From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience

Jia Luo

cs.AIApr 13, 2026

From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience

Authors: Jia Luo

Organizations: Huazhong University and Science and Technology Wuhan, China

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.

Figures & tables

Figure 1: The ReflectiChain framework operates as a closed-loop, adaptive system that bridges the “grounding gap” in supply chain decision-making through a dual-stage reflection process: initially, the system synthesizes multimodal inputs to facilitate Reflection-in-Action , where candidate interventions are sampled and filtered through a dual-path latent rehearsal that concurrently optimizes for semantic compliance and physical grounding via the SC-WM’s spatiotemporal dynamics. Following execution and immediate feedback buffering in the high-fidelity environment, the system triggers Reflection-on-Action , utilizing a retrospective hindsight mechanism to resolve temporal credit assignment by re-evaluating historical decisions with future context, which is ultimately operationalized into policy gradients that fundamentally evolve the actor’s underlying reasoning weights via test-time LoRA adaptation.

Metric (Goal)	PPO (Classic RL)	Qwen2.5-7B	InternLM2.5	ReflectiChain (Ours)	Ideal Ref.
Total Cash (CEE $\uparrow$ )	0.00	410,125.00	547,650.00	894,320.00	$>1{,}000\mathrm{K}$
Avg Compliance (RCI $\uparrow$ )	0.00	100.00	100.00	95.40	$>85.00$
Bullwhip Index (BWI $\approx 1$ )	0.000	0.0117	0.0938	1.1500	$1.0\sim 1.5$
Operability Ratio (OR $\uparrow$ )	0.0%	13.3%	26.7%	88.5%	$>80.0\%$
Average Risk Level (ARL $\downarrow$ )	100.00	73.27	68.45	35.20	$<40.00$

Table 1: Metric-level comparison of ReflectiChain against baselines under macroeconomic shocks. PPO completely fails to navigate semantic constraints, while vanilla LLMs suffer from severe decision paralysis (low OR).

Variant	Step Reward	Retro Score	Observed Failure Mode (Why it fails)
Full ReflectiChain	0.184	85.0	N/A (Optimal Pareto frontier achieved).
w/o World Model	0.052	72.5	Grounding Gap: Agent selects actions that sound logically correct but yield negative physical rewards.
w/o Retrospective RL	0.112	70.0	Static Myopia: Agent cannot extract gradients from historical errors; performance oscillates randomly.
w/o Internal Reflection	-0.045	55.0	Greedy Collapse: Agent fails to recognize out-of-distribution cascading risks before execution.

Table 2: Ablation study demonstrating distinct failure modes when core components are removed.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 2: Semi-Sim flowchart for spatiotemporal risk propagation dynamics in the semiconductor supply chain. The procedure can be interpreted as Algorithm A . Module I: Initialization takes the network topology $G(V,E)$ , dependency weights $w_{ji}$ , initial node risk $R_{i,t}$ , recovery rate $\gamma$ , and activation threshold $\tau$ to establish the operational baseline. Module II: Endogenous Attenuation computes intrinsic recovery through $R_{\mathrm{internal}}=(1-\gamma)R_{i,t}$ , capturing internal resilience and inventory-buffer effects. Module III: Exogenous Filtering evaluates upstream shock transmission by $\Delta R_{j\to i}=w_{ji}\cdot\max(0,R_{j,t}-\tau)$ , so only disruptions exceeding the threshold trigger cascading impact. Module IV: Spatial Aggregation sums all activated upstream disturbances as $R_{\mathrm{external}}=\sum_{j\in N_{\mathrm{pred}}(i)}\Delta R_{j\to i}$ to form the localized external pressure on node $i$ . Module V: State Transition updates the next-step risk according to $R_{i,t+1}=R_{\mathrm{internal}}+R_{\mathrm{external}}$ , thereby advancing the global system from time $t$ to $t+1$ and checking whether the network approaches systemic-collapse limits.

