arXiv Machine Learning

AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows

arXiv AI

Jul 14

Agentic Routing: The Harness-Native Data Flywheel

arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.

By Xinchen Liu, Hang Zhou, Yingjie Zong, Yuchuan Tian, Liuyang Song, Shuo Zhang, Yulong Li, Wei He, Mengyu Zheng, Runke Liu, Siyang Cheng, Xiang Kuang, Hailin Hu, Kai Han, Yunhe Wang

llms agents benchmarks

arXiv Machine Learning

Jul 27

TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI

arXiv:2607. 22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.

By Ritik Raj, Souvik Kundu, Sarbartha Banerjee, Dheemanth Joshi, Ishita Vohra, Tushar Krishna

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 4

Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark

arXiv:2608. 00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it.

By Natan Vidra, Alina Kapanova, Arun Kanhai, Spurthi Setty

llms agents benchmarks

arXiv AI

Jul 2

SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks

arXiv:2607. 00053v1 Announce Type: cross Abstract: Large language models (LLMs) embedded in multi-turn agentic harnesses are reshaping software engineering (SWE), but routing every task to a frontier model is wasteful when many issues admit cheap fixes.

By Seongho Son, Sangwoong Yoon, Jiahua Tang, Shuhan Wang, Lorenz Wolf, Ilija Bogunovic

llms agents

arXiv AI

Jun 29

Agent-as-a-Router: Agentic Model Routing for Coding Tasks

arXiv:2606. 22902v3 Announce Type: replace Abstract: Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct domains, yet none dominate all.

By Pengfei Zhou, Zhiwei Tang, Yixing Ma, Jiasheng Tang, Yizeng Han, Zhenglin Wan, Fanqing Meng, Wei Wang, Bohan Zhuang, Wangbo Zhao, Yang You

llms agents benchmarks

arXiv AI

Sep 10

Do Dynamic Routers Need Memory? HeRo: History-Aware Routing for Efficient LLM Inference

arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, ho...

By Hongjin Lin, Wentao Wan, Keze Wang

llms benchmarks

arXiv Machine Learning

1d ago

Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving

arXiv:2609.23085v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajector...

By Muhammad Abdur Rab Siddiqui, Daniela Rojas, Chen Yang, Wenqi Cui, Yuanyuan Shi, Yize Chen

llms agents fine-tuning benchmarks

arXiv Computation and Language

1d ago

Rubric-Guided Process Reward for Stepwise Model Routing

arXiv:2605.29310v2 Announce Type: replace-cross Abstract: Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent...

By Shenghao Ye, Yu Guo, Zhengheng Li, Shuangwu Chen, Jian Yang

reinforcement-learning benchmarks

arXiv AI

Aug 18

RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning

arXiv:2608. 15817v1 Announce Type: new Abstract: The growing ecosystem of large language models (LLMs) offers huge potential to optimize performance-cost trade-offs.

By Shihong Huang, Shengjie Wang, Hong Ma, Zhou Xu

llms reinforcement-learning benchmarks

arXiv AI

Sep 1

ProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs

arXiv:2608.25992v2 Announce Type: replace Abstract: Multi-agent large language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative re...

By Songyuan Li, Ahmed M. Abdelmoniem, Shiqiang Wang

llms rag agents nlp benchmarks

arXiv AI

Aug 7

MACRO: Markov Chain Routing of Transformer Layers

arXiv:2608. 05872v1 Announce Type: cross Abstract: Standard Large Language Models (LLMs) execute layers sequentially.

By Pawe{\l} Batorski, Abtin Pourhadi, Akylgali Aitaza, Przemys{\l}aw Spurek, Paul Swoboda

llms benchmarks

arXiv AI

Aug 11

Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents

arXiv:2604. 05164v3 Announce Type: replace-cross Abstract: As LLM reasoning performance plateaus, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces even for simple queries.

By Neharika Jali, Anupam Nayak, Gauri Joshi

llms agents reinforcement-learning benchmarks

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