15 citations · 43 across the 70 of their papers we have counts for
9 papers · 1 filter
Beyond Top- Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Wang Wei, Tiankai Yang, Samyadeep Basu +7
Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redu…
Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents
Harshitha Kolukuluru, Reshma Ashok, Kirat Arora +7
Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional eviden…
KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim +3
To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user's evolving underst…
Personalized Auto-Research: Towards a True AI Co-Scientist
Bo Ni, Franck Dernoncourt, Hongjie Chen +5
AI co-scientists that generate hypotheses, retrieve related work, design experiments, execute code, and draft full papers are beginning to change how research is carried out. Despi…
GRASP: GRanularity-Aware Search Policy for Agentic RAG
Varun Gandhi, Jaewook Lee, Shantanu Todmal +4
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.…
FORTIS: Benchmarking Over-Privilege in Agent Skills
Shawn Li, Chenxiao Yu, Han Wang +8
Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as…
Sparse Personalized Text Generation with Multi-Trajectory Reasoning
Bo Ni, Haowei Fu, Qinwen Ge +10
As Large Language Models (LLMs) advance, personalization has become a key mechanism for tailoring outputs to individual user needs. However, most existing methods rely heavily on d…
Anticipatory Planning for Multimodal AI Agents
Yongyuan Liang, Shijie Zhou, Yu Gu +6
Recent advances in multimodal agents have improved computer-use interaction and tool-usage, yet most existing systems remain reactive, optimizing actions in isolation without reaso…
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs
Paiheng Xu, Gang Wu, Xiang Chen +6
Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specifi…