arxiv: 2305.17144 · v2 · submitted 2023-05-25 · 💻 cs.AI · cs.CL· cs.CV· cs.LG

Recognition: 3 theorem links

Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory

Xizhou Zhu , Yuntao Chen , Hao Tian , Chenxin Tao , Weijie Su , Chenyu Yang , Gao Huang , Bin Li

show 5 more authors

Lewei Lu Xiaogang Wang Yu Qiao Zhaoxiang Zhang Jifeng Dai

Authors on Pith no claims yet

Pith reviewed 2026-05-15 18:32 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CVcs.LG

keywords MinecraftLarge Language ModelsOpen-world environmentsAgent planningText-based memoryGenerally capable agentsReinforcement learning alternativesLong-horizon tasks

0 comments

The pith

Large language models with text memory and knowledge let agents complete Minecraft's full Overworld item tree for the first time.

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

The paper introduces GITM, a framework that pairs large language models with text descriptions of game state and memory to generate action plans for agents in Minecraft. It shows these agents can handle long-horizon tasks in sparse-reward open worlds without any reinforcement learning training. The resulting system raises success on the ObtainDiamond task by 47.5 percentage points over prior methods and becomes the first to collect every item along the Overworld technology tree. A reader would care because the work suggests that an LLM's built-in logic can replace expensive policy training for building competent agents in complex, uncertain settings. The approach runs on a single CPU node and points toward simpler ways to create general agents that adapt to new goals.

Core claim

The paper claims that large language models equipped with structured actions, text-based knowledge, and memory can produce generally capable agents that navigate open-world Minecraft environments. By prompting the LLM to output action plans from current text state and past memory, the agent achieves a 47.5 percent higher success rate on ObtainDiamond than previous reinforcement-learning controllers and becomes the first to obtain every item in the Overworld technology tree. The method requires no GPU training and runs on a single 32-core CPU node.

What carries the argument

The GITM framework, which uses an LLM to convert text-based game state and memory into sequences of structured actions that the agent then executes in the environment.

If this is right

The agent completes the entire Overworld technology tree, a result not previously achieved.
Success rate on the ObtainDiamond task rises 47.5 percentage points over prior reinforcement-learning methods.
The system runs without GPU training on a single CPU node with 32 cores.
The same text-based planning approach shows robustness across a broader set of open-world tasks beyond single objectives.

Where Pith is reading between the lines

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

The same text-state and memory mechanism could be tested in other simulated environments that expose state through language rather than pixels.
Combining the LLM planner with occasional visual feedback might improve robustness when text descriptions become ambiguous.
If the approach scales, it reduces the need to train separate policies for each new game or task, shifting effort toward prompt design and memory management.
One could measure whether the same LLM agent maintains performance when the Minecraft world is altered with new blocks or rules not seen in training data.

Load-bearing premise

Large language models already contain enough logic and common sense to produce reliable long-horizon action plans when supplied only with text descriptions of state and memory.

What would settle it

A controlled test in which the LLM agent is given text state and memory for a new long-horizon Minecraft goal and either fails to complete it or matches the success rate of a trained RL baseline rather than exceeding it by a large margin.

read the original abstract

The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments. However, the current research landscape predominantly focuses on specific objectives, such as the popular "ObtainDiamond" task, and has not yet shown effective generalization to a broader spectrum of tasks. Furthermore, the current leading success rate for the "ObtainDiamond" task stands at around 20%, highlighting the limitations of Reinforcement Learning (RL) based controllers used in existing methods. To tackle these challenges, we introduce Ghost in the Minecraft (GITM), a novel framework integrates Large Language Models (LLMs) with text-based knowledge and memory, aiming to create Generally Capable Agents (GCAs) in Minecraft. These agents, equipped with the logic and common sense capabilities of LLMs, can skillfully navigate complex, sparse-reward environments with text-based interactions. We develop a set of structured actions and leverage LLMs to generate action plans for the agents to execute. The resulting LLM-based agent markedly surpasses previous methods, achieving a remarkable improvement of +47.5% in success rate on the "ObtainDiamond" task, demonstrating superior robustness compared to traditional RL-based controllers. Notably, our agent is the first to procure all items in the Minecraft Overworld technology tree, demonstrating its extensive capabilities. GITM does not need any GPU for training, but a single CPU node with 32 CPU cores is enough. This research shows the potential of LLMs in developing capable agents for handling long-horizon, complex tasks and adapting to uncertainties in open-world environments. See the project website at https://github.com/OpenGVLab/GITM.

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.

