ASI Technical Radar: Compounding Intelligence
A curated map of the papers, model drops, benchmarks, and research systems that matter most for building ASI: post-AGI pathways, recursive self-improvement, automated AI research, architecture discovery, and code-evaluable scientific search.
Which bottleneck—evaluation quality, search efficiency, transfer, or inspectability—most limits today's compounding AI research loops?
This radar organizes the current ASI literature and frontier-lab releases around one claim: superintelligence is less likely to arrive as a single artifact than as a stack of compounding loops. Models write code, code evaluates models, agents design stronger agents, and scientific workflows become cheaper, faster, and more parallel.
North star
From AGI to ASI is the best current orientation document. It frames the transition from AGI to artificial general superintelligence around four pathways: scaling AGI, AI paradigm shifts, recursive improvement, and large-scale multi-agent collectives.
Core technical clusters
- Recursive self-improvement: Darwin Godel Machine, Hyperagents, Huxley-Godel Machine, and MetaAI recursive self-design.
- Automated AI R&D: AI Scientist, Live-SWE-agent, CodeEvolve, AlphaEvolve, AIRA, AutoResearchClaw, Co-Scientist, Claude Science, and Gemini for Science.
- Frontier model drops: GPT-5.6 Sol, Claude Fable 5, Claude Sonnet 5, Gemini 3.5 Flash, Antigravity Agent, Gemini Omni Flash, and Llama 4.
- Evaluation-grounded discovery: systems that accept only changes validated by tests, benchmarks, proof checks, simulations, or domain evaluators.
- Collective intelligence: ensembles of specialized agents that produce stronger search, review, repair, and synthesis than a single prompt loop.
Build pattern
The practical pattern across these papers is simple:
archive = seed_systems()
while budget.remaining():
parent = select_promising_or_diverse(archive)
proposal = model.modify(parent.code, parent.logs, objective)
score = evaluate_in_sandbox(proposal)
if score.valid and score.beats_acceptance_bar:
archive.add(proposal, score)
The hard research problems are not the loop syntax. They are evaluator design, sample efficiency, transfer across domains, keeping the search open-ended, and making the resulting systems inspectable enough for deployment.
Current priority
Treat every entry in this library as a component in an ASI research stack: scaling tells us what raw capability can buy, automated R&D shows how progress can compound, model drops reveal what labs are productizing, and governance measurement keeps that acceleration legible.