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arxiv: 2606.05622 · v1 · pith:F5UPDFXWnew · submitted 2026-06-04 · 💻 cs.CL

AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints

Jiayu Liu , Cheng Qian , Zhenhailong Wang , Bingxuan Li , Jiateng Liu , Heng Wang , Jeonghwan Kim , Yumeng Wang
show 3 more authors
Xiusi Chen Yi R. Fung Heng Ji
This is my paper

Pith reviewed 2026-06-28 01:39 UTC · model grok-4.3

classification 💻 cs.CL
keywords adaptive planningLLM agentsinteractive benchmarkworld constraintsuser constraintsmulti-turn protocolplanning evaluationhousehold tasks
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The pith

AdaPlanBench shows that LLM agents achieve at most 67.75% accuracy when adapting to progressively revealed world and user constraints.

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

The paper introduces AdaPlanBench to evaluate how well LLM agents adaptively plan and re-plan when world and user constraints are not fully known in advance but revealed over multiple turns of interaction. It builds the benchmark on 307 household tasks using a scalable pipeline to add dual constraints, then applies a protocol where agents receive feedback only after proposing a violating plan and must revise accordingly. Experiments across ten leading LLMs find that this form of planning stays difficult, with the strongest model reaching 67.75% accuracy and results worsening as constraints accumulate, especially user constraints. Failures commonly trace to weaker physical grounding. A sympathetic reader would care because real planning tasks routinely involve constraints that surface gradually during execution.

Core claim

AdaPlanBench demonstrates that adaptive planning under dual constraints remains challenging for LLM agents, with the best of ten leading models reaching only 67.75% accuracy, performance degrading as more constraints accumulate, and user constraints proving especially difficult due to weaker physical grounding and reduced re-planning effectiveness.

What carries the argument

AdaPlanBench's multi-turn revelation protocol, which discloses hidden constraints only when an agent's proposed plan violates them and requires iterative revision under accumulating feedback.

If this is right

  • Accuracy falls as the number of accumulated constraints rises during interaction.
  • User constraints create a larger performance gap than world constraints.
  • Many errors originate from insufficient physical grounding in the models.
  • The benchmark functions as a testbed for evaluating dual-constrained interactive planning.

Where Pith is reading between the lines

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

  • Explicit constraint-tracking modules added to agents might reduce the observed accuracy drop.
  • Applying the same revelation protocol outside household tasks could expose domain-specific adaptation limits.
  • Running the benchmark with human participants would test whether simulated feedback matches real constraint disclosure.
  • The degradation pattern suggests language-only planning may require hybrid systems that maintain explicit constraint lists.

Load-bearing premise

The constraint construction pipeline and multi-turn revelation protocol accurately model real-world progressively disclosed world and user constraints without introducing artificial biases in difficulty or feedback quality.

What would settle it

An experiment in which the same tasks are solved at near-perfect accuracy once all constraints are supplied upfront instead of revealed progressively through violation feedback.

Figures

Figures reproduced from arXiv: 2606.05622 by Bingxuan Li, Cheng Qian, Heng Ji, Heng Wang, Jeonghwan Kim, Jiateng Liu, Jiayu Liu, Xiusi Chen, Yi R. Fung, Yumeng Wang, Zhenhailong Wang.

