Trinity 以共享能力組織多模態智能體演化

Computer Science > Artificial Intelligence arXiv:2610.

36077 (cs) [Submitted on 26 Oct 2026 (v1), last revised 30 Oct 2026 (this version, v2)] Title:The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents Authors:Weidong Tang, Ruiheng Wang, Jingxuan Zhang, Hexin Wang, Haoyuan Chen, Zhengfeiyang Zhang, Yixing Ma, Zichen Jiang, Ziyu Guo, Haodong Li, Zhengbo Jiao, Zhiqing Cui, Xinxiang Yin, Shuo Liang, Zhenglin Wan, Hongzhan Lin, Wangbo Zhao, Kaipeng Zhang, Pengfei Zhou, Odin Zhang, Yang You View a PDF of the paper titled The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents, by Weidong Tang and 20 other authors View PDF HTML (experimental) Abstract:Multimodal intelligence can be viewed as human-guided co-evolution between worlds and agents through interaction.

However, scaling this process faces two bottlenecks: constructing heterogeneous, verifiable environments is costly, and manual Harness iteration cannot keep pace with growing task diversity and complexity.

A key insight is that tasks with different modalities and surface forms can share the same underlying capability requirements.

We therefore introduce The Trinity, a recursive self-improvement framework coupling world generation with Harness evolution through three roles: Creator, Explorer, and Evolver.

Drawing on verified execution trajectories, Evolver shifts the basis of task organization from surface categories to shared requirements, reorganizing the original task tree into a hierarchical capability tree.

We introduce Capability-Driven Real2Sim2Real: for heterogeneous tasks at the same node, Creator abstracts their common requirements into simplified, verifiable simulated worlds for capability learning, while evaluation on original tasks measures transfer.

Along this capability tree, Explorer progressively evolves reusable capability modules through selective inheritance, forming a Harness tree whose validated updates accumulate across iterations within a fixed topology.

The resulting Harnesses specialize in different capabilities, motivating a Capability-Aware Harness Router that uses public task information to select compatible module configurations from the tree.

Our evolved Harnesses improve mean performance by 34 points across 28 training benchmarks and 26 points across 18 unseen benchmarks, including two real-world embodied tasks.Comments: Tech Report Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.

CV) Cite as: arXiv:2610.36077 [cs.AI] (or arXiv:2610.36077v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2610.

36077 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Weidong Tang [view email] [v1] Mon, 26 Oct 2026 18:03:34 UTC (42,027 KB) [v2] Fri, 30 Oct 2026 04:15:41 UTC (42,027 KB) Full-text links: Access Paper: View a PDF of the paper titled The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents, by Weidong Tang and 20 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.

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