{
  "annotation_id": "db6ecb07-eaa2-45af-bd92-2eae78eeab0b",
  "date_created": "2026-02-17T05:53:15.096000Z",
  "date_modified": "2026-02-17T05:53:15.096000Z",
  "file_hash": "4138e3a4308cebcd12b92867263411dbe223249ff3439757c93738ef0b958664",
  "private": false,
  "record": {
    "abstract": "Large language model (LLM)-based agents are widely deployed in user-facing services but remain error-prone in new tasks, tend to repeat the same failure patterns, and show substantial run-to-run variability. Fixing failures via environment-specific training or manual patching is costly and hard to scale. To enable self-evolving agents in user-facing service environments, we propose WISE-Flow, a workflow-centric framework that converts historical service interactions into reusable procedural experience by inducing workflows with prerequisite-augmented action blocks. At deployment, WISE-Flow aligns the agent\u0027s execution trajectory to retrieved workflows and performs prerequisite-aware feasibility reasoning to achieve state-grounded next actions. Experiments on ToolSandbox and $\\tau^2$-bench show consistent improvement across base models.",
    "arxiv_id": "2601.08158",
    "authors": [
      "Yuqing Zhou",
      "Zhuoer Wang",
      "Jie Yuan",
      "Hong Wang",
      "Samson Koelle",
      "Ziwei Zhu",
      "Wei Niu"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "WISE-Flow: Workflow-Induced Structured Experience for Self-Evolving Conversational Service Agents",
    "url": "https://arxiv.org/abs/2601.08158",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4956a07e-0b09-40ab-9a3b-a443bdc100e1",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}