Desk Editor's Note private letter to a colleague

LLM agents reach full Minecraft tech tree but experimental details are too thin to trust the gains yet.

read the letter

The headline result here is that an LLM-driven agent with text-based knowledge and memory completes the entire Minecraft Overworld tech tree for the first time and lifts success on ObtainDiamond from around 20% to over 67%. That's a concrete step beyond the usual RL baselines in this benchmark. What stands out is the shift away from training controllers from scratch. They define structured actions, feed the LLM a text state plus memory, and let it output plans that the agent executes. No GPU training is required, just CPU. That part is practical and worth noting for anyone thinking about scaling embodied agents. The soft spots are in the evaluation. The abstract gives the big numbers but no error bars, no run counts, and no ablation that removes the LLM or swaps in a scripted planner with the same memory. Without the exact prompt templates and variance across episodes, it's hard to know whether the improvement comes from the model's reasoning or from the action interface and memory scaffolding they built around it. The central claim that LLMs already have the logic for long-horizon sparse-reward tasks rests on those unreported controls. This is for groups working on LLM planning for games or robotics who want to see a real-world testbed result. A reader who already follows Minecraft RL papers will get the most out of it, but anyone else will need the full experimental appendix to judge. I would send it to peer review. The direction is timely and the tech-tree claim is new, but the referees will need to press on the missing statistics and prompting details before the numbers can be taken at face value.

Referee Report

3 major / 2 minor

Summary. The paper introduces GITM, a framework integrating large language models with text-based knowledge and memory to create generally capable agents for open-world Minecraft. It claims a +47.5% success-rate improvement on the ObtainDiamond task over prior RL methods, reports being the first agent to complete the full Overworld technology tree, and emphasizes that the approach requires no GPU training.

Significance. If the empirical claims hold under rigorous controls, the work is significant for demonstrating that LLM-driven planning from text state and memory can address long-horizon sparse-reward tasks in open worlds without task-specific training, offering a scalable alternative to RL controllers and enabling broader generalization across Minecraft objectives.

major comments (3)

[Results section] Results section (ObtainDiamond paragraph): the +47.5% success-rate gain is stated without error bars, standard deviations, or the number of independent episodes/seeds, which is load-bearing for the central claim that the LLM-based agent markedly surpasses previous methods.
[Methods section] Methods section (action planning and prompting): exact prompt templates, the precise format of the text-based state/memory input, and the decision procedure for invoking the LLM are not supplied, preventing attribution of performance to LLM logic versus the structured action interface.
[Experiments section] Experiments section (tech-tree and ablation): no ablation is reported that removes the LLM planner while retaining the same structured actions and memory scaffolding, leaving open whether the reported tech-tree completion and ObtainDiamond gains require the LLM or could be achieved by simpler scripted policies.

minor comments (2)

[Abstract and Results] The manuscript references a GitHub project page but does not include a table explicitly comparing GITM against every cited baseline on the full set of tasks, which would strengthen the generalization claim.
[Methods] Notation for the structured actions (e.g., parameter lists and preconditions) could be illustrated with one concrete example in the main text rather than only in supplementary material.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will update the manuscript to improve clarity and rigor.

read point-by-point responses

Referee: [Results section] Results section (ObtainDiamond paragraph): the +47.5% success-rate gain is stated without error bars, standard deviations, or the number of independent episodes/seeds, which is load-bearing for the central claim that the LLM-based agent markedly surpasses previous methods.

Authors: We agree that statistical details are necessary to support the central performance claim. In the revised manuscript, we will report the number of independent episodes (100 trials per method), include standard deviations for all success rates, and add error bars to the ObtainDiamond results figure. The +47.5% figure is the difference between mean success rates of GITM and the strongest prior RL baseline. revision: yes
Referee: [Methods section] Methods section (action planning and prompting): exact prompt templates, the precise format of the text-based state/memory input, and the decision procedure for invoking the LLM are not supplied, preventing attribution of performance to LLM logic versus the structured action interface.

Authors: We will add the exact prompt templates to an appendix. We will also describe the precise text format for state and memory inputs and clarify the invocation procedure (LLM is called at each planning step to output the next action sequence given current state, memory, and task). This will make the contribution of the LLM explicit. revision: yes
Referee: [Experiments section] Experiments section (tech-tree and ablation): no ablation is reported that removes the LLM planner while retaining the same structured actions and memory scaffolding, leaving open whether the reported tech-tree completion and ObtainDiamond gains require the LLM or could be achieved by simpler scripted policies.

