activity
20192026
most citedGLM-130B: An Open Bilingual Pre-trained Model

298 citations · 600 across the 25 of their papers we have counts for

collaborators

29 papers

cs.CL2026

IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse

Yushi Bai, Qian Dong, Ting Jiang +5

Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and serving cost. Sparse…

cs.LG2026★ 1 cited

GLM-5: from Vibe Coding to Agentic Engineering

GLM-5-Team, :, Aohan Zeng +184

We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…

cs.CL2025

OckBench: Measuring the Efficiency of LLM Reasoning

Zheng Du, Hao Kang, Song Han +2

Large language models (LLMs) such as GPT-5 and Gemini 3 have pushed the frontier of automated reasoning and code generation. Yet current benchmarks emphasize accuracy and output qu…

cs.CL2025★ 4 cited

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

5 Team, Aohan Zeng, Xin Lv +167

We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that s…

cs.CL2025

InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior

Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3

Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…

cs.CV2025★ 1 cited

GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

V Team, Wenyi Hong, Wenmeng Yu +90

We present GLM-4.1V-Thinking, GLM-4.5V, and GLM-4.6V, a family of vision-language models (VLMs) designed to advance general-purpose multimodal understanding and reasoning. In this…