ニュース
TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
arXiv cs.AI · 公開日 · 読了3分
30秒で要点
- 何が起きたか
- TRACE-Router routes agentic AI tasks to different LLMs once at admission, then pins all calls to that model, updating routing policy using final task outcome.
- なぜ重要か
- Enterprise engineers deploying multi-model AI systems where task-level accuracy and latency matter more than optimizing individual LLM calls independently.
- 注意点
- Paper is recent preprint with limited real-world deployment data; effectiveness depends on having meaningful delayed task-level feedback signals available.
この要約を音声で聴く
記事より
-->
Computer Science > Artificial Intelligence
arXiv:2607.22465v1 (cs)
[Submitted on 24 Jul 2026 (this version), latest version 27 Jul 2026 ( v2 )]
Title: TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
Authors: Ritik Raj , Souvik Kundu , Sarbartha Banerjee , Dheemanth Joshi , Ishita Vohra , Tushar Krishna
View a PDF of the paper titled TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI, by Ritik Raj and 5 other authors
View PDF HTML (experimental)
Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. However, agentic applications execute as long-horizon workflows whose quality is determined only by a delayed, task-level outcome. This mismatch prevents per-call routers from correctly attributing feedback to individual routing decisions. Towards mitigating this, we present TRACE-Router, a task-level routing framework that aligns routing with the unit of supervision. TRACE-Router assigns each task to a model once at admission using a contextual bandit, pins all subsequent LLM calls to the selected backend, and updates its policy using the task's terminal reward, jointly accounting for accuracy and latency. By leveraging delayed task feedback,
原文からの抜粋です。全文は配信元でお読みください。
- agent
- agentic
- llm
- language model
The Agent Architect
1つのパターン、1つのトレードオフ、1つの本番障害事例。エージェントシステムを構築する人のための短い週刊ブリーフィング。
週1回のメール、ワンクリックで購読解除できます。アドレスはブリーフィングの送信のみに使用します。