Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era
Please give a thumbs up to this comment if you found it helpful!
\n
If you want recommendations for any Paper on Hugging Face checkout this Space
\n
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: \n\n@librarian-bot\n\t recommend
\n","updatedAt":"2026-01-14T01:37:20.697Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7303509712219238},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[{"reaction":"👍","users":["SDSHAMMER"],"count":1}],"isReport":false}},{"id":"696b8b9985619ece0dd078ba","author":{"_id":"65243980050781c16f234f1f","avatarUrl":"/avatars/743a009681d5d554c27e04300db9f267.svg","fullname":"Avi","name":"avahal","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false},"createdAt":"2026-01-17T13:16:09.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"arXivlens breakdown of this paper 👉 https://arxivlens.com/PaperView/Details/megaflow-large-scale-distributed-orchestration-system-for-the-agentic-era-5563-b3cd7717\n\n- Executive Summary\n- Detailed Breakdown\n- Practical Applications","html":"
\n","updatedAt":"2026-01-17T13:16:09.420Z","author":{"_id":"65243980050781c16f234f1f","avatarUrl":"/avatars/743a009681d5d554c27e04300db9f267.svg","fullname":"Avi","name":"avahal","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7032687067985535},"editors":["avahal"],"editorAvatarUrls":["/avatars/743a009681d5d554c27e04300db9f267.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2601.07526","authors":[{"_id":"6965c626fc8c4ecc02c7f9b2","user":{"_id":"64c38871f9cd765462fa1a17","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64c38871f9cd765462fa1a17/yuIlVcqeDlQVKsUF8uEl3.jpeg","isPro":false,"fullname":"Lei Zhang","user":"Lemoncoke","type":"user"},"name":"Lei Zhang","status":"claimed_verified","statusLastChangedAt":"2026-01-13T15:45:28.900Z","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9b3","name":"Mouxiang Chen","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9b4","name":"Ruisheng Cao","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9b5","name":"Jiawei Chen","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9b6","name":"Fan Zhou","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9b7","name":"Yiheng Xu","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9b8","user":{"_id":"646df403ad20c6fa4f30b7ec","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/646df403ad20c6fa4f30b7ec/Q64-XMghOcBoo3itZDGYA.jpeg","isPro":false,"fullname":"Jiaxi Yang","user":"jx-yang","type":"user"},"name":"Jiaxi Yang","status":"claimed_verified","statusLastChangedAt":"2026-02-03T10:09:27.851Z","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9b9","name":"Liang Chen","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9ba","name":"Changwei Luo","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9bb","name":"Kai Zhang","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9bc","name":"Fan Yan","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9bd","name":"KaShun Shum","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9be","user":{"_id":"660297295c467e8c06c684b0","avatarUrl":"/avatars/b721b512148ae118cd99464ae909bb3a.svg","isPro":false,"fullname":"zjj","user":"zjj1233","type":"user"},"name":"Jiajun Zhang","status":"claimed_verified","statusLastChangedAt":"2026-01-13T15:45:31.001Z","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9bf","name":"Zeyu Cui","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9c0","name":"Hu Feng","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9c1","name":"Junyang Lin","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9c2","name":"Binyuan Hui","hidden":false},{"_id":"6965c626fc8c4ecc02c7f9c3","name":"Min Yang","hidden":false}],"publishedAt":"2026-01-12T13:25:33.000Z","submittedOnDailyAt":"2026-01-13T01:47:40.945Z","title":"MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era","submittedOnDailyBy":{"_id":"64c38871f9cd765462fa1a17","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64c38871f9cd765462fa1a17/yuIlVcqeDlQVKsUF8uEl3.jpeg","isPro":false,"fullname":"Lei Zhang","user":"Lemoncoke","type":"user"},"summary":"The rapid development of interactive and autonomous AI systems signals our entry into the agentic era. Training and evaluating agents on complex agentic tasks such as software engineering and computer use requires not only efficient model computation but also sophisticated infrastructure capable of coordinating vast agent-environment interactions. However, no open-source infrastructure can effectively support large-scale training and evaluation on such complex agentic tasks. To address this challenge, we present MegaFlow, a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads. MegaFlow abstracts agent training infrastructure into three independent services (Model Service, Agent Service, and Environment Service) that interact through unified interfaces, enabling independent scaling and flexible resource allocation across diverse agent-environment configurations. In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization. By enabling such large-scale agent training, MegaFlow addresses a critical infrastructure gap in the emerging agentic AI landscape.","upvotes":23,"discussionId":"6965c626fc8c4ecc02c7f9c4","ai_summary":"MegaFlow is a distributed orchestration system that enables large-scale training and evaluation of agents on complex tasks by providing efficient scheduling, resource allocation, and task management through modular services.","ai_keywords":["distributed orchestration system","agent-environment interactions","large-scale training","resource allocation","task management","modular services","concurrent agent tasks","system stability","resource utilization"],"organization":{"_id":"64c8b5837fe12ecd0a7e92eb","name":"Qwen","fullname":"Qwen","avatar":"https://cdn-uploads.huggingface.co/production/uploads/620760a26e3b7210c2ff1943/-s1gyJfvbE1RgO5iBeNOi.