Figure 1
Figure 1. Figure 1: Overview of AdaPlanBench. Top: data construction, where dual constraints are constructed for each query. Middle: runtime interaction, where the agent proposes plans, receives feedback on violated constraints, and re-plans iteratively. Bottom: an example trajectory showing how hidden constraints are progressively disclosed during interaction. As shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Model performance under increasing constraint burden. Performance drops [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Selected model rubric scores across interaction turns under [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Model performance under Emid with additional constraint tracking module. Ex￾plicitly providing prior disclosed constraints brings only limited improvement on accuracy. trend suggests that models struggle to maintain coherent and constraint-consistent planning once they must continuously incorporate newly revealed requirements into an existing plan. The degradation is substantially milder for stronger model… view at source ↗
Figure 5
Figure 5. Figure 5: Model performance under Emid with rubric-based refinement. Additional feedback yields only modest recovery and often destabilizes planning. Qwen3-14B Llama-3.3-70B-Instruct Gemini-3-Flash GPT-5-Mini 0.00 0.25 0.50 0.75 Accuracy Qwen3-14B Llama-3.3-70B-Instruct Gemini-3-Flash GPT-5-Mini 0.8 0.9 1.0 Valid Plan Rate Constraint Setting World Only User Only Both [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Model performance under [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of GPT-5’s physical grounding failure. 39 [PITH_FULL_IMAGE:figures/full_fig_p039_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Example of GPT-5’s effectiveness failure. 40 [PITH_FULL_IMAGE:figures/full_fig_p040_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example of Gemini-3.1-Pro’s physical grounding failure. 41 [PITH_FULL_IMAGE:figures/full_fig_p041_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example of Gemini-3.1-Pro’s effectiveness failure. 42 [PITH_FULL_IMAGE:figures/full_fig_p042_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: A data instance in AdaPlanBench with constructed environment profile [PITH_FULL_IMAGE:figures/full_fig_p043_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: A data instance in AdaPlanBench with constructed environment profile [PITH_FULL_IMAGE:figures/full_fig_p044_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: A data instance in AdaPlanBench with constructed environment profile [PITH_FULL_IMAGE:figures/full_fig_p045_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The query filtering prompt used to filter out unwanted queries in the data [PITH_FULL_IMAGE:figures/full_fig_p047_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Prompt for generating user feedback on plan violations. The figure illustrates the [PITH_FULL_IMAGE:figures/full_fig_p048_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Prompt used for world-constraint violation judgment. The figure illustrates the [PITH_FULL_IMAGE:figures/full_fig_p049_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Prompt used for user-constraint violation judgment. The figure illustrates the [PITH_FULL_IMAGE:figures/full_fig_p050_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Runtime prompt template. Placeholders enclosed in “ [PITH_FULL_IMAGE:figures/full_fig_p051_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Judge consistency by rubric. Lower values indicate higher agreement among [PITH_FULL_IMAGE:figures/full_fig_p051_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Human and LLM-judge alignment on Feasibility and Physical Plausibility. [PITH_FULL_IMAGE:figures/full_fig_p052_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Human and LLM-judge alignment on Logical Step Ordering and Effectiveness. [PITH_FULL_IMAGE:figures/full_fig_p052_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Human and LLM-judge alignment on Concreteness and Safety. [PITH_FULL_IMAGE:figures/full_fig_p052_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Human and LLM-judge alignment on Consequence Awareness and Autonomy. [PITH_FULL_IMAGE:figures/full_fig_p052_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Example interface for trajectory-level human review. The figure shows a complete [PITH_FULL_IMAGE:figures/full_fig_p054_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Human annotation interface for evaluating the quality of simulated user feedback. [PITH_FULL_IMAGE:figures/full_fig_p055_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Human annotation interface for rubric-based plan evaluation. The figure il [PITH_FULL_IMAGE:figures/full_fig_p056_26.png] view at source ↗
read the original abstract

Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through interaction. However, existing benchmarks still underexplore adaptive planning under such progressively revealed dual constraints. To address this gap, we introduce AdaPlanBench, a dynamic interactive benchmark for evaluating whether Large Language Model (LLM) agents can adaptively plan and re-plan under progressively revealed world and user constraints. AdaPlanBench is built on 307 household tasks, with a scalable constraint construction pipeline that augments each task with dual constraints. At runtime, agents interact with the environment in a multi-turn protocol where hidden constraints are revealed only when the agent proposes a plan that violates them, requiring iterative plan revision under accumulating feedback. This makes planning challenging, as agents must infer and track constraints from feedback while re-planning effectively. Experiments on ten leading LLMs show that adaptive planning under dual constraints remains challenging, with the best model reaching only 67.75% accuracy. We further observe that performance degrades as more constraints accumulate, with user constraints posing a particularly large challenge and failures often stemming from weaker physical grounding and reduced effectiveness. These results establish AdaPlanBench as a testbed for dual-constrained interactive planning and highlight the challenge of reliable adaptation to dynamically revealed constraints in LLM agents.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces AdaPlanBench, a dynamic interactive benchmark built on 307 household tasks augmented via a scalable pipeline with dual world and user constraints that are progressively revealed only upon plan violation in a multi-turn protocol. It evaluates ten leading LLMs, reporting that the best model reaches 67.75% accuracy under accumulating constraints, with performance degrading as more constraints are revealed and user constraints proving especially difficult; failures are attributed to weaker physical grounding and reduced re-planning effectiveness. The work positions the benchmark as a testbed for dual-constrained adaptive planning.

