1 citations · 2 across the 15 of their papers we have counts for
16 papers
Terminus-4B: Can a Smaller Model Replace Frontier LLMs at Agentic Execution Tasks?
Spandan Garg, Vikram Nitin, Yufan Huang
Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibilities like search, debugging or…
FastContext: Training Efficient Repository Explorer for Coding Agents
Shaoqiu Zhang, Maoquan Wang, Yuling Shi +12
Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code c…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
ORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents
Kenan Li, Qirui Jin, Liao Zhu +13
Recent advances in Language Model (LM) agents have increasingly focused on repository-level software engineering (SWE) tasks, leading to various agentic workflows, training methods…
SWE-Edit: Rethinking Code Editing for Efficient SWE-Agent
Yikai Zhang, Jiaxin Pei, Kenan Li +9
Large language model agents have made strong progress on software engineering, yet current systems suffer from a context coupling problem: the standard code editing interface confl…
RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms
Kenan Li, Rongzhi Li, Linghao Zhang +17
Language model (LM) agents have driven substantial progress in automated software engineering (SWE), yet building and testing software repositories at scale remains a largely manua…
Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?
Spandan Garg, Yufan Huang
While significant progress has been made in automating various aspects of software development through coding agents, there is still significant room for improvement in their bug f…
Saving SWE-Bench: A Benchmark Mutation Approach for Realistic Agent Evaluation
Spandan Garg, Benjamin Steenhoek, Yufan Huang
Current benchmarks for evaluating software engineering agents, such as SWE-Bench Verified, are predominantly derived from GitHub issues and fail to accurately reflect how developer…
Lita: Light Agent Uncovers the Agentic Coding Capabilities of LLMs
Hankun Dai, Maoquan Wang, Mengnan Qi +6
Large language models (LLMs) are increasingly being applied to programming tasks, ranging from single-turn code completion to autonomous agents. Current code agent designs frequent…
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…
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation
Xing Zhang, Jiaheng Wen, Fangkai Yang +11
The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages. However, existing…
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…
Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension
Mengnan Qi, Yufan Huang, Yongqiang Yao +3
Large language models (LLMs) has experienced exponential growth, they demonstrate remarkable performance across various tasks. Notwithstanding, contemporary research primarily cent…
Rethinking the Instruction Quality: LIFT is What You Need
Yang Xu, Yongqiang Yao, Yufan Huang +4
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data. Existing qua…
SUT: Active Defects Probing for Transcompiler Models
Mengnan Qi, Yufan Huang, Maoquan Wang +5
Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program transl…
Program Translation via Code Distillation
Yufan Huang, Mengnan Qi, Yongqiang Yao +4
Software version migration and program translation are an important and costly part of the lifecycle of large codebases. Traditional machine translation relies on parallel corpora…