Pith. sign in

REVIEW 44 references

BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks

T0 review · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A backward-reasoning LLM agent with recursive goal decomposition, state-consistency repair, and stage memory outperforms forward-planning baselines on Minecraft task planning.

arxiv 2505.14079 v3 pith:OHHKOHAZ submitted 2025-05-20 cs.CL

Weihong Du , Wenrui Liao , Binyu Yan , Hongru Liang , Anthony G. Cohn , Wenqiang Lei This is my paper
classification cs.CL
keywords agentreasoningstateplanningtasksbackwardcomplexgoal
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

Most LLM agents plan a complex task by starting with what they have and deciding the next action: mine wood, craft planks, craft a pickaxe, and so on. The authors argue this forward direction fails when the goal is far from the starting state. Their agent, BAR, instead plans backward. For a goal like 'obtain a diamond pickaxe', it asks: what single last action achieves this? The answer is 'craft a diamond pickaxe'. Then it asks: what sub-goals must be true before that action? It collects those sub-goals, puts them in a queue, and repeats the process until no sub-goals remain. The final plan is the reverse of the decomposition steps.

Backward planning can ignore the physical world, so BAR includes a repair step. It rates the steps of the backward plan, picks suspicious segments, and asks a forward-planning prompt to fill in the missing steps, for example adding 'dig down' before 'mine stone' because stone is underground. A third module, stage memory, stores previous decomposition results whose plans executed successfully in the Minecraft environment, and reuses them when the same sub-goal appears in a new task.

In static tests with no environment interaction, BAR with GPT-4 improves accuracy, F1, and edit distance over chain-of-thought, reverse chain, self-refine, tree-of-thought, DEPS, plan-and-solve, and o1. In dynamic tests, BAR with stage memory performs even better. However, the dynamic evaluation builds memory from the same 53 tasks it then re-plans, so the gains may partly come from memorizing the test set rather than from a generalizable memory mechanism.

Extended reading notes

Core claim

The load-bearing claim is that backward reasoning from the terminal state is superior to forward reasoning for complex planning: 'our agent achieves SOTA performance on all 5 groups under all three metrics' (Table 1) in static planning, and the dynamic agent with stage memory further improves these results. If the paper is correct, an LLM planner can substantially improve long-horizon Minecraft planning by recursively decomposing goals from the end, then repairing state conflicts with forward reasoning.

Load-bearing premise

The dynamic planning evaluation assumes that storing decomposition results from executing plans on the 53 evaluation tasks and then re-planning the same 53 tasks is a valid test of memory-augmented planning. If stage memory is only helpful because the target goals appeared during memory construction, the reported dynamic gains (Table 2) do not generalize to novel tasks; the paper does not hold out tasks during memory building.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method depends on LLM internal knowledge, expert annotations, execution success as a quality signal, and hand-chosen hyperparameters. There are no new physical entities; the stage memory is a data structure, not an invented physical entity.

free parameters (3)
  • anchor threshold t = 5
    Chosen to select start anchor steps in step-scoring; no sensitivity analysis across thresholds, only comparison of step scoring vs sliding window.
  • anchor interval k = 3
    Number of steps between start and end anchors in the state-consistency module; set without exhaustive tuning.
  • stage memory success threshold = 0.3
    Decomposition results with execution success rate at least 0.3 are reused in dynamic planning; threshold chosen by hand.
assumptions (4)
  • domain assumption The LLM's parametric knowledge of Minecraft recipes and spatial constraints is sufficiently accurate for backward decomposition.
    Goal decomposition prompt in Appendix C relies on LLM memorized knowledge, not an external knowledge base.
  • domain assumption Expert-annotated ground-truth plans for the 53 tasks are correct, unique, and complete.
    All textual metrics compare against these annotations (Section 4.1, Appendix B.1).
  • domain assumption Execution success rate of a plan in the Minecraft environment reflects the accuracy of the corresponding goal decomposition.
    Stage memory stores decompositions with success rate at least 0.3 (Section 4.3).
  • domain assumption High-level step encapsulation (craft, smelt, equip, mine, dig down) preserves the semantics of plans so that plan quality on steps transfers to execution quality.
    Environment execution uses heuristic rules and MineDreamer for low-level control (Appendix B.3.1).

