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arXiv AI By Hengqi Liu, Ziyuan Liu, Yang Qin, Wanting Zhou, Zichuan Wang, Jiachen Liang, Xu Chu, Shaowei Chen, Zhaokai Luo, Mu Chuan

Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training

Read the original on arXiv AI →

Navalia is a framework that advances cowork agents by combining verifiable task synthesis with long‑horizon post‑training. It selects supporting evidence from corporate financial reports, builds executable computation chains, and uses independent LLM review and programmatic checks to ensure evidence consistency and answer correctness. The verified tasks are then used for trajectory collection, supervised fine‑tuning, and reinforcement learning, yielding consistent performance gains over the base model across five financial and office‑task benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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