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REVIEW 4 major objections 5 minor 4 cited by

Planning with Reasoning using Vision Language World Model

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims a world model trained on unlabeled videos to predict goals and action-state trajectories in natural language beats much larger prompting-based planners, and that a self-supervised cost-minimizing critic makes it better stil

desk verdict Solid systems paper on language-based world modeling, but the central 'understands physical dynamics' claim rests on LLM-generated state labels that are never verified against real state changes. read the letter →

arxiv 2509.02722 v2 pith:HNJ2C5W4 submitted 2025-09-02 cs.AI

Delong Chen , Theo Moutakanni , Willy Chung , Yejin Bang , Ziwei Ji , Allen Bolourchi , Pascale Fung This is my paper · ORCID
classification cs.AI
keywords visionlanguageworldmodelmodelingproceduralplanninglanguage-basedabstractioncostminimizationself-supervisedcriticTreeofCaptionsvisualforassistance
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

This paper tries to show that a high-level world model for planning can be learned directly from unlabeled videos, with the future represented in natural language instead of pixels or latent vectors. The Vision Language World Model (VLWM) is trained to turn a video context into a goal description plus an interleaved sequence of actions and world-state changes, using an automatic pipeline that compresses video into a Tree of Captions and refines it with LLM self-refinement. On top of this predictive model, the paper adds a self-supervised critic that scores how close a predicted action-state rollout comes to the goal, turning plan generation into cost minimization (system-2 planning). The paper reports state-of-the-art Visual Planning for Assistance results, a +27% Elo gain in human plan-preference evaluation, and strong results on RoboVQA and WorldPrediction, arguing that language-based world states are an efficient and interpretable substrate for long-horizon planning.

What carries the argument

Tree of Captions: a hierarchy of video captions over adaptively segmented windows, produced by hierarchical agglomerative clustering of Perception Encoder features and PerceptionLM captioning; it compresses 1.1 TB of Ego4D video into under 900 MB of text. LLM Self-Refine extracts the goal/interpretation/action-state training targets from this tree. The critic is a 1B language model trained with a ranking loss (Eq. 2) to assign lower cost to valid goal progress and higher cost to distractors or shuffled steps; it supplies the semantic-distance cost for system-2 plan search.

What would settle it

Take held-out instructional videos, adversarially replace the state-description portion of extracted training targets with descriptions from a different task while keeping actions and visuals intact, then test whether VLWM rollouts and critic cost rankings degrade; if they do not, the state descriptions are not doing the claimed dynamics work. A cheaper check: have humans compare VLWM's predicted world-state text against the actual next visual frames.

Watch

Extended reading notes

Core claim

The central claim is Eq. 1: conditioned on visual context and a goal config, VLWM predicts the goal's interpretation and a trajectory of interleaved actions and world-state deltas <A0, ΔS0>, ..., <AN, ΔSN>. This single objective bundles three levels of world modeling — goal inference, action anticipation, and action-conditioned dynamics prediction — so the same model can act reactively (system-1) by decoding a plan, or reflectively (system-2) by rolling out multiple candidate actions and selecting the one with lowest cost assigned by a separately trained critic. The paper argues that generating language descriptions of state changes, rather than raw future frames, sidesteps pixel-level ill-p

Load-bearing premise

The training targets extracted by Llama-4 Self-Refine from Tree of Captions are treated as faithful, goal-directed ground truth — with no external verification — so any hallucinated or commonsense-invented state descriptions are learned as world dynamics.