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"64c38871f9cd765462fa1a17","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64c38871f9cd765462fa1a17/yuIlVcqeDlQVKsUF8uEl3.jpeg","isPro":false,"fullname":"Lei Zhang","user":"Lemoncoke","type":"user"},{"_id":"660297295c467e8c06c684b0","avatarUrl":"/avatars/b721b512148ae118cd99464ae909bb3a.svg","isPro":false,"fullname":"zjj","user":"zjj1233","type":"user"},{"_id":"63eb30275c837d9968f3a2c7","avatarUrl":"/avatars/f0da56bca6760b9a79133bba6eb4379d.svg","isPro":false,"fullname":"Jiawei Chen","user":"jiawei1998","type":"user"},{"_id":"628f6e5ab90dde28ef57d293","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/628f6e5ab90dde28ef57d293/AxNzR2nvrND6Rf3RPkYMk.jpeg","isPro":false,"fullname":"Fan Zhou","user":"koalazf99","type":"user"},{"_id":"6384c07fdfffab4824ff45fb","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1669644372381-noauth.jpeg","isPro":false,"fullname":"Zeyao Ma","user":"KAKA22","type":"user"},{"_id":"646df403ad20c6fa4f30b7ec","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/646df403ad20c6fa4f30b7ec/Q64-XMghOcBoo3itZDGYA.jpeg","isPro":false,"fullname":"Jiaxi Yang","user":"jx-yang","type":"user"},{"_id":"67244a81aa8556c561925ab6","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/w-vZ0uwYACagrNq-H1oyO.jpeg","isPro":false,"fullname":"Shaohang Wei","user":"SylvainWei","type":"user"},{"_id":"663a255beb73b1a397656dc1","avatarUrl":"/avatars/83cbb595391e3413d8f1f159c2aff512.svg","isPro":false,"fullname":"Zhixun Li","user":"Zhixun-Li","type":"user"},{"_id":"65d801194c28f74f7f5e8059","avatarUrl":"/avatars/f18622ba64e4c19c1288ccdc1761cf4b.svg","isPro":false,"fullname":"Junfei Wu","user":"Hyperwjf","type":"user"},{"_id":"689e0304aafdf21ed7ba6f29","avatarUrl":"/avatars/6ba9236b0625eb2f5dfd8f44c425d732.svg","isPro":false,"fullname":"Qingbin Li","user":"BetuBin","type":"user"},{"_id":"6552dda31d1a7cefd8e5700a","avatarUrl":"/avatars/46fb8f3b3e73a443fcfea1fa29225007.svg","isPro":false,"fullname":"zhang","user":"BoHanMint","type":"user"},{"_id":"64f44021220c2e5e96628595","avatarUrl":"/avatars/43bcb389760d1037dde1f81c4398d260.svg","isPro":true,"fullname":"gdwind LQ","user":"gdwind","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":0,"organization":{"_id":"64c8b5837fe12ecd0a7e92eb","name":"Qwen","fullname":"Qwen","avatar":"https://cdn-uploads.huggingface.co/production/uploads/620760a26e3b7210c2ff1943/-s1gyJfvbE1RgO5iBeNOi.png"}}">
MegaFlow is a distributed orchestration system that enables large-scale training and evaluation of agents on complex tasks by providing efficient scheduling, resource allocation, and task management through modular services.
AI-generated summary
The rapid development of interactive and autonomous AI systems signals our entry into the agentic era. Training and evaluating agents on complex agentic tasks such as software engineering and computer use requires not only efficient model computation but also sophisticated infrastructure capable of coordinating vast agent-environment interactions. However, no open-source infrastructure can effectively support large-scale training and evaluation on such complex agentic tasks. To address this challenge, we present MegaFlow, a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads. MegaFlow abstracts agent training infrastructure into three independent services (Model Service, Agent Service, and Environment Service) that interact through unified interfaces, enabling independent scaling and flexible resource allocation across diverse agent-environment configurations. In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization. By enabling such large-scale agent training, MegaFlow addresses a critical infrastructure gap in the emerging agentic AI landscape.
The rapid development of interactive and autonomous AI systems signals our entry into the agentic era. Training and evaluating agents on complex agentic tasks such as software engineering and computer use requires not only efficient model computation but also sophisticated infrastructure capable of coordinating vast agent-environment interactions. However, no open-source infrastructure can effectively support large-scale training and evaluation on such complex agentic tasks. To address this challenge, we present MegaFlow, a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads. MegaFlow abstracts agent training infrastructure into three independent services (Model Service, Agent Service, and Environment Service) that interact through unified interfaces, enabling independent scaling and flexible resource allocation across diverse agent-environment configurations. In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization. By enabling such large-scale agent training, MegaFlow addresses a critical infrastructure gap in the emerging agentic AI landscape.