Elamur Research⌕
☰
home / papers / 2026 / EvoRoute: Experience-Driven Self-Routing LLM Agent Systems

EvoRoute: Experience-Driven Self-Routing LLM Agent Systems

Paper · 2026 · Guibin Zhang, Haiyang Yu, Kaiming Yang et al.

Summary

Multi-model agentic systems for deep research perform well but often run slowly and cost a lot. The authors frame this as the Agent System Trilemma: it is hard to get high accuracy, low monetary cost and fast completion at the same time. Their solution, EvoRoute, chooses an LLM backbone for every individual step of an agent workflow rather than fixing one model per agent role. Before each step, it does three things:

  • It looks up similar past sub-task executions in a growing experience base, matching on agent role, task similarity and tool usage.
  • It removes candidate models that are Pareto-dominated on performance, cost and delay.
  • It makes the final choice with a lightweight Thompson-sampling decision step.

The experience base grows during an optimization phase that explores several trajectories in a tree. At inference time, the system follows a single path. EvoRoute was added to existing frameworks (Smolagent, CK-Pro, ReAct) and tested on five benchmarks, including GAIA and BrowseComp+. Accuracy stayed level or rose, while expenses fell by as much as 80% and completion time shrank by more than 70%. This matters because it makes such agent systems more practical to deploy.

Key points

  • Averaged across all tested configurations, EvoRoute reaches 74.60% accuracy, versus 74.27% for Gemini-2.5-Pro. It costs $138.40 instead of $521.61 and takes 92.23h instead of 123.00h.

  • On BrowseComp+, EvoRoute scores 38.72%, compared with 33.50% for Claude-4. It costs $79.30 versus $220.50 and roughly halves latency (10.33h vs. 20.15h).

  • In the ablation on GAIA Level 1, the full model reaches 83.02%. Removing a component lowers that score:

    Removed componentAccuracy drop
    Cold-start knowledge base13.21 points
    Multi-faceted retrieval11.22 points
    Thompson sampling6.52 points
    Pareto filtration1.52 points

  • Routing follows the difficulty of each role. The Plan Agent most often gets Gemini-2.5-Pro (37.4%), while the File Agent leans on the cheaper Qwen3-14B (25.79%).

  • The authors describe "architectural overfitting": Gemini-2.5-Pro+Smolagent scores 31.7% on DS-1000, below the simpler Qwen3-14b+ReAct at 38.2%.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓National University of SingaporeSingapore · Singapore180 works · 34,304 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Guibin Zhang, Haiyang Yu, Kaiming Yang, Bingli Wu, Fei Huang, Yongbin Li and Shuicheng Yan. 2026. EvoRoute: Experience-Driven Self-Routing LLM Agent Systems. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1771

@inproceedings{zhang2026evoroute,
  title = {{EvoRoute: Experience-Driven Self-Routing LLM Agent Systems}},
  author = {Guibin Zhang, Haiyang Yu, Kaiming Yang, Bingli Wu, Fei Huang, Yongbin Li and Shuicheng Yan},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1771},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1771}
}