Editorial extensions

If this is right

  • If VLWM is correct, high-level planning can be trained at scale from raw, unlabeled, imperfect videos by predicting language abstractions instead of pixels, avoiding the cost of generative video prediction.
  • System-2 cost-minimizing search over VLWM rollouts measurably beats the same model's reactive system-1 decoding (Elo 1261 vs 992), suggesting inference-time reasoning helps even when training demonstrations are imperfect.
  • The self-supervised critic generalizes beyond its training domain: 92.7% on egocentric goal-achievement detection and 72.9% on open grounded planning robot subsets, outperforming semantic-similarity baselines.
  • VLWM's interleaved action-state format gives SOTA VPA results with an 8B model, beating a 70B-assisted VidAssist on most metrics, so world-model pretraining is a viable alternative to scaling the planner alone.
  • Goal interpretation and world-state descriptions are load-bearing for the critic: removing them hurts OOD goal-achievement detection, especially on egocentric data.

Reading between the lines

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

  • A test the authors did not run: whether the world-state descriptions are genuinely grounded predictions or fluent text that leaks the plan; a caption-vs-state correlation check on held-out videos would settle it.
  • The critic's learned cost could plausibly double as a dense reward for RL fine-tuning of the planner — the paper uses it only for selection, not for further training.
  • Language as the state bottleneck means planning quality is capped by the descriptive coverage of the Tree of Captions and the Self-Refine extraction; a weaker captioner should degrade planning in a predictable, measurable way.
  • System-2 search width (20 candidates here) is a compute-accuracy dial; plotting Elo versus candidate count would show where additional rollouts stop paying off.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes VLWM, a vision-language model trained, given a config and visual context, to predict a goal description, a goal interpretation, and an interleaved action/world-state trajectory of the form specified in Eq. (1). Training targets are produced by compressing videos into a Tree of Captions, then using Llama-4 Self-Refine to extract structured plans. At inference, system-1 decodes plans autoregressively; system-2 generates multiple candidate rollouts and selects the plan with the lowest cost assigned by a critic trained with ranking losses on valid versus distractor/shuffled steps. The paper evaluates on VPA benchmarks (COIN and CrossTask), a new PlannerArena human-preference study, RoboVQA, critic goal-achievement detection, and WorldPrediction-PP, claiming state-of-the-art VPA performance, a +27% Elo improvement for system-2 over system-1, and top results on RoboVQA and WorldPrediction.

Significance. If the central claim is established, the contribution is significant: a scalable recipe for training high-level, language-based world models from uncurated video, with a self-supervised critic enabling test-time search. The pipeline has several strengths: the Tree-of-Captions compression is compute-efficient; the training corpus is large; several evaluations (OGP, WorldPrediction-PP, RoboVQA) are external to the training distribution; and the qualitative cost-minimizing versus cost-maximizing examples in Appendix D.2 are convincing demonstrations of the critic's behavior. However, the central construct-validity claim—that VLWM learns physical dynamics rather than text-style fluency—is not established by the current experiments, because the state labels are generated by the same LLM pipeline used for target construction and the benchmarks largely evaluate action ordering or language plausibility. The paper also overstates the VPA result and lacks statistical support for the PlannerArena Elo claim.

