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The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents

Summary

A new arXiv paper introduces The Trinity, a recursive self-improvement framework that couples world generation with Harness evolution through Creator, Explorer, and Evolver roles, reorganizing tasks into a capability tree. 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.

SourcearXiv AIAuthor: 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
The Trinity: Capability-Driven Recursive Self-Improvement for Multimodal Agents
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[Submitted on 26 Oct 2026]

Title:The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents

View a PDF of the paper titled The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents, by Weidong Tang and 19 other authors

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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.

Subjects:

Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2610.36077 [cs.AI]

(or arXiv:2610.36077v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36077

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Weidong Tang [view email] [v1] Mon, 26 Oct 2026 18:03:34 UTC (42,027 KB)

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Key points

  • Tasks with different modalities and surface forms can share underlying capability requirements, motivating a hierarchical capability tree.
  • The framework couples world generation and Harness evolution via Creator, Explorer, and Evolver, with a Capability-Aware Harness Router for module selection.
  • Evolved Harnesses gain 34 points on 28 training benchmarks and 26 points on 18 unseen benchmarks, including two real-world embodied tasks.

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