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VersE, a Verified Self-Evolving optimizer for agent harnesses, improves all four evaluated harness optimizers on held-out SWE-rebench tasks and newer out-of-distribution tasks in five languages.
The model's hidden state provides a way to read, steer, and check tool choice before a call is made, suggesting that the model's hidden state provides a way to read, steer, and check tool choice before a call is made.
A three-tier diagnostic taxonomy shows that verification/feedback and planning failures dominate execution/grounding errors, while a single scalar success rate can not explain, and connects these findings to newer long-horizon CUA benchmarks and derive stage-specific design rules for CUA evaluation.
The architecture and the Muon optimizer together shift the optimal learning rate and batch size upwards, render batch-size warmup unnecessary, and substantially improve stability under stress tests.
Qwen-CUA is introduced, a native computer-use agent with a 397B-A17B Qwen mixture-of-experts backbone that outperforms Qwen3.7 and remains competitive with leading proprietary systems, and scalable verifiable interaction and hybrid tool use as key directions.
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