31 citations · 52 across the 13 of their papers we have counts for
14 papers
MMSkills: Towards Multimodal Skills for General Visual Agents
Kangning Zhang, Shuai Shao, Qingyao Li +8
Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable co…
SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle
Hao Guan, Lingyue Fu, Shao Zhang +8
As autonomous code agents move toward end-to-end software development, evaluating their practical autonomy becomes critical. Current benchmarks hide friction by testing agents in p…
DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education
Lingyue Fu, Haowei Yuan, Datong Chen +5
In programming education, Debugging and Teaching (DT) task is a common scenario where students receive assistance in correcting their erroneous code. The task involves multiple inp…
CATArena: Evaluating Evolutionary Capabilities of Code Agents via Iterative Tournaments
Lingyue Fu, Xin Ding, Linyue Pan +9
Current evaluation for Large Language Model (LLM) code agents predominantly focus on generating functional code in single-turn scenarios, which fails to evaluate the agent's capabi…
Automatically Benchmarking LLM Code Agents through Agent-Driven Annotation and Evaluation
Lingyue Fu, Bolun Zhang, Hao Guan +7
Recent advances in code agents have enabled automated software development at the project level, supported by large language models (LLMs). However, existing benchmarks for code ag…
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks
Lingyue Fu, Hao Guan, Bolun Zhang +10
The evaluation of Large Language Models (LLMs) for software engineering has shifted towards complex, repository-level tasks. However, existing benchmarks predominantly rely on coar…
LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis
Weiming Zhang, Lingyue Fu, Qingyao Li +7
Cognitive diagnosis (CD) plays a crucial role in intelligent education, evaluating students' comprehension of knowledge concepts based on their test histories. However, current CD…
AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing
Lingyue Fu, Ting Long, Jianghao Lin +6
Knowledge Tracing (KT) monitors students' knowledge states and simulates their responses to question sequences. Existing KT models typically follow a single-step training paradigm,…
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction
Xinmeng Hou, Lingyue Fu, Chenhao Meng +3
Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opin…
SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model
Lingyue Fu, Hao Guan, Kounianhua Du +6
Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational…
CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation
Kounianhua Du, Jizheng Chen, Renting Rui +7
Utilizing large language models to generate codes has shown promising meaning in software development revolution. Despite the intelligence shown by the large language models, their…
Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges
Qingyao Li, Lingyue Fu, Weiming Zhang +6
Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with stu…
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
Lingyue Fu, Huacan Chai, Shuang Luo +13
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researcher…
An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages
Lingyue Fu, Jianghao Lin, Weiwen Liu +4
To provide click simulation or relevance estimation based on users' implicit interaction feedback, click models have been much studied during recent years. Most click models focus…