activity
20232026
most citedPersonal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

31 citations · 42 across the 39 of their papers we have counts for

collaborators
Showing cs.CLShow all

25 papers · 1 filter

cs.CL2026

Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering

Yifan Wang, Xinkui Lin, Yongxiu Xu +9

Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversation…

cs.CL2026

TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents

Wenhao Wu, Menghao Zhang, Xin Wang +3

Reliable deployment of LLM agents in user-facing products depends not on raw task-solving ability but on consistency and limit-awareness: behaving the same way across repeated tria…

cs.CL2026

Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation

Chris Han, Pengzhi Gao, Pei Fu +1

We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply G…

cs.CL2026

UI-MOPD: Multi-Platform On-Policy Distillation for Unified GUI Agents

Niu Lian, Tongbo Chen, Zhehao Yu +8

Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform interaction. However, unified mul…

cs.CL2026

ExPosST: Explicit Positioning with Adaptive Masking for LLM-Based Simultaneous Machine Translation

Yuzhe Shang, Pengzhi Gao, Yazheng Yang +4

Large language models (LLMs) have recently demonstrated promising performance in simultaneous machine translation (SimulMT). However, applying decoder-only LLMs to SimulMT introduc…

cs.CL2026

Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models

Yuzhe Shang, Pengzhi Gao, Wei Liu +2

Open large language models (LLMs) have demonstrated improving multilingual capabilities in recent years. In this paper, we present a study of open LLMs for multilingual machine tra…