major comments (4)
  1. [§2.1.2, Appendix B.2, Eq. (1)] The training targets of Eq. (1) are generated by Llama-4 Self-Refine from PerceptionLM captions. The only faithfulness check is the prompt instruction in Appendix B.2, which is self-contradictory: it forbids hallucinating or inferring from commonsense knowledge, yet it requires each world-state paragraph to discuss 'motivation, rationale, and purpose', 'what the change enables', and whether it is 'ready for future steps'—content not observable in the video. Consequently, the predicted ΔS are not grounded state annotations, and the 'world model that understands physical dynamics' claim is non-identifiable: VLWM may be optimizing text-style fluency rather than learning genuine world dynamics. The authors should either provide external verification of predicted state changes (e.g., a human-annotated sample of state transitions, or a benchmark checking predicted ΔS against measured states) o
  2. [§2.2.1, §3.4.1, Table 5] The critic is trained on positive/negative pairs constructed from the same Self-Refine trajectories, and the in-domain goal-achievement evaluation (VLWM-Instruct and VLWM-Ego) uses trajectories and distractors drawn from that same distribution. The near-perfect accuracy (98.4% and 92.7%) therefore partly reflects self-consistency of the label-generation pipeline, not goal achievement. The OGP and WorldPrediction-PP evaluations are more independent, but OGP is action-only and WorldPrediction-PP evaluates the critic on externally provided captions, not on VLWM's predicted ΔS. The current experiments do not break the circularity for the system-2 planning claim. Please add an evaluation with human-verified or externally annotated state changes, or an ablation with an alternative label source.
  3. [Table 2, Abstract] The abstract claims 'state-of-the-art VPA performance on both benchmark evaluations,' but Table 2 shows VLWM trailing VidAssist on 4 of 12 metrics: CrossTask T=3 mAcc 36.4 vs. 36.7; T=3 mIoU 48.3 vs. 48.9; T=4 SR 7.2 vs. 7.4; T=4 mIoU 51.1 vs. 51.6. The claim should be qualified (e.g., average improvement, 8 of 12 metrics). In addition, no error bars or significance tests are reported; several margins are small, so the superiority is not established with the current evidence. Please report variance across seeds/initializations and adjust the abstract accordingly.
  4. [§3.2.2, Table 3] PlannerArena is based on only 550 battle pairs with five annotators, and Elo scores are reported without confidence intervals. With K=32, the claimed +27% Elo improvement of system-2 over system-1 (1261 vs. 992) may be within sampling noise. Inter-annotator agreement is computed on only 100 samples (Fleiss' κ=0.63). The claim that system-2 improves over system-1 by +27% Elo needs bootstrap confidence intervals or a significance test before it can be considered load-bearing.
minor comments (5)
  1. [§3.1.1] '32 frames in 4482 resolution' is unclear; probably a typo for 448×448 or 448². Please clarify.
  2. [Table 1] Table header says 'Statics' and 'EPIC-KITHCNES-100'; both should be corrected to 'Statistics' and 'EPIC-KITCHENS-100'.
  3. [§3.2.1] 'procedual' in the description of VPA benchmarks should be 'procedural'.
  4. [References] Several references are incomplete or malformed: 'RoboMamba' lacks a paper title, and the OpenOrca entry has a URL beginning 'https://https://'. Please check all entries.
  5. [§3.3, Table 4] The text reports BLEU-4 values (VLWM 55.6 vs. RoboBrain 55.1), but Table 4 only lists BLEU-1. Either add the multi-gram columns or align the text with the table.

Circularity Check

1 steps flagged · score 4.0 of 10

Critic's in-domain goal-achievement test loops back to the same Self-Refine pipeline; central VPA/RoboVQA/PlannerArena results remain externally grounded.

  1. fitted input called prediction [§3.4.1 Goal Achievement Detection (Datasets); cf. §3.1.2, §2.2.1]
    "We construct testing sample from two sources. 1) Vision-language World Modeling (VL WM): 4,410 action-state trajectories extracted with Tree of Captions and Self-Refine. ... Since VLWM-critic-1B is trained on HowTo100M trajectories, we exclude it and only sample data from other sources of instruction videos (COIN, CrossTask, YouCook2), and egocentric recordings (EgoExo4D, EPIC-KITCHENS-100)."

    The critic is trained on positive/negative pairs built from the same Self-Refine pipeline that generates the VLWM targets (§3.1.2: paired data from HowTo100M and NaturalReasoning; §2.2.1: valid next steps vs. distractors/shuffles). The VLWM goal-achievement test uses trajectories extracted by the same Tree-of-Captions + Self-Refine procedure, only from different source datasets. Thus the near-ceiling in-domain accuracy (98.4/92.7) largely measures self-consistency of the extraction pipeline's textual notion of 'valid progress' rather than externally verified physical state changes. The OGP and WorldPrediction results provide independent checks, so the circularity is partial, not total.

