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

• URL: http://arxiv.org/abs/2606.10359v1
• Date: Tue, 09 Jun 2026 03:18:44 GMT
• Title: ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience
• Abstract summary: We introduce REFLECTICHAIN to bridge the gap between large language models and reinforcement learning.<n> REFLECTICHAIN separates uncertainty separation, knowledge-boundary detection, and empirical Bayesian policy updating.<n>On Semi-Sim, 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.
• Score: 2.160835522257474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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