{
  "annotation_id": "205c6722-69ab-4409-a2e3-b0c25cb561e1",
  "date_created": "2026-02-17T05:53:15.101000Z",
  "date_modified": "2026-02-17T05:53:15.101000Z",
  "file_hash": "0a843ce8c6f526fd917bc9f9b604ac749eaa2b14c8f43f6807987e44be76d81a",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.08156",
    "authors": [
      "Arin Gopalan Yadav",
      "Varad Dherange",
      "Kumar Shivam"
    ],
    "categories": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Project Synapse: A Hierarchical Multi-Agent Framework with Hybrid Memory for Autonomous Resolution of Last-Mile Delivery Disruptions",
    "url": "https://arxiv.org/abs/2601.08156",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e3b63efc-41a3-4d7d-9b53-f449951d154a",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}