{
  "schema_version": "1.0",
  "id": "s13:https://arxiv.org/abs/2607.22465v1",
  "slug": "2607-22465v1-1g7nw7j",
  "url": "https://feed7.dev/p/2607-22465v1-1g7nw7j",
  "title": "TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI",
  "why_included": "TRACE-Router selects one model per agent task, keeps every call on that backend, and learns from the final outcome. Its benchmarks suggest task-level routing can improve accuracy and latency together.",
  "summary": "TRACE-Router chooses a model when a task arrives, pins every subsequent call to that backend, then updates a contextual-bandit policy from the terminal reward. The reward accounts for accuracy and latency, without explicit task-complexity estimates.",
  "practical_implication": "For long-running coding agents, route at the workflow boundary rather than treating each model call independently. The reported gains include **7–8 accuracy points on tau2-Bench** and **7.1 accuracy points with 36% lower latency on Terminal-Bench**.",
  "agent_context": "TRACE-Router chooses a model when a task arrives, pins every subsequent call to that backend, then updates a contextual-bandit policy from the terminal reward. The reward accounts for accuracy and latency, without explicit task-complexity estimates.\n\nFor long-running coding agents, route at the workflow boundary rather than treating each model call independently. The reported gains include **7–8 accuracy points on tau2-Bench** and **7.1 accuracy points with 36% lower latency on Terminal-Bench**.\n\nThe evidence covers **three agentic benchmarks** and reports Pareto improvements, but the material does not describe deployment overhead, model-switching constraints, or behavior on real coding workloads.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2607.22465v1",
    "published_at": "2026-07-24T16:29:06.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "infra",
  "domains": [
    "coding"
  ],
  "topics": [
    "gateways",
    "agent-reliability",
    "model-selection"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The evidence covers **three agentic benchmarks** and reports Pareto improvements, but the material does not describe deployment overhead, model-switching constraints, or behavior on real coding workloads."
  ],
  "lifecycle": "Current",
  "published_at": "2026-07-24T16:29:06.000Z",
  "modified_at": "2026-07-24T16:29:06.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/2607-22465v1-1g7nw7j",
    "json": "https://feed7.dev/p/2607-22465v1-1g7nw7j.json",
    "markdown": "https://feed7.dev/p/2607-22465v1-1g7nw7j.md"
  }
}