References

65 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Joshua Aizenman, Menzie David Chinn, and Hiro Ito. 2013. The “impossible trinity” hypothesis in an era of global imbalances: Measurement and testing. Review of international economics, 21(3):447–458. find
  2. Jun Shern Chan, Neil Chowdhury, Oliver Jaffe, James Aung, Dane Sherburn, Evan Mays, Giulio Starace, Kevin Liu, Leon Maksin, Tejal Patwardhan, Lilian Weng, and Aleksander Madry. 2025. Mle-bench: Evaluating machine learning agents on machine learning engineering. Preprint, arXiv:2410.07095.
  3. Kaiyuan Chen, Yixin Ren, Yang Liu, Xiaobo Hu, Haotong Tian, Tianbao Xie, Fangfu Liu, Haoye Zhang, Hongzhang Liu, Yuan Gong, Chen Sun, Han Hou, Hui Yang, James Pan, Jianan Lou, Jiayi Mao, Jizheng Liu, Jinpeng Li, Kangyi Liu, and 14 others. 2025a. xbench: Tracking agents productivity scaling with prof… — [arXiv:2506.13651](https://arxiv.org/abs/2506.13651) find
  4. Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Matei Zaharia, James Zou, and Ion Stoica. 2025b. Optimizing model selection for compound ai systems. Preprint, arXiv:2502.14815. find
  5. Shuhao Chen, Weisen Jiang, Baijiong Lin, James T Kwok, and Yu Zhang. 2024. Routerdc: Query-based router by dual contrastive learning for assembling large language models. arXiv preprint arXiv:2409.19886. — [arXiv:2409.19886](https://arxiv.org/abs/2409.19886) find
  6. Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Sahel Sharifymoghaddam, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, and Jimmy Lin. 2025c. Browsecomp-plus: A more fair and… — [arXiv:2508.06600](https://arxiv.org/abs/2508.06600) find
  7. Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E. Gonzalez, and Ion Stoica. 2024. Chatbot arena: An open platform for evaluating llms by human preference. Preprint, arXiv:2403.04132.
  8. Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, and 1 others. 2025. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities. arX…
  9. Dujian Ding, Ankur Mallick, Shaokun Zhang, Chi Wang, Daniel Madrigal, Mirian Del Carmen Hipolito Garcia, Menglin Xia, Laks V. S. Lakshmanan, Qingyun Wu, and Victor Rühle. 2025. BEST-route: Adaptive LLM routing with test-time optimal compute. In Forty-second International Conference on Machine Learni… find
  10. Yihong Dong, Xue Jiang, Zhi Jin, and Ge Li. 2023. Self-collaboration Code Generation via ChatGPT. arXiv e-prints, arXiv:2304.07590. find
  11. Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, and 1 others. 2023. Palm-e: An embodied multimodal language model. find
  12. Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Anumanchipalli, Kurt Keutzer, and Amir Gholami. 2025. Plan-and-act: Improving planning of agents for long-horizon tasks. arXiv preprint arXiv:2503.09572. — [arXiv:2503.09572](https://arxiv.org/abs/2503.09572) find
  13. Tianqing Fang, Zhisong Zhang, Xiaoyang Wang, Rui Wang, Can Qin, Yuxuan Wan, Jun-Yu Ma, Ce Zhang, Jiaqi Chen, Xiyun Li, Hongming Zhang, Haitao Mi, and Dong Yu. 2025. Cognitive kernel-pro: A framework for deep research agents and agent foundation models training. Preprint, arXiv:2508.00414. — [arXiv:2508.00414](https://arxiv.org/abs/2508.00414) find
  14. Tao Feng, Yanzhen Shen, and Jiaxuan You. 2024. Graphrouter: A graph-based router for llm selections. Preprint, arXiv:2410.03834. — [arXiv:2410.03834](https://arxiv.org/abs/2410.03834) find
  15. Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Erkang, Zhu, Friederike Niedtner, Grace Proebsting, Griffin Bassman, Jack Gerrits, Jacob Alber, Peter Chang, Ricky Loynd, Robert West, Victor Dibia, Ahmed Awadallah, Ece Kamar, Rafah Hosn, and Saleema Amershi. 2024. Magentic-o… find