Significance. If the empirical claims hold after methodological clarification, AdaPlanBench supplies a needed evaluation framework for LLM agents handling progressively disclosed dual constraints, a setting closer to real-world deployment than static benchmarks. The reported performance ceiling and degradation trends, if statistically supported, would usefully quantify current limitations in feedback tracking and iterative revision. The scalable construction pipeline is a practical strength that could enable future extensions.

major comments (2)
  1. [Experiments section] Experiments section: the central performance claims (best-model accuracy of 67.75%, degradation with accumulating constraints, and differential difficulty of user vs. world constraints) are stated without error bars, confidence intervals, statistical significance tests, or per-task distributions across the 307 tasks. This directly affects the ability to assess whether the reported ceiling and trends are reliable.
  2. [Sections 3–4] Benchmark construction and evaluation protocol (Sections 3–4): the manuscript provides limited detail on validation of the constraint construction pipeline and multi-turn revelation mechanism, including how constraints were checked for realism, non-redundancy, and absence of systematic bias in difficulty or feedback quality. These elements are load-bearing for the claim that the benchmark accurately models progressively revealed dual constraints.
minor comments (1)
  1. [Figures and Tables] Figure captions and tables should explicitly state the number of runs or seeds used to produce the accuracy numbers and degradation curves.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The two major comments highlight important areas for improving the reliability and transparency of our claims. We address each point below and commit to revisions that strengthen the manuscript without altering its core contributions.

read point-by-point responses

  1. Referee: [Experiments section] Experiments section: the central performance claims (best-model accuracy of 67.75%, degradation with accumulating constraints, and differential difficulty of user vs. world constraints) are stated without error bars, confidence intervals, statistical significance tests, or per-task distributions across the 307 tasks. This directly affects the ability to assess whether the reported ceiling and trends are reliable.

    Authors: We agree that the absence of statistical measures limits the assessment of reliability. The reported figures are point estimates from a single evaluation pass over the 307 tasks. In the revised manuscript we will add bootstrapped 95% confidence intervals for all accuracy numbers, include per-task performance distributions (e.g., histograms or summary statistics), and report statistical significance tests (paired McNemar tests for degradation trends and constraint-type differences). These additions will appear in the Experiments section and associated tables. revision: yes

  2. Referee: [Sections 3–4] Benchmark construction and evaluation protocol (Sections 3–4): the manuscript provides limited detail on validation of the constraint construction pipeline and multi-turn revelation mechanism, including how constraints were checked for realism, non-redundancy, and absence of systematic bias in difficulty or feedback quality. These elements are load-bearing for the claim that the benchmark accurately models progressively revealed dual constraints.

    Authors: We acknowledge that the current description of validation steps is brief. While the pipeline was designed with explicit checks for non-redundancy and realism, these procedures were not fully documented. In the revision we will expand Sections 3 and 4 with a new subsection on validation that details: (i) the manual review protocol and inter-annotator agreement for a sampled subset of constraints, (ii) automated checks for redundancy and overlap, and (iii) analysis of feedback quality and difficulty balance across world versus user constraints. This will directly support the claim that the benchmark models progressively revealed dual constraints. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces AdaPlanBench as an empirical benchmark for LLM agents under progressively revealed dual constraints, with evaluation results reported directly from experiments on ten models (best at 67.75% accuracy). No equations, derivations, fitted parameters, or load-bearing self-citations appear in the abstract or described methodology; the constraint pipeline, multi-turn protocol, and performance observations are defined and measured independently without reducing to self-defined quantities or prior author work by construction. This is a self-contained benchmark paper whose central claims rest on external experimental data rather than internal circular reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities; the contribution is a new evaluation protocol and dataset construction method rather than a theoretical derivation.

pith-pipeline@v0.9.1-grok · 5804 in / 1097 out tokens · 20951 ms · 2026-06-28T01:39:38.508968+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

63 extracted references · 6 canonical work pages · 3 internal anchors

  1. [1]

    URLhttps://doi.org/10.1145/3472208

    doi: 10.1145/3472208. URLhttps://doi.org/10.1145/3472208. Centers for Disease Control and Prevention (CDC) and others. A home fall prevention checklist for older adults, 2015. Matthew Chang, Gunjan Chhablani, Alexander Clegg, Mikael Dallaire Cote, Ruta Desai, Michal Hlavac, Vladimir Karashchuk, Jacob Krantz, Roozbeh Mottaghi, Priyam Parashar, Siddharth Pa...

    work page doi:10.1145/3472208 2015

  2. [2]

