arXiv:2606. 02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
By Yiheng Shu, Bernal Jim\'enez Guti\'errez, Saisri Padmaja Jonnalagedda, Yuguang Yao, Huan Sun, Yu Su
arXiv:2606. 02461v2 Announce Type: replace Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
By Yiheng Shu, Bernal Jim\'enez Guti\'errez, Saisri Padmaja Jonnalagedda, Yuguang Yao, Huan Sun, Yu Su
Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained requests in static environments.
The paper proposes a hierarchical architecture for long-horizon language‑model agents that must operate over days or weeks without forgetting. It introduces three key components: time‑scale levels that store bounded summaries, a clocked tick as the basic action unit, and cascaded intelligence that escalates tasks to more capable models only after review failures. A ten‑day experiment demonstrated that the agent maintained continuity across context resets, adapted its behavior based on early knowledge, and identified where learned components could be integrated.
By Erik Nijkamp, Anurag Koul, Egor Pakhomov, Bo Pang
arXiv:2608. 10875v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as personal assistants.
By Xiaohongshu Inc
arXiv:2609.17416v1 Announce Type: new
Abstract: Voice agents built on LLMs follow a rigid listen-think-speak loop that inserts seconds of dead air before every reply. We show that continuous-time cog...
By Bojie Li, Noah Shi