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

Researchers introduce ReflectiChain, an AI system that combines large language models with reinforcement learning to improve supply chain resilience by bridging the gap between semantic understanding and physical optimization. The framework demonstrates 33% improvement in decision consistency and maintains 82.3% operational efficiency under adversarial disruptions through a dual-learning approach that separates different types of uncertainty.

ReflectiChain addresses a critical limitation in current AI-driven supply chain systems: the disconnect between semantic reasoning in LLMs and optimization capabilities in reinforcement learning. Traditional approaches either interpret policies without physical grounding or optimize flows without understanding unstructured constraints, leaving supply chains vulnerable to complex disruptions. This research introduces a Generative Supply Chain World Model that encodes heterogeneous networks into a structured latent space while preserving physical conservation laws, enabling more realistic simulations of supply chain behavior.

The innovation lies in the double-loop learning architecture, which systematically separates epistemic uncertainty (reducible through better policies) from aleatoric uncertainty (inherent stochasticity). Testing on Semi-Sim, a semiconductor supply chain benchmark with SIR risk propagation and multiple perturbation types, reveals significant improvements in decision consistency and operational resilience. The system maintains functionality under adversarial shocks and exhibits anti-fragile properties—actually improving performance under moderate stress.

For industry stakeholders, this framework has implications for supply chain management, risk assessment, and AI safety in autonomous systems. Organizations managing complex networks could leverage these epistemic mechanisms for more robust decision-making during disruptions. The research identifies three operational mechanisms—uncertainty separation, knowledge-boundary detection, and empirical Bayesian updating—that enhance transparency in AI-driven supply chain decisions.

The work's limitations across five categories suggest the framework remains experimental, but the foundation for bridging symbolic and statistical AI in logistics applications appears solid. Future applications may extend beyond semiconductors to pharmaceuticals, manufacturing, and financial networks.

• →ReflectiChain combines LLMs and reinforcement learning to solve the semantic-physical gap in supply chain optimization systems.
• →The framework achieves 33% improvement in decision consistency and maintains 82.3% operability under adversarial disruptions.
• →Double-loop learning separates epistemic uncertainty (policy-driven) from aleatoric uncertainty (stochastic), improving interpretability.
• →The system exhibits anti-fragile behavior, gaining 40.2% performance improvement under moderate operational pressure.
• →Knowledge-boundary detection and empirical Bayesian updating enable more transparent AI decision-making in complex logistics networks.