2 citations · 3 across the 18 of their papers we have counts for
6 papers · 1 filter
Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks
Mengyu Zheng, Kai Han, Boxun Li +13
General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not b…
An Empirical Study of World Model Quantization
Zhongqian Fu, Tianyi Zhao, Kai Han +3
World models learn an internal representation of environment dynamics, enabling agents to simulate and reason about future states within a compact latent space for tasks such as pl…
Diffusion In Diffusion: Reclaiming Global Coherence in Semi-Autoregressive Diffusion
Linrui Ma, Yufei Cui, Kai Han +1
One of the most compelling features of global discrete diffusion language models is their global bidirectional contextual capability. However, existing block-based diffusion studie…
ROOT: Robust Orthogonalized Optimizer for Neural Network Training
Wei He, Kai Han, Hang Zhou +4
The optimization of large language models (LLMs) remains a critical challenge, particularly as model scaling exacerbates sensitivity to algorithmic imprecision and training instabi…
LLM Data Selection and Utilization via Dynamic Bi-level Optimization
Yang Yu, Kai Han, Hang Zhou +4
While large-scale training data is fundamental for developing capable large language models (LLMs), strategically selecting high-quality data has emerged as a critical approach to…
Mixture of Lookup Experts
Shibo Jie, Yehui Tang, Kai Han +4
Mixture-of-Experts (MoE) activates only a subset of experts during inference, allowing the model to maintain low inference FLOPs and latency even as the parameter count scales up.…