most citedHow Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings

4 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures

Yujian Liu, Li An, Zelin He +5

Autonomous agents can increasingly propose ideas, modify code, and run experiments, creating an opportunity to accelerate scientific discovery using teams of collaborative AI resea…

cs.CL2026

Practical Online KV Cache Compaction for LLM Agents: An Empirical Study

Yujian Liu, Jiabao Ji, Li An +4

LLM agents accumulate long trajectories of reasoning steps, tool calls, and environment feedback, making the KV cache a major inference bottleneck. KV cache compaction can reduce t…

cs.AI2026

Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL

Jiabao Ji, Yujian Liu, Li An +4

Large language model agents often spend substantial wall-clock time waiting for tool call results. Tool-call speculation can hide this latency by predicting and pre-executing an ag…

cs.CL2026

VISUALSKILL: Multimodal Skills for Computer-Use Agents

Ziyan Jiang, Li An, Yujian Liu +5

Computer-use agents (CUAs) approach human-level performance on standardised benchmarks but still struggle on long-horizon tasks and unseen software. Existing skill libraries addres…

cs.CL2026★ 4 cited

How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings

Yujian Liu, Jiabao Ji, Li An +3

Agent skills, which are reusable, domain-specific knowledge artifacts, have become a popular mechanism for extending LLM-based agents, yet formally benchmarking skill usage perform…

cs.CR2025

A Reinforcement Learning Framework for Robust and Secure LLM Watermarking

Li An, Yujian Liu, Yepeng Liu +3

Watermarking has emerged as a promising solution for tracing and authenticating text generated by large language models (LLMs). A common approach to LLM watermarking is to construc…

cs.CR2025

Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning

Li An, Yujian Liu, Yepeng Liu +3

Watermarking has emerged as a promising technique for detecting texts generated by LLMs. Current research has primarily focused on three design criteria: high quality of the waterm…