9 papers
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…
LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
Jiacheng Lin, Kun Qian, Arvind Srinivasan +15
Personalization today is fundamentally platform-centric: services build user representations from the behavioral fragments they observe. Yet no platform can construct a complete pi…
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…
Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
Shubhangi Upasani, Chen Wu, Jay Rainton +4
Test-time adaptation enables large language models (LLMs) to modify their behavior at inference without updating model parameters. A common approach is many-shot prompting, where l…
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation
Chan-Wei Hu, Yueqi Wang, Shuo Xing +4
Large Vision-Language Models (LVLMs) have made remarkable strides in multimodal tasks such as visual question answering, visual grounding, and complex reasoning. However, they rema…