full rationale

The paper's main external evaluations are not circular: VPA (COIN/CrossTask) uses standard human-annotated benchmarks; RoboVQA is an established robotics VQA benchmark; PlannerArena uses human pairwise preferences; and the critic is also tested on OOD OGP and on WorldPrediction-PP, whose 570 samples are human-verified. These give the central planning and critic claims genuine independent content. The clearest circularity is in the intrinsic goal-achievement evaluation: both the critic's training signals and the VLWM-subset test trajectories are generated by the same Tree-of-Captions + Llama-4 Self-Refine pipeline, so high in-domain scores partly reflect stylistic self-consistency, not grounded state dynamics. The paper also cites the authors' own WorldPrediction benchmark when claiming SoTA, but because that benchmark is human-verified and uses external Qwen captions, the self-citation is not load-bearing circularity. The unverified LLM-extracted state labels are a correctness/falsifiability concern rather than a formal reduction of outputs to inputs, so they do not raise the score further. Overall: partial circularity confined to the critic's in-domain evaluation; the headline empirical results retain external support.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The system rests on the fidelity of the caption and LLM extraction pipeline, the sufficiency of language as a world-state representation, and a handful of hand-set hyperparameters. The most important unfalsified premise is that LLM Self-Refine targets are correct goal-directed ground truth rather than linguistically coherent text.

free parameters (6)
  • Critic ranking margin = 1
    Margin in Eq. 2 chosen by hand; sensitivity not tested.
  • Cost centering weight lambda = 0.01
    Small regularization weight in Eq. 2; chosen without reported sweep.
  • Self-Refine iterations = 2
    Number of refinement rounds for LLM plan extraction; more rounds untested.
  • Minimum caption segment duration = 5 seconds
    Segments shorter than 5 seconds are excluded from Tree of Captions.
  • Target windows per video = 5
    First 5 BFS nodes sampled per video for plan extraction.
  • System-2 search width = 20 candidate plans
    Number of rollouts used in PlannerArena system-2 evaluation; no ablation on width.
assumptions (4)
  • domain assumption PerceptionLM and Perception Encoder captions faithfully capture the visual content needed for goal and state inference.
    The entire abstraction pipeline and all training targets start from these captions; errors propagate to VLWM and critic. Invoked in §2.1.1.
  • ad hoc to paper LLM Self-Refine produces correct, non-hallucinated action and world-state targets.
    The prompts in Appendix B.2 instruct faithfulness, but there is no external verification that extracted trajectories correspond to physically valid plans; VLWM and critic are trained on them. Invoked in §2.1.2.
  • domain assumption Natural language is a sufficient abstract state representation for high-level planning.
    The paper chooses text over pixels or latent embeddings; cost is defined as semantic distance between text states. This choice is central throughout §2 and Eq. 1.
  • domain assumption The ranking loss in Eq. 2 makes the critic's cost approximate semantic distance to the goal.
    Self-supervised signals (valid, distractor, shuffled) are assumed to recover true goal achievement; no formal or empirical guarantee beyond the paper's own evals. Invoked in §2.2.1.

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Cite this review

Pith. "Pith review of Planning with Reasoning using Vision Language World Model." pith.science (2026). https://pith.science/paper/HNJ2C5W4

@misc{pith2026250902722,
  author       = {Pith},
  title        = {Pith review of: Planning with Reasoning using Vision Language World Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HNJ2C5W4}},
  note         = {Machine review of arXiv:2509.02722}
}
read the original abstract

Effective planning requires strong world models, but high-level world models that can understand and reason about actions with semantic and temporal abstraction remain largely underdeveloped. We introduce the Vision Language World Model (VLWM), a foundation model trained for language-based world modeling on natural videos. Given visual observations, the VLWM first infers the overall goal achievements then predicts a trajectory composed of interleaved actions and world state changes. Those targets are extracted by iterative LLM Self-Refine conditioned on compressed future observations represented by Tree of Captions. The VLWM learns both an action policy and a dynamics model, which respectively facilitates reactive system-1 plan decoding and reflective system-2 planning via cost minimization. The cost evaluates the semantic distance between the hypothetical future states given by VLWM roll-outs and the expected goal state, and is measured by a critic model that we trained in a self-supervised manner. The VLWM achieves state-of-the-art Visual Planning for Assistance (VPA) performance on both benchmark evaluations and our proposed PlannerArena human evaluations, where system-2 improves the Elo score by +27% upon system-1. The VLWM models also outperforms strong VLM baselines on RoboVQA and WorldPrediction benchmark.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Grounding Spatial Relations in a Compact World Model: Instruction Leakage and a Goal-Free Dynamics Fix