how reviews work

0 comments
Cite this review

Pith. "Pith review of BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks." pith.science (2026). https://pith.science/paper/OHHKOHAZ

@misc{pith2026250514079,
  author       = {Pith},
  title        = {Pith review of: BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OHHKOHAZ}},
  note         = {Machine review of arXiv:2505.14079}
}
read the original abstract

Large language model (LLM) based agents have shown great potential in following human instructions and automatically completing various tasks. To complete a task, the agent needs to decompose it into easily executed steps by planning. Existing studies mainly conduct the planning by inferring what steps should be executed next starting from the agent's initial state. However, this forward reasoning paradigm doesn't work well for complex tasks. We propose to study this issue in Minecraft, a virtual environment that simulates complex tasks based on real-world scenarios. We believe that the failure of forward reasoning is caused by the big perception gap between the agent's initial state and task goal. To this end, we leverage backward reasoning and make the planning starting from the terminal state, which can directly achieve the task goal in one step. Specifically, we design a BAckward Reasoning based agent (BAR). It is equipped with a recursive goal decomposition module, a state consistency maintaining module and a stage memory module to make robust, consistent, and efficient planning starting from the terminal state. Experimental results demonstrate the superiority of BAR over existing methods and the effectiveness of proposed modules.

Figures

Figures reproduced from arXiv: 2505.14079 by the authors.

Figure 1
Figure 1. A toy example showing the essential advantage of backward reasoning over forward reasoning for task [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The conflict of the agent’s physical states during planning. In Minecraft, [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Our proposed backward reasoning based agent. The agent consists of three key modules. Module (a) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Collected tasks on the technology tree in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Results of the dynamic planning experiments [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Results of the ablation study for state con [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Results of the ablation study for state consis [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Computation demonstration of the three adopted textual metrics. B.3 Details of the Simulation Environment B.3.1 High-level Steps Encapsulation As our work focuses on task planning instead of generating fine-grained machine instructions, based on the work of Jarvis-1 (W…
Figure 9
Figure 9. Figure 9: The performance of different models on plans [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: Sample diversity of different models on tasks [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]
Figure 11
Figure 11. Figure 11: Results of ablation study for state consistency [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 12
Figure 12. Figure 12: Results of ablation study for state consistency [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]
Figure 13
Figure 13. Figure 13: Results of ablation study for state consistency [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: Results of ablation study for state consistency [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

44 extracted references · 40 canonical work pages

  1. [1]

    Obtain 1 wooden_pickaxe

    work page

  2. [2]

    I will give you a goal to achieve in Minecraft and the generated initial plan to achieve this goal

    Dig down with wooden_pickaxe ======== ======== Goal: {new goal} Thought: Assistant: Prompt used to rate for all the steps in the initial plan in section 3.2 is as follows: System: You are a helpful assistant in Minecraft. I will give you a goal to achieve in Minecraft and the generated initial plan to achieve this goal. You need to rate for all the steps ...

    work page

  3. [3]

    Craft 2 stick Next Step:

    work page

  4. [4]

    craft 1 craft- ing_table

    Minedreamer: Learning to follow instructions via chain-of-imagination for simulated-world control. arXiv preprint arXiv:2403.12037. Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Wei- jie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, et al. 2023. Ghost in the Minecraft: Generally capable agents for open-world environments via large languag...

    arXiv 2023

  5. [6]

    Mine 3 stone with wooden_pickaxe

    Mine 3 stone with wooden_pickaxe Thought: As the stone only exists in below the ground, when executing the step “Mine 3 stone with wooden_pickaxe”, the agent should be below the ground. However, before this step the agent is gathering materials and crafting items above the ground. So the latter half of the steps in the initial plan may not be executed suc...

