18 citations · 20 across the 14 of their papers we have counts for
16 papers
GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning
Zesheng Yang, Lingling Zhang, Xinyu Zhang +4
Chain-of-thought reasoning has substantially improved the problem-solving capabilities of multimodal large language models. Fine-grained visual evidence, however, remains difficult…
QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
Heng Wang, Yifei Li, Lingling Zhang +4
Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferenc…
Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning
Pengyu Li, Zhitao Gao, Lingling Zhang +5
Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an order-of-magnitude inference co…
LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification
Yanrui Wu, Lingling Zhang, Xinyu Zhang +5
Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world…
AERO: Autonomous Evolutionary Reasoning Optimization via Endogenous Dual-Loop Feedback
Zhitao Gao, Jie Ma, Xuhong Li +5
Large Language Models (LLMs) have achieved significant success in complex reasoning but remain bottlenecked by reliance on expert-annotated data and external verifiers. While exist…
$\textbf{AGT$^{AO}$}$: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality
Pengyu Li, Lingling Zhang, Zhitao Gao +5
While Large Language Models (LLMs) have achieved remarkable capabilities, they unintentionally memorize sensitive data, posing critical privacy and security risks. Machine unlearni…
PathFinder: Advancing Path Loss Prediction for Single-to-Multi-Transmitter Scenario
Zhijie Zhong, Zhiwen Yu, Pengyu Li +3
Radio path loss prediction (RPP) is critical for optimizing 5G networks and enabling IoT, smart city, and similar applications. However, current deep learning-based RPP methods lac…
Finding Kissing Numbers with Game-theoretic Reinforcement Learning
Chengdong Ma, Théo Tao Zhaowei, Pengyu Li +7
Since Isaac Newton first studied the Kissing Number Problem in 1694, determining the maximal number of non-overlapping spheres around a central sphere has remained a defining chall…
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
Pengyu Li, Zhijie Zhong, Tong Zhang +3
Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction…
EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection
Pengyu Li, Chenhe Liu, Tengfei Li +3
The detection of small objects, particularly traffic signs, is a critical subtask within object detection and autonomous driving. Despite the notable achievements in previous resea…
Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation
Jiyi Chen, Pengyu Li, Yutong Wang +2
This work proposes a deep learning (DL)-based framework, namely Sim2Real, for spectral signal reconstruction in reconstructive spectroscopy, focusing on efficient data sampling and…
Neural Collapse in Multi-label Learning with Pick-all-label Loss
Pengyu Li, Xiao Li, Yutong Wang +1
We study deep neural networks for the multi-label classification (MLab) task through the lens of neural collapse (NC). Previous works have been restricted to the multi-class classi…
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
HanQin Cai, Longxiu Huang, Pengyu Li +1
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both samp…
Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents
Pengyu Li, Christine Tseng, Yaxuan Zheng +4
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as label…
Analysis of Legal Documents via Non-negative Matrix Factorization Methods
Ryan Budahazy, Lu Cheng, Yihuan Huang +7
The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assist…
COVID-19 Literature Topic-Based Search via Hierarchical NMF
Rachel Grotheer, Yihuan Huang, Pengyu Li +7
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available.…