    cs.AI 2026-07 conditional novelty 7.0 of 10

    Goal-conditioned world models transcribe instructions instead of perceiving spatial relations when the instruction names the scored quantity, and removing the goal from the dynamics fixes it.

  2. Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Selecting 30 diverse environments by LLM-annotated ability coverage and training with harness-weakening plus state-scale curriculum improves multimodal agent success over naive scaling.

  3. OSEF: One-Step Evidence Fusion for Cross-Video Scene Procedure Planning

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A new benchmark and model for cross-video procedure planning where evidence retrieval, window localization, and action planning are scored jointly; OSEF improves video-and-plan success over hard-selection baselines.

  4. RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures

    cs.RO 2026-07 unverdicted novelty 5.0 of 10

    RoboTALES uses hierarchical LLM subgoals and VLM reward feedback to keep video-model futures task-aligned, then trains robot policies that beat baselines on RoboCasa and LIBERO10 long-horizon tasks.

Reference graph

Works this paper leans on

109 extracted references · 80 canonical work pages · cited by 4 Pith papers

  1. [1]

    Identify a sequence of physical actions that meaningfully advance the task progress; Omit vague, redundant, or purely presentational steps

  2. [2]

    Avoid describing actions from the tutor’s or demonstrator’s voice

    Each action is one informative imperative sentence said from the actor’s perspective. Avoid describing actions from the tutor’s or demonstrator’s voice

  3. [3]

    They must fall within <min_start> and <max_end> and do not overlap with each other

    Infer the span of each action according the provided timestamps. They must fall within <min_start> and <max_end> and do not overlap with each other

  4. [4]

    For example, the final result may appear at the beginning of the video

    Be selective - time in the video may be non-linear. For example, the final result may appear at the beginning of the video. Such actions should be skipped. **World State**

  5. [5]

    Use imperative voice and instructional or tutoral style

    Explain how the action is performed according to the provided captions. Use imperative voice and instructional or tutoral style. 21

  6. [6]

    Provide elaborated discussion of the motivation, rationale, and purpose behind the action

  7. [7]

    Discuss all relevant objects (can be both physical object or abstract concept, or the actor itself) whose states are changed by the action

  8. [8]

    Highlight the causal relationship between actions and states

    Cover various aspects, such as status, position, condition, temperature, etc. Highlight the causal relationship between actions and states

Show all 109 references
  1. [9]

    They are autoregressive and shouldn’t conflict with each other

    Be logically coherent and semantically connected with neighboring steps. They are autoregressive and shouldn’t conflict with each other

  2. [10]

    Provide in-depth analysis. Perspectives may include (but are not limited to): * Implications of state changes * What the change enables; whether it is (or is not) ready for future steps * Whether and how the change advances or contributes to the overall goal * Whether it satis...

  3. [11]

    **Goal Identification**

    Organize the discussion into a single coherent paragraph, it should be comprehensive and detailed, but also avoid redundency and ensure readibility. **Goal Identification**

  4. [12]

    Summarize the overall achievements by the actions during <min_start> to <max_end> (not the entire video)

  5. [13]

    Feel free to use multiple sentences if appropriate

    Ensure comprehensive coverage. Feel free to use multiple sentences if appropriate

  6. [14]

    But it should not be a simple concatenation of individual action names

    Use imperative voice. But it should not be a simple concatenation of individual action names

  7. [15]

    do x by doing y

    It summarize WHAT is achived (e.g., aggregation and abstraction of state changes) but not HOW it is achieved (e.g., "do x by doing y"). **Goal Interpretation**

  8. [16]

    Now,

    Infer and describe the initial state of the environment before any action is taken. Only describe task-relevant aspects. Start with "Now, ..."