    work page

  6. [11]

    Mine 3 log with barehand - 10

    work page

  7. [12]

    Craft 1 crafting_table - 3

    work page

  8. [13]

    Craft 1 wooden_pickaxe - 3

    work page

Show all 44 references
  1. [14]

    collect 3 stone

    Mine 3 stone with wooden_pickaxe - 5 ======== ======== Goal: {goal of new task} Initial Plan: {generated initial plan} Thought: Assistant: Prompt used to complement the partial plan be- tween the given start and end anchor steps in sec- tion 3.2 is as follows: System: You are ...

  2. [18]

    Mine 3 stone with wooden_pickaxe Complementary Partial Plan:

  3. [22]

    Dig down with wooden_pickaxe

    Mine 3 stone with wooden_pickaxe Thought: The initial plan ignores one important step “Dig down with wooden_pickaxe” that is presented in the complementary partial plan. As the stone is in below ground, the agent should reach below ground first and then mine the stone. Corrected Plan:

  4. [27]

    Meta-Llama-3-8B- Instruct

    Mine 3 stone with wooden_pickaxe ======== ======== Goal: {goal of new task} Initial Plan: {generated initial plan} Complementary Partial Plan: {complementary partial plan} Thought: Assistant: D Details of Baseline Models In this section, we will provide the details of the base...

    2022

  5. [32]

    Recipe: {recipe of new task}

    Mine 3 stone with wooden_pickaxe ======== ======== Goal: {goal of new task}. Recipe: {recipe of new task}. Thought: Assistant: Reverse Chain (Zhang et al., 2024).In contrast to Chain-of-thought, Reverse Chain utilizes back- ward reasoning based thought chains to generate the p...

    2024

  6. [37]

    Correct" or

    Mine 3 stone with wooden_pickaxe ======== ======== Goal: {goal of new task}. Recipe: {recipe of new task}. Thought: Assistant: Self-Refine (Madaan et al., 2024).Self-Refine first generates an initial plan using the same prompt as Chain-of-Thought. The generated initial plan is...

    2024

  7. [40]

    Dig down with wooden_pickaxe 6.Mine 3 log with wooden_pickaxe

  8. [41]

    Crafting 1 wooden_pickaxe

    Mine 3 stone with wooden_pickaxe What’s wrong with the plan? Please give me the feedback and list the mistakes if the plan is wrong. Incorrect. Step 4 "Crafting 1 wooden_pickaxe" must be done on a crafting_table. But the plan does not include "Craft 1 crafting_table" before St...

  9. [43]

    Collecting materials on above the ground when the agent is already in below the ground. ======== ======== Goal: {goal of new task} Recipe: {recipe of new task} Plan: {generated initial plan of new task} What’s wrong with the plan? Please give me the feedback and list the mista...

  10. [47]

    Mine 3 log with wooden_pickaxe

  11. [48]

    Crafting 1 wooden_pickaxe

    Mine 3 stone with wooden_pickaxe Feedback: Incorrect. Step 4 "Crafting 1 wooden_pickaxe" must be done on a crafting_table. But the plan does not include "Craft 1 crafting_table" before Step 4, so the first mistake is "ignoring obtaining one crafting_table before using it to cr...

  12. [49]

    Ignoring obtaining one crafting_table before using it to craft other tools

  13. [50]

    Refined Plan:

    Collecting materials on above the ground when the agent is already in below the ground. Refined Plan:

  14. [55]

    OpenAI-gpt-4

    Mine 3 stone with wooden_pickaxe ======== ======== Goal: {goal of new task} Recipe: {recipe of new task} Initial Plan: {initial plan of new task} Feedback: {feedback of the initial plan} Refined Plan: Assistant: Tree-of-thought (Yao et al., 2024).Tree-of- thought generates the...