    Generative Agents: Interactive Simulacra of Human Behavior

    URLhttps://www.osha.gov/sites/default/files/publications/OSHA3886.pdf. Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. Generative agents: Interactive simulacra of human behavior, 2023. URLhttps://arxiv.org/abs/2304.03442. Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidl...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/s12369-021-00853-y 2023

  3. [3]

    UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

    URLhttps://arxiv.org/abs/2509.02544. Huaxiaoyue Wang, Nathaniel Chin, Gonzalo Gonzalez-Pumariega, Xiangwan Sun, Neha Sunkara, Maximus Adrian Pace, Jeannette Bohg, and Sanjiban Choudhury. Apricot: 19 Preprint. Under review. Active preference learning and constraint-aware task planning with llms, 2024a. URL https://arxiv.org/abs/2410.19656. Jiayin Wang, Fen...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2024.emnlp-main 2024

  4. [4]

    Jiayin Wang, Fengran Mo, Weizhi Ma, Peijie Sun, Min Zhang, and Jian-Yun Nie

    URLhttps://aclanthology.org/2024.emnlp-main.210/. Jiayin Wang, Fengran Mo, Weizhi Ma, Peijie Sun, Min Zhang, and Jian-Yun Nie. A user- centric multi-intent benchmark for evaluating large language models. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.),Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 3...

    work page doi:10.18653/v1/2024.emnlp-main.210 2024

  5. [5]

    The Rise and Potential of Large Language Model Based Agents: A Survey

    ISSN 1472-6955. doi: 10.1186/s12912-025-04245-9. URL https://doi.org/10.1186/ s12912-025-04245-9. Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou,...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1186/s12912-025-04245-9 2023

  6. [6]

    Hainiu Xu, Runcong Zhao, Lixing Zhu, Jinhua Du, and Yulan He

    URLhttps://arxiv.org/abs/2402.01622. Hainiu Xu, Runcong Zhao, Lixing Zhu, Jinhua Du, and Yulan He. OpenToM: A comprehen- sive benchmark for evaluating theory-of-mind reasoning capabilities of large language models. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.),Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistic...

    work page doi:10.18653/v1/2024.acl-long.466 2024

  7. [7]

    I dislike quiet atmosphere

    and use the query rewriter to produce short, method-agnostic household queries so as to broaden the downstream action space. Denoting the raw query by qraw, the rewritten query is q=M rw (qraw) . The rewriter removes explicit resource constraints, such astools available: ...orusing only ..., while preserving the original task goal. We then apply a strict ...

    2025

  8. [8]

    Wash your hands: At the **sink**, wet your **hands** with **water**, lather thoroughly with **soap** for 20 seconds, rinse with **water**, and let excess drip into the **sink**

  9. [9]

    Rinse the orange: Hold the **orange** under running **water** and rub the peel with your **hands** to clean it

  10. [10]

    Lay the **two chopsticks** parallel across the rim of the **serving glass**, spaced about a finger-width apart, creating a stable bridge above the glass opening

    Set up the drip station: Place the **serving glass** in the basin of the **sink**. Lay the **two chopsticks** parallel across the rim of the **serving glass**, spaced about a finger-width apart, creating a stable bridge above the glass opening

  11. [11]

    Peel under running water: With a gentle stream of **water** flowing, pierce the orange peel near the top with your thumbnail, pull off the peel in large sections without squeezing the fruit, and drop the peel directly into the **trash bin**

  12. [12]

    Segment under water onto a plate: Still under the running **water**, pull the peeled **orange** apart along its natural seams into individual segments without pressing; place the segments spread out on the **plate**

  13. [13]

    Freeze the segments: Put the **plate** of segments in the **freezer** on a flat shelf and leave it there for 4 hours to fully freeze and rupture the juice sacs

  14. [14]

    Position frozen segments: Remove the **plate** from the **freezer**, set it beside the **sink**, and use the **tongs** to place roughly one quarter of the frozen segments evenly spaced across the **two chopsticks** directly over the **serving glass**

  15. [15]

    Drip batch 1: Let the frozen segments thaw and drip into the **serving glass** for 10 minutes at room temperature without touching or pressing them

  16. [16]

    Drip batch 2: Use the **tongs** to move the first batch of segments from the chopsticks into the **trash bin**, place the next quarter of frozen segments on the chopsticks over the glass, and wait 10 minutes

  17. [17]

    Drip batch 3: With the **tongs**, discard the second batch into the **trash bin**, set the third quarter of frozen segments on the chopsticks, and wait another 10 minutes

  18. [18]

    Drip batch 4: With the **tongs**, discard the third batch into the **trash bin**, place the final quarter of frozen segments on the chopsticks, and wait a final 10 minutes

  19. [19]

    Finish and clean up: Lift the **two chopsticks** off the glass and set them in the **sink**, place any remaining orange remnants in the **trash bin**, rinse the **tongs**, **plate**, and chopsticks with **water**, wash your **hands** with **soap** and **water** again, and the juice collected in the **serving glass** is ready to drink. Physical Rubric Scor...

