Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptions
Abstract
This paper introduces Project Synapse, a novel agentic framework designed for the autonomous resolution of last-mile delivery disruptions. Synapse employs a hierarchical multi-agent architecture in which a central Resolution Supervisor agent performs strategic task decomposition and delegates subtasks to specialized worker agents responsible for tactical execution. The system is orchestrated using LangGraph to manage complex and cyclical workflows. To validate the framework, a benchmark dataset of 30 complex disruption scenarios was curated from a qualitative analysis of over 6,000 real-world user reviews. System performance is evaluated using an LLM-as-a-Judge protocol with explicit bias mitigation.
Keywords
Cite
@article{arxiv.2601.08156,
title = {Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptions},
author = {Arin Gopalan Yadav and Varad Dherange and Kumar Shivam},
journal= {arXiv preprint arXiv:2601.08156},
year = {2026}
}
Comments
We propose and evaluate a hierarchical LLM-driven multi-agent framework for adaptive disruption management in last-mile logistics, integrating planning, coordination, and natural-language reasoning. The system is validated through simulation-based experiments and qualitative analysis. Includes figures and tables. 33 pages