arXiv Machine Learning
Jun 24
HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning
arXiv:2509. 24118v2 Announce Type: replace Abstract: Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare.
Inference efficiency
Quantization, distillation, pruning and serving work aimed at the same accuracy for less memory, latency and money.
arXiv AI
Jun 24
MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching
arXiv:2606. 24433v1 Announce Type: cross Abstract: Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains insufficiently studied.
arXiv AI
Jun 24
Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation
arXiv:2606. 24206v1 Announce Type: cross Abstract: Recent breakthroughs in 3D generation have advanced notably with the development of text-to-image diffusion model.
arXiv AI
Jun 24
ARIA: Adaptive Region-Based Importance Allocation for Conditional Diffusion Distillation
arXiv:2606. 23898v1 Announce Type: cross Abstract: Distilling conditional diffusion models aims to transfer the behavior of a large teacher to a smaller student while preserving alignment across conditioning inputs.
arXiv AI
Jun 24
Cost-Optimal Decision Diagrams for Stochastic Boolean Function Evaluation
arXiv:2606. 24672v1 Announce Type: new Abstract: In many decision-making scenarios, acquiring information incurs different costs.
arXiv AI
Jun 24
Sol Video Inference Engine: Agent-Native Full-Stack Acceleration Framework for Efficient Video Generation
arXiv:2606. 23743v1 Announce Type: cross Abstract: Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost.
arXiv Machine Learning
Jun 24
Configurable Holography: Towards Display and Scene Adaptation
arXiv:2405. 01558v4 Announce Type: replace-cross Abstract: Rendering holograms for holographic displays is often an iterative and computationally costly process.
arXiv Machine Learning
Jun 24
A Comparative Study of Bayesian Contextual Bandits for Real-Time Warehouse Sorter Optimization
arXiv:2606. 23977v1 Announce Type: new Abstract: Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehouse environments.
arXiv Machine Learning
Jun 24
Grouped Query Experts: Mixture-of-Experts on GQA Self-Attention
arXiv:2606. 20945v2 Announce Type: replace Abstract: Self-attention is central to Transformer performance and is often the most expensive part of the Transformer at long context lengths because its pairwise token interactions scale quadratically with sequence length.
arXiv AI
Jun 24
WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models
arXiv:2604. 08558v2 Announce Type: replace-cross Abstract: Recent decoder-only autoregressive text-to-speech (AR-TTS) models produce high-fidelity speech, but their memory and compute costs scale quadratically with sequence length due to full self-attention.
arXiv AI
Jun 24
HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training
arXiv:2606. 20189v3 Announce Type: replace-cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).
arXiv AI
Jun 24
ZONOS2 Technical Report
arXiv:2606. 24320v1 Announce Type: cross Abstract: We present ZONOS2 8B, our latest TTS model, which achieves state-of-the-art naturalness, prosody, and voice cloning fidelity.
arXiv AI
Jun 24
Grounded Chess Reasoning in Language Models via Master Distillation
arXiv:2603. 20510v2 Announce Type: replace Abstract: Language models often lack grounded reasoning capabilities in specialized domains where training data is scarce but bespoke systems excel.
arXiv Machine Learning
Jun 24
EnerInfer: Energy-Aware On-Device LLM Inference
arXiv:2606. 23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck.
arXiv Machine Learning
Jun 24
Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints
arXiv:2606. 24353v1 Announce Type: cross Abstract: Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving.
arXiv AI
Jun 24
FP8 is All You Need (Part 2): Efficient Ozaki-Bailey Style FFT Through Tensor-core Garner Reformulation and Kulisch Escape Route
arXiv:2606. 23698v1 Announce Type: cross Abstract: NVIDIA's Blackwell Ultra (B300) cuts FP64 vector throughput to ~1.
arXiv AI
Jun 24
Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach
arXiv:2606. 24237v1 Announce Type: new Abstract: Federated Graph Learning facilitates collaborative graph modeling across distributed clients while preserving data privacy.
arXiv AI
Jun 24
DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs
arXiv:2606. 24089v1 Announce Type: cross Abstract: Recent advances in control have enabled bipedal-wheeled robots to traverse slopes and single-step obstacles, yet long staircase traversal remains challenging as current teacher-student frameworks suffer from weakened dynamics-aware representations and incomplete terrain geometry encoding.
arXiv AI
Jun 24
The Professor: Multi-Teacher Unsupervised Prompt Distillation for Vision-Language Models
arXiv:2606. 23897v1 Announce Type: cross Abstract: Prompt distillation compresses large vision-language models (VLMs) such as CLIP into lightweight student models by matching teacher predictions on unlabeled domain images.
arXiv AI
Jun 24
On the Position Bias of On-Policy Distillation
arXiv:2606. 22600v2 Announce Type: replace-cross Abstract: On-Policy Distillation (OPD) improves the learning efficiency of standard reinforcement learning through dense, token-level supervision from teachers.