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arxiv: 2605.30931 · v1 · pith:RMSCZFYZnew · submitted 2026-05-29 · 💻 cs.CL

MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft

Tianjie Ju , Yueqing Sun , Zheng Wu , Wei Zhang , Yaqi Huo , Xi Su , Qi Gu , Xunliang Cai
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Gongshen Liu Zhuosheng Zhang
This is my paper

Pith reviewed 2026-06-28 22:35 UTC · model grok-4.3

classification 💻 cs.CL
keywords MineExplorerMLLM agentsopen-world explorationMinecraft benchmarkmulti-hop tasksembodied reasoningReAct formulationmulti-agent synthesis
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The pith

MLLM agents handle single Minecraft tasks but degrade sharply on longer trajectories requiring hidden prerequisite coordination.

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

The paper presents MineExplorer, a benchmark designed to test how well multimodal large language model agents can explore and act in Minecraft's open world over extended periods. It filters tasks to emphasize general reasoning over game-specific knowledge and builds multi-hop tasks by composing atomic ones, using a multi-agent process to create reliable test cases with task graphs and evaluators. Experiments reveal that capable models succeed on many isolated steps yet lose performance when they must track and fulfill unstated dependencies across sequences. This evaluation matters because sustained exploration in changing environments is a core requirement for agents to operate usefully outside narrow, scripted settings.

Core claim

MineExplorer demonstrates that open-world exploration remains challenging for MLLM agents in Minecraft. Strong models manage many single-hop tasks yet degrade sharply when hidden prerequisites must be coordinated over longer trajectories. The benchmark filters atomic tasks to reduce reliance on domain-specific knowledge, organizes them under a ReAct-style formulation into implicit multi-hop instances, and employs a multi-agent synthesis workflow to design task graphs, sandbox scenes, and rule-based milestone evaluators. Human evaluation confirms the multi-agent workflow yields more reliable instances than single-agent baselines, while further analysis shows task difficulty tracks completion

What carries the argument

The multi-agent synthesis workflow that jointly designs task graphs, sandbox scenes, and rule-based milestone evaluators to produce reliable multi-hop instances from filtered atomic tasks.

If this is right

  • Open-world exploration in dynamic environments stays difficult for current MLLM agents even when they succeed on short tasks.
  • Performance declines as trajectories lengthen and hidden dependencies accumulate.
  • Task difficulty level correlates directly with observed agent completion rates.
  • Increasing model size or enabling thinking modes does not produce consistent gains in exploration success.

Where Pith is reading between the lines

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

  • Short-horizon benchmarks may overestimate agent readiness for realistic open-world use.
  • Methods to surface and manage implicit prerequisites could become a priority for improving long-horizon MLLM behavior.
  • The multi-agent construction approach might transfer to creating evaluation sets in other simulated environments.
  • Difficulty tracking could guide the design of progressive training curricula for exploration agents.

Load-bearing premise

Filtering atomic tasks to remove heavy reliance on Minecraft-specific knowledge yields instances that reflect general open-world reasoning, and the multi-agent synthesis workflow produces significantly more reliable instances than single-agent methods.

What would settle it

A test in which advanced MLLM agents maintain comparable success rates on the benchmark's multi-hop tasks as on its single-hop tasks, or in which human raters judge single-agent generated instances as equally reliable as multi-agent ones.

Figures

Figures reproduced from arXiv: 2605.30931 by Gongshen Liu, Qi Gu, Tianjie Ju, Wei Zhang, Xi Su, Xunliang Cai, Yaqi Huo, Yueqing Sun, Zheng Wu, Zhuosheng Zhang.

