Research Papers
Hugging Face Survey Unifies Progress Reward Modeling for Robotic Learning
A new comprehensive survey from Hugging Face provides a unified framework for progress reward modeling in robotics, addressing the...
Inside DeepSeek's DSpark: How Speculative Decoding Speeds Up LLM Inference Without Losing Quality
DeepSeek introduces DSpark, a speculative decoding technique that accelerates large language model inference while maintaining out...
OpenAI Study: AI Expands Worker Roles, Blurs Job Boundaries
New research from OpenAI reveals that ChatGPT users are taking on tasks across different roles, effectively expanding their job sc...
Multimodal Speaker Verification Threatens Anonymization, Study Finds
A new study shows that aggregating audio, prosodic, and linguistic cues across multiple anonymized utterances can significantly im...
VisCo: Hugging Face Researchers Use LLMs as Intrinsic Encoders for Visual Token Compression
VisCo introduces a training-efficient self-compression framework that reuses a pretrained VLM as an intrinsic compressor, achievin...
Spectral Alignment (SPA): A Lightweight Fix for Exposure Bias in Diffusion Models
Researchers propose Spectral Alignment (SPA), a guidance-based method that calibrates the power spectrum of intermediate predictio...
Training-Free Method Solves Revisit Inconsistency in Autoregressive Video Generation
Researchers propose a training-free approach that uses 3D engine correspondences to maintain consistent appearance when autoregres...
ID-V2V: Netflix and Hugging Face Introduce Identity-Preserving Video Restylization
A new research paper from Netflix and Hugging Face, to appear at SIGGRAPH Asia 2026, presents ID-V2V, a video-to-video framework t...
Multi-Head Latent Control: Lightweight Layer Enables Smarter LLM Agent Decisions
Hugging Face researchers introduce Multi-Head Latent Control, a lightweight layer that reads hidden states from frozen LLMs to pro...
O-VAD: Training-Free Agentic Framework Outperforms Frontier VLMs in Industrial Video Anomaly Detection
Researchers introduce O-VAD, a training-free agentic framework that uses object-centric tracking and reasoning to detect anomalies...
Hugging Face Releases LAMAR: A Language-Aware Multilingual Reranker
LAMAR is a new multilingual cross-encoder reranker that balances semantic relevance and language coherence, outperforming existing...
Hugging Face Researchers Introduce Three-Body Scattering Modeling for One-Step Generation
A new generative framework called Three-Body Scattering Modeling (TBSM) achieves state-of-the-art one-step image generation on Ima...