NVIDIA Isaac GR00T N1.7: Open Reasoning VLA Model for Humanoid Robots
nvidia
• • 18NEW Articles from Team or Enterprise organizations will get promoted to the main section.
Camera Choice For Robotics Data
satpalsr
• • 1MP3 Bitrate Sensitivity in Audio-Multimodal LLMs: A Per-Model Evaluation
danielrosehill
• Auron: Depth-Efficient Language Models via Hybrid Recurrent-Attention Weight Sharing
nyxia
• • 2vindex-infer — Run LLMs without CUDA, without PyTorch, from flat binary files
cronos3k
• • 1easyaligner: Forced alignment of text and audio, made easy
KBLab
• • 5Building OmniBench Aegis Env: A Reproducible Multi-Domain OpenEnv for Agent Evaluation
AGIreflex
• 1.37x Faster on Alibaba's 80B Code Model: EAGLE3 for Qwen3-Coder-Next
lujangusface
• 80% of AI LLM Fine-Tuning Compute Is Wasted — Here's How We Proved It
cronos3k
• • 1Querying a Neural Network Like a Database — and Making That Tool Available to Everyone
cronos3k
• • 1lucky_pick_scheduler
juiceb0xc0de
• • 1Darwin-TTS: We Gave a TTS Model 3% of an LLM's Brain — It Started Showing Emotion
FINAL-Bench
• • 131.7x Faster on a 218B Model: EAGLE3 Speculative Decoding for GLM-4.7
lujangusface
• • 1Stop benchmarking inference providers
SaylorTwift
• • 8Latest Agentic AI Trends to Watch in 2026: Market Shifts, Adoption Patterns, and What Comes Next
daya-shankar
• • 1When Speech AI Meets the Long Tail of Languages: Inside the VAANI Dataset
ARTPARK-IISc
• • 9Agentic AI vs Generative AI vs Predictive AI: What's the Difference? (2026 Guide)
daya-shankar
• Nucleus-Image: Scaling Text-to-Image with Sparse Mixture of Experts
NucleusAI
• • 11Releasing LiteCoder-Terminal-SFT
Lite-Coder
• • 4"Darwin-27B-Opus: Surpassing the Foundation Model Without Training"
FINAL-Bench
• • 16