Figure 1
Figure 1. Figure 1: Overview of MINEEXPLORER. We first construct atomic task sets by separating open-world knowledge from Minecraft-specific priors, and then map the retained tasks to various capabilities. We further synthesize implicit multi-hop tasks and instantiate them with a multi-agent workflow for benchmark construction. The resulting benchmark places agents in dynamic environments and evaluates their progress with rul… view at source ↗
Figure 2
Figure 2. Figure 2: Statistics and examples of the atomic task pool before and after filtering Minecraft-specific knowledge [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Relationship between rule-based milestone [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Human annotation interface for evaluating benchmark quality and agent execution performance. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Agreement between Claude-Opus-4.6 human annotations and automated milestone detection. ment of 86.8%, indicating that the multi-agent workflow produces reliable milestone evaluators. We remove all instances with inconsistent annota￾tions from the final benchmark. E Fine-grained Results on Multi-hop Tasks [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: TSR of Claude-Opus-4.6 and LLaMA-3.2- 90B-Vision-Instruct across task difficulty levels over three independent runs, with error bars indicating stan￾dard deviation. concrete powder blocks and mine at least one of them. The agent identifies the blue blocks at the be￾ginning of the episode, continues exploring the sur￾rounding area, and successfully mines the nearby brown concrete powder at around 22 second… view at source ↗
Figure 12
Figure 12. Figure 12: Example trajectory of a successful episode in M [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Example trajectory of a failed episode in M [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
read the original abstract

Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic open worlds remains unclear. Existing embodied and game-based benchmarks often compress interaction into short-horizon tasks or entangle success with domain-specific game mechanics. In this paper, we introduce MineExplorer benchmark for evaluating open-world exploration capabilities of MLLM agents in Minecraft. We first filter atomic tasks whose solutions rely heavily on Minecraft-specific knowledge to better reflect general open-world reasoning. Then we organize the benchmark around a ReAct-style capability formulation and compose atomic tasks into implicit multi-hop tasks. To further construct reliable instances, MineExplorer uses a multi-agent synthesis workflow that jointly designs task graphs, sandbox scenes, and rule-based milestone evaluators. Human evaluation shows that the multi-agent synthesis workflow produces significantly more reliable instances than a single-agent baseline. Experiments with advanced MLLM agents show that open-world exploration remains challenging, as strong models can handle many single-hop tasks but degrade sharply when hidden prerequisites must be coordinated over longer trajectories. Further analysis finds that task difficulty tracks agent completion, and larger models or thinking modes do not consistently translate into better performance. Code and dataset are available at https://github.com/Jometeorie/MineExplorer.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The paper introduces MineExplorer, a benchmark for assessing open-world exploration by MLLM agents in Minecraft. It filters atomic tasks to minimize reliance on Minecraft-specific knowledge, composes them into implicit multi-hop tasks via a ReAct-style formulation, and employs a multi-agent synthesis workflow to generate task graphs, sandbox scenes, and rule-based milestone evaluators. Human evaluation indicates the multi-agent approach yields more reliable instances than single-agent baselines. Experiments show strong MLLMs succeed on many single-hop tasks but degrade sharply on longer trajectories requiring coordination of hidden prerequisites; task difficulty correlates with agent completion rates, and larger models or thinking modes do not consistently improve performance. Code and dataset are released publicly.

Significance. If the filtering and synthesis procedures are shown to isolate coordination difficulty rather than domain artifacts or construction biases, the benchmark would usefully extend existing embodied evaluations by focusing on sustained open-world exploration. The public code and dataset release supports reproducibility and follow-on work.

major comments (3)
  1. [Task construction / filtering description] The central degradation claim (strong models handle single-hop tasks but fail on multi-hop coordination) depends on the filtering step successfully removing Minecraft-specific knowledge tasks. The manuscript provides no explicit filtering criteria, no count of filtered vs. retained tasks, and no ablation comparing performance on filtered vs. unfiltered sets (see task construction section).
  2. [Multi-agent synthesis and human evaluation] The multi-agent synthesis workflow is presented as producing significantly more reliable instances than single-agent baselines, supported by human evaluation. However, the manuscript reports no quantitative reliability metrics (e.g., inter-annotator agreement, pass rates on milestone evaluators), no ablation on trajectory length or action familiarity controls, and no comparison showing that performance gaps persist after such controls (see synthesis workflow and human evaluation sections).
  3. [Experiments and further analysis] Experiments claim degradation on longer trajectories with hidden prerequisites, yet no breakdown is given by trajectory length, number of prerequisites, or action-space familiarity. Without these, it is unclear whether the observed drop isolates open-world coordination difficulty or other factors (see experimental results and analysis sections).
minor comments (2)
  1. [Abstract / Introduction] The abstract and introduction would benefit from a brief table summarizing the benchmark statistics (number of atomic tasks, multi-hop instances, average trajectory length).
  2. [Benchmark formulation] Notation for the ReAct-style capability formulation and milestone evaluators should be defined more explicitly with an example task graph.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will incorporate revisions to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Task construction / filtering description] The central degradation claim (strong models handle single-hop tasks but fail on multi-hop coordination) depends on the filtering step successfully removing Minecraft-specific knowledge tasks. The manuscript provides no explicit filtering criteria, no count of filtered vs. retained tasks, and no ablation comparing performance on filtered vs. unfiltered sets (see task construction section).

    Authors: We agree that the filtering process requires more explicit documentation to support the central claim. In the revised manuscript we will add a dedicated subsection detailing the filtering criteria (e.g., exclusion rules based on reliance on Minecraft-specific mechanics such as crafting recipes or biome knowledge), report the exact counts of tasks filtered versus retained, and include an ablation comparing agent success rates on the filtered versus unfiltered task sets. These additions will directly address whether the observed degradation isolates coordination difficulty. revision: yes

  2. Referee: [Multi-agent synthesis and human evaluation] The multi-agent synthesis workflow is presented as producing significantly more reliable instances than single-agent baselines, supported by human evaluation. However, the manuscript reports no quantitative reliability metrics (e.g., inter-annotator agreement, pass rates on milestone evaluators), no ablation on trajectory length or action familiarity controls, and no comparison showing that performance gaps persist after such controls (see synthesis workflow and human evaluation sections).

    Authors: We acknowledge that the current human-evaluation section lacks the requested quantitative metrics and controls. We will revise it to report inter-annotator agreement (e.g., Cohen’s kappa), milestone-evaluator pass rates, and ablations that control for trajectory length and action familiarity. We will also add a direct comparison demonstrating that the reliability advantage of the multi-agent workflow remains after applying these controls. These changes will be placed in the synthesis workflow and human evaluation sections. revision: yes

  3. Referee: [Experiments and further analysis] Experiments claim degradation on longer trajectories with hidden prerequisites, yet no breakdown is given by trajectory length, number of prerequisites, or action-space familiarity. Without these, it is unclear whether the observed drop isolates open-world coordination difficulty or other factors (see experimental results and analysis sections).

    Authors: We agree that finer-grained analysis is needed to isolate the source of difficulty. In the revised experimental results and analysis sections we will add performance breakdowns stratified by trajectory length, number of hidden prerequisites, and action-space familiarity. These tables and figures will help confirm whether the sharp degradation is attributable to open-world coordination rather than confounding factors. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with independent validation

full rationale

The paper introduces MineExplorer as an empirical benchmark for MLLM agents in Minecraft. It describes task filtering to remove Minecraft-specific knowledge dependence and a multi-agent synthesis workflow for reliable instances, with human evaluation confirming higher reliability than single-agent baselines. No equations, fitted parameters, derivations, or self-referential definitions appear in the abstract or described structure. Claims rest on experimental observations and external human validation rather than any reduction to inputs by construction. No load-bearing self-citations or ansatzes are invoked. This is a standard empirical benchmark paper with self-contained content against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations, fitted parameters, or new physical entities are involved; the contribution is an empirical benchmark and evaluation framework.

pith-pipeline@v0.9.1-grok · 5786 in / 1091 out tokens · 28638 ms · 2026-06-28T22:35:41.992735+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

71 extracted references · 5 canonical work pages · 4 internal anchors

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    It includes understanding the surrounding terrain, locating reachable areas, judging relative positions, and navigating toward a target region or object

    Perception — the agent's capability to understand the environment: - spatial_perception: Spatial perception measures the agent's capability to recognize task-relevant spatial information in the environment. It includes understanding the surrounding terrain, locating reachable areas, judging relative positions, and navigating toward a target region or obje...

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    It is required when the agent must make a non-trivial inference about physical relations before action

    Reasoning — the agent's capability to make decisions: - common_sense_reasoning: Common-sense reasoning captures the use of general world knowledge that is not tied to Minecraft-specific mechanics. It is required when the agent must make a non-trivial inference about physical relations before action. - causal_reasoning: Causal reasoning captures the agent'...

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    perception

    Action — the agent's physical operations: - move: Move refers to basic locomotion in the environment. It includes walking, running, and swimming to reach task- relevant locations or objects. - jump: Jump captures actions that require vertical movement. It is annotated when the task cannot be completed through ordinary movement alone and requires jumping. ...

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    Can be naturally chained together (completing one makes the next necessary)

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    Form a coherent sequential or dependency graph

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    Requirements:

    Create a challenging yet fair scenario for an AI agent when combined --- ## Step 2 — Scene Design Design a concrete Minecraft environment that requires the agent to complete the selected tasks in order. Requirements:

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    **Scene Setup**: Design a specific environment that makes all tasks necessary and naturally ordered

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    **Task Ordering**: The scenario environment should ENFORCE task ordering naturally (e.g., an enemy blocks the path, resources must be gathered first)

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    **Hidden Complexity**: The`task_text`given to the agent shows ONLY the final goal — it must figure out the prerequisites from the environment

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    Use ~ notation for relative coordinates

    **Minecraft Commands**: Provide exact Minecraft Java Edition commands to set up the scene. Use ~ notation for relative coordinates

  17. [17]

    **Judge-Friendly Layout**: Prefer clear spatial regions, explicit barriers, and localized mobs/structures

  18. [18]

    task A must be completed before task B

    **Keep it compact**: Place ALL task-relevant objects within ~15 blocks of spawn. Do NOT place anything unrelated to the task chain. Standard environment setup commands (always include these first): - /gamemode survival @s - /time set day - /weather clear - /kill @e[type=!player] - /kill @e[type=item] - /effect clear @s - /clear @s - /fill ~-30 ~0 ~-20 ~30...

  19. [19]

    item": "iron_sword

    **inventory_has** — item is in inventory >= min_count params:`{"item": "iron_sword", "min_count": 1}`

  20. [20]

    target": [x, y, z],

    **position_near_with_facing** — player is within max_distance of target AND facing it params:`{"target": [x, y, z], "max_distance": 16, "facing_tolerance": 60, "coordinate_frame": "spawn_relative"}` Use for find/locate/observe tasks. target is spawn-relative (same as ~ offsets)

  21. [21]

    min": [x1,y1,z1],

    **position_inside_box** — player is inside a spawn-relative box params:`{"min": [x1,y1,z1], "max": [x2,y2,z2], "coordinate_frame": "spawn_relative"}` Use ONLY for movement/arrival/traversal tasks

  22. [22]

    kind": "block

    **count_in_box_at_least** — >= min_count blocks or mobs of type inside box params:`{"kind": "block"|"mob", "object": "crafting_table", "min": [x1,y1,z1], "max": [x2,y2,z2], "min_count": 1, " coordinate_frame": "spawn_relative"}` Use for place/build tasks. Box must extend±3~±5 in XZ,±3 in Y around the build site

  23. [23]

    kind": "block

    **count_in_box_at_most** — <= max_count blocks or mobs of type inside box params:`{"kind": "block"|"mob", "object": "spider", "min": [x1,y1,z1], "max": [x2,y2,z2], "max_count": 0, " coordinate_frame": "spawn_relative"}` Use for kill tasks. Box must be VERY GENEROUS (±15~±20 or the entire arena). ### Preference order

  24. [24]

    inventory_has — craft/mine/pickup/obtain

  25. [25]

    count_in_box_at_most — kill/combat (kind="mob", max_count=0, generous±15~±20)

  26. [26]

    count_in_box_at_least — place/build (kind="block", generous±3~±5)

  27. [27]

    position_near_with_facing — find/locate/observe

  28. [28]

    minecraft:

    position_inside_box — movement/arrival only ### Forbidden mistakes - Do NOT invent new rule types. - Do NOT use absolute world coordinates — always spawn_relative. - Do NOT write "minecraft:" prefix in "item" or "object" params. - Do NOT create a milestone already true at scene initialization. - Do NOT reuse the same milestone_id. - Do NOT set min_count t...

