标题： DR. WELL：基于符号世界模型的具身LLM多智能体协作动态推理与学习 显示英文标题

标题： DR. WELL: Dynamic Reasoning and Learning with Symbolic World Model for Embodied LLM-Based Multi-Agent Collaboration

摘要： 协作多智能体规划要求智能体在部分信息和有限通信的情况下做出联合决策。 轨迹层级的协调经常失败，因为时间或运动的微小偏差会引发冲突。 符号规划通过提高抽象层次并提供一种最小的动作词汇来缓解这一挑战，这些动作能够实现同步和集体进展。 我们提出了DR. WELL，一个用于协作多智能体规划的去中心化神经符号框架。 协作通过一个两阶段协商协议展开：智能体首先提出带有推理的候选角色，然后在共识和环境约束下承诺一个联合分配。 承诺之后，每个智能体独立生成并执行其角色的符号计划，而无需揭示详细轨迹。 计划通过一个共享世界模型与执行结果相结合，该模型编码当前状态，并在智能体行动时进行更新。 通过在符号计划上进行推理而非原始轨迹，DR. WELL避免了脆弱的步骤级对齐，并实现了可重用、可同步和可解释的高层操作。 在协作推块任务上的实验表明，智能体在不同回合中能够适应，动态世界模型捕捉到了可重用的模式，提高了任务完成率和效率。 在协作推块任务上的实验表明，我们的动态世界模型通过协商和自我优化提高了任务完成度和效率，以时间开销为代价换取更高效的合作策略。 显示英文摘要

摘要： Cooperative multi-agent planning requires agents to make joint decisions with partial information and limited communication. Coordination at the trajectory level often fails, as small deviations in timing or movement cascade into conflicts. Symbolic planning mitigates this challenge by raising the level of abstraction and providing a minimal vocabulary of actions that enable synchronization and collective progress. We present DR. WELL, a decentralized neurosymbolic framework for cooperative multi-agent planning. Cooperation unfolds through a two-phase negotiation protocol: agents first propose candidate roles with reasoning and then commit to a joint allocation under consensus and environment constraints. After commitment, each agent independently generates and executes a symbolic plan for its role without revealing detailed trajectories. Plans are grounded in execution outcomes via a shared world model that encodes the current state and is updated as agents act. By reasoning over symbolic plans rather than raw trajectories, DR. WELL avoids brittle step-level alignment and enables higher-level operations that are reusable, synchronizable, and interpretable. Experiments on cooperative block-push tasks show that agents adapt across episodes, with the dynamic world model capturing reusable patterns and improving task completion rates and efficiency. Experiments on cooperative block-push tasks show that our dynamic world model improves task completion and efficiency through negotiation and self-refinement, trading a time overhead for evolving, more efficient collaboration strategies.