Title: MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Cultural Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: lifelong learning

theory of mind

LLMs

cultural learning

in-context learning
Abstract: Embodied agents powered by large language models (LLMs), such as Voyager, promise open-ended competence in worlds such as Minecraft. However, when powered by open-weight LLMs they still falter on elementary tasks after domain-specific fine-tuning. We propose MindForge, a generative-agent framework for cultural lifelong learning through explicit perspective taking. We introduce three key innovations: (1) a structured theory of mind representation linking percepts, beliefs, desires, and actions; (2) natural inter-agent communication; and (3) a multi-component memory system. Following the cultural learning framework, we test MindForge in both instructive and collaborative settings within Minecraft. In an instructive setting with GPT-4, MindForge agents powered by open-weight LLMs significantly outperform their Voyager counterparts in basic tasks yielding $3\times$ more tech-tree milestones and collecting $2.3\times$ more unique items than the Voyager baseline. Furthermore, in fully collaborative settings, we find that the performance of two underachieving agents improves with more communication rounds, echoing the Condorcet Jury Theorem. MindForge agents demonstrate sophisticated behaviors, including expert-novice knowledge transfer, collaborative problem solving, and adaptation to out-of-distribution tasks through accumulated cultural experiences.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nMindForge introduces a framework for cultural lifelong learning in embodied agents through explicit perspective taking. It uses (1) a structured theory of mind representation linking percepts, beliefs, desires, and actions; (2) natural inter-agent communication; and (3) a multi-component memory system. In Minecraft experiments, MindForge agents significantly outperform their Voyager counterparts. In fully collaborative settings, even agents that fail in isolation succeed together, with performance scaling over multiple communication rounds. Through these mechanisms, agents exhibit expert–novice knowledge transfer, collaborative problem solving, and adaptation to out-of-distribution tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. MindForge equips agents with structured Belief-Desire-Intention (BDI) models that link percepts, beliefs, desires, and actions, enabling them to form and update mental models of both themselves and their collaborators. This framework allows agents to reason recursively about partner states and generate more targeted, context-appropriate advice during collaboration.\n2. MindForge agents lead to significant gains with open-weight LLMs in experiments, both in the Voyager setting and fully collaborative settings.\n3. The integration of episodic, semantic, and procedural memory subsystems enables MindForge agents to keep and leverage past experiences. Ablation studies show that removing episodic memory leads to drops in task completion rates, and semantic memory facilitates both post-collaboration performance improvements and out-of-distribution generalization.\n\nWeaknesses:\n1. MindForge functions more as an engineering framework than a typical research paper. While it touches on many concepts (lifelong learning, collaborative learning, cultural learning), its core research contributions, particularly how it differentiates from other collaboration frameworks, are not clearly presented.\n2. MindForge agents can initiate dialogue only in a hardcoded fashion—specifically, the weaker agent must fail a task before requesting help, limiting flexibility in dynamic environments and potentially missing opportunities for proactive collaboration.\n3. MindForge introduces additional computational overhead due to multi-round communication. However, the paper does not quantify how much slower or more resource-intensive (e.g., in terms of API calls) MindForge is compared to non-collaborative baselines, nor does it explore different strategies or parameter settings to balance efficiency and performance.\n\nQUESTIONS:\n1. The current design requires a weaker agent to completely fail a task before initiating dialogue. Can the communication protocol be made more flexible? This would improve efficiency and allow better generalization across different tasks.\n2. Would the framework generalize beyond Minecraft, such as being evaluated in another embodied environment? Since the paper claims it is a general framework for diverse settings, this would demonstrate whether the structured ToM representations, memory subsystems, and communication mechanisms transfer effectively.\n3. Can the compute–performance tradeoff be more precisely quantified? This would provide clearer insights and enhance the framework’s practical utility.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose MindForge, a method that extends an LLM-powered embodied agent with a structured representation inspired by the theory of mind. This representation enables agents to reason about the beliefs, goals, and capabilities of others. The study focuses on inter-agent communication in two scenarios: when agents have differing capabilities and when they are equally capable. The proposed method is evaluated in the Minecraft environment, with Voyager serving as the main baseline.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\n* The paper is well-constructed and clearly written, making the proposed ideas and methodology easy to follow.\n* The ablation studies are detailed and well-structured, providing valuable insights into the contribution of each component.\n\n**Weaknesses**:\n\n* Figure 6 is unclear; it is not specified which experimental condition corresponds to each colored line, making interpretation difficult. Clarifying the legend or labeling would improve readability.\n\nQUESTIONS:\n- During the collaborative setting, does the strong agent (e.g., a human expert or GPT-4) actively interact with the environment? Or is it treated more like an oracle, where the weaker agent queries it without requiring the strong agent to update its own beliefs about the environment?\n\n- The agent's beliefs are represented using a {question: answer} format. How are these questions generated? What mechanism determines which aspects of the environment are relevant or worth querying about?\n\n- Have the authors tested scenarios involving more than two agents with similar capability levels? If so, how does the communication and belief sharing scale in multi-agent settings?\n\n- In the case where two agents of similar ability interact, do their beliefs converge over time, or does each agent maintain its own distinct belief system? For example, if there are two valid strategies to reach the same goal, have the authors observed cases where each agent have a different belief about the strategy to adopt?\n\n- Have the authors explored settings where GPT-4 interacts with only one of the two agents in a collaborative task? If so, does the uninformed agent benefit indirectly from its partner’s interaction with GPT-4—e.g., through shared communication or updated beliefs?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

