7 citations · 27 across the 29 of their papers we have counts for
31 papers
ProductWebBench: Evaluating Website Continuity Beyond Task Completion
Jiajun Wu, Jian Yang, Yaxin Du +9
Frontend coding agents are usually graded on one question: did the requested target get built. Real engineering changes carry a second obligation, because the rest of the site has…
HiRAE: Hierarchical Representation Autoencoding with Residual Budgets
Xuanyu Zhu, Yan Bai, Yang Shi +5
Pretrained visual representations support image generation, but may not fully preserve the fine-grained details needed for faithful reconstruction. Meanwhile, intermediate encoder…
TUE-Detector: A Tool-Using Expert MLLM-Based Detector for AI-Generated Videos
Yichen Wu, Haoxuan Qu, Yongxing Dai +5
AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perform this task reliably, a ke…
CANVAS: Consistency-Aware Navigation via Visual Adaptive Sampling for Long-Context Text-to-SVG Generation
Yichen Wu, Haoxuan Qu, Yihang Lou +2
Autoregressive large models have recently advanced Text-to-SVG generation from simple icons to complex, long-context graphics, yet standard autoregressive decoding often fails to m…
StainFlow: Entity-Stain Tracking and Evidence Linking for Process Rewards in GUI Agents
Haojie Hao, Longkun Hao, Yihang Lou +8
Reinforcement Learning (RL) has become a promising approach for improving GUI Agents in long-horizon, stochastic digital environments, but trajectory-level success feedback is too…
MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
Zhichao Yang, Yuanze Hu, Haojie Hao +7
Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning over screen affordances, mul…
Mechanistic Insights into Functional Sparsity in Multimodal LLMs via CoRe Heads
Ruoxi Sun, Quantong Qiu, Juntao Li +3
While Multimodal Large Language Models (MLLMs) demonstrate remarkable proficiency on complex vision-language tasks, the mechanisms by which they extract query-relevant visual featu…
Speculative Rollback Correction for Quality-Diverse Web Agent Imitation
Longkun Hao, Hongyu Lin, Hao Li +11
Training interactive web agents through imitation learning from expert trajectories has emerged as a highly effective approach. However, determining the optimal timing for expert i…
ToolFG: Towards Well-Grounded Fine-Grained Image Classification
Yu Xue, Haoxuan Qu, Zhuoling Li +4
Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel paradigm for solving FGIC by pro…
HTMLCure: Turning Browser Experience into State Guided Repair for Interactive HTML
Jiajun Wu, Jian Yang, Tuney Zheng +4
LLMs can now produce full HTML pages, but many of those pages are only superficially correct: they render once, then fail under scroll, hover, click, resize, or gameplay. Evaluatio…
A Case for Agentic Tuning: From Documentation to Action in PostgreSQL
Hongyu Lin, Mingyu Li, Weichen Zhang +4
Documentation has long guided computer system tuning by distilling expert knowledge into per-parameter recommendations. Yet such guides capture only what experts conclude, discardi…
OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models
Morunliu Yang, Ruotao Xu, Le Li +6
Omnimodal large language models (OmniLLMs) have recently gained increasing attention for unified audio-video understanding. However, processing long multimodal token sequences intr…
Beyond the Last Layer: Multi-Layer Representation Fusion for Visual Tokenization
Xuanyu Zhu, Yan Bai, Yang Shi +4
Representation autoencoders that reuse frozen pretrained vision encoders as visual tokenizers have achieved strong reconstruction and generation quality. However, existing methods…
MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection
Xiran Zhao, Jing Jin, Yan Bai +6
Industrial anomaly detection is critical for manufacturing quality control, yet existing datasets mainly focus on static images or sparse views, which do not fully reflect continuo…
MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading
Baibei Ji, Xiaoyang Weng, Juntao Li +3
Long-context reasoning tasks have motivated agent-memory approaches that process document chunks sequentially while maintaining a dynamically updated memory, avoiding the quadratic…
Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks
Jing Jin, Hao Liu, Yan Bai +9
Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains challenging. STEM reasoning is a…
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
Jinhu Fu, Yan Bai, Longzhu He +4
Large language models (LLMs) can effectively handle outdated information through knowledge editing. However, current approaches face two key limitations: (I) Poor generalization: M…
Diagnosing and Repairing Unsafe Channels in Vision-Language Models via Causal Discovery and Dual-Modal Safety Subspace Projection
Jinhu Fu, Yihang Lou, Qingyi Si +3
Large Vision-Language Models (LVLMs) have achieved impressive performance across multimodal understanding and reasoning tasks, yet their internal safety mechanisms remain opaque an…
VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining
Xuanyu Zhu, Yuhao Dong, Rundong Wang +9
Recent advancements extend Multimodal Large Language Models (MLLMs) beyond standard visual question answering to utilizing external tools for advanced visual tasks. Despite this pr…
Demonstration Attack against In-Context Learning for Code Intelligence
Yifei Ge, Weisong Sun, Yihang Lou +7
Recent advancements in large language models (LLMs) have revolutionized code intelligence by improving programming productivity and alleviating challenges faced by software develop…
Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
Yulu Gan, Yan Bai, Yihang Lou +4
Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly foc…
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning
Jinze Li, Yan Bai, Yihang Lou +4
Few-shot class-incremental learning(FSCIL) focuses on designing learning algorithms that can continually learn a sequence of new tasks from a few samples without forgetting old one…
Bayesian Evidential Learning for Few-Shot Classification
Xiongkun Linghu, Yan Bai, Yihang Lou +4
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like mach…
Switchable Representation Learning Framework with Self-compatibility
Shengsen Wu, Yan Bai, Yihang Lou +3
Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrai…