Pith. sign in

REVIEW 5 major objections 5 minor 1 cited by

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

T0 review · 5 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper contends that extracting symbolic world rules as executable code aligns an LLM world model with environment dynamics, letting an agent reach 98% success on ALFWorld after four iterations without any training.

desk verdict Solid incremental systems paper with real empirical gains, but the 'world alignment' claim is narrower than stated because the rule-based world model only covers deterministic transitions. read the letter →

arxiv 2504.15785 v1 pith:UITKPTYG submitted 2025-04-22 cs.AI

Siyu Zhou , Tianyi Zhou , Yijun Yang , Guodong Long , Deheng Ye , Jing Jiang , Chengqi Zhang This is my paper · ORCID
classification cs.AI
keywords LLMagentsworldmodelsalignmentneurosymboliclearningactionrulesknowledgegraphsscenemodel-predictivecontrol
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 contends that a pretrained LLM can be turned into an accurate world model for an agent without finetuning, by learning the environment's own symbolic knowledge from exploration. The knowledge — action rules, a knowledge graph, and a scene graph — is extracted by prompting the LLM to reason inductively over real and predicted trajectories, compiled into executable code rules, and used to override the LLM's raw success/failure predictions when they conflict with the environment. The agent plans by asking this neurosymbolic world model to check candidate actions, receiving feedback and suggestions, and replanning until the model approves. On Mars and ALFWorld, the authors report state-of-the-art results, including 98% success on ALFWorld after four iterations and an average Mars score rising from 4.0% to 15.3%. If correct, the method shows that a small amount of complementary symbolic knowledge, not the LLM's prior alone, is what makes LLM-based world models reliable.

What carries the argument

The load-bearing mechanism is the neurosymbolic learning pipeline together with the world-model overlay. It runs in four stages: compare real and predicted trajectories and sort transitions into correctly and incorrectly predicted; use LLM inductive reasoning to extract action rules, a knowledge graph of requires/consumes/enables constraints, and a scene graph of spatial and global facts; translate these into executable code rules of the form `def CodeRule_m(obs, action, KG/SG): return feedback, suggestion, flag`; and prune the rule set with a greedy maximum-coverage algorithm that keeps only rules correcting mispredicted transitions. During model-predictive control, the MAPEXECUTE wrapper applies active code rules to the LLM world model's prediction; when a rule disagrees, the rule's output overrides the LLM and the agent uses the generated feedback to propose a new action. The world model's predictive task is deliberately reduced to binary success/failure on the assumption that the next observation follows deterministically from that outcome.

What would settle it

Take a deterministic benchmark, inject one stochastic transition — for example, make a mining action succeed with probability 0.5 because hostile creatures sometimes appear — and measure the code rules' cover rate on mispredicted transitions over iterations. If the rule learner labels the same action sometimes success and sometimes failure, the cover rate will stall near chance and agent success will degrade far below the reported 98% and 15.3%, confirming that the binary deterministic-transition assumption is the load-bearing premise.

Watch

Extended reading notes

Core claim

The paper's central claim is that the gap between an LLM's prior knowledge and an environment's actual dynamics is narrow but decisive, and that the missing piece can be learned as compact symbolic knowledge. WALL-E 2.0 performs world alignment by running the LLM as a world model, comparing its predicted trajectories against real ones, and limiting the prediction task to whether each action transition succeeds or fails. It then prompts the LLM for inductive reasoning to extract action rules, a knowledge graph, and a scene graph, translates them into Python functions, and prunes them by solving a maximum set coverage problem over the initially mispredicted transitions. During planning, these code rules override the LLM's predictions when they disagree and supply feedback and suggestions to the agent, which replans in a model-predictive-control loop. The authors attribute the performance gains to this alignment: learned rules raise the cover rate of failed predictions from 0.0% to 92.3%, and the method outperforms all compared baselines on Mars and reaches 98% success in ALFWorld after four iterations.

Load-bearing premise

The method's rules assume the next state is determined by whether an action succeeds or fails; if the environment is stochastic, or a successful action can lead to different next states, the learned world model cannot faithfully predict what happens next.

