activity
20232026
most citedTowards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey

9 citations · 11 across the 10 of their papers we have counts for

collaborators

11 papers

cond-mat.quant-gas2026

Analog Cosmology in a Quenched Bose-Einstein Condensate Bubble

Ruotong Zhai, Brendan Rhyno, Zain Mehdi +3

Recent interest and realization of ultracold bosons confined in shell-shaped, or bubble, traps have opened up several directions in the study of Bose-Einstein condensates (BECs) in…

cs.IR2026

Risk-Aware Reranking for Agentic Tool Retrieval

Qinfei Li, Xiaoxuan Dong, Jin Zhang +6

Tool retrieval determines which external tools are exposed to an LLM agent for a user query or task, making retrieval a critical pre-execution safety boundary. Unlike document retr…

cs.AI2026

Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?

Kaiwen Zheng, Junchen Fu, Wenhao Deng +3

Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable descriptio…

cs.IR2026

RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation

Wenhao Deng, Junchen Fu, Hanwen Du +6

Sequential recommender systems rely on a single forward pass to encode user interaction histories and predict the next item. Increasing inference-time computation through latent re…

cs.IR2026

Stream-aware Side Adaptation for Large Pre-trained Multimodal Embedding Models in Sequential Recommendation

Junchen Fu, Kaiwen Zheng, Ioannis Arapakis +4

Recently, large pretrained multimodal embedding models such as Qwen3-VL Embedding have shown strong promise for sequential recommendation, as they provide reusable semantic item re…

cs.AI2026

SkillAudit: From Fixed-Suite Benchmarking to Skill-Centered Assessment

Dexu Yu, Youhua Li, Zhaoyang Guan +12

Agent skills have become a practical way to extend large language model agents, but the growing skill ecosystem still lacks a reliable way to judge whether a skill is worth deployi…

cs.LG2026

RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data

Peiyan Hu, Haodong Feng, Hongyuan Liu +13

Predicting the evolution of complex physical systems remains a central problem in science and engineering. Despite rapid progress in scientific Machine Learning (ML) models, a crit…

cs.MM2026

Benchmarking Multimodal Large Language Models for Missing Modality Completion in Product Catalogues

Junchen Fu, Wenhao Deng, Kaiwen Zheng +5

Missing-modality information on e-commerce platforms, such as absent product images or textual descriptions, often arises from annotation errors or incomplete metadata, impairing b…

cs.LG2025

On the Guidance of Flow Matching

Ruiqi Feng, Chenglei Yu, Wenhao Deng +2

Flow matching has shown state-of-the-art performance in various generative tasks, ranging from image generation to decision-making, where generation under energy guidance (abbrevia…

cs.SE2023★ 9 cited

Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey

Shihan Dou, Junjie Shan, Haoxiang Jia +7

Code cloning, the duplication of code fragments, is common in software development. While some reuse aids productivity, excessive cloning hurts maintainability and introduces bugs.…

cs.IR2023★ 2 cited

Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights

Ruyu Li, Wenhao Deng, Yu Cheng +3

Text-based collaborative filtering (TCF) has emerged as the prominent technique for text and news recommendation, employing language models (LMs) as text encoders to represent item…