AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research
Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analysis, posing a significant challenge for current language models. Most existing approaches follow a plan-then-write paradigm, whose performance heavily depends on the quality of the initial outline. However, constructing a comprehensive outline itself demands strong reasoning ability, causing current deep research systems to rely almost exclusively on closed-source or online large models. This reliance raises practical barriers to deployment and introduces safety and privacy concerns for user-authored data. In this work, we present AgentCPM-Report, a lightweight yet high-p...
| Authors | Yishan Li, Wentong Chen, Yukun Yan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 February 2026 |
| Last updated at source | 6 February 2026 |
Popularity, last 2 days
0 now. Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- openbmb/AgentCPM-Report-GGUFModel · cited by
- openbmb/AgentCPM-ReportModel · cited by
Models and datasets referencing this paper
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 19 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.