7 citations · 15 across the 13 of their papers we have counts for
16 papers
Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments
Jie Wu, Zhenru Zhang, Beichen Zhang +11
As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic, executable environments remain scarce. However, environments are what…
OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation
Xiaomeng Hu, Yinger Zhang, Fei Huang +7
AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import…
InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration
Yunkun Wang, Yue Zhang, Guochang Li +5
Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose. While existing LLM-based self-repair approaches conduct inte…
CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
Xue Jiang, Yihong Dong, Mengyang Liu +10
While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between their training on textual patterns and th…
Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model
Yihong Dong, Zhaoyu Ma, Xue Jiang +10
Diffusion language models (DLMs) are emerging as a compelling alternative to the dominant autoregressive paradigm, offering inherent advantages in parallel generation and bidirecti…
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization
Yihong Dong, Xue Jiang, Yongding Tao +11
Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). However, it struggles to break thro…
Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format
Dingzirui Wang, Xuanliang Zhang, Rongyu Cao +8
Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning…
Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
Yingwei Ma, Yongbin Li, Yihong Dong +5
Recent advancements in software engineering agents have demonstrated promising capabilities in automating program improvements. However, their reliance on closed-source or resource…
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
Yunkun Wang, Yue Zhang, Zhen Qin +5
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhausti…
LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues
Yalan Lin, Yingwei Ma, Rongyu Cao +4
Reproducing buggy code is the first and crucially important step in issue resolving, as it aids in identifying the underlying problems and validating that generated patches resolve…
Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement
Yingwei Ma, Rongyu Cao, Yongchang Cao +7
Recent advancements in LLM-based agents have led to significant progress in automatic software engineering, particularly in software maintenance and evolution. Despite these encour…
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks
Dingzirui Wang, Xuanliang Zhang, Qiguang Chen +9
In-context learning (ICL) is an effective approach to help large language models (LLMs) adapt to various tasks by providing demonstrations of the target task. Considering the high…
EvoCodeBench: An Evolving Code Generation Benchmark with Domain-Specific Evaluations
Jia Li, Ge Li, Xuanming Zhang +6
How to evaluate Large Language Models (LLMs) in code generation remains an open question. Existing benchmarks have two limitations - data leakage and lack of domain-specific evalua…
Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
Zhenyu Pan, Rongyu Cao, Yongchang Cao +5
Code completion, a key downstream task in code generation, is one of the most frequent and impactful methods for enhancing developer productivity in software development. As intell…
Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration
Yingwei Ma, Qingping Yang, Rongyu Cao +3
This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue reso…
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
Jia Li, Ge Li, Yunfei Zhao +15
How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories…