In-DRAM Bulk Bitwise Execution Engine
arxiv.org
A research paper describing Ambit, a method for performing bulk bitwise operations inside DRAM to reduce data movement. Summary based on its abstract.
Gemt
Flere links fra arxiv.org
Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models
A study comparing distilled byte-level and token-level language models. It finds that byte models start behind at low compute but can reach a higher performance ceiling with more training.
1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
A research paper describing CPU inference techniques for BitNet b1.58 language models.
EvoOntology: A Self-Evolving Ontology Layer for Data Agents
A research paper describing an ontology layer that adapts over time to support data-oriented AI agents.
Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents
A research paper examining context-management methods for language-model agents performing long sequences of tool calls.
WorldKG: A World-Scale Geographic Knowledge Graph
Abstract page for arXiv paper 2109.10036: WorldKG: A World-Scale Geographic Knowledge Graph.
Image-to-Image Translation with Conditional Adversarial Networks
The pix2pix paper investigates learning mappings between images, with applications including colourisation and generating pictures from edge or label maps. Summary based on its abstract.