12 papers
Adam's Law: Textual Frequency Law on Large Language Models
Hongyuan Adam Lu, Z. L., Victor Wei +5
While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…
Search Self-play: Pushing the Frontier of Agent Capability without Supervision
Hongliang Lu, Yuhang Wen, Pengyu Cheng +7
Reinforcement learning with verifiable rewards (RLVR) has become the mainstream technique for training LLM agents. However, RLVR highly depends on well-crafted task queries and cor…
"The Whole Is Greater Than the Sum of Its Parts": A Compatibility-Aware Multi-Teacher CoT Distillation Framework
Jin Cui, Jiaqi Guo, Ruixuan Yang +6
Chain-of-Thought (CoT) reasoning empowers Large Language Models (LLMs) with remarkable capabilities but typically requires prohibitive parameter scales. CoT distillation has emerge…
FAST: Topology-Aware Frequency-Domain Distribution Matching for Coreset Selection
Jin Cui, Boran Zhao, Jiajun Xu +3
Coreset selection compresses large datasets into compact, representative subsets, reducing the energy and computational burden of training deep neural networks. Existing methods ar…
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…
Listen, Look, Drive: Coupling Audio Instructions for User-aware VLA-based Autonomous Driving
Ziang Guo, Feng Yang, Xuefeng Zhang +6
Vision Language Action (VLA) models promise an open-vocabulary interface that can translate perceptual ambiguity into semantically grounded driving decisions, yet they still treat…