3 citations · 3 across the 3 of their papers we have counts for
4 papers
Stateless Language Agents: Scaling Long-Horizon Automated Research
Qizheng Zhang, Changxiu Ji, Isaac Sun +15
Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate o…
SETA: Scaling Environments for Terminal Agents
Qijia Shen, Zhiqi Huang, Vamsidhar Kamanuru +19
Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs). Among these, the te…
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Qizheng Zhang, Changran Hu, Shubhangi Upasani +10
Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evi…
Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environments
Vinay Venkatesh, Vamsidhar R Kamanuru, Lav Kumar +1
This paper proposes Edge-FIT (Federated Instruction Tuning on the Edge), a scalable framework for Federated Instruction Tuning (FIT) of Large Language Models (LLMs). Traditional Fe…