  9. [17]

    Interpret the goal in detail by discussing objects needs to be what state such that goal can be considered achieved

  10. [18]

    To achieve the goal,

    Start with "To achieve the goal, ...". You can also include related technical specifications if applicable

  11. [19]

    Description of the desired world state should be grounded in the provided context and aggregate all the state changes caused by the actions

  12. [20]

    needed or invovled in the action steps and explain the functional rationale

    Discuss all objects, tools, materials, dependencies, etc. needed or invovled in the action steps and explain the functional rationale

  13. [21]

    You are preparing by thinking and analyzing the task

    Use the tone as if you are now at the starting time of the video (<min_start>) and tasked to plan towards the given goal. You are preparing by thinking and analyzing the task

  14. [22]

    Importantly, you should avoid the leakage of any action plan information in this section

    Provide one paragraph and ensure its coherence and readibility. Importantly, you should avoid the leakage of any action plan information in this section. **Overall Requirements**

  15. [23]

    Maintain faithfulness to the provided video content; Do not hallucinate or infer based on commonsense knowledge

  16. [24]

    Timestamps should be in the same format as <min_start> and <max_end>

    The output must strictly follow the given YAML format. Timestamps should be in the same format as <min_start> and <max_end>

  17. [25]

    Except for the start and end times of the action, don’t mention exact timestamp anywhere in your output

  18. [26]

    Instead, refer to the actions, objects, and environements directly

    Don’t use ’the video’ / ’the segment’ in any part of the output. Instead, refer to the actions, objects, and environements directly

  19. [27]

    Ignore task-irrelevant information such as appearance which does not affect the task

    Use specific functional description when referring to objects. Ignore task-irrelevant information such as appearance which does not affect the task

  20. [28]

    Avoid repetition and redundancy

    Ensure comprehensiveness and detail in your output, but also conherence and readibility. Avoid repetition and redundancy. 22 C Tree-of-Captions Example Figure 8 Structure of Tree of captions (bottom) extracted from video (top). Each box is associated with a corresponding video...

  21. [29]

    Saute chopped onions and garlic in oil

  22. [30]

    Add ground beef to the pan and break it up

  23. [31]

    Add seasoning to the beef mixture

  24. [32]

    Add tomato sauce to the beef mixture and stir

  25. [33]

    Fill taco shells with the beef mixture

  26. [34]

    Add cheese to the tacos

  27. [35]

    Serve the tacos Cost Maximizing Plan (cost = 3.03):

  28. [36]

    Add959 existing ingredients to the slow cooker

  29. [37]

    Mix the ingredients in the slow cooker

  30. [38]

    Add taco seasoning to the slow cooker

  31. [39]

    Stir the contents of the slow cooker

  32. [40]

    Open the slow cooker lid

  33. [41]

    Serve the cooked beef mixture onto a tortilla

  34. [42]

    Add cheese to the taco

  35. [43]

    Fold the tortilla ### Goal: Make Kerala Fish Curry Cost Minimizing Plan (cost = -4.36):

  36. [44]

    Dry roast the chilies in a pan over medium heat

  37. [45]

    Add ginger and garlic to the pan and mix with the chilies

  38. [46]

    Add water and tamarind paste to the pan and mix

  39. [47]

    Add salt and sugar to the curry and stir

  40. [48]

    Add fish to the curry and cook until done

  41. [49]

    Add water to the curry and stir

  42. [50]

    Simmer the curry and stir occasionally

  43. [51]

    Garnish with cilantro

  44. [52]

    Serve the curry Cost Maximizing Plan (cost = -0.04):

  45. [53]

    AddTransition metal pieces

  46. [54]

    Add coriander powder

  47. [55]

