7 papers
SKILL-DISCO: Distilling and Compiling Agent Traces into Reusable Procedural Skills
Zhongxin Guo, Danrui Qi, Hanwen Gu +2
Agents often repeatedly solve similar task instances from scratch, leading to unnecessary reasoning cost and long execution traces. Prior work has explored workflow reuse and execu…
Sigma-MoE-Tiny Technical Report
Qingguo Hu, Zhenghao Lin, Ziyue Yang +12
Mixture-of-Experts (MoE) has emerged as a promising paradigm for foundation models due to its efficient and powerful scalability. In this work, we present Sigma-MoE-Tiny, an MoE la…
SIGMA: An AI-Empowered Training Stack on Early-Life Hardware
Lei Qu, Lianhai Ren, Peng Cheng +12
An increasing variety of AI accelerators is being considered for large-scale training. However, enabling large-scale training on early-life AI accelerators faces three core challen…
EpiCoder: Encompassing Diversity and Complexity in Code Generation
Yaoxiang Wang, Haoling Li, Xin Zhang +10
Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature t…
FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation
Wei Li, Xin Zhang, Zhongxin Guo +6
Implementing new features in repository-level codebases is a crucial application of code generation models. However, current benchmarks lack a dedicated evaluation framework for th…
Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization
Yuanye Liu, Jiahang Xu, Li Lyna Zhang +6
Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design. While recent research has fo…