Editorial extensions

If this is right

  • No RL or finetuning is needed to align an LLM world model: an iteratively updated set of code rules is sufficient for substantial prediction and planning gains in a new environment.
  • Because pruning keeps only rules that fix real mispredictions, the agent's knowledge stays compact and it does not need to carry the full history of past trajectories in context.
  • Encoding rules as executable code makes the world model's checks deterministic and reduces the variance of prompt-based rule following that hurts natural-language-rule methods.
  • Combining action rules with a knowledge graph and a scene graph is necessary: ablations show each component contributes, and the improvement comes mostly from applying the symbolic knowledge to the world model rather than to the agent's prompt.
  • The method is designed for environments that contradict the LLM's priors, and the reported gains are largest where other methods rely on misleading commonsense.

Reading between the lines

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

  • The binary success/failure design implies the approach is best suited to rule-governed, deterministic environments; extending the rules to stochastic transitions would require probabilistic success rates rather than hard flags, a step the paper identifies as future work.
  • Since the code rules are compact and environment-specific, a natural transfer test would be to take a pruned rule set learned in one world and apply it to a structurally similar world, measuring whether alignment transfers without new exploration.
  • The maximum-coverage pruning objective is defined on the LLM's current mispredictions, so the selected rules may patch the model's specific errors rather than capture the full dynamics; validating rules on held-out mispredictions would separate these two explanations.
  • The MPC loop's efficiency rests on the LLM being a good action proposer; in environments where the LLM's heuristics are poor, the world model can veto bad actions but cannot invent good ones, so the ceiling may be set by the proposer's prior.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper proposes WALL-E 2.0, a training-free pipeline that aligns an LLM-based world model with environment dynamics by learning symbolic knowledge (action rules, knowledge graphs, scene graphs) from exploration trajectories, converting that knowledge into executable code rules, and using a maximum-coverage pruning step to keep the rule set compact. The aligned world model is used inside an agent that applies model-predictive control (MPC), where the LLM agent proposes actions and the world model checks them against the learned rules. Experiments on Mars and ALFWorld report improved reward/score and success rates over prior LLM agents and RL baselines, and ablations suggest the symbolic knowledge and its placement in the world model are the main contributors.

Significance. If the results hold, WALL-E 2.0 offers a practical, interpretable way to correct LLM priors in environments with counter-commonsense mechanics without fine-tuning, and the code-rule mechanism provides a verifiable interface between symbolic constraints and LLM predictions. The paper includes useful ablations (Tables 3–5) and a diagnostic cover-rate measure (Section 4.4) that directly tracks how much of the LLM's misprediction is corrected by learned rules. The reported ALFWorld success rate of 98% is notable, though its statistical robustness needs verification. The significance is tempered by the narrow scope of the 'world model' (binary success/failure prediction), the acknowledged inability to handle stochastic transitions, and a mismatch between the MPC framing and the one-step implementation.

major comments (5)
  1. [3.1.1, Appendix F, Eq. (9)] The binary success/failure world model in Section 3.1.1 assumes deterministic transitions, but the paper's own Appendix F concedes that Mars includes stochastic outcomes (e.g., resource gathering at night can sometimes succeed) and Section 4.3 reports high variance from cows shooting arrows. On such transitions, a learned rule that always predicts failure is a policy bias rather than a predictive world model, and the cover-rate metric (Eq. 9) is not well-defined for stochastic transitions because a failure-predicting rule covers only the failure realizations. The authors should either restrict the world-model predictions and cover-rate evaluation to deterministic transitions, model stochastic outcomes probabilistically, or explicitly justify the conservative-failure treatment as a safety policy rather than an accurate world model.
  2. [3.2, Appendix E.1] The abstract and introduction claim an 'LLM-based MPC framework' with look-ahead optimization of future steps, but Algorithm 2 is a one-step action feasibility filter: the loop only replans a single action until it passes the code-rule checks, and Appendix E.1 confirms 'We employ a one-step MPC approach.' This is a mismatch between the stated contribution and the implemented method; either implement multi-step look-ahead planning or reframe the contribution as a one-step predictive filter.
  3. [4.3, Table 1] The text states that WALL-E 2.0 surpasses IfR 'in every aspect' (Figure 5), but Table 1 shows IfR outperforming WALL-E 2.0 on the TERR. TASK. row (reward 6.9±1.8 vs 5.8±2.2; score 7.1±2.5 vs 4.7±2.0) and on the SURV. TASK. reward row (3.3±1.4 vs 3.2±1.4). The superiority claim should be restricted to average results or the specific settings where it holds.
  4. [Table 2, Figure 4] ALFWorld results are reported as single success rates without error bars or trial counts, despite using GPT-3.5-Instruct with stochastic sampling. The 98% versus RAFA's 95% difference may be within run-to-run noise. The authors should report multiple runs with standard deviations or confidence intervals and specify the number of trials per task type.
  5. [Abstract, Conclusion, Section 4.2] The abstract and conclusion say WALL-E 2.0 surpasses baselines in Mars by 16.1%–51.6% of 'success rate', but Section 4.2 and Table 1 define and report 'Reward' and 'Score', not success rate. The 16.1%–51.6% range corresponds to reward improvements. The metric name should be corrected throughout the abstract and conclusion.
minor comments (5)
  1. [Figure 3 caption] The caption has duplicate stage numbering: it lists '(2) learning new symbolic knowledge...; (4) translating symbolic knowledge to code; and (4) Code rule set pruning.' The numbering should be corrected to 2, 3, 4.
  2. [Appendix B.1] In the prompt text, 'Please use you knowledge' should be 'Please use your knowledge'.
  3. [Appendix C.2] In the structured observation format, 'inital_state' appears as a key in the ALFWorld action rules (Appendix D.2); this should be 'initial_state' for consistency with the main text.
  4. [Section 4.5, Table 4] The text reports that applying symbolic knowledge in the world model yields 'about a 46.3% improvement' and in the agent 'about a 30.9% gain', but the numbers in Table 4 give (6.7−3.8)/6.7 ≈ 43.3% for the world-model case and (5.5−3.8)/5.5 ≈ 30.9% for the agent case; please clarify the formula or adjust the percentages.
  5. [Algorithm 2, Section 3.1.2] The values of REPLAN LIMIT (Algorithm 2) and the context window k (Section 3.1.2) are never specified; please report their values for reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

There is no material circularity: the central performance and cover-rate results are measured on held-out benchmark data, and the only self-citation (WALL-E 1.0) is not load-bearing.

full rationale

WALL-E 2.0's derivation chain is: collect real trajectories; use LLM inductive reasoning to extract action rules, knowledge graphs, and scene graphs; translate them into executable code rules; prune rules by maximum coverage of incorrectly predicted training transitions; and apply the rules in MAPEXECUTE to override LLM world-model predictions. The supervision throughout is the environment's actual transition outcomes, not the reported performance numbers. The main results are on held-out data: ALFWorld uses a 134-task testing split, and Mars results are averaged over trials after fixed learning episodes, so task success is not a fitted value. The cover-rate metric (Appendix E.3, Eq. 9) is also a held-out generalization measure: rules learned from training episodes are evaluated on mispredicted transitions collected from test trajectories, with correctness judged against real outcomes. The fact that the pruning objective (Eq. 5) uses the same coverage notion on training mispredictions does not make the test cover rate circular; the sets differ, and the reported 0% to 92.3% increase is evidence that the learned rules correct out-of-sample LLM errors. The only self-citation, WALL-E 1.0 (Zhou et al., 2024), serves as a baseline and prior framework, not as a load-bearing external theorem. The binary success/failure simplification (Section 3.1.1) and the admitted inability to handle stochastic dynamics (Appendix F) are scope limitations rather than circularities. Thus no step reduces by construction to its own input; the appropriate finding is minor or no circularity (score 2).

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical or theoretical entities. Its 'inventions' are data structures (knowledge graphs, scene graphs) and code rules, which are not new postulated things in the sense of the graviton problem. The central claim rests on the LLM's inductive reliability and the determinism of the test environments, both listed as axioms.

free parameters (3)
  • Code rule set limit l = Not fixed; ablated as 5, 3, 1
    Section 3.1.4 says 'a large l leads to better performance' and Table 5 ablates l=5,3,1, but no principled selection criterion is given. This hyperparameter controls rule-set size and directly affects final performance.
  • Context window k for rule extraction = Unspecified
    In Eq. (1), k defines the finite context window of past transitions used for inductive rule extraction. No value is reported in the paper, which affects which transitions contribute to the learned rules.
  • REPLAN LIMIT = Unspecified
    Algorithm 2 uses REPLAN LIMIT as the stopping condition for the MPC revision loop, but no value is given. It controls how many times the agent can revise a proposed action.
assumptions (3)
  • domain assumption Environment transitions are deterministic enough that success/failure determines the next observation.
    Section 3.1.1: 'ot+1 can be derived from the success or failure of an action.' This is load-bearing for the code-rule design and is acknowledged as invalid for stochastic settings in Appendix F.
  • domain assumption An LLM can perform reliable inductive reasoning over trajectories to extract accurate, general rules.
    Stage 2 (Section 3.1.2) relies on fInductiveReasoning, implemented by prompting an LLM. The paper provides no guarantee that extracted rules are correct, complete, or free of hallucination.
  • domain assumption Generated Python code rules are executed correctly and strictly override LLM predictions.
    Section 3.2 states 'the outputs of the code rules override the predictions of the LLM world model.' This assumes the generated code is bug-free and that the structured state representation accurately reflects the true environment state.

