5 citations · 6 across the 29 of their papers we have counts for
24 papers · 1 filter
Evaluating Parameter Efficient Methods for RLVR
Qingyu Yin, Yulun Wu, Zhennan Shen +6
We systematically evaluate Parameter-Efficient Fine-Tuning (PEFT) methods under the paradigm of Reinforcement Learning with Verifiable Rewards (RLVR). RLVR incentivizes language mo…
LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning
Haoyan Xu, Ruizhi Qian, Zhengtao Yao +10
Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed…
Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection
Xi Xiao, Zhuxuanzi Wang, Mingqiao Mo +6
The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require cost…
Read the Scene, Not the Script: Outcome-Aware Safety for LLMs
Rui Wu, Yihao Quan, Zeru Shi +3
Safety-aligned Large Language Models (LLMs) still show two dominant failure modes: they are easily jailbroken, or they over-refuse harmless inputs that contain sensitive surface si…
JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation
Siheng Wan, Zhengtao Yao, Zhengdao Li +9
Modern Text-to-Image (T2I) generation increasingly relies on token-centric architectures that are trained with self-supervision, yet effectively fusing text with visual tokens rema…
C3-OWD: A Curriculum Cross-modal Contrastive Learning Framework for Open-World Detection
Siheng Wang, Zhengdao Li, Yanshu Li +12
Object detection has advanced significantly in the closed-set setting, but real-world deployment remains limited by two challenges: poor generalization to unseen categories and ins…
Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention
Jiaqian Li, Yanshu Li, Ligong Han +2
Implicit in-context learning (ICL) has newly emerged as a promising paradigm that simulates ICL behaviors in the representation space of large language models (LLMs), aiming to att…
TRiCo: Triadic Game-Theoretic Co-Training for Robust Semi-Supervised Learning
Hongyang He, Xinyuan Song, Yangfan He +5
We introduce TRiCo, a novel triadic game-theoretic co-training framework that rethinks the structure of semi-supervised learning by incorporating a teacher, two students, and an ad…
A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond
Xiaodong Qu, Andrews Damoah, Joshua Sherwood +12
Artificial Intelligence (AI) agents have rapidly evolved from specialized, rule-based programs to versatile, learning-driven autonomous systems capable of perception, reasoning, an…
CATP: Contextually Adaptive Token Pruning for Efficient and Enhanced Multimodal In-Context Learning
Yanshu Li, Jianjiang Yang, Zhennan Shen +3
Modern large vision-language models (LVLMs) convert each input image into a large set of tokens that far outnumber the text tokens. Although this improves visual perception, it als…
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Zhaochen Su, Peng Xia, Hangyu Guo +12
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-cent…
RoadBench: A Vision-Language Foundation Model and Benchmark for Road Damage Understanding
Xi Xiao, Yunbei Zhang, Janet Wang +9
Accurate road damage detection is crucial for timely infrastructure maintenance and public safety, but existing vision-only datasets and models lack the rich contextual understandi…
Taming the Tri-Space Tension: ARC-Guided Hallucination Modeling and Control for Text-to-Image Generation
Jianjiang Yang, Ziyan Huang, Yanshu li +2
Despite remarkable progress in image quality and prompt fidelity, text-to-image (T2I) diffusion models continue to exhibit persistent "hallucinations", where generated content subt…
ReLoop: "Seeing Twice and Thinking Backwards" via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding
Jianjiang Yang, Yanshu li, Ziyan Huang
While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in open-ended visual question answering, they remain vulnerable to hallucinations. These are output…
Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer
Wenquan Lu, Yuechuan Yang, Kyle Lee +2
Chain-of-thought (CoT) reasoning has enabled transformer-based language models to excel at complex mathematics and multi-step planning. However, in standard decoder-only architectu…
ViP-CLIP: Visual-Perception Prompting with Unified Alignment for Zero-Shot Anomaly Detection
Ziteng Yang, Jingzehua Xu, Yanshu Li +3
Zero-shot anomaly detection (ZSAD) aims to detect anomalies without any target domain training samples, relying solely on external auxiliary data. Existing CLIP-based methods attem…
TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration
Yanshu Li, Jianjiang Yang, Tian Yun +3
Multimodal in-context learning (ICL) has emerged as a key mechanism for harnessing the capabilities of large vision-language models (LVLMs). However, its effectiveness remains high…
Make LVLMs Focus: Context-Aware Attention Modulation for Better Multimodal In-Context Learning
Yanshu Li, Jianjiang Yang, Ziteng Yang +8
Multimodal in-context learning (ICL) is becoming a key capability that allows large vision-language models (LVLMs) to adapt to novel tasks without parameter updates, which expands…
From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agent
Minjie Shen, Yanshu Li, Lulu Chen +4
Manus AI is a general-purpose AI agent introduced in early 2025, marking a significant advancement in autonomous artificial intelligence. Developed by the Chinese startup Monica.im…
Advancing Multimodal In-Context Learning in Large Vision-Language Models with Task-aware Demonstrations
Yanshu Li
Multimodal in-context learning (ICL) has emerged as a key capability of Large Vision-Language Models (LVLMs), driven by their increasing scale and applicability. Despite its promis…
Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation
Zhiqing Cui, Jiahao Yuan, Hanqing Wang +3
Scientific diagrams are vital tools for communicating structured knowledge across disciplines. However, they are often published as static raster images, losing symbolic semantics…
MIV: Towards Efficient and Fine-grained Multimodal In-Context Learning via Representation Engineering
Yanshu Li, Yi Cao, Hongyang He +5
Multimodal in-context learning (ICL) equips Large Vision-language Models (LVLMs) with the ability to adapt to new tasks via multiple user-provided demonstrations, without requiring…
Visual Variational Autoencoder Prompt Tuning
Xi Xiao, Yunbei Zhang, Yanshuh Li +5
Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of f…
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
Chengyan Wu, Bolei Ma, Yihong Liu +7
Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. Ho…