2 citations · 2 across the 12 of their papers we have counts for
15 papers
Zebra3D: Efficient Hybrid Foundation Models For Scalable 3D Reconstruction
Utkarsh Saxena, Ruijing Yang, Mehdi Rezagholizadeh +3
Feed-forward 3D foundation models reconstruct scenes from unposed images, but the quadratic cost of global attention limits their scalability to large view sets. We present Zebra3D…
CORAL-KV: Compact Cross-Layer Prediction and Residual Coding for Autoregressive Video KV Caches
Albert Ge, Akash Haridas, Utkarsh Saxena +5
Autoregressive video diffusion models maintain a growing attention key-value (KV) cache, making memory a key bottleneck for long video generation. Recently, clustering-based quanti…
AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization
Ji Liu, Saptarshi Majumder, Yiqing Huang +14
We introduce AMDKernelVault, an open HIP and Triton kernel corpus and training framework for recent AMD CDNA GPUs. Existing LLM-based kernel agents are largely CUDA/NVIDIA-centric…
AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification
Ji Liu, Puyuan Yang, Rongzhang Zheng +18
High-performance ML systems increasingly rely on GPU kernels whose editable source is unavailable, generated, or too distant from final machine code to expose remaining optimizatio…
Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion
Shivam Singh, Saptarshi Majumder, Pratik Prabhanjan Brahma +2
Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos. We introduce pause-and-think-T, a reasoning-centr…
PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning
Daize Dong, Junlin Chen, Haolong Jia +9
Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale. However, reinforcement learning (RL) on MoE-based LLMs often suffers from training instab…