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

• URL: http://arxiv.org/abs/2604.11041v1
• Date: Mon, 13 Apr 2026 06:14:15 GMT
• Title: From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience
• Abstract summary: This paper introduces ReflectiChain, a cognitive agentic framework tailored for resilient macroeconomic supply chain planning.<n>Under extreme scenarios such as export bans and material shortages, ReflectiChain achieves a 250% improvement in average step rewards.
• Score: 2.160835522257474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning [76.93011742289768]
  Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories.<n>This trajectory-level imitation encourages memorization of instance-specific steps rather than acquisition of transferable problem-solving skills.<n>We propose Strategy-Guided Policy Optimization, which replaces instance-level trajectory imitation with reusable strategy distillation.
  arXiv  Detail & Related papers  (2026-06-23T02:14:12Z)
• ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience [2.160835522257474]
  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.
  arXiv  Detail & Related papers  (2026-06-09T03:18:44Z)
• Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL [0.8546394944138487]
  We formalize text-based world modeling as a steerable transition-dynamics problem decomposed into initial state.<n>We curate 239,403 grounded state-action trajectories spanning nine open-source environments and twelve frontier model families.<n>We introduce a plug-and-play GRPO training framework with deterministic state checks, and perform zero-shot transfer ablations on three OOD environments.
  arXiv  Detail & Related papers  (2026-05-07T00:40:32Z)
• LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning [90.86828952599147]
  We present LaST-R1, a novel reinforcement learning framework designed to harness "latent reasoning-before-acting" policies.<n>LaST-R1 achieves a near-perfect 99.9% average success rate on the LIBERO benchmark.<n>In real-world deployments, LaST-R1 yields up to a 22.5% average improvement over SOTA supervised fine-tuning approach.
  arXiv  Detail & Related papers  (2026-04-30T17:59:52Z)
• TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training [53.93696896939915]
  Training tool-use agents typically rely on Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks.<n>We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology.<n>TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-02T10:38:54Z)
• RISE: Self-Improving Robot Policy with Compositional World Model [52.227523057681786]
  We present RISE, a scalable framework of robotic reinforcement learning via imagination.<n>At its core is a Compositional World Model that predicts multi-view future via a controllable dynamics model, and (ii) evaluates imagined outcomes with a progress value model.<n>These components are integrated into a closed-loop self-improving pipeline that continuously generates imaginary rollouts, estimates advantages, and updates the policy in imaginary space without costly physical interaction.
  arXiv  Detail & Related papers  (2026-02-11T17:43:36Z)
• Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling [49.41422138354821]
  We propose a principled reward modeling framework that integrates non-negative factor analysis into the Bradley-Terry preference model.<n>BNRM represents rewards through a sparse, non-negative latent factor generative process.<n>We show that BNRM substantially mitigates reward over-optimization, improves robustness under distribution shifts, and yields more interpretable reward decompositions than strong baselines.
  arXiv  Detail & Related papers  (2026-02-11T08:14:11Z)
• PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution [64.15555230987222]
  PACEvolve is a framework designed to robustly govern the agent's context and search dynamics.<n>We demonstrate that PACEvolve provides a systematic path to consistent, long-horizon self-improvement.
  arXiv  Detail & Related papers  (2026-01-15T18:25:23Z)
• Reflecting with Two Voices: A Co-Adaptive Dual-Strategy Framework for LLM-Based Agent Decision Making [24.534365665776672]
  Large language model (LLM) agents often rely on external demonstrations or retrieval-augmented planning.<n>We propose DuSAR - a demonstration-free framework that enables a single frozen LLM to perform co-adaptive reasoning.<n>On ALFWorld and Mind2Web, DuSAR achieves state-of-the-art performance with open-source LLMs.
  arXiv  Detail & Related papers  (2025-12-09T08:44:59Z)
• AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models [75.214287449744]
  We introduce a framework for post-training policy refinement built around an Impartial World Model.<n>Our primary contribution is to teach this model to be honest about danger.<n>We demonstrate through extensive experiments, that our model significantly outperforms baselines in predicting failures.
  arXiv  Detail & Related papers  (2025-11-25T13:57:24Z)
• R-WoM: Retrieval-augmented World Model For Computer-use Agents [15.812606459788471]
  Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments.<n>We probe two core capabilities of world models--future state prediction and reward estimation--through three tasks.<n>We propose the Retrieval-augmented World Model (R-WoM), which grounds simulations by incorporating factual, up-to-date knowledge retrieved from external tutorials.
  arXiv  Detail & Related papers  (2025-10-13T19:52:04Z)
• Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios [4.735413508037063]
  This paper proposes a momentum-constrained hybrid trajectory optimization framework (MHHTOF) tailored for assistive navigation in visually impaired scenarios.<n>It integrates trajectory sampling generation, optimization and evaluation with residual deep reinforcement learning (DRL)<n> Experimental results demonstrate that the proposed LSTM-BResPPO achieves significantly faster convergence, attaining stable policy performance in approximately half the training required by the PPO.
  arXiv  Detail & Related papers  (2025-09-19T04:33:39Z)
• Expert Insight-Based Modeling of Non-Kinetic Strategic Deterrence of Rare Earth Supply Disruption:A Simulation-Driven Systematic Framework [3.5516803380598074]
  This study constructs a quantifiable modelling framework to simulate non-kinetic strategic deterrence pathways in rare earth supply disruption scenarios.<n>Data is derived from expert interviews and scenario analyses centered on U.S.-China dynamics in ISR, electronic warfare, and rare earth control.<n>Results show institutional signals have strong tempo and path-coupling effects, capable of causing rapid degradation of strategic capabilities.
  arXiv  Detail & Related papers  (2025-06-13T10:18:59Z)
• Deep Active Inference Agents for Delayed and Long-Horizon Environments [1.693200946453174]
  AIF agents rely on accurate immediate predictions and exhaustive planning, a limitation that is exacerbated in delayed environments.<n>We propose a generative-policy architecture featuring a multi-step latent transition that lets the generative model predict an entire horizon in a single look-ahead.<n>We evaluate our agent in an environment that mimics a realistic industrial scenario with delayed and long-horizon settings.
  arXiv  Detail & Related papers  (2025-05-26T11:50:22Z)
• Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator [50.191655141020505]
  Reinforcement Learning (RL) has demonstrated impressive capabilities in robotic control but remains challenging due to high sample complexity, safety concerns, and the sim-to-real gap.<n>We introduce Offline Robotic World Model (RWM-O), a model-based approach that explicitly estimates uncertainty to improve policy learning without reliance on a physics simulator.
  arXiv  Detail & Related papers  (2025-04-23T12:58:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.