3 citations · 6 across the 9 of their papers we have counts for
10 papers
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…
P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads
Yun Luo, Futing Wang, Qianjia Cheng +28
The transition from symbolic manipulation to science-grade reasoning represents a pivotal frontier for Large Language Models (LLMs), with physics serving as the critical test ancho…
Rethinking Code Complexity Through the Lens of Large Language Models
Chen Xie, Xiaodong Gu, Yuling Shi +1
Code complexity metrics such as cyclomatic complexity have long been used to assess software quality and maintainability. With the rapid advancement of large language models (LLMs)…
Confidence as a Reward: Transforming LLMs into Reward Models
He Du, Bowen Li, Chengxing Xie +3
Reward models can significantly enhance the reasoning capabilities of large language models (LLMs), but they typically require extensive curated data and costly training. To mitiga…
EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translation
Chaofan Wang, Tingrui Yu, Beijun Shen +6
Translating legacy C codebases to Rust is increasingly demanded for building safety-critical systems. While various approaches have emerged for this task, they face inherent trade-…
SWE-bench Goes Live!
Linghao Zhang, Shilin He, Chaoyun Zhang +12
The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs…
Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs
Chen Xie, Mingsheng Jiao, Xiaodong Gu +1
While large language models (LLMs) have been widely applied to code generation, they struggle with generating entire deep learning projects, which are characterized by complex stru…
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale
Linghao Zhang, Junhao Wang, Shilin He +13
Large Language Models have advanced automated software development, however, it remains a challenge to correctly infer dependencies, namely, identifying the internal components and…
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
Chengxing Xie, Bowen Li, Chang Gao +4
Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks. One significant application of LLMs is in tackling software engineering cha…
A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models
Chengxing Xie, Difan Zou
Recent studies have highlighted their proficiency in some simple tasks like writing and coding through various reasoning strategies. However, LLM agents still struggle with tasks t…