mlsanityData to 7 Oct 2026

SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through LLM-driven Multi-Agent Simulation

arXiv 2601.14566 ↗First posted 21 Jan 2026cs.HCcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, with enterprises as interconnected nodes, play a pivotal role in organizational success. However, optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making and Game Theory. Traditional approaches, grounded in mathematical simplifications and managerial heuristics, fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in LLMs create opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. We present SCSimulator, a visual analytics framework that integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection. It simulates SC evolution via adaptive network structures and enterprise behaviors, which are visualized via interpretable interfaces. By combining CoT reasoning with XAI techniques, it generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities. Developed through iterative co-design with SC experts and industry managers, SCSimulator serves as a proof-of-concept, offering methodological contributions and practical insights for future research on SC decision-making and interactive AI-driven analytics. Usage scenarios and a user study demonstrate the system's effectiveness and usability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 16 of its 92 references that are in the map. First posted on arXiv 21 Jan 2026.

Builds on

16 of 92 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations13 Apr
  2. 2024What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents20 Feb
  3. 2023EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities16 Oct
  4. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 12

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016Variational Graph Auto-Encoderscited by 1,373
  3. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  4. 2025Small Language Models are the Future of Agentic AIcited by 254
  5. 2025Levels of Autonomy for AI Agentscited by 45
  6. 2022Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workerscited by 28
  7. 2024Very Large-Scale Multi-Agent Simulation in AgentScopecited by 18
  8. 2024Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chainscited by 9
  9. 2025MASS: Muli-agent simulation scaling for portfolio constructioncited by 9
  10. 2021Wielding Occam's razor: Fast and frugal retail forecastingcited by 4
  11. 2020Scalability of Network Visualisation from a Cognitive Load Perspectivecited by 3
  12. 2022LargeNetVis: Visual Exploration of Large Temporal Networks Based on Community Taxonomiescited by 1

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  1. 2026SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation
  2. 2026Dynamics of Cognitive Heterogeneity: Investigating Behavioral Biases in Multi-Stage Supply Chains with LLM-Based Simulation
  3. 2025LLMs for Supply Chain Management
  4. 2026Optimizing Service Operations via LLM-Powered Multi-Agent Simulation
  5. 2024Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking
  6. 2024InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
  7. 2025Rethinking Supply Chain Planning: A Generative Paradigm
  8. 2025PartnerMAS: An LLM Hierarchical Multi-Agent Framework for Business Partner Selection on High-Dimensional Features