# Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptions

arXiv: https://arxiv.org/abs/2601.08156 · HTML: https://mlsanity.com/paper/2601.08156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 10 of 10)

- [Self-Preference Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2410.21819/) (2024)
- [LLM Agents Making Agent Tools](https://mlsanity.com/paper/2502.11705/) (2025)
- [From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users](https://mlsanity.com/paper/2508.17281/) (2025)
- [Analytics and Machine Learning in Vehicle Routing Research](https://mlsanity.com/paper/2102.10012/) (2021)
- [When AIs Judge AIs: The Rise of Agent-as-a-Judge Evaluation for LLMs](https://mlsanity.com/paper/2508.02994/) (2025)
- [The Silent Judge: Unacknowledged Shortcut Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2509.26072/) (2025)
- [A Taxonomy of Hierarchical Multi-Agent Systems: Design Patterns, Coordination Mechanisms, and Industrial Applications](https://mlsanity.com/paper/2508.12683/) (2025)
- [InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios](https://mlsanity.com/paper/2509.22502/) (2025)
- [Assistant-Guided Mitigation of Teacher Preference Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2505.19176/) (2025)
- [Memory Management and Contextual Consistency for Long-Running Low-Code Agents](https://mlsanity.com/paper/2509.25250/) (2025)

## Built on by (most cited first, 1 of 1)

- [STEM Agent: A Self-Adapting, Tool-Enabled, Extensible Architecture for Multi-Protocol AI Agent Systems](https://mlsanity.com/paper/2603.22359/) (2026)

## Abstract (arXiv)

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.