Our research team is dedicated to superalignment, multimodal emergent behavior, and ensuring our models don't just circle back—they move forward.
We double-click on the scaling laws governing superalignment in 2T+ parameter models. By leveraging p-tuning and knowledge distillation, we achieve emergent reasoning without the stochastic parrot effect commonly seen in smaller context windows.
Download PDF (4.2 MB) ↗Boiling the ocean of tokens requires granular quantization. We propose a new INT4-based LoRA adapter pipeline that preserves semantic nuance while increasing tokens/sec by 400% on sovereign AI infrastructure.
Download PDF (2.1 MB) ↗A deep dive into why your agentic workflows fail when the model lacks bandwidth. We introduce the "Synergy Index" for measuring model-human alignment during complex chain-of-thought tasks.
Download PDF (8.4 MB) ↗