    2024

  15. [58]

    Craft 1 crafting_table ======== ======== Goal: {goal of new task} Recipe: {recipe of new task} Already Generated Plan: {generated partial plan} Next Step: Assistant: After generating multiple candidate plans, the best plan is selected by taking the average result of three time...

  16. [61]

    craft 1 wooden_pickaxe Evaluation: sure ======== ======== Goal: obtain 1 wooden_pickaxe. Plan:

  17. [63]

    OpenAI-gpt-4

    craft 1 wooden_pickaxe Evaluation: impossible ======== ======== Goal: {goal of new task} Plan: {generated plan of new task} Evaluation: Assistant: DEPS (Wang et al., 2023c).DEPS first gener- ates the initial plan and then re-generates the plan through integrating the descripti...

  18. [67]

    Dig down with wooden_pickaxe

  19. [68]

    Mine 3 stone with wooden_pickaxe Task goal achieving:

  20. [69]

    I obtain 3 log by mining with barehand

  21. [70]

    I obtain 9 planks using 3 log

  22. [71]

    I obtain 2 stick using 2 planks, I have 7 planks left

  23. [72]

    I obtain 1 crafting_table using 4 planks, I have 3 planks left

  24. [73]

    I obtain 1 wooden_pickaxe using 2 stick and 3 planks on 1 crafting_table using, I have 0 planks left and 0 stick left

  25. [74]

    I reach below the ground by digging down with wooden_pickaxe

  26. [75]

    OpenAI-gpt-4

    I obtain 3 stone by mining with wooden_pickaxe In conclusion, I have achieved the task goal by obtaining 3 stone. ======== ======== Goal: {goal of new task} Recipe: {recipe of new task} Let’s first understand the task and devise a plan to achieve the task goal. Then, let’s car...

    2024

  27. [76]

    Mine 2 log with barehand

  28. [79]

    Craft 8 planks completed, there are 0 log, 8 planks in my inventory

    Mine 3 stone with wooden_pickaxe Observation: Mine 2 log with barehand completed, there are 2 log in my inventory. Craft 8 planks completed, there are 0 log, 8 planks in my inventory. Craft 2 stick completed, there are 6 planks, 2 stick in my inventory. Craft 1 crafting_table ...

  29. [80]

    Mine 3 log with barehand

  30. [81]

    Craft 1 crafting_table

  31. [82]

    Craft 1 wooden_pickaxe

  32. [83]

    Craft 9 planks completed, there are 0 log, 9 planks in my inventory

    Mine 3 stone with wooden_pickaxe User: Observation: Mine 3 log with barehand completed, there are 3 log in my inventory. Craft 9 planks completed, there are 0 log, 9 planks in my inventory. Craft 2 stick completed, there are 7 planks, 2 stick in my inventory. Craft 1 crafting_...

  33. [84]

    This is because our agent can narrow down the situations that the agent needs to consider for goal decomposition through the recursive goal decomposition module

    In contrast, our agent shows no significant vari- ation in performance when the length of the plans increases. This is because our agent can narrow down the situations that the agent needs to consider for goal decomposition through the recursive goal decomposition module. Ther...

  34. [2022]

    Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark

    Selection-inference: Exploiting large language models for interpretable logical reasoning.arXiv preprint arXiv:2205.09712. Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. 2021. Explaining answers with entail- ...

    2021 arXiv

  35. [2023]

    Yinger Zhang, Hui Cai, Xierui Song, Yicheng Chen, Rui Sun, and Jing Zheng

    Skill reinforcement learning and planning for open-world long-horizon tasks.arXiv preprint arXiv:2303.16563. Yinger Zhang, Hui Cai, Xierui Song, Yicheng Chen, Rui Sun, and Jing Zheng. 2024. Reverse chain: A generic-rule for LLMs to master multi-API planning. InFindings of the ...

    2024 arXiv

  36. [2024]

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao

    Tree of thoughts: Deliberate problem solving with large language models.Advances in Neural Information Processing Systems, 36. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. React: Synergizing reasoning and acting in language ...

    2023 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.