how reviews work

0 comments
Cite this review

Pith. "Pith review of WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents." pith.science (2026). https://pith.science/paper/UITKPTYG

@misc{pith2026250415785,
  author       = {Pith},
  title        = {Pith review of: WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UITKPTYG}},
  note         = {Machine review of arXiv:2504.15785}
}
read the original abstract

Can we build accurate world models out of large language models (LLMs)? How can world models benefit LLM agents? The gap between the prior knowledge of LLMs and the specified environment's dynamics usually bottlenecks LLMs' performance as world models. To bridge the gap, we propose a training-free "world alignment" that learns an environment's symbolic knowledge complementary to LLMs. The symbolic knowledge covers action rules, knowledge graphs, and scene graphs, which are extracted by LLMs from exploration trajectories and encoded into executable codes to regulate LLM agents' policies. We further propose an RL-free, model-based agent "WALL-E 2.0" through the model-predictive control (MPC) framework. Unlike classical MPC requiring costly optimization on the fly, we adopt an LLM agent as an efficient look-ahead optimizer of future steps' actions by interacting with the neurosymbolic world model. While the LLM agent's strong heuristics make it an efficient planner in MPC, the quality of its planned actions is also secured by the accurate predictions of the aligned world model. They together considerably improve learning efficiency in a new environment. On open-world challenges in Mars (Minecraft like) and ALFWorld (embodied indoor environments), WALL-E 2.0 significantly outperforms existing methods, e.g., surpassing baselines in Mars by 16.1%-51.6% of success rate and by at least 61.7% in score. In ALFWorld, it achieves a new record 98% success rate after only 4 iterations.

Figures

Figures reproduced from arXiv: 2504.15785 by the authors.

Figure 1
Figure 1. WALL-E 2.0 mining a diamond on Mars. Step 1-2: The agent makes decisions via MPC with the initial unaligned world model, resulting in a failed action for mining iron. Step 3: leveraging previous trajectories and world model predictions, WALL-E 2.0 learns symbolic knowledge, including rules, knowledge graphs, and scene graphs. Step 4-5: The learned symbolic knowledge helps the world model make accurate predictions an… view at source ↗
Figure 2
Figure 2. Overview of WALL-E 2.0. The agent determines ac￾tions to take via MPC, which optimizes future steps’ actions by interacting with a neurosymbolic world model. The world model adopts an LLM whose predictions are aligned with environment dynamics through code rules converted from symbolic knowledge (action rules, knowledge/scene graph) learned via inductive rea￾soning from real trajectories and predicted trajectories. … view at source ↗
Figure 3
Figure 3. NeuroSymbolic Learning of Code Rules. WALL-E 2.0 iteratively refines the symbolic knowledge with the agent’s actual trajectories in the environment and the world model’s predicted trajectories. The NeuroSymbolic learning takes 4 stages: (1) comparing predicted and actual trajectories; (2) learning new symbolic knowledge from real trajectories; (4) translating symbolic knowledge to code; and (4) Code rule set pruning… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: WALL-E 2.0 vs. baselines on 134 testing tasks from the ALFWorld. WALL-E 2.0 exhibiting superior planning ability and achieves the highest success rate after only 4 iterations, significantly surpassing other baselines. WALL-E 2.0 excels in planning and task-solving with…
Figure 5
Figure 5. Figure 5: Comparison between WALL-E 2.0 and IfR (the best baseline in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Left: WALL-E 2.0 code rules’ cover rate (higher the better) over neurosymsbolic learning iterations in Mars. The cover rate measures the percentage of LLM failed predictions that can be corrected by our world model. Right: Comparison between WALL-E 2.0’s world model an…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions

    cs.AI 2025-11 conditional novelty 6.0 of 10

    MAPs is a new amusement-park simulator benchmark on which frontier LLM agents score 7–15% of human performance, exposing persistent gaps in long-horizon planning, active learning, spatial reasoning, and handling stoch...

