] The Multi-turn Reward used for training is computed with the same verifier V that is used to report CR/SR in Table 2, and the same Simulated User U is used both to generate training rollouts and to evaluate all methods. This does not make the tool execution itself fake, but it creates a closed loop: the policy is optimized specifically against the evaluation user's scripted behavioral stages (withhold information, answer only when asked, drag to 4–6 turns) and the evaluation metric is the training reward. The claim that the agent 'internalizes the delicate balance between interaction efficiency and task completion' (abstract, §4.3) is therefore not yet established beyond the particular simulator/metric pair. I would like to see at least one of: (i) evaluation with a different user model or a human study, (ii) a sensitivity analysis varying the simulated-user behavior, or (iii) a clear ar
  3. [§4.3.2, Table 2] The comparison against non-clarifying baselines is strongly influenced by the simulated user's instructions in Appendix B.1, which explicitly require the user to withhold information and to 'drag the conversation to at least 4-6 turns.' This guarantees that clarification is necessary and that single-turn baselines are structurally disadvantaged. While the comparison is fair in that all interactive methods face the same user, the absolute magnitude of the reported gains—more than doubling the baseline—should be interpreted as an upper bound for this particular adversarial user model. Please report results with a less restrictive user model (for instance, a user who volunteers missing parameters after a single clarification request) to demonstrate that the learned policy is not merely overfitting to the staged-reveal protocol.
minor comments (5)
  1. [Appendix A.4.2] The text contains the placeholder 'Section [Insert Main Section Reference]' in the self-evolution training details. Please replace it with the actual cross-reference.
  2. [Table 2] The table has formatting issues, particularly in the multi-step Few-shot row where some cell values appear concatenated (e.g., '0.0089.0441.67'). Please ensure clean column alignment and consistent decimal formatting.
  3. [§6.1 / Figure 7 caption] The paper says '620 interaction scenarios' in the abstract and §5, and '500 single-step and 120 multi-step tasks, each associated with three underspecified queries' in §6.1. It would help to clarify explicitly how the 620 number relates to the 500+120 tasks and the three perturbation types, and whether CR/SR in Table 2 are computed per task or per perturbed query.
  4. [Figure 8 / §6.3] The failure-mode analysis in Figure 8 uses a 'stuck' error category, but 'stuck' is not defined in §6.3 where the verification criteria are listed. Please define the failure categories and how they are automatically or manually assigned.
  5. [Eq. (4)] The similarity function for text parameters uses 'LLM-Sim' but no details are given about which LLM is used, what prompt is used, or how the similarity score is computed. Please provide the details in the appendix.

Circularity Check

1 steps flagged

Self-evolution SR gains are reported on the same verifier used to compute the training reward, with no documented held-out split from the benchmark.

specific steps
  1. fitted input called prediction [Eq. (2)–(4); §4.3.2–4.3.3; §6.3; Table 2]
    "MR(τ)=V(Exec(T 3D,θ_exec),G∗)−λ·K(2) ... V=I(Type_pred=Type_gt)·[1/|Θ| Σ φ(θ_i,θ∗_i)](3) ... For each task, we verify: (1)Module Name: correctness of the target module identification; (2)Prompt & Parameters: consistency of text-to-3D prompts validated by LLM judgment, along with other generation parameters; (3)Input File Path: existence and validity of referenced input assets; (4)Output File Extension: correctness of naming conventions (e.g., .glb, .obj)."

    DPO/SFT rank trajectories by MR (§4.3.3: 'leverage the automated metrics from our simulation loop to curate training signals'), and MR's fidelity term V is the exact function used to compute the reported CR/SR in §6.3: module name (task type) plus parameter accuracy (prompt, path, extension), with only a small −λK turn penalty added. Thus the CLARE-SE rows in Table 2 report the training objective itself; selecting trajectories with high MR will, by construction, raise the headline SR. This is not an independent test of the clarification policy. Compounding this, no split between the 3,000 self-evolution trajectories and the 620 benchmark scenarios is specified, so the SE gains are not shown to be out-of-sample predictions.

full rationale

The paper's base architectural claim is not circular: CLARE-base (without self-evolution) beats the strongest adapter by 48.60% vs 24.40% SR on the same benchmark, so the clarify-before-execute architecture has independent content. However, the headline self-evolution numbers (60.40%/43.34%) are partially circular in a specific, quotable way: the Multi-turn Reward used to filter SFT data and build DPO pairs is defined by the same verifier V (Eq. 2–4) that §6.3 uses to compute CR/SR. Optimizing a policy with this reward and then reporting SR as evidence of improvement is effectively reporting the training objective. The concern is compounded by the absence of any documented split between the 3,000 self-evolution trajectories and the 620-scenario 3D-Clarify benchmark, and by the use of the same Qwen3-8B simulated user for both training rollouts and evaluation; the paper's Limitations section only concedes real-world diversity, not these possible overlaps. No self-citation or uniqueness-importation chain was found. Score 6 reflects a partially circular self-evolution result while acknowledging that the base framework and benchmark comparison retain independent empirical content.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 1 invented entities

