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, verif
The Trinity framework recursively improves multimodal agents by organizing tasks into a capability tree, generating simulated training worlds, and routing specialized harness modules to boost performance on diverse benchmarks.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
The Trinity:Capability-Driven Recursive Self-Improvement for Multimodal Agents was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 23 checks on 3 different days. Its best position was #1 of 30, reached on Oct 28, 13:28 UTC. It dropped off the list after Oct 30, 03:28 UTC.
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Not at the moment. It was last on the list on Oct 30, 03:28 UTC, after 3 days of trending.
#1 on Hugging Face · Daily papers, on Oct 28, 13:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 28, 2026 and has been on the list on 3 days (23 checks) since.