Research author

Wei Chen

13 AI research papers in the One9Founders library, with summaries and links to original sources.

Papers by Wei Chen

Aug 26, 2026
cs.CV

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

Junxiang Xu, Ruisi Wang, Fanyi Pu, et al.

37
Aug 6, 2026
cs.AI

Contextual Information Policy Optimization for Search Agents

Xingyu Guo, Wei Chen, Linlin Yang, et al.

Aug 5, 2026
cs.HC

ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation

Xiaoyan Gu, Yifang Wang, Wenqing Zheng, et al.

Aug 3, 2026
cs.IR

Requirement--Evidence Alignment for Compositional E-Commerce Queries

Weihao Shen, Wei Chen, Fuwei Zhang, et al.

Aug 3, 2026
cs.IR

Unpaired Modality-Agnostic Generative Recommendation

Weihao Shen, Wei Chen, Fuwei Zhang, et al.

Jun 5, 2026
cs.CL

M$^3$Exam: Benchmarking Multimodal Memory for Realistic User-Agent Interactions

Zhengjun Huang, Wenxuan Liu, Zhoujin Tian, et al.

Apr 29, 2026
cs.CV

ViCrop-Det: Spatial Attention Entropy Guided Cropping for Training-Free Small-Object Detection

Hui Wang, Hongze Li, Wei Chen, et al.

Apr 17, 2026
cs.CL

Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language

Dianqing Lin, Tian Lan, Jiali Zhu, et al.

Apr 15, 2026
cs.CL

MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging

Zhijie Bao, Fangke Chen, Licheng Bao, et al.

Apr 14, 2026
cs.AI

DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding

Hao Yan, Yuliang Liu, Xingchen Liu, et al.

Apr 7, 2026
cs.IR

Data, Not Model: Explaining Bias toward LLM Texts in Neural Retrievers

Wei Huang, Keping Bi, Yinqiong Cai, et al.

Apr 7, 2026
cs.LG

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

Qimin Zhong, Hao Liao, Haiming Qin, et al.

Mar 19, 2026
cs.LG

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Edward Lin, Sahil Modi, Siva Kumar Sastry Hari, et al.

SOL-ExecBench is a new benchmark for evaluating AI systems that optimize GPU kernels by measuring how close they get to the theoretical maximum performance (Speed-of-Light) that the hardware can achieve, rather than just comparing against other software implementations. It includes 235 real optimization problems from actual AI models and uses a special pipeline called SOLAR to calculate what perfect performance would look like for each kernel. The benchmark includes safeguards to prevent cheating and focuses on modern NVIDIA Blackwell GPUs with various precision formats.

GPU optimizationbenchmarkingCUDA kernelsagentic AI
Wei Chen — AI Research Papers | One9Founders