    Finish with coconut oil ### Goal: Use Tapping Gun Cost Minimizing Plan (cost = -7.58):

  48. [56]

    Insert the gun head into the Tapping gun

  49. [57]

    Remove the existing gun head from the Tapping gun

  50. [58]

    Handle the Tapping gun to prepare it for a new gun head

  51. [59]

    Clean the Tapping gun

  52. [60]

    Prepare the Tapping gun for use by loading it with a new gun head

  53. [61]

    Load the Tapping gun with a new gun head

  54. [62]

    Demonstrate the final preparation of the Tapping gun

  55. [63]

    Emphasize the completion of the task

  56. [64]

    Conclude the demonstration

  57. [65]

    Fill the Tapping gun with disposable scoops Cost Maximizing Plan (cost = 3.46):

  58. [66]

    Ignited the lighter to produce a flame

  59. [67]

    Insert the gun head into the tag brand

  60. [68]

    Load the Tapping gun with degrees

  61. [69]

    Insert the Tapping gun into the red compartment

  62. [70]

    Aer.YEAR4 doesn’t come off easily ### Goal: Replace SIM Card Cost Minimizing Plan (cost = -5.43):

  63. [71]

    Eject the SIM card tray using a SIM card ejector tool

  64. [72]

    Remove the SIM card from the tray

  65. [73]

    Place the SIM card into the tray 25

  66. [74]

    Reinsert the SIM card tray into the phone

  67. [75]

    Ensure the SIM card is properly seated and the tray is fully inserted

  68. [76]

    Adjust and finalize the SIM card tray insertion

  69. [77]

    Verify the SIM card tray is fully inserted and functional Cost Maximizing Plan (cost = 4.24):

  70. [78]

    Use the SIM card ejector tool to eject the SIM card tray

  71. [79]

    Remove the SIM card tray from the phone

  72. [80]

    Place the SIM card into the SIM card tray

  73. [81]

    Insert the SIM card tray back into the phone

  74. [82]

    Place the phone on a flat surface

  75. [83]

    Display the final result or a related message ### Goal: Use Rice Cooker To Cook Rice Cost Minimizing Plan (cost = -4.31):

  76. [84]

    Put the washed rice into the rice cooker

  77. [85]

    Measure the water level

  78. [86]

    Cook the rice using the rice cooker

  79. [87]

    Open the rice cooker and fluff the cooked rice

  80. [88]

    Serve the cooked rice Cost Maximizing Plan (cost = 3.91):

  81. [89]

    Place the strainer over the rice cooker

  82. [90]

    Add water to the rice cooker

  83. [91]

    Start the rice cooker

  84. [92]

    Analytical balance assessment

  85. [93]

    Open the rice cooker to check the cooked rice

  86. [94]

    Scoop the cooked rice from the rice cooker ### Goal: Make Chinese Spring Rolls Cost Minimizing Plan (cost = -4.44):

  87. [95]

    Pour oil into a wok and heat it

  88. [96]

    Add chopped garlic and ginger to the heated oil in the wok

  89. [97]

    Cook chopped vegetables such as carrots and onions in the wok

  90. [98]

    Add noodles and various sauces to the wok and stir-fry

  91. [99]

    Prepare spring roll sheets and fill them with the cooked mixture

  92. [100]

    Fry the filled spring rolls in hot oil until they are golden brown

  93. [101]

    Serve the fried spring rolls on a plate Cost Maximizing Plan (cost = -0.59):

  94. [102]

    Pour vegetable oil into a wok

  95. [103]

    Add chopped garlic and ginger to the wok

  96. [104]

    Stir-fry chopped onions

  97. [105]

    Add chopped carrots and capsicum to the wok

  98. [106]

    Add noodles and purified water to the wok

  99. [107]

    Drain the cooked noodles

  100. [108]

    Mix the cooked noodles with chopped parsley, salt, and pepper

  101. [109]

    Prepare the cooking vessel for frying 26

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.