  20. [20]

    Make the area safe and stable: Lay the **towel** on a sturdy table, put on **safety glasses**, set the **table lamp** on the towel, turn the switch to OFF, and unplug it by gripping the plug with your **hands** and pulling straight out

  21. [21]

    Remove fragile parts: Unscrew and remove the **light bulb** by hand, lift off the shade, and place both on the **towel** so they cannot fall while you handle the cord

  22. [22]

    Identify the damaged section: Use your **hands** to extend the cord just enough to see its full length, locate the damaged spot, and wipe a hand-length around that area with the **dry cloth** so the damage is clearly visible and free of dust

  23. [23]

    Disable any chance of accidental use: Slide the **plastic bag with handles** over the plug so the prongs are fully covered, pull the bag’s handles tight around the cord just above the plug, and tie the handles into two tight overhand knots with your **hands** so the plug cannot be inserted into an outlet

  24. [24]

    DO NOT USE – DAMAGED WIRING

    Add a clear warning: Use the **permanent marker** to write “DO NOT USE – DAMAGED WIRING” and today’s date on the outside of the bag that now covers the plug so the warning is obvious at a glance

  25. [25]

    Bundle the cord without stressing the damage: Form large, loose coils of the cord with your **hands**, starting from the plug end, and keep the damaged section straight and outside the coil so it is not bent; finish by tucking the bag-covered plug under and back over one coil turn to hold the bundle in place without knots on the damaged area

  26. [26]

    Isolate the lamp from everyday use: Place the lamp body into the **box with lid**, lay the bundled cord inside with the damaged section straight and on top for visibility, set the removed **light bulb** beside the lamp, and close the lid

  27. [27]

    Effectiveness Rubric Score: 1.33 Human Crafted Reason: This plan is safe, but it does not answer the task

    Store out of service and tidy up: Carry the closed **box with lid** to a high shelf or closet so it is out of circulation, confirm the bag knots over the plug are tight, then put away the **towel** and **dry cloth** so the workspace is clean and safe. Effectiveness Rubric Score: 1.33 Human Crafted Reason: This plan is safe, but it does not answer the task...

  28. [28]

    Fill the **Large bowl** completely with **Ice cubes** from your kitchen freezer

  29. [29]

    Carry the **Large bowl** to the bathroom and hold it just above the water surface in the toilet bowl, ensuring the bowl itself does not touch the toilet or the water

  30. [30]

    Gently slide the **Ice cubes** directly from the **Large bowl** into the toilet to fill the trap area, ensuring a soft entry to completely avoid any splashing or spreading of contaminated droplets

  31. [31]

    Press the flush handle on the toilet tank to immediately release a normal cycle of water into the bowl

  32. [32]

    Allow the flush to push the solid mass of **Ice cubes** downward, utilizing their physical weight and volume as a temporary mechanical ram to instantly force the clog through the plumbing trap without any waiting period

  33. [33]

    Leave the **Ice cubes** to flush down the drain and naturally melt away, completely avoiding the need to retrieve, handle, dispose of, or store any object that has touched the contaminated toilet water

  34. [34]

    Physical Rubric Score: 1.33 Human Crafted Reason: This plan fails because the proposed mechanism is physi- cally wrong

    Take the clean **Large bowl** back to the kitchen and thoroughly wash your bare hands at the sink using **Hand soap** and warm **Water**. Physical Rubric Score: 1.33 Human Crafted Reason: This plan fails because the proposed mechanism is physi- cally wrong. Ice cubes do not sink into the toilet trap as a compact mechanical ram; they float, separate, and m...

