# Paper: SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation

---
type: paper
title: "SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation"
authors: Yanjia Li, Kelcy Kexin Han, Tianrui Hu, Yi-Fan Cao, Huamin Qu, Sicheng Song
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.24694
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-23
updated_at: 2026-06-23
status: queued
relevance: high
topics:
  - multi-agent
  - rag
  - reasoning
  - world-model
methods:
  -
benchmarks:
  -
models:
  -
datasets:
  - cs.HC
related_concepts:
  -
related_jobs:
  -
related_experiments:
  -
related_projects:
  -
collection_score: 14
collection_queries: multi-agent-llm
---

## One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

## Why Collected

- matched queries: multi-agent-llm
- inferred topics: multi-agent, rag, reasoning, world-model
- arXiv categories: cs.HC
- collection score: 14

## Review Checklist

- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?

## Links

- arXiv: https://arxiv.org/abs/2606.24694