comments: SUMMARY:
The authors propose a framework for agent memory and decision making in complex environments.

STRENGTHS AND WEAKNESSES:
Strengths:
- demonstrates benefit of agent architecture improvements on smaller llms
- runs multiple ablations on architecture to help identify key components
- architecture performs well empirically on low parameter, high complexity situations

Weaknesses:
- main results shown in table 3 are difficult to parse
  - reporting success rate and task length side by side confuses which results are better than others
  - I believe that I should be comparing each mindforge result to the open weight voyager results with the corresponding model, but that's not clear when looking at the table
- some figures are nearly unreadable
  - figure 1
  - figure 3
  - figure 5
- lack of mentioned related work in LLM memory and belief architectures
  - Expel: Llm agents are experiential learners
  - CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
  - Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
  - Describe, explain, plan and select: interactive planning with llms enables open-world multi-task agents
  - Adapt: As-needed decomposition and planning with language models

QUESTIONS:
How do you believe your work compares to other belief and memory architectures such as ExpeL, CLIN, SSO, DEPS, ADAPT?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes MINDFORGE, a multi-module framework that augments open-weight LLM-based embodied agents with a causal Theory-of-Mind template, episodic + semantic + procedural memory, and multi-round natural-language communication. Knowledge is primarily distilled from a single expert agent. During inference, two MINDFORGE agents can collaborate, exchanging beliefs and action plans to refine their policies without gradient updates. Experiments are conducted in Minecraft, where the authors report higher success rates on two basic resource-collection tasks and deeper advancement along the tech tree relative to the open-weight Voyager baseline.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Clear architectural decomposition: The paper articulates how ToM, memory subsystems, and communication interact, with helpful diagrams and prompt listings.\n2. Initial evidence that social interaction with an expert can offset smaller model capacity: Even with open-weight LLMs, the distilled agent surpasses Voyager on the reported metrics.\n\nWeakness:\n1. Limited novelty in the core learning mechanism: The main performance gains come from distilling reasoning produced by a stronger expert model; the distillation pipeline itself follows standard practice.\n2. Questionable effectiveness under purely collaborative use: When two identical MINDFORGE agents collaborate—without an expert—the paper reports a performance drop after communication, hinting that the proposed belief-exchange may introduce noise rather than useful signal.\n3. Narrow evaluation scope: Quantitative success-rate numbers are provided only for dirt and wood collection, two of the simplest Minecraft tasks, leaving the bulk of the MineDojo/Minecraft benchmark unexplored.\n4. Insufficient ablations for a complex system: MINDFORGE chains many hand-engineered modules (three memory types, BigToM belief graph, skill library, conversation scheduler). The importance of each piece is unclear.\n\nQUESTIONS:\n1. Broaden the task set: It would be great include success-rate or reward curves on a wider range of MineDojo goals spanning different difficulties.\n2. Deeper ablation: Provide separate ablations that disable each memory subsystem (episodic, semantic, procedural) and the ToM causal graph to quantify individual contribution.\n3. Compare communication cost to expert cost: Multi-round peer communication may involve more cumulative LLM tokens than a single expert rollout. Please report token counts, latency for (a) expert-only, (b) distilled MINDFORGE collaborative.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}