5 citations · 18 across the 24 of their papers we have counts for
29 papers
OpticalRec: Unified Optical Vision-Language Representation for Multimodal Recommendation
Yueqi Wang, Zitian Guo, Yupeng Hou +9
Recent advances in vision-language modeling have substantially improved multimodal encoding, retrieval and reasoning. Yet for multimodal recommendation, encoding rich item vision-l…
The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models
Shuo Xing, Zilin Dai, Chengyuan Qian +7
While Large Language Models (LLMs) have demonstrated striking capabilities on frontier mathematical problems, it remains unclear whether they possess the structural mathematical un…
CinematicVQA: Benchmarking Film-Grammar Reasoning in Large Vision-Language Models
Shuo Xing, Pooja Verlani, Balu Adsumilli +1
Cinematography, the craft of visual storytelling through framing, lighting, and camera operation, fundamentally shapes how audiences perceive and emotionally engage with video cont…
CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
Fangzhou Lin, Peiran Li, Lingyu Xu +12
Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edits and do not fully capture th…
PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
Lingyu Jiang, Zirui Li, Shuo Xing +7
Large Reasoning Language Models (LRMs) tackle complex tasks through test-time scaling, generating long-form Chain-of-Thought (CoT) trajectories during inference. These trajectories…
CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning
Fangzhou Lin, Shuo Xing, Peiran Li +6
Parallel reasoning, where a generator samples many candidate solutions and an aggregator selects the best, is one of the most effective forms of test-time scaling in large language…
AdaptFuse: Training-Free Sequential Preference Inference via Externalized Bayesian Computation
Fangzhou Lin, Zilin Dai, Peiran Li +8
Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a manner consistent with Bayesian infer…
Let the Abyss Stare Back Adaptive Falsification for Autonomous Scientific Discovery
Peiran Li, Fangzhou Lin, Shuo Xing +5
Autonomous scientific discovery is entering a more dangerous regime: once the evaluator is frozen, a sufficiently strong search process can learn to win the exam without learning t…
ConsID-Gen: View-Consistent and Identity-Preserving Image-to-Video Generation
Mingyang Wu, Ashirbad Mishra, Soumik Dey +5
Image-to-Video generation (I2V) animates a static image into a temporally coherent video sequence following textual instructions, yet preserving fine-grained object identity under…
Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding
Fangzhou Lin, Qianwen Ge, Lingyu Xu +7
AI systems increasingly produce fluent, correct, end-to-end outcomes. Over time, this erodes users' ability to explain, verify, or intervene. We define this divergence as the Capab…
Traversal-as-Policy: Log-Distilled Gated Behavior Trees as Externalized, Verifiable Policies for Safe, Robust, and Efficient Agents
Peiran Li, Jiashuo Sun, Fangzhou Lin +5
Autonomous LLM agents fail because long-horizon policy remains implicit in model weights and transcripts, while safety is retrofitted post hoc. We propose Traversal-as-Policy: dist…
BibAgent: An Agentic Framework for Traceable Miscitation Detection in Scientific Literature
Peiran Li, Fangzhou Lin, Shuo Xing +6
Citations are the bedrock of scientific authority, yet their integrity is compromised by widespread miscitations: ranging from nuanced distortions to fabricated references. Systema…
Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models
Minghe Shen, Zhuo Zhi, Chonghan Liu +3
Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribution rather than inducing new cap…
Q-Router: Agentic Video Quality Assessment with Expert Model Routing and Artifact Localization
Shuo Xing, Soumik Dey, Mingyang Wu +5
Video quality assessment (VQA) is a fundamental computer vision task that aims to predict the perceptual quality of a given video in alignment with human judgments. Existing perfor…
LLMs Can Get "Brain Rot": A Pilot Study on Twitter/X
Shuo Xing, Junyuan Hong, Yifan Wang +5
We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To unveil junk effects, we…
VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results
Yixiao Li, Xin Li, Chris Wei Zhou +28
This paper presents the ISRGC-Q Challenge, built upon the Image Super-Resolution Generated Content Quality Assessment (ISRGen-QA) dataset, and organized as part of the Visual Quali…
Demystifying the Visual Quality Paradox in Multimodal Large Language Models
Shuo Xing, Lanqing Guo, Hongyuan Hua +5
Recent Multimodal Large Language Models (MLLMs) excel on benchmark vision-language tasks, yet little is known about how input visual quality shapes their responses. Does higher per…
SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems
Peiran Li, Xinkai Zou, Zhuohang Wu +9
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled powerful autonomous agents capable of complex reasoning and multi-modal tool use. Des…
V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving
Xuewen Luo, Fengze Yang, Fan Ding +5
Autonomous driving (AD) has achieved significant progress, yet single-vehicle perception remains constrained by sensing range and occlusions. Vehicle-to-Everything (V2X) communicat…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation
Chan-Wei Hu, Yueqi Wang, Shuo Xing +4
Large Vision-Language Models (LVLMs) have made remarkable strides in multimodal tasks such as visual question answering, visual grounding, and complex reasoning. However, they rema…
Can Large Vision Language Models Read Maps Like a Human?
Shuo Xing, Zezhou Sun, Shuangyu Xie +6
In this paper, we introduce MapBench-the first dataset specifically designed for human-readable, pixel-based map-based outdoor navigation, curated from complex path finding scenari…
UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving
Yuping Wang, Xiangyu Huang, Xiaokang Sun +4
We introduce UniOcc, a comprehensive, unified benchmark and toolkit for occupancy forecasting (i.e., predicting future occupancies based on historical information) and occupancy pr…
DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning
Chengxuan Qian, Shuo Xing, Shawn Li +2
Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities. However, the intrinsic heterogeneity of diverse mo…