2026-06-15原文2 min readUpdated: 2026-06-15

Hybrid Open-Ended Tri-Evolution Makes Better Deep Researcher

The Hybrid Open-Ended Tri-Evolution (HOTE) framework uses hybrid-mode reinforcement learning to collaboratively evolve a proposer, solver and judge based on web-scale knowledge, enabling autonomous agent evolution for open-ended deep research tasks. An 8B model trained with HOTE surpasses stronger static models and state-of-the-art methods with less time overhead.

SourcearXiv AIAuthor: Hongming Piao, Chi Liu, Mengzhuo Chen, Yan Shu, Derek Li, Ying Wei, Bryan Dai

[2606.13710] Hybrid Open-Ended Tri-Evolution Makes Better Deep Researcher

[Submitted on 10 Jun 2026]

Title:Hybrid Open-Ended Tri-Evolution Makes Better Deep Researcher

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Abstract:Deep research and agent evolution serve as de-facto tasks for AI agents in real-world applications toward artificial general intelligence. The former enables autonomous retrieval and integration of information in open-ended environments to tackle open-ended research tasks, yet it is constrained by the static parametric deep research capabilities of agent systems. The latter allows agents to autonomously interact with the environment to gain experiences that evolve model capabilities. However, its effectiveness has been widely validated only on verifiable tasks with standard answers, leaving a gap with open-ended research tasks. To bridge these two critical tasks, we propose the Hybrid Open-Ended Tri-Evolution (HOTE) framework, which leverages hybrid-mode reinforcement learning to facilitate the collaborative evolution of a proposer, solver and judge based on web-scale knowledge, moving toward autonomous evolving agents in open-ended tasks and environments. Extensive experiments on three long-form deep research benchmarks demonstrate that the 8B model trained via HOTE surpasses the strongest static open 8-32B models as well as those trained by state-of-the-art deep research training methods with less time overhead, and further verify that the evolution of all three modules in HOTE is indispensable.

Subjects:

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as: arXiv:2606.13710 [cs.AI]

(or arXiv:2606.13710v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.13710

arXiv-issued DOI via DataCite

Submission history

From: Hongming Piao [view email] [v1] Wed, 10 Jun 2026 15:03:42 UTC (8,116 KB)

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