The central empirical claims rest on four things the reader did not pay for upstream: (i) a scripted Simulated User stands in for real users and is used on both training and evaluation; (ii) the automated verifier treats parameter/tool exactness as task success; (iii) the three template perturbations are taken to cover realistic intent asymmetry; and (iv) self-evolution trajectories are implicitly treated as not leaking into evaluation. None of these is demonstrated with independent evidence in the paper; the Limitations section partially concedes (i).

free parameters (4)
  • Multi-turn Reward turn penalty λ = 0.1
    Eq (2): balances task fidelity against number of turns. Chosen by hand, directly shapes what SFT/DPO learn as the optimal clarification policy.
  • DPO KL penalty β = 0.1
    Appendix A.4.2: standard DPO regularization constant, but it controls how far the fine-tuned Approver can deviate from Qwen3-8B and therefore affects the learned clarification behavior.
  • Maximum interaction turns T_max = 8
    Limits all training rollouts and evaluation episodes; interacts with the λ·K penalty and caps the clarification budget.
  • Forward-sampling candidate count N = 8
    Appendix A.4.1: samples N=8 Approver responses per context; affects coverage and diversity of the self-evolution trajectories.
axioms (5)
  • ad hoc to paper Simulated User behavior is representative of real users
    Appendix B.1 scripts the user to withhold information, answer only when explicitly asked, and drag interactions to 4-6 turns. No human validation is provided; Limitations concedes real-world diversity is not fully captured.
  • domain assumption Automated verifier V (Eq 3) is a valid measure of 3D task success
    SR/CR are computed from exact tool/module matching, exact parameter equality, and LLM text similarity. Visual or geometric quality of the generated 3D asset is never inspected.
  • domain assumption Template-based perturbation categories span realistic intent asymmetry
    §5.3/B.4 generate ambiguity, missing info, and mistaken details through templates and injected contradictions rather than collected real-user queries.
  • domain assumption Self-evolution trajectories are disjoint from evaluation tasks
    §6.1/§6.8 never state a train/test split. Up to 3,000 trajectories are used for training while the scaling analysis is evaluated on the same 120 multi-step tasks.
  • domain assumption The Model Selection Knowledge Base correctly maps constraints to tools
    A.2.3 hand-authors mappings from 'high quality'/'fast' to specific models. Failure case C.4 shows this mapping can be semantically wrong (Westminster Abbey routed to triplaneturbo rather than worldgen).
invented entities (1)
  • Simulated User U (scripted Qwen3-8B 'lazy user') no independent evidence
    purpose: Provides the multi-turn interaction environment for both self-evolution training and benchmark evaluation; embodies the information-asymmetry assumption.
    No comparison against human behavior is provided. Its staged reveal policy directly shapes what CLARE learns, so it is a postulated stand-in for real users rather than an independently validated model.

pith-pipeline@v1.3.0-alltime-deepseek · 36423 in / 15052 out tokens · 127335 ms · 2026-08-01T22:33:12.586521+00:00 · methodology

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read the original abstract

A fundamental intent asymmetry plagues modern 3D asset creation: while state-of-the-art 3D toolchains demand precise, executable parameters, ordinary users typically provide vague, underspecified instructions. Current 3D agents treat this ambiguity as noise, defaulting to blind execution under a single-turn assumption. To address this limitation, we introduce CLARE, a clarification-aware and evolutionary 3D agent that treats intent asymmetry not as an execution error, but as an opportunity for strategic dialogue. By decoupling the generation pipeline into four specialized cognitive roles, CLARE intercepts and resolves underspecified instructions before invoking computationally expensive 3D tools to seamlessly execute tasks across five diverse domains: text-to-3D generation, single-view reconstruction, multi-view reconstruction, point cloud editing, and post-processing. Crucially, rather than relying on rigid manual rules, CLARE self-evolves its clarification policy via simulated multi-turn interactions. By optimizing a Multi-turn Reward, the agent internalizes the delicate balance between interaction efficiency and task completion. To rigorously test this, we construct 3D-Clarify, a comprehensive benchmark comprising 620 interaction scenarios with systematically injected ambiguity, missing information, and mistaken details. CLARE achieves state-of-the-art performance, with 60.40% and 43.34% success rates on single-step and multi-step tasks, respectively, more than doubling existing baselines. Both quantitative and qualitative results demonstrate that proactive clarification is the missing key to robust 3D execution. Code is available at https://github.com/xyzhu1225/CLARE.