  16. Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin. 2023. LLM-blender: Ensembling large language models with pairwise ranking and generative fusion. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14165–14178, Toronto, Canada. Assoc…
  17. Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2024. Swe-bench: Can language models resolve real-world github issues? Preprint, arXiv:2310.06770.
  18. Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Jeevesh Juneja, Congchao Wang, Zifeng Wang, Alec Go, Chen-Yu Lee, Pradeep Shenoy, Rina Panigrahy, Aditya Krishna Menon, and Sanjiv Kumar. 2025. Universal model routing for efficient llm inference. Preprint, arXiv:2502.08773. — [arXiv:2502.08773](https://arxiv.org/abs/2502.08773) find
  19. Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, and 1 others. 2023. Dspy: Compiling declarative language model calls into self-improving pipelines. arXiv preprint arXiv:2310.03714.
  20. Dimitrios Koutsoupakis. 2021. Are stable cryptocurrencies exempted from impossible trinity? Journal of Economic Studies, 48(8):1480–1496. find
  21. Evan Frick, Connor Chen, Joseph Tennyson, Tianle Li, Wei-Lin Chiang, Anastasios N. Angelopoulos, and Ion Stoica. 2025. Prompt-to-leaderboard. Preprint, arXiv:2502.14855. find
  22. Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen tau Yih, Daniel Fried, Sida Wang, and Tao Yu. 2022. Ds-1000: A natural and reliable benchmark for data science code generation. Preprint, arXiv:2211.11501. find
  23. Hongcheng Gao, Yue Liu, Yufei He, Longxu Dou, Chao Du, Zhijie Deng, Bryan Hooi, Min Lin, and Tianyu Pang. 2025. Flowreasoner: Reinforcing query-level meta-agents. Preprint, arXiv:2504.15257. — [arXiv:2504.15257](https://arxiv.org/abs/2504.15257) find
  24. Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023. CAMEL: communicative agents for "mind" exploration of large language model society. In NeurIPS.
  25. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the math dataset. NeurIPS.
  26. Manling Li, Shiyu Zhao, Qineng Wang, Kangrui Wang, Yu Zhou, Sanjana Srivastava, Cem Gokmen, Tony Lee, Erran Li Li, Ruohan Zhang, and 1 others. 2024. Embodied agent interface: Benchmarking llms for embodied decision making. Advances in Neural Information Processing Systems, 37:100428–100534. find
  27. Mengkang Hu, Yuhang Zhou, Wendong Fan, Yuzhou Nie, Bowei Xia, Tao Sun, Ziyu Ye, Zhaoxuan Jin, Yingru Li, Qiguang Chen, Zeyu Zhang, Yifeng Wang, Qianshuo Ye, Bernard Ghanem, Ping Luo, and Guohao Li. 2025. Owl: Optimized workforce learning for general multi-agent assistance in real-world task automati… — [arXiv:2505.23885](https://arxiv.org/abs/2505.23885) find
  28. Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang, Benjamin Keigwin, Gaurav Ranganath, Kurt Keutzer, and Shriyash Kaustubh Upadhyay. 2024a. Routerbench: A benchmark for multi-llm routing system. Preprint, arXiv:2403.12031. — [arXiv:2403.12031](https://arxiv.org/abs/2403.12031) find
  29. Shengran Hu, Cong Lu, and Jeff Clune. 2024b. Automated design of agentic systems. arXiv preprint arXiv:2408.08435. find
  30. Zexi Liu, Yuzhu Cai, Xinyu Zhu, Yujie Zheng, Runkun Chen, Ying Wen, Yanfeng Wang, Weinan E, and Siheng Chen. 2025. Ml-master: Towards ai-for-ai via integration of exploration and reasoning. Preprint, arXiv:2506.16499. — [arXiv:2506.16499](https://arxiv.org/abs/2506.16499) find
  31. Keming Lu, Hongyi Yuan, Runji Lin, Junyang Lin, Zheng Yuan, Chang Zhou, and Jingren Zhou. 2023. Routing to the expert: Efficient reward-guided ensemble of large language models. Preprint, arXiv:2311.08692. find
  32. Tula Masterman, Sandi Besen, Mason Sawtell, and Alex Chao. 2024. The landscape of emerging ai agent architectures for reasoning, planning, and tool calling: A survey. arXiv preprint arXiv:2404.11584. find
