From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience
Summary: arXiv:2604.11041v1 Announce Type: new
Abstract: 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.
Introduction
The semiconductor industry is currently experiencing a tumultuous period marked by various external pressures. The need for resilience in supply chains has never been more pressing. Traditional LLM planners are often ill-equipped to navigate the complexities of dynamic geopolitical environments, leading to suboptimal decision-making.
Challenges Faced by Conventional LLM Planners
Conventional LLM planners encounter significant obstacles when dealing with unpredictable events, known as “Policy Black Swan” events. These challenges can manifest in several ways:
- Decision Paralysis: Inability to make timely decisions due to overwhelming complexity.
- Grounding Gap: Lack of integration between virtual models and physical environments, leading to ineffective planning.
Introducing ReflectiChain
ReflectiChain emerges as a solution to these issues, offering a cognitive framework that enhances macroeconomic supply chain planning. Key features of ReflectiChain include:
- Latent Trajectory Rehearsal: This innovative approach enables planners to simulate potential future scenarios based on historical data, facilitating better decision-making.
- Retrospective Agentic RL: This mechanism allows for adaptive policy evolution during real-time deployment, ensuring that planners can respond effectively to emerging challenges.
Performance Evaluation
ReflectiChain has undergone rigorous testing on the Semi-Sim benchmark, demonstrating remarkable improvements in operational efficiency:
- 250% improvement in average step rewards over leading LLM baselines.
- Restoration of the Operability Ratio (OR) from 13.3% to over 88.5%.
- Ensured robust gradient convergence throughout the evaluation process.
Conclusion
The introduction of ReflectiChain marks a significant advancement in supply chain resilience strategies. By integrating physical grounding with advanced learning mechanisms, this framework provides a comprehensive solution to the challenges posed by today’s volatile geopolitical landscape. The findings underscore the importance of bridging the gap between semantic reasoning and physical reality for effective long-horizon strategic planning.