Reference graph

Works this paper leans on

27 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    Rt-1: Robotics transformer for real-world control at scale

    Brohan, A., Brown, N., Carbajal, J., Chebotar, Y ., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., et al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.06817,

    arXiv

  2. [5]

    J., Wang, Z., Wang, D

    Hao, S., Gu, Y ., Ma, H., Hong, J. J., Wang, Z., Wang, D. Z., and Hu, Z. Reasoning with language model is plan- ning with world model.arXiv preprint arXiv:2305.14992,

    arXiv

  3. [6]

    and Shu, T

    Hu, Z. and Shu, T. Language models, agent models, and world models: The law for machine reasoning and plan- ning. arXiv preprint arXiv:2312.05230,

    arXiv

  4. [7]

    Optimus-1: Hybrid multimodal memory empow- ered agents excel in long-horizon tasks

    Li, Z., Xie, Y ., Shao, R., Chen, G., Jiang, D., and Nie, L. Optimus-1: Hybrid multimodal memory empow- ered agents excel in long-horizon tasks. arXiv preprint arXiv:2408.03615,

    arXiv

  5. [9]

    Reason for future, act for now: A principled framework for autonomous llm agents with provable sam- ple efficiency

    Liu, Z., Hu, H., Zhang, S., Guo, H., Ke, S., Liu, B., and Wang, Z. Reason for future, act for now: A principled framework for autonomous llm agents with provable sam- ple efficiency. arXiv preprint arXiv:2309.17382,

    arXiv

  6. [10]

    Chatrule: Mining logical rules with large language models for knowledge graph reasoning

    Luo, L., Ju, J., Xiong, B., Li, Y .-F., Haffari, G., and Pan, S. Chatrule: Mining logical rules with large language models for knowledge graph reasoning. arXiv preprint arXiv:2309.01538,

    arXiv

  7. [11]

    and Fleuret, F

    Micheli, V . and Fleuret, F. Language models are few-shot butlers. arXiv preprint arXiv:2104.07972,

    arXiv

  8. [12]

    Can llms follow simple rules? arXiv preprint arXiv:2311.04235, 2023a

    Mu, N., Chen, S., Wang, Z., Chen, S., Karamardian, D., Aljeraisy, L., Hendrycks, D., and Wagner, D. Can llms follow simple rules? arXiv preprint arXiv:2311.04235, 2023a. Mu, Y ., Zhang, Q., Hu, M., Wang, W., Ding, M., Jin, J., Wang, B., Dai, J., Qiao, Y ., and Luo, P. Embod- iedgpt: Vision-language pre-training via embodied chain of thought. arXiv preprin...

    arXiv

Show all 27 references
  1. [13]

    Proximal policy optimization algorithms

    Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347,

    arXiv

  2. [15]

    ALFRED: A benchmark for interpreting grounded instructions for everyday tasks

    Shridhar, M., Thomason, J., Gordon, D., Bisk, Y ., Han, W., Mottaghi, R., Zettlemoyer, L., and Fox, D. ALFRED: A benchmark for interpreting grounded instructions for everyday tasks. In CVPR, 2020a. Shridhar, M., Yuan, X., C ˆot´e, M.-A., Bisk, Y ., Trischler, A., and Hausknech...

    2010 arXiv

  3. [16]

    Worldcoder, a model- based llm agent: Building world models by writing code and interacting with the environment

    Tang, H., Key, D., and Ellis, K. Worldcoder, a model- based llm agent: Building world models by writing code and interacting with the environment. arXiv preprint arXiv:2402.12275, 2024a. Tang, X., Li, J., Liang, Y ., Zhu, S.-c., Zhang, M., and Zheng, Z. Mars: Situated inductiv...

    arXiv

  4. [18]

    Autogen: Enabling next-gen llm applications via multi-agent conversation framework

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

    arXiv

  5. [19]

    L., et al

    Wu, X., Guan, T., Li, D., Huang, S., Liu, X., Wang, X., Xian, R., Shrivastava, A., Huang, F., Boyd-Graber, J. L., et al. Autohallusion: Automatic generation of hallucination benchmarks for vision-language models. arXiv preprint arXiv:2406.10900,

    arXiv

  6. [20]

    Making large language models into world models with precondition and effect knowledge