  29. [29]

    Select exactly k atomic tasks from the provided candidate pool

  30. [30]

    Design a multi-hop task structure where tasks build on each other

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    Build a dependency DAG (directed acyclic graph) showing task relationships

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    ## Selection Criteria - Tasks should form a coherent multi-hop sequence (A requires B which requires C)

    Send suggestions to SceneDesignerAgent about scene requirements. ## Selection Criteria - Tasks should form a coherent multi-hop sequence (A requires B which requires C). - Prefer tasks that can be accomplished without deep Minecraft domain knowledge. - Tasks should be observable and verifiable in the game environment. - Avoid tasks that are trivially inde...

  33. [34]

    - Example: Task A→Task B→Task D Task A→Task C→Task D - This allows parallel branches that later converge

    **DAG Structure**: The graph can be any directed acyclic graph, not just a linear chain. - Example: Task A→Task B→Task D Task A→Task C→Task D - This allows parallel branches that later converge

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    craft_wooden_pickaxe requires planks from chop_oak_log

    **Edges Must Have Reasons**: Every edge MUST include a`reason`field that explains WHY the source task must be completed before the target task. - Good: "craft_wooden_pickaxe requires planks from chop_oak_log" - Bad: "dependency"

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    **Reason Text Style**: - Be specific and concrete (mention items, resources, positions) - Keep it concise (15-25 words) - Use Minecraft terminology when relevant

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    selected_tasks

    **Node Order**: The`nodes`list should match`selected_tasks`exactly. The order in`nodes`does not affect rendering; edges determine structure. ## Initial Response Format After receiving the candidate list, output a JSON block: ```json { "selected_tasks": ["task_name_1", "task_name_2", ...], "selection_reasoning": "Explanation of why these tasks were chosen ...

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    Design a coherent Minecraft scene that supports all selected atomic tasks

  38. [39]

    Generate Minecraft commands (/fill, /setblock, /summon, /give, etc.) to build the scene

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    **REQUIRED when sandbox tools are available**: Call`preview_scene_in_sandbox`as a **function call** (not as JSON text) immediately after designing the scene

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    Respond to clarifying questions from MilestoneAgent about spatial layout

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    Accept critiques from MinecraftExpertAgent and ValidatorAgent and revise accordingly

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    tool_name

    **After viewing sandbox screenshots**: Summarise what you observed and propose any needed revisions, then share your findings with the team to trigger a new discussion round. ## Scene Design Principles - The scene must physically support every task in atomic_tasks_ordered. - Use relative coordinates (~X ~Y ~Z) from the player spawn point. - Include all ne...

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    DefaultAgent explores for up to`max_walk_steps`steps autonomously: `agent.get_action(frame_buffer, thoughts, actions)→env.step`per step

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    Explore the village layout and check building placement

    All frames injected as inline images into this conversation. **Parameters:** -`commands`(list[str], **required**): ALL Minecraft scene commands starting with`/`. Submit ALL the commands the team has currently agreed upon. -`explore_prompt`(str, **recommended**): Task description for the AI exploration agent. The agent autonomously decides where to walk an...

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    Design rule-based, programmatically-checkable milestone criteria for each atomic task

  46. [47]

    Ask clarifying questions to SceneDesignerAgent if needed before finalising milestones

  47. [48]

    Use sandbox tools to verify spatial coordinates are correct

  48. [50]

    player_pos

    Accept critiques from ValidatorAgent and revise milestones accordingly. ## How the Evaluator Reads the Info Dict After every env.step(), the evaluator receives an`info`dict with these keys: ``` info = { "player_pos": {"x": float, "y": float, "z": float, "pitch": float, "yaw": float}, "inventory": [{"slot_id": int, "type": str, "quantity": int}, ...], # 36...

