26 citations · 59 across the 24 of their papers we have counts for
29 papers
SPROUT: Scaling Agentic Reinforcement Learning for Long-Horizon Software Engineering
Mengyang Liu, Weiming Wang, Haixin Nan +6
Reinforcement learning (RL) has driven substantial progress in coding agents by relying on verified successful trajectories as learning signals. However, such trajectories are scar…
OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime
Chun-Wah Hsu, Kai Gong, Yu Wu +12
Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically assume configured organizations…
PRAXIS: Graph-Grounded Tacit Knowledge for Domain Code Generation
Xue Jiang, Tianyu Zhang, Lingwei Wu +7
LLM agents have achieved strong performance on general software engineering tasks, yet struggle with domain-specific code generation. We identify the root cause as the agent's lack…
Archer: Adaptive Reuse of Cached Hidden States for Efficient Rollback in Diffusion Language Models
Xuning He, Zinan Sheng, Qingyuan Shi +5
Diffusion language models (DLMs) can revise earlier predictions as context evolves, but rollback requires costly full-sequence recomputation. Conventional key-value (KV) caching as…
Where Is the Tradeoff in Using Third-Party API Routers for Agentic Software Development?
Donghao Fu, Jingxin Li, Xue Jiang +1
Third-party API routers have become a common layer that unifies access across increasingly diverse LLM providers. In coding-agent workflows, high-autonomy operation is widely adopt…
Multi-agent Collaboration with State Management
Mengyang Liu, Taozhi Chen, Zhenhua Xu +2
Recent advances in multi-agent systems have shown great potential for solving complex tasks. However, when multiple agents edit a shared codebase concurrently, their changes can si…
Agentic Reverse Engineering: From I/O to Code with Discovery Agent
Yihong Dong, Jiaru Qian, Haoran Zhang +7
The automatic synthesis of a program from any form of specification is regarded as a holy grail of computer science. Fueled by LLMs, NL2Code has achieved tremendous success, yet th…
TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steering
Yuan Sui, Yulin Chen, Yibo Li +6
When language model agents tackle complex software engineering tasks, they often degrade over long trajectories, which we define as *agent drift*. We focus on two recurring failure…
Think Anywhere in Code Generation
Xue Jiang, Tianyu Zhang, Ge Li +8
Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer. However, this approach suffers from…
Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy
Yihong Dong, Jianha Xiao, Xue Jiang +7
The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack systematic evaluation based on comput…
KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?
Xue Jiang, Ge Li, Jiaru Qian +12
Large language models (LLMs) excel at general programming but struggle with domain-specific software development, necessitating domain specialization methods for LLMs to learn and…
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…
A Survey on Code Generation with LLM-based Agents
Yihong Dong, Xue Jiang, Jiaru Qian +4
Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm. Distinct from previous code generation techniques, code genera…
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…
Large Language Model Unlearning for Source Code
Xue Jiang, Yihong Dong, Huangzhao Zhang +9
While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringe…
Rethinking Repetition Problems of LLMs in Code Generation
Yihong Dong, Yuchen Liu, Xue Jiang +2
With the advent of neural language models, the performance of code generation has been significantly boosted. However, the problem of repetitions during the generation process cont…
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…
Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs
Yuqi Zhu, Ge Li, Xue Jiang +4
Chain-of-Thought (CoT) reasoning has been demonstrated as an effective technique for improving the problem-solving capabilities of large language models (LLMs) in the context of co…
Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling
Yihong Dong, Ge Li, Xue Jiang +8
Periodicity, as one of the most important basic characteristics, lays the foundation for facilitating structured knowledge acquisition and systematic cognitive processes within hum…
ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation
Xue Jiang, Yihong Dong, Yongding Tao +4
Large language models (LLMs) have achieved impressive performance in code generation recently, offering programmers revolutionary assistance in software development. However, due t…
FAN: Fourier Analysis Networks
Yihong Dong, Ge Li, Yongding Tao +7
Despite the remarkable successes of general-purpose neural networks, such as MLPs and Transformers, we find that they exhibit notable shortcomings in modeling and reasoning about p…
Exploring Data-Efficient Adaptation of Large Language Models for Code Generation
Xue Jiang, Yihong Dong, Zhiyuan Fan +3
Although Large Language Models (LLMs) have made significant progress in code generation, they still struggle with code generation tasks in specific scenarios. These scenarios usual…
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models
Yihong Dong, Xue Jiang, Huanyu Liu +4
Recent statements about the impressive capabilities of large language models (LLMs) are usually supported by evaluating on open-access benchmarks. Considering the vast size and wid…