activity
20242026
most citedSocial Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?

1 citations · 2 across the 12 of their papers we have counts for

collaborators
Showing 2026Show all

7 papers · 1 filter

cs.AI2026

Dr. Claw: An AI Scientist Workspace for Vibe Research

Dingjie Song, Hanrong Zhang, Dawei Liu +10

Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fragments across chat tools, ID…

cs.AI2026

COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows

Zongxia Li, Dawei Liu, Fuxiao Liu +6

Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently. As these workflows become repeated and domain-specific, agent…

cs.AI2026★ 1 cited

Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills

Dawei Liu, Zongxia Li, Hongyang Du +4

As LLM agents act across personal applications, web browsers, and other interfaces, their reusable skill libraries can scale to thousands of skills. This scale introduces two chall…

cs.CL2026

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Zhengqing Yuan, Hanchi Sun, Lichao Sun +1

We present MegaTrain, a memory-centric system that efficiently trains 100B+ parameter large language models at full precision on a single GPU. Unlike traditional GPU-centric system…

cs.CL2026

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

Kai Zhang, Zhengqing Yuan, Cheng Peng +10

Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are ei…

cs.CL2026

CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era

Kaiwen Shi, Weixiang Sun, Zheyuan Zhang +3

Scientific research relies on citation integrity, yet large language models (LLMs) have introduced a critical risk: fabricated references that appear plausible but correspond to no…

cs.OS2026

Horizon-LM: A RAM-Centric Architecture for LLM Training

Zhengqing Yuan, Lichao Sun, Yanfang Ye

The rapid growth of large language models (LLMs) has outpaced the evolution of single-GPU hardware, making model scale increasingly constrained by memory capacity rather than compu…