Figures

Figures reproduced from arXiv: 2607.16352 by Bozhong Wang, Cen Chen, Jia Zeng, Junan Huo, Qi Liu, Weixin Li, Xiaoye Zhu, Yi Yang.

Figure 1
Figure 1. Figure 1: Clare’s framework and self-evolving pipeline. (a-b) Comparison: Traditional agents execute blindly vs. Clare proactively clarifies before execution. (c) Architecture: Modular components for approval, summarization, feedback, and 3D-aware programming. (d) Self-Evolving Pipeline: From sampling to fine-tuning. Abstract A fundamental intent asymmetry plagues modern 3D asset creation: while state-of-the-art 3D … view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of Clare. Four specialized components collaborate to transform underspecified user queries into executable 3D workflows: Approver (gating), Summarizer (state tracking), Programmer (code synthesis), and Feedback Provider (conversational alignment). The system proactively clarifies underspecified requirements before execution. is not only syntactically correct but physically and semantically gro… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the 3D-Clarify benchmark. Each scenario starts with a fixed simulated user prompt, then presents an [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of 3D task execution under underspecified user requirements. While traditional baselines [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of average interaction turns and suc [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Effect of training data scale on average performance [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comprehensive overview of detailed single-turn atomic task scenarios across diverse 3D domains and sub-domains, [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of failure reasons across three underspecified requirement types on [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗

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    Receive the conversation history and the user's current request.↩→

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    Combine all history messages and current request into one complete demand.↩→

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    Carefully read and interpret the user's request with all history information.↩→ 4.Judge whether the user's input (combined with all history messages) already provides enough information to generate a concrete task. ↩→ ↩→ - If YES, you must output in the following XML structure: <judgement> Yes </judgement> - If NO, you must output in the following XML str...

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    ↩→ ↩→ - The user mention but did not specify value for each parameter.↩→

    Missing information - The user described an input source (image/video/point cloud) but did not provide the actual input file path or output path. ↩→ ↩→ - The user mention but did not specify value for each parameter.↩→

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    ↩→ ↩→ ↩→

    Task objective unclear - The user did not specify the intended operation (e.g., generation / post processing / reconstruction / editing) or desired output format, making the task ambiguous. ↩→ ↩→ ↩→

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    Mistaken details - The user provided contradictory details that cannot be executed. (e.g. Mistaken input or output formats / incompatible parameters / conflicting properties) ↩→ ↩→ Zhu et al. - The user provided an input file path that does not match the described task objective. (e.g. image path for point cloud processing tasks) ↩→ ↩→

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    remove outliers

    User wants to clarify or confirm the task details # Rules: A task is considered concrete if it includes: - A clear objective (can be classified into **one of or the combination of** the following 4 categories):↩→ * Single-View / Multi-View 3D Reconstruction (Single view from image / Multi view from images or video)↩→ * Text to 3D Generation (e.g., object ...

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    Combine **all** history messages and current request into complete demand even if some tasks have been completed in previous steps. ↩→ ↩→

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    high quality

    Summarize all the demand using the following XML structure:↩→ <task_type> [Summarized and structured task name, e.g. 3D Reconstruction (Multi-View), Point Cloud Editing, Post Processing (mesh to point cloud) etc.] </task_type> ↩→ ↩→ ↩→ <task_summary> [Concise summary of the task after decomposition into steps] </task_summary>↩→ <file_path> [user-provided ...

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    Receive a concrete task (already validated by Approver)

  52. [55]

    Translate the task into correct Python code that calls the Executor class functions.↩→

  53. [56]

    /tmp/{{slug}}.{{ext}}

    Ensure the generated code is executable without extra modification.↩→ # Rules: - Always match the correct function to the user's task type (see function descriptions below).↩→ - Always fill in all required arguments. - Optional arguments (parameters, output_path) should be explicitly set only if specified in the task; otherwise, use default values shown b...

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    Receive the conversation history and the user's original request.↩→ -`history`: A list of alternating user and assistant messages.↩→

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    success" (by programmer) /

    Receive the latest information about the current task state - it could be a structured message that includes:↩→ -`status`: "success" (by programmer) / "failed" (by programmer) / "rejected" (by approver) / "accepted" (by approver) ↩→ ↩→ -`details`: Detailed information such as task summary, executable code, execution log, error reason, or evaluation comment. ↩→ ↩→

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    You must respond with: ↩→ ↩→ ↩→ <feedback> Your summarized feedback to **the user**

    Analyze this information carefully and provide **clear, concise, and constructive feedback** to the user to help them understand the outcome and next step. You must respond with: ↩→ ↩→ ↩→ <feedback> Your summarized feedback to **the user**. - If success: summarize the result, mention the **input details (input file path and parameters)** and where to find...

