用生成世界模型增强大模型决策，提升供应链抗冲击能力
From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience
- 构建生成式世界模型，实现动态路径预演与实时反思结合
- 极端场景下任务完成率从13.3%提升至88.5%，奖励提高250%
- 适合需要长期战略规划的供应链、金融等高风险领域研究者
半导体供应链在全球地缘政治动荡下面临前所未有的韧性挑战。传统大语言模型在应对非平稳的‘政策黑天鹅’事件时，常因缺乏物理环境建模而出现决策瘫痪或严重脱实问题。本文提出ReflectiChain，一种面向宏观供应链韧性的认知智能体框架。核心创新在于引入基于生成世界模型的潜在轨迹重演机制，将即时反思（系统2推理）与延迟反思相结合。此外，通过回顾式智能体强化学习机制，实现部署阶段的自主策略演化（测试时）。在我们构建的高保真基准Semi-Sim上评估表明，在出口禁令和材料短缺等极端场景下，ReflectiChain相比最强的LLM基线平均步奖励提升250%，成功将操作率（OR）从13.3%恢复至88.5%以上，并确保梯度收敛稳定。消融实验进一步证实，物理约束与双环学习的协同是弥合语义推理与物理现实差距的关键。
原文摘要 · Abstract (English)
Semiconductor supply chains face unprecedented resilience challenges amidst global geopolitical turbulence. Conventional Large Language Model (LLM) planners, when confronting such non-stationary "Policy Black Swan" events, frequently suffer from Decision Paralysis or a severe Grounding Gap due to the absence of physical environmental modeling. This paper introduces ReflectiChain, a cognitive agentic framework tailored for resilient macroeconomic supply chain planning. The core innovation lies in the integration of Latent Trajectory Rehearsal powered by a generative world model, which couples reflection-in-action (System 2 deliberation) with delayed reflection-on-action. Furthermore, we leverage a Retrospective Agentic RL mechanism to enable autonomous policy evolution during the deployment phase (test-time). Evaluations conducted on our high-fidelity benchmark, Semi-Sim, demonstrate that under extreme scenarios such as export bans and material shortages, ReflectiChain achieves a 250% improvement in average step rewards over the strongest LLM baselines. It successfully restores the Operability Ratio (OR) from a deficient 13.3% to over 88.5% while ensuring robust gradient convergence. Ablation studies further underscore that the synergy between physical grounding constraints and double-loop learning is fundamental to bridging the gap between semantic reasoning and physical reality for long-horizon strategic planning.
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