  33. Grégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun, and Thomas Scialom. 2023. Gaia: a benchmark for general ai assistants. In The Twelfth International Conference on Learning Representations.
  34. Fan Nie, Lan Feng, Haotian Ye, Weixin Liang, Pan Lu, Huaxiu Yao, Alexandre Alahi, and James Zou. 2025. Weak-for-strong: Training weak meta-agent to harness strong executors. Preprint, arXiv:2504.04785. find
  35. Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M Waleed Kadous, and Ion Stoica. 2024. Routellm: Learning to route llms with preference data. Preprint, arXiv:2406.18665. find
  36. Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, and Rafael Rafailov. 2024. Agent q: Advanced reasoning and learning for autonomous ai agents. arXiv preprint arXiv:2408.07199. find
  37. Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. 2023. Communicative agents for software development. 25 pages, 9 figures, 2 tables. find
  38. Toran Bruce Richards and et al. 2023. Auto-gpt: An autonomous gpt-4 experiment. https://github.com/ Significant-Gravitas/Auto-GPT. ↗
  39. Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen. 2020. A tutorial on thompson sampling. Preprint, arXiv:1707.02038. — [arXiv:1707.02038](https://arxiv.org/abs/1707.02038) find
  40. Dingfeng Shi, Jingyi Cao, Qianben Chen, Weichen Sun, Weizhen Li, Hongxuan Lu, Fangchen Dong, Tianrui Qin, King Zhu, Minghao Liu, Jian Yang, Ge Zhang, Jiaheng Liu, Changwang Zhang, Jun Wang, Yuchen Eleanor Jiang, and Wangchunshu Zhou. 2025a. Taskcraft: Automated generation of agentic tasks. Preprint,… — [arXiv:2506.10055](https://arxiv.org/abs/2506.10055) find
  41. Yexuan Shi, Mingyu Wang, Yunxiang Cao, Hongjie Lai, Junjian Lan, Xin Han, Yu Wang, Jie Geng, Zhenan Li, Zihao Xia, Xiang Chen, Chen Li, Jian Xu, Wenbo Duan, and Yuanshuo Zhu. 2025b. Aime: Towards fully-autonomous multi-agent framework. Preprint, arXiv:2507.11988. — [arXiv:2507.11988](https://arxiv.org/abs/2507.11988) find
  42. KV Aditya Srivatsa, Kaushal Kumar Maurya, and Ekaterina Kochmar. 2024. Harnessing the power of multiple minds: Lessons learned from llm routing. Preprint, arXiv:2405.00467. find
  43. Arsene Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, and Joumana Ghosn. 2022. Ddxplus: A new dataset for automatic medical diagnosis. Preprint, arXiv:2205.09148. — [arXiv:2205.09148](https://arxiv.org/abs/2205.09148) find
  44. Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. 2020. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems, 33:5776–5788. find
  45. Jason Wei, Zhiqing Sun, Spencer Papay, Scott McKinney, Jeffrey Han, Isa Fulford, Hyung Won Chung, Alex Tachard Passos, William Fedus, and Amelia Glaese. 2025. Browsecomp: A simple yet challenging benchmark for browsing agents. Preprint, arXiv:2504.12516. find
  46. Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu, Changxing Liu, Hao Zhao, Siheng Chen, and Yanfeng Wang. 2024. Editable scene simulation for autonomous driving via collaborative llm-agents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15077–15087. find
  47. Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, and Hung yi Lee. 2024. Streambench: Towards benchmarking continuous improvement of language agents. Preprint, arXiv:2406.08747. — [arXiv:2406.08747](https://arxiv.org/abs/2406.08747) find
  48. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. Autogen: Enabling next-gen llm applications via multi-agent conversation framework.
  49. Shirley Wu, Parth Sarthi, Shiyu Zhao, Aaron Lee, Herumb Shandilya, Adrian Mladenic Grobelnik, Nurendra Choudhary, Eddie Huang, Karthik Subbian, Linjun Zhang, and 1 others. 2025. Optimas: Optimizing compound ai systems with globally aligned local rewards. arXiv preprint arXiv:2507.03041. find
  50. Xu Yang, Xiao Yang, Shikai Fang, Bowen Xian, Yuante Li, Jian Wang, Minrui Xu, Haoran Pan, Xinpeng Hong, Weiqing Liu, and 1 others. 2025. R&d-agent: Automating data-driven ai solution building through llm-powered automated research, development, and evolution. arXiv preprint arXiv:2505.14738. — [arXiv:2505.14738](https://arxiv.org/abs/2505.14738) find
  51. Yijun Yang, Tianyi Zhou, Kanxue Li, Dapeng Tao, Lu-song Li, Li Shen, Xiaodong He, Jing Jiang, and Yuhui Shi. 2024. Embodied multi-modal agent trained by an llm from a parallel textworld. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 26275–26285. find
  52. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600.
  53. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2023. React: Synergizing reasoning and acting in language models. In The Eleventh International Conference on Learning Representations.
  54. Yanwei Yue, Guibin Zhang, Boyang Liu, Guancheng Wan, Kun Wang, Dawei Cheng, and Yiyan Qi. 2025. Masrouter: Learning to route llms for multi-agent systems. arXiv preprint arXiv:2502.11133.
  55. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. 2024. Textgrad: Automatic "differentiation" via text. Preprint, arXiv:2406.07496.
  56. Guibin Zhang, Kaijie Chen, Guancheng Wan, Heng Chang, Hong Cheng, Kun Wang, Shuyue Hu, and Lei Bai. 2025a. Evoflow: Evolving diverse agentic workflows on the fly. arXiv preprint arXiv:2502.07373. — [arXiv:2502.07373](https://arxiv.org/abs/2502.07373) find
  57. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. 2025b. Multi-agent architecture search via agentic supernet. arXiv preprint arXiv:2502.04180. — [arXiv:2502.04180](https://arxiv.org/abs/2502.04180) find
  58. Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. 2024a. G-designer: Architecting multi-agent communication topologies via graph neural networks. arXiv preprint arXiv:2410.11782. — [arXiv:2410.11782](https://arxiv.org/abs/2410.11782) find
  59. Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, and Chenglin Wu. 2024b. AFlow: Automating Agentic Workflow Generation. arXiv preprint. ArXiv:2410.10762. find
  60. Wentao Zhang, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, Yahui Zhou, and Bo An. 2025c. Agentorchestra: A hierarchical multi-agent framework for general-purpose task solving. Preprint, arXiv:2506.12508. — [arXiv:2506.12508](https://arxiv.org/abs/2506.12508) find
  61. Yi-Kai Zhang, De-Chuan Zhan, and Han-Jia Ye. 2025d. Capability instruction tuning: A new paradigm for dynamic llm routing. Preprint, arXiv:2502.17282. — [arXiv:2502.17282](https://arxiv.org/abs/2502.17282) find
  62. Yiqun Zhang, Hao Li, Chenxu Wang, Linyao Chen, Qiaosheng Zhang, Peng Ye, Shi Feng, Daling Wang, Zhen Wang, Xinrun Wang, Jia Xu, Lei Bai, Wanli Ouyang, and Shuyue Hu. 2025e. The avengers: A simple recipe for uniting smaller language models to challenge proprietary giants. Preprint, arXiv:2505.19797. — [arXiv:2505.19797](https://arxiv.org/abs/2505.19797) find
  63. Sipeng Zheng, Jiazheng Liu, Yicheng Feng, and Zongqing Lu. 2023. Steve-eye: Equipping llm-based embodied agents with visual perception in open worlds. arXiv preprint arXiv:2310.13255. — [arXiv:2310.13255](https://arxiv.org/abs/2310.13255) find
  64. Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, and Ningyu Zhang. 2024. Knowagent: Knowledge-augmented planning for llm-based agents. arXiv preprint arXiv:2403.03101. find
  65. Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber. 2024. Gptswarm: Language agents as optimizable graphs. In Forty-first International Conference on Machine Learning. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/zhang-2026-evoroute-experience-driven-self-routing/