    Xie, K., Yang, I., Gunerli, J., and Riedl, M. Making large language models into world models with precondition and effect knowledge. arXiv preprint arXiv:2409.12278,

    arXiv

  7. [21]

    Enabling large language models to learn from rules

    Yang, W., Lin, Y ., Zhou, J., and Wen, J. Enabling large language models to learn from rules. arXiv preprint arXiv:2311.08883, 2023a. Yang, Y ., Zhou, T., Li, K., Tao, D., Li, L., Shen, L., He, X., Jiang, J., and Shi, Y . Embodied multi-modal agent trained by an llm from a par...

    arXiv

  8. [22]

    Failures pave the way: En- hancing large language models through tuning-free rule accumulation

    Yang, Z., Li, P., and Liu, Y . Failures pave the way: En- hancing large language models through tuning-free rule accumulation. arXiv preprint arXiv:2310.15746, 2023b. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y . React: Synergizing reasoning and ...

    arXiv

  9. [23]

    Wall-e: World alignment by rule learning improves world model-based llm agents

    Zhou, S., Zhou, T., Yang, Y ., Long, G., Ye, D., Jiang, J., and Zhang, C. Wall-e: World alignment by rule learning improves world model-based llm agents. arXiv preprint arXiv:2410.07484,

    arXiv

  10. [24]

    Minigpt-4: Enhancing vision-language understanding with advanced large language models

    Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023a. Zhu, X., Chen, Y ., Tian, H., Tao, C., Su, W., Yang, C., Huang, G., Li, B., Lu, L., Wang, X., ...

    arXiv

  11. [25]

    used a Bayesian adaptive Markov Decision Process to guide LLMs in planning future trajectories, prompting them to predict future states. While these approaches demonstrate the potential of using LLMs as world models, they often require extensive fine-tuning or rely heavily on ...

    2024

  12. [26]

    partially addresses this issue by extracting knowledge graphs from historical trajectories, enabling more flexible knowledge transfer across tasks. However, it still relies heavily on logging trajectories and custom scripts tailored to a specific environment’s data format for ...

    2023

  13. [27]

    Action failed: Not enough {resource} to make {tool_name}

    < amount: feedback = f"Action failed: Not enough {resource} to make {tool_name}." suggestion = f"Collect more {resource} to make {tool_name}." return feedback, False, suggestion # If all checks pass return "Action executed successfully.", True, "" def Rule_1_place(state, actio...

    2022

  14. [2017]

    Reflexion: Language agents with verbal reinforcement learning.(2023)

    Shinn, N., Cassano, F., Labash, B., Gopinath, A., Narasimhan, K., and Yao, S. Reflexion: Language agents with verbal reinforcement learning.(2023). arXiv preprint cs.AI/2303.11366,

    2023 arXiv

  15. [2018]

    Physically grounded vision- language models for robotic manipulation

    Gao, J., Sarkar, B., Xia, F., Xiao, T., Wu, J., Ichter, B., Ma- jumdar, A., and Sadigh, D. Physically grounded vision- language models for robotic manipulation. arXiv preprint arXiv:2309.02561, 2023a. Gao, P., Han, J., Zhang, R., Lin, Z., Geng, S., Zhou, A., Zhang, W., Lu, P.,...

    arXiv

  16. [2021]

    Mastering diverse domains through world models

    10 W ALL-E 2.0: World Alignment by NeuroSymbolic Learning Hafner, D., Pasukonis, J., Ba, J., and Lillicrap, T. Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104,

    arXiv

  17. [2022]

    S., Feng, J., Ko- rneev, N., Tenenbaum, J

    Wong, L., Mao, J., Sharma, P., Siegel, Z. S., Feng, J., Ko- rneev, N., Tenenbaum, J. B., and Andreas, J. Learning adaptive planning representations with natural language guidance. arXiv preprint arXiv:2312.08566,

    arXiv

  18. [2023]

    Benchmarking the spectrum of agent capabilities

    Hafner, D. Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780,

    arXiv

  19. [2024]

    Aligning cyber space with physical world: A comprehensive survey on embodied ai

    Liu, Y ., Chen, W., Bai, Y ., Luo, J., Song, X., Jiang, K., Li, Z., Zhao, G., Lin, J., Li, G., et al. Aligning cyber space with physical world: A comprehensive survey on embodied ai. arXiv preprint arXiv:2407.06886,

    arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.