2 citations · 4 across the 7 of their papers we have counts for
8 papers
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration
Feng Lin, Xiaotian Yu, Yifan Yang +1
Strong individual agents do not guarantee effective collaboration. Communication must align what one agent conveys with how another interprets and uses it. This dependency makes th…
Agentic Kernel Optimization: Generating State-of-the-Art GPU Kernels Without Hand-Written CUDA
Mao Luo, Hongbin Li, Feng Lin +2
We study whether general-purpose code agents can produce state-of-the-art GPU kernels without any manually written CUDA code. We investigate this question using representative work…
Reg-TTR, Test-Time Refinement for Fast, Robust and Accurate Image Registration
Lin Chen, Yue He, Fengting Zhang +4
Traditional image registration methods are robust but slow due to their iterative nature. While deep learning has accelerated inference, it often struggles with domain shifts. Emer…
Vision Language Models: A Survey of 26K Papers
Fengming Lin
We present a transparent, reproducible measurement of research trends across 26,104 accepted papers from CVPR, ICLR, and NeurIPS spanning 2023-2025. Titles and abstracts are normal…
Not All Attention Heads Are What You Need: Refining CLIP's Image Representation with Attention Ablation
Feng Lin, Marco Chen, Haokui Zhang +3
This paper investigates the role of attention heads in CLIP's image encoder. Building on interpretability studies, we conduct an exhaustive analysis and find that certain heads, di…
REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation
Xizhe Xue, Guoting Wei, Hao Chen +4
The rapid evolution of Vision Language Models (VLMs) has catalyzed significant advancements in artificial intelligence, expanding research across various disciplines, including Ear…
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
Hanling Yi, Feng Lin, Hongbin Li +3
This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel \textbf{A}uto-\textbf{…
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
Feng Lin, Hanling Yi, Hongbin Li +4
Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this…