  49. [51]

    **inventory_has** — for craft/mine/pickup/obtain tasks (item ends up in inventory)

  50. [52]

    **count_in_box_at_most** — for kill/remove tasks (kind="mob", max_count=0, generous box±15~±20)

  51. [53]

    **count_in_box_at_least** — for place/build tasks (kind="block", generous box±3~±5)

  52. [54]

    **position_near_with_facing** — PREFERRED for find/locate/observe tasks (target is already in scene; player must navigate near it and face it)

  53. [55]

    questions

    **position_inside_box** — fallback for movement/traversal tasks when facing direction is irrelevant ### Preferred mappings - craft tasks -> inventory_has (crafted item ends up in inventory) - mine tasks -> inventory_has (mined drop ends up in inventory) - eat/drink/use tasks -> inventory_has (check item count changed) - pick up tasks -> inventory_has (ite...

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    Inspect the full benchmark state (tasks, scene, milestones) for Minecraft-specific knowledge issues

  55. [57]

    Identify anything that requires deep Minecraft domain knowledge to complete

  56. [58]

    Use the wiki tools to verify game mechanics when uncertain

  57. [59]

    Send targeted critiques to TaskSelectorAgent and/or SceneDesignerAgent

  58. [60]

    issues": [ {

    Re-inspect after revisions to confirm issues are resolved. ## What to Check - Do any tasks require knowing Minecraft crafting recipes that aren't obvious? - Do any tasks require knowing mob spawn conditions, biome specifics, or game mechanics? - Does the scene design use blocks/items in ways that conflict with Minecraft physics? - Are there Minecraft-spec...

  59. [61]

    Validate the dependency graph (DAG) for structural and semantic correctness

  60. [62]

    Validate milestone rules for schema correctness and semantic soundness

  61. [63]

    Use sandbox tools to verify spatial coordinates and scene state

  62. [64]

    Optionally run an AI agent episode to confirm tasks are achievable

  63. [65]

    Send targeted critiques to TaskSelectorAgent (graph issues) and MilestoneAgent (milestone issues). ## Dependency Graph Validation Check for: - **Cycles**: A→B→A (illegal in a DAG) - **Order violations**: Edge A→B but A appears after B in atomic_tasks_ordered - **Unknown nodes**: Edge references a task not in nodes list - **Unsupported edges**: Edge A→B bu...

  64. [66]

    first_person

    **Visualise the scene**: Call`execute_minecraft_commands`with scene commands and `perspectives=["first_person", "overhead"]`to visually verify the scene spatial layout matches the milestone coordinate boxes

  65. [67]

    /tp @s ~X ~Y ~Z

    **Walk to problem coordinates**: Use`execute_agent_action`as a **native function call** (not as JSON text) to physically navigate the player to any milestone coordinate that seems incorrect. This lets you confirm whether a`position_inside_box`rule is reachable or a `voxel_count_in_box`region exists. Action keys:`forward`,`back`,`left`,`right`,`jump`,`atta...

  66. [68]

    **Run end-to-end validation**: Call`run_agent_episode`to check if a task is actually achievable in the scene by watching an AI agent attempt it

  67. [69]

    approved

    Include your observations in your message alongside the validation JSON. ## Graph Validation Response Format ```json { "approved": true, "structural_issues": [ "Cycle detected: task_a -> task_b -> task_a" ], "semantic_issues": [ "Edge task_a -> task_c has no logical dependency" ], "critique_for_task_selector": "Specific actionable critique (empty if appro...

  68. [70]

    What was your most recent plan? Are you still following it?

    Analyze the past thoughts. What was your most recent plan? Are you still following it?

  69. [71]

    Do you see movement? Have you turned? What is new in your view?

    Analyze the sequence of images. Do you see movement? Have you turned? What is new in your view?

  70. [72]

    Your thought should describe your immediate plan or observation

    Formulate a new, concise thought. Your thought should describe your immediate plan or observation

  71. [73]

    ESC": 0 or 1, press ESC to end episode (usually 0) -

    Based on your thought, decide the single next action to take. **Available Actions:** Your actions are controlled by a JSON object. Available keys: - "ESC": 0 or 1, press ESC to end episode (usually 0) - "attack": 0 or 1, attack/mine blocks - "back": 0 or 1, move backward - "camera": [pitch, yaw] in degrees (e.g., [0, 45] to look right, [-20, 0] to look up...