6 citations · 13 across the 11 of their papers we have counts for
13 papers
VeeReview: Measuring Build-Verify Pairs in Agent-Authored Pull Requests from a V-Model Perspective
Gabriel Dautovi, Jie JW Wu
Context: Reviewing a pull request requires verification evidence that addresses the proposed change. Coding agent benchmarks and studies of pull request outcomes do not directly as…
VReAct: Verification as an Architectural Layer for LLM Agents: A V-Model Design, and a Pilot Study of Its Deterministic Core
Ali Afoud, Jie JW Wu
Large language model (LLM) agents built on the ReAct pattern concentrate four responsibilities in one model: selecting a strategy, choosing each action, formatting it, and judging…
AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning
Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao +10
Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes…
AssumptionMiner: Extracting, Tracing, and Revising Implicit Assumptions in LLM Code Generation
Jie "JW" Wu
Large language models (LLMs) generate code from natural-language prompts, yet real-world prompts rarely provide complete specifications. When prompts leave input formats, error han…
AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise
Bowen Tian, Caixue He, Jiemin Wu +4
Editing complex, long-form knowledge in Large Language Models remains a significant challenge due to the difficulty of maintaining generation coherence. Existing autoregressive met…
Internalize the Temperature: On-Policy Self-Distillation as Policy Reheater for Reinforcement Learning
Xuewei Yang, Jiachen Yu, Jie Wu +3
Reinforcement learning from verifiable rewards improves the reasoning ability of large language models, but often suffers from entropy collapse, in which increasingly concentrated…
MANTRA: a Framework for Multi-stage Adaptive Noise TReAtment During Training
Zixiao Zhao, Fatemeh H. Fard, Jie JW Wu
The reliable application of deep learning models to software engineering tasks hinges on high-quality training data. Yet, large-scale repositories inevitably introduce noisy or mis…
GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents
Jie JW Wu, Ayanda Patrick Herlihy, Ahmad Saleem Mirza +2
With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies. However, deploying online experiments de…
Can Code Language Models Learn Clarification-Seeking Behaviors?
Jie JW Wu, Manav Chaudhary, Davit Abrahamyan +3
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, a gap remains between their output and the problem-solving strategies of h…
A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages
Sathvik Joel, Jie JW Wu, Fatemeh H. Fard
Large Language Models (LLMs) have shown impressive capabilities in code generation for popular programming languages. However, their performance on Low-Resource Programming Languag…
MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair
Meghdad Dehghan, Jie JW Wu, Fatemeh H. Fard +1
Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and test…
HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent
Jie JW Wu, Fatemeh H Fard
Large language models (LLMs) have significantly improved their ability to perform tasks in the field of code generation. However, there is still a gap between LLMs being capable co…
Investigating the Efficacy of Large Language Models for Code Clone Detection
Mohamad Khajezade, Jie JW Wu, Fatemeh Hendijani Fard +2
Large Language Models (LLMs) have demonstrated remarkable success in various natural language processing and software engineering tasks, such as code generation. The LLMs are mainl…