Since Transformers are parallel, why do we have to wait for AI responses?

You type a question into the chat screen and hit the send button. Sometimes the reply starts immediately, but other times ...
Tech Times on MSN

Graph architecture replaces transformer attention, extrapolating to 4 million tokens in linear time

GTFormer proposes replacing Transformer self-attention with a hierarchical graph neural network, achieving zero-shot length ...
Tech Times on MSN

Foundry Robotics raises $100M for AI-native defense factory, acquires Avec Robotics

Foundry Robotics raised a $100M oversubscribed Series A led by Marlinspike Partners to build an AI-powered autonomous ...
news.lavx.hu

Cartesia's Hierarchical Networks Show 10x Compute Efficiency Gains Over Tokenized Transformers

Cartesia Research demonstrates that Hierarchical Networks (H-Nets) learning dynamic tokenization from raw bytes achieve up to ...
SoftBank

SoftBank Demonstrates Higher 5G Uplink Throughput with a Transformer Neural Receiver

SoftBank demonstrated up to a 60.5% improvement in 5G uplink L1 throughput under interference using a Transformer-based neural receiver integrated into NVIDIA Aerial. OTA testing with a commercial ...
BIOENGINEER

Deep learning hits 97% accuracy on satellite benchmarks, review finds

A systematic review finds deep learning exceeds 97 percent accuracy on remote sensing benchmarks but struggles with ...
GitHub

README.md

Native HIP text-to-image generation with MOSS-Image-SFT from OpenMOSS: a 42-block single-stream diffusion transformer (about 5B parameters), a Qwen3.5 9B text encoder and the FLUX.2 small VAE decoder.