AI research

prxhub is an open registry for AI research bundles, built around verifiable .prx artifacts, search, provenance, and agent-friendly publishing via MCP and CLI.

A research report on the current MCP ecosystem, with live crawl numbers, verification rates, category breakdowns, and examples of both strong and weak MCP-positive sites.

An early-access AI research assistant that presents a cited mindmap of AI agent safety and alignment research, with expandable nodes, source-backed claims, and built-in verification checks.

A sourced model and short report exploring whether Europe could train a sovereign frontier AI model using public compute it already owns, with reproducible code, datasets, and a PDF report.

An open-source research framework for structured LLM evaluation, claim verification, and source-grounded reflective reasoning. The repo describes modular components for retrieval, semantic scoring, skeptical claim checking, and benchmark-style epistemic assessment.

MiroThinker is a science-focused AI research app that emphasizes prediction, verification, and evidence-backed answers. The page also points to a MiroMind app and suggests use cases across finance, medicine, and regulation.

An opinionated write-up on where multi-agent systems have and have not delivered value, with concrete comparisons across coding, images, CAD, and BIM, plus a description of Blade’s evidence-first architecture.

Agora-1 is a multi-agent world model from Odyssey that simulates shared real-time environments for up to four participants, human or AI, with a focus on gaming, robotics, reinforcement learning, and foundation model research.

Apple Machine Learning Research paper proposing LaDiR, a reasoning framework that combines a VAE-based latent space with latent diffusion to improve LLM text reasoning and iterative refinement.

Research paper introducing hyperagents, a self-referential agent framework that combines a task agent and a meta agent into one editable program. The abstract describes a DGM-based system that improves both task performance and its own improvement process across domains.

A GitHub research project that measures how gpt-4.1 responds when asked to pick a random number between 1 and 100, using 10,000 API calls and comparing the results to a uniform baseline.