Figure 7: Global Correlation Matrix of the Triple Feedback RL System variables.

Explore similar work

Jun 9, 2026

ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience

AI agents in supply chains face a fundamental epistemic gap: large language models (LLMs) interpret policies but lack physical grounding, while reinforcement learning (RL) optimizes flows but is semantically blind to unstructured constraints. We introduce REFLECTICHAIN, bridging this gap through a Generative Supply Chain World Model (SC-WM) - encoding heterogeneous supply networks into a 6-dim graph-latent space with physical conservation - and Double-Loop Learning that separates epistemic uncertainty (KL-trust-region-bounded policy adaptation) from aleatoric uncertainty (stochastic latent rollouts). On Semi-Sim, a 10-node semiconductor benchmark with SIR risk propagation, 6 perturbation types, and 10 policy constraint templates, REFLECTICHAIN improves Rationale Consistency Score by 33.0% (p < 0.0001, d = 2.78), maintains 82.3% operability under adversarial shocks, and exhibits anti-fragile behavior (+40.2% gain under moderate pressure). We identify three operational epistemic mechanisms - uncertainty separation, knowledge-boundary detection, and empirical Bayesian policy updating - and discuss five limitation categories.

Jia Luo

School of Foreign Languages, Huazhong University of Science and Technology, Wuhan 430074, China.

Constrained RL LLM-Guided RL Latent World Models Supply Chain Management Robust RL

Ask PDF

May 13, 2026

GenAI-Driven Approach to RISC-V Supply Chain Exploration

This paper presents an LLM-empowered workflow for RISC-V supply chain analysis, integrating Vision-Language Models (VLMs) and Model-Driven Engineering (MDE) to enable comprehensive, multimodal data-driven insights. The proposed approach addresses the challenges of heterogeneous and unstructured supply chain data by leveraging LLMs for textual understanding and VLMs for extracting information from visual artifacts such as diagrams, tables, and scanned documents. These models collaboratively identify key entities and relationships, which are then organized into a knowledge graph representing supply chain components and their interdependencies. For analytical reasoning, the workflow incorporates MDE techniques and constraint-based modeling to enable formal validation of dependencies, detection of bottlenecks, and assessment of risks. The synergy between LLM- and VLM-based semantic understanding and MDE-based formal analysis supports both exploratory and systematic evaluation of supply chain resilience. A human-in-the-loop mechanism further enables interactive querying and expert validation. The approach is evaluated in RISC-V ecosystem scenarios, demonstrating its effectiveness in generating actionable insights, enhancing transparency, and supporting decision-making in complex semiconductor supply chains.

Nenad Petrovic, Andre Schamschurko, Yingjie Xu +1

Chair of Robotics, Artificial Intelligence and Real-Time Systems Technical University of Munich, Munich, Germany

Supply Chain Management

Ask PDF

Sep 3, 2026

Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations

Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this task, but existing methods primarily focus on individual optimization models. Extending them to heterogeneous decision pipelines is challenging because a requirement may admit multiple intervention paths with different downstream effects. We formulate requirement-driven adaptation as the joint selection of an intervention route and an admissible module-level change, and propose a graph-constrained agentic framework in which domain agents expose admissible reformulation interfaces and a central processor searches over bounded intervention paths. Candidates are validated and compared using downstream KPIs. In collaboration with a large retail partner, we evaluate 100 warehouse requirements elicited from practitioner interviews, with GPT, Qwen, and DeepSeek as base LLMs. Relative to direct LLM reformulation, our framework improves correctness and end-to-end success across all three models, raising end-to-end success from 72--76% to 79--83%.

Lei Zheng, Liping Yang, Zihao Li +3

School of Business and Management, Hong Kong University of Science and Technology · School of Management, University of Science and Technology of China · Institute of Operations Research and Analytics, National University of Singapore +1

LLM Agent Orchestration LLM Decision-Making Supply Chain Management

Ask PDF