1 citations · 1 across the 10 of their papers we have counts for
11 papers
Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models
Xingyu Mou, Zijin Huang, Tianze Zhang +5
Block diffusion is the dominant approach for scaling discrete diffusion language models (dLLMs), as fixed-size blocks preserve parallel decoding while keeping quadratic attention c…
LLaDA2.1: Speeding Up Text Diffusion via Token Editing
Tiwei Bie, Maosong Cao, Xiang Cao +47
While LLaDA2.0 showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generati…
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Tiwei Bie, Maosong Cao, Kun Chen +28
This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (A…
InnoGym: Benchmarking the Innovation Potential of AI Agents
Jintian Zhang, Kewei Xu, Jingsheng Zheng +10
LLMs and Agents have achieved impressive progress in code generation, mathematical reasoning, and scientific discovery. However, existing benchmarks primarily measure correctness,…
dInfer: An Efficient Inference Framework for Diffusion Language Models
Yuxin Ma, Lun Du, Lanning Wei +20
Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, leveraging denoising-based generation to enable inherent parallel…
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations
Yujie Luo, Zhuoyun Yu, Xuehai Wang +6
Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to in…
LLaDA-MoE: A Sparse MoE Diffusion Language Model
Fengqi Zhu, Zebin You, Yipeng Xing +23
We introduce LLaDA-MoE, a large language diffusion model with the Mixture-of-Experts (MoE) architecture, trained from scratch on approximately 20T tokens. LLaDA-MoE achieves compet…
Reflective Paper-to-Code Reproduction Enabled by Fine-Grained Verification
Mingyang Zhou, Quanming Yao, Lun Du +2
Reproducing machine learning papers is essential for scientific progress but remains challenging for both humans and automated agents. Existing agent-based methods often struggle t…
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
Yixin Ou, Yujie Luo, Jingsheng Zheng +9
Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems. LLM-driven data science agents promise to automate the entire machine l…
Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey
Da Zheng, Lun Du, Junwei Su +6
Problem-solving has been a fundamental driver of human progress in numerous domains. With advancements in artificial intelligence, Large Language Models (LLMs) have emerged as powe…
OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System
Yujie Luo, Xiangyuan Ru, Kangwei Liu +10
We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news,…