  35. [35]

    Clear a clean, open space on the hard **Floor** to serve as a completely flat pressing surface

  36. [36]

    Lift the **Mattress** off your bed frame and set it temporarily aside

  37. [37]

    Lay the completely dry **Wrinkled shirt** perfectly flat directly against the cleared space on the **Floor**

  38. [38]

    Pat the collar, sleeves, front panels, and back panel of the **Wrinkled shirt** flat against the **Floor** using your open palms to ensure no folded creases remain in the fabric

  39. [39]

    Lift the **Mattress** and lower it gently and evenly directly over the **Wrinkled shirt** so that the entire garment is completely covered by the flat underside of the mattress

  40. [40]

    Leave the **Mattress** resting undisturbed on top of the **Wrinkled shirt** for one hour, allowing its broad, heavy weight to mechanically press the dry fabric flat against the hard floor without any heat, moisture, or manual stretching

  41. [41]

    Lift the **Mattress** off the **Wrinkled shirt** and return it to the bed frame

  42. [42]

    World Constraints

    Pick up the newly smoothed, completely dry **Wrinkled shirt** from the **Floor** so it is ready to be worn immediately. Effectiveness Rubric Score: 1.67 Human Crafted Reason: This plan is ineffective because it omits the key ingredients that make ironing work: heat, moisture, and concentrated rigid pressure. A mattress is soft and compliant, so it will no...

  43. [43]

    Explicitly mention {tools placeholder} {prefs placeholder} {refine rubrics} if they are provided in the judge feedback

  44. [44]

    **DO NOT introduce new issues not mentioned in judge feedback**

    ONLY point out the problems found by the judge. **DO NOT introduce new issues not mentioned in judge feedback**

  45. [45]

    Do NOT answer the task or suggest solutions / plans yourself

  46. [46]

    If multiple kinds of judge feedback are present, combine them into one coherent user message in a natural way

  47. [47]

    If the judge’s feedback indicates that any previously disclosed user preference or tool constraint has been violated, first state the violations that occurred for the first time, then clearly emphasize any repeated violations and explicitly instruct the assistant not to repeat them again

  48. [48]

    You must directly output your response without any explanation

  49. [49]

    You should use a human-like tone, as if you are a real user providing feedback to the assistant

  50. [50]

    Your job is only to write the user’s message to the assistant

    Do not output a plan, do not output <answer start>, and do not output [Used Tools / Entities] or [Step description] . Your job is only to write the user’s message to the assistant. {memory} Your answer: Figure 15: Prompt for generating user feedback on plan violations. The figure illustrates the instruction template used to simulate a user response based ...

  51. [51]

    ##<coresponding number for tool/object 1>## (tool/object 1)

  52. [52]

    <A NUMBER>. <AN OBJECT>

    ## Example output: If the banned tools are “... <A NUMBER>. <AN OBJECT> ...” and you think this is a violation, you should output: <answer start>: - [Result]: **YES** - [Details]: The plan uses the following banned tools/objects:

  53. [53]

    ##<A NUMBER>## (<AN OBJECT>)

  54. [56]

    The number of the violated and banned tools must be wrapped in double hash marks ## ##, and the tool name must be included in a parenthesis after the number

  55. [58]

    Your answer: Figure 16: Prompt used for world-constraint violation judgment

    You may include reasoning before <answer start>, but once <answer start> is given, it will be treated as your final answer. Your answer: Figure 16: Prompt used for world-constraint violation judgment. The figure illustrates the instruction template provided to LLM judges for determining whether a proposed plan uses any unavailable tools. 49 Preprint. Unde...

  56. [59]

    ##<corresponding number for user preference 1>## (user preference 1)

  57. [60]

    <A NUMBER>. <A PREF>

    ## Example output: If the user preferences are “... <A NUMBER>. <A PREF> ...” and you think this is a violation, you should output: <answer start>: - [Result]: **YES** - [Details]: The plan violates the following user preferences:

  58. [61]

    ##<A NUMBER>## (<A PREF>)

  59. [62]

    **YES** indicates a violation; **NO** indicates no violation

  60. [63]

    The Result (**YES** / **NO**) must be wrapped in double asterisks ** **

  61. [64]

    Any violated user preferences must be wrapped in double hash marks ## ##, and the preference must be included in a parenthesis after the number

  62. [65]

    If there are no violations, the Details section may be left empty

  63. [66]

    <answer start>

    You may include reasoning before <answer start>, but once <answer start> is given, it will be treated as your final answer. Your answer: Figure 17: Prompt used for user-constraint violation judgment. The figure illustrates the instruction template provided to LLM judges for determining whether a proposed plan violates any user preferences. 50 Preprint. Un...