71 citations · 235 across the 92 of their papers we have counts for
7 papers · 1 filter
UltraData-Code: From Raw Repositories to Algorithmically Dense and Task-Oriented Code Data
Chengying Tu, Hengyu Zhao, Shuaikang Xue +13
Code generation has become a core capability of large language models (LLMs), and code data is central to the pre-training process that develops it. As obtaining public code of suf…
What is Missing from AI Post-Training AI: An Empirical Analysis
Joy Jia Yin Lim, Xin Huang, Hao Peng +5
Large language model (LLM) agents can now post-train an LLM end-to-end, raising the prospect of recursive self-improvement (RSI). Yet this progress is measured by aggregate benchma…
Representational Equality in Cross-country Value Simulation: A Systematic Analysis of Large Language Models
Xiaowen Jian, Xinyi Mou, Daisong Gong +3
Traditional methods for studying human opinions often struggle to support representative and scalable research across countries. Large language models (LLMs) can serve as scalable…
VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation
Yaoxiang Wang, Qi Shi, ShangZhan Li +6
LLMs have recently demonstrated strong capabilities in automatic RTL code generation, achieving high syntactic and functional correctness. However, most methods focus on functional…
Data Science and Technology Towards AGI Part I: Tiered Data Management
Yudong Wang, Zixuan Fu, Hengyu Zhao +14
The development of artificial intelligence can be viewed as an evolution of data-driven learning paradigms, with successive shifts in data organization and utilization continuously…
SE-Bench: Benchmarking Self-Evolution with Knowledge Internalization
Jiarui Yuan, Tailin Jin, Weize Chen +1
True self-evolution requires agents to act as lifelong learners that internalize novel experiences to solve future problems. However, rigorously measuring this foundational capabil…
CPMobius: Iterative Coach-Player Reasoning for Data-Free Reinforcement Learning
Ran Li, Zeyuan Liu, Yinghao Chen +8
Large Language Models (LLMs) have demonstrated strong potential in complex reasoning, yet their progress remains fundamentally constrained by reliance on massive high-quality human…