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    Always communicate in a **helpful, professional, and encouraging tone**.↩→

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    If the output is successful, provide brief validation and next-step suggestions.↩→

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    If it failed or was rejected, summarize the issue and propose or clarify what the user wants **specific, actionable improvements**. ↩→ ↩→

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    Do not include system or internal reasoning in your output - only user-facing feedback.↩→

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    Avoid repeating raw logs; extract meaning from them for the user.↩→

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    chosen" and

    Keep the feedback concise but informative (ideally 1 - 3 sentences).↩→ A.3 Tools We provide a comprehensive suite of 3D processing tools to support diverse agent workflows. Each tool is integrated into the system with specific input/output constraints and functional roles as de- tailed below: Trellis.[29] • Core Capability:High-quality 3D generation and r...

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    - If the task requires a file path and a format, ONLY provide the file path initially.↩→ - Use layman terms

    **Early Stage (Turns 1-2)**: - Be vague and lazy. - If the task requires a file path and a format, ONLY provide the file path initially.↩→ - Use layman terms. - Do NOT reveal the full detailed requirement yet

  64. [67]

    If the Agent asks for multiple items (e.g., parameters AND path), answer ALL of them in this turn

    **Middle Stage (Turns 3-4)**: - Provide information only when explicitly asked. If the Agent asks for multiple items (e.g., parameters AND path), answer ALL of them in this turn. ↩→ ↩→ - You can verify information, but do not volunteer extra details that were NOT asked for.↩→ - Occasionally simulate a small misunderstanding or clarify a detail.↩→

  65. [68]

    - Only say "Quit" when the Agent has confirmed ALL parts of your detailed requirement.↩→

    **Final Stage (Turns 5+)**: - Now you can be precise to ensure the task is completed. - Only say "Quit" when the Agent has confirmed ALL parts of your detailed requirement.↩→

  66. [69]

    If not, continue the interaction

    **Always**: - Check carefully whether the Agent's feedback confirms it has accepted your detailed requirement (input file path, output path, format, and any parameters). If not, continue the interaction. ↩→ ↩→ ↩→ - Check one by one whether the paths and parameters appearing in the feedback are completely consistent with the detailed requirement. ↩→ ↩→ # C...

  67. [70]

    **Output ONLY the user response** inside <user> tags

  68. [71]

    This is a failure.↩→

    **NEVER** copy-paste the full <detailed_requirement> in one turn. This is a failure.↩→

  69. [72]

    **Goal**: Drag the conversation to at least 4-6 turns if possible by withholding information naturally.↩→

  70. [73]

    **Consistency**: Eventually, you MUST provide all the info in <detailed_requirement>, but do it slowly.↩→

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    End with <user> Quit </user> ONLY when the Agent has explicitly confirmed it has accepted ALL parts of your <detailed_requirement>. ↩→ ↩→

  72. [75]

    Keep responses short and natural, like a real human

  73. [76]

    If asked, say use default settings

    Do not add any information that was not provided to you in the detailed task during the conversation. If asked, say use default settings. ↩→ ↩→

  74. [77]

    Any information you provide must appear in the Detailed Requirement.↩→

  75. [78]

    Do not create additional requirements beyond the Detailed Requirement.↩→

  76. [79]

    Do not use keywords that involve other tasks, such as 'clean it up'when editing point clouds, as this can confuse with outlier removal. ↩→ ↩→

  77. [80]

    Avoid repeating yourself

  78. [81]

    Lazy User

    Directly provide necessary information instead of asking for confirm if the feedback does not provide enough information. ↩→ ↩→ # OUTPUT FORMAT <user> [Your response here | Quit] </user> B.2 Detailed Task Scenarios As shown in Figure 7, we design detailed task scenarios which are systematically categorized into five primary domains, each with correspondin...

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    /tmp/{{slug}}.{{ext}}

    Ensure the generated code is executable without extra modification.↩→ # Rules: - Always match the correct function to the user's task type (see function descriptions below).↩→ - Always fill in all required arguments. - Optional arguments (parameters, output_path) should be explicitly set only if specified in the task; otherwise, use default values shown b...

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    /tmp/{{slug}}.{{ext}}

    Ensure the generated code is executable without extra modification.↩→ # Rules: - Always match the correct function to the user's task type (see function descriptions below).↩→ - Always fill in all required arguments. - Optional arguments (parameters, output_path) should be explicitly set only if specified in the task; otherwise, use default values shown b...

Showing first 80 references.