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

4 citations · 4 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Conditional Performance Guarantee for Large Reasoning Models

Jianguo Huang, Hao Zeng, Bingyi Jing +2

Large reasoning models have shown strong performance through extended chain-of-thought reasoning, yet their computational cost remains significant. Probably approximately correct (…

cs.CL2025

SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs

Ruixuan Huang, Hao Zeng, Hantao Huang +4

Post-training quantization (PTQ) plays a crucial role in the democratization of large language models (LLMs). However, existing low-bit quantization and sparsification techniques a…

econ.TH2025

Identity-Compatible Auctions

Haoyuan Zeng

This paper studies the incentives of the seller and buyers to shill bid in a single-item auction. An auction is seller identity-compatible if the seller cannot profit from pretendi…

econ.TH2025

Persuasive Selection in Signaling Games

Haoyuan Zeng

This paper introduces a novel criterion, persuasiveness, to select equilibria in signaling games. In response to the Stiglitz critique, persuasiveness focuses on the comparison acr…

econ.TH2025

Mechanism Design with Information Leakage

Samuel Häfner, Marek Pycia, Haoyuan Zeng

We study the design of mechanisms -- e.g., auctions -- when the designer does not control information flows between mechanism participants. A mechanism equilibrium is leakage-proof…

cs.CL20254 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…