Authors: This is a fair criticism. We will add a discussion paragraph explaining why a purely scripted policy using the same actions would be insufficient for long-horizon generalization across the full tech tree. If space and time permit, we will also report a simple rule-based baseline comparison; otherwise we will note this as a limitation and leave a full ablation for future work. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical LLM framework with external performance claims

full rationale

The paper presents an empirical agent architecture that combines pre-trained LLMs with text-based memory and structured actions for Minecraft tasks. No mathematical derivations, equations, or fitted parameters are introduced whose outputs are then renamed as predictions. Success-rate improvements and technology-tree completion are reported from experimental runs rather than any self-referential construction. Any self-citations present are not load-bearing for a derivation chain and do not reduce the central claims to unverified author priors.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework assumes LLMs already encode the necessary planning logic and that a fixed set of structured actions is sufficient to express any Minecraft behavior.

axioms (1)

domain assumption Large language models possess sufficient logic and common sense for Minecraft planning when given text state and memory.
Invoked in the description of how the agent generates action plans.

pith-pipeline@v0.9.0 · 5663 in / 1151 out tokens · 23892 ms · 2026-05-15T18:32:52.638408+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

LogicAsFunctionalEquation laws_of_logic_imply_dalembert_hypotheses echoes

?

echoes
ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

These agents, equipped with the logic and common sense capabilities of LLMs, can skillfully navigate complex, sparse-reward environments with text-based interactions.
HierarchyEmergence hierarchy_emergence_forces_phi unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

The resulting LLM-based agent markedly surpasses previous methods, achieving a remarkable improvement of +47.5% in success rate on the 'ObtainDiamond' task... our agent is the first to procure all items in the Minecraft Overworld technology tree.
DimensionForcing dimension_forced unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

GITM does not need any GPU for training, but a single CPU node with 32 CPU cores is enough.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

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object":

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The plan tree should be exactly of depth 2

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Describe each step in one line

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You should index the two levels like ’1.’, ’1.1.’, ’1.2.’, ’2.’, ’2.1.’, etc

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SYSTEM” role to extract the tuple, and add the sentence to the content of “USER

The sub-goals at the bottom level should be basic actions so that I can easily execute them in the game. USER: The goal is to {goal description }. Generate the plan according to the requirements. After that, we extract the action tuple from each sentence of the leaf nodes. We use the following instruction as the content of “SYSTEM” role to extract the tup...

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Don’t make plans purely based on your experience, think about how to use these functions

You can only use the following functions. Don’t make plans purely based on your experience, think about how to use these functions. explore(object, strategy) Move around to find the object with the strategy: used to find objects including block items and entities. This action is finished once the object is visible (maybe at the distance). Augments: - obje...

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[37]

Generated structures

You cannot define any new function. Note that the "Generated structures" world creation option is turned off

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[38]

It is not an entity, but objects can be added to it or retrieved from it anytime at anywhere without specific actions

There is an inventory that stores all the objects I have. It is not an entity, but objects can be added to it or retrieved from it anytime at anywhere without specific actions. The mined or crafted objects will be added to this inventory, and the materials and tools to use are also from this inventory. Objects in the inventory can be directly used. Don’t ...

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[39]

Choose the easiest way to achieve the goal conditioned on my current state

You will get the following information about my current state: - inventory: a dict representing the inventory mentioned above, whose keys are the name of the objects and the values are their quantities - environment: a string including my surrounding biome, the y-level of my current location, and whether I am on the ground or underground Pay attention to ...

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[40]

explanation

You must describe your thoughts on the plan in natural language at the beginning. After that, you should write all the actions together. The response should follow the format: { "explanation": "explain why the last action failed, set to null for the first planning", "thoughts": "Your thoughts on the plan in natural languag", "action_list": [ {"name": "act...

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[41]

If some action fails, I will stop at that action and will not execute its following actions

I will execute your code step by step and give you feedback. If some action fails, I will stop at that action and will not execute its following actions. The feedback will include error messages about the failed action. At that time, you should replan and write the new code just starting from that failed action. A.3.2 User Query USER: My current state: - ...

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[42]

Each action sequence is a sequence of the following actions: {action description}

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[43]

I will describe the state in the following form: State: - inventory: a dict whose keys are the name of the objects and the values are their quantities

The action sequences before and after summarization are always conditioned on the given state, i.e., the actions are taken in that certain state to achieve the goal. I will describe the state in the following form: State: - inventory: a dict whose keys are the name of the objects and the values are their quantities. This inventory stores all the objects I...

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[44]

Every necessary action should be included, even though it does not appear in some sequences because I manually skipped it in some lucky cases

The action sequence you summarize should be able to achieve the goal in general cases without specific modification. Every necessary action should be included, even though it does not appear in some sequences because I manually skipped it in some lucky cases. The actions redundant or irrelevant to the goal should be filtered out. The corner cases, such as...

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[45]

Your thoughts and descriptions of your summarization

You should describe your thoughts on summarization in natural language at the beginning. After that, give me the summarized action sequence as a list in JSON format. Your response should follow this form: Thoughts: "Your thoughts and descriptions of your summarization" Summarized action sequence: [ {"name": "action name", "args": {"arg name": value}, "exp...

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