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.
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.
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.
]]>ASI governance should be judged by the same standard. It should increase the rate at which capable systems can be shipped responsibly, not turn uncertainty into paralysis.
Measuring AI R&D Automation is the most useful current governance paper in this library because it treats acceleration as something to measure. If AI begins performing a large share of AI research, institutions need live telemetry on both capability progress and oversight capacity.
LifeSciBench and GeneBench-Pro point toward domain-grounded scientific evaluations. The Google DeepMind AI Control Roadmap and OpenAI third-party evaluation playbook point toward the deployment side: harness design, supervision, controls, and evidence trails for agentic systems.
]]>The key ASI question is whether stronger models can turn longer context, code execution, and domain reasoning into reliable biological discovery. A benchmark like this is most valuable when it rewards reproducible analysis steps, defensible intermediate artifacts, and error recovery.
Scientific superintelligence needs better measurement than “can answer a hard question.” GeneBench-Pro points toward evals where a system must make progress through messy data, domain assumptions, and multi-step analytical workflows.
]]>ASI systems will not be text-only. Video, audio, simulation, world models, and interactive editing all contribute to agents that can model environments, communicate plans, and test ideas visually.
The important signal is a shift toward “any input to any output” systems with iterative editing loops. That is a foundation for simulation-rich scientific, robotic, and design workflows.
]]>The important word is “auditable.” Scientific agents need source code, message history, data provenance, and human-readable explanations if they are going to be trusted in real research workflows.
AI workbenches are where frontier models meet reproducibility. The systems that matter most will not merely answer questions; they will leave trails that other scientists and agents can inspect, rerun, and improve.
]]>The model overview also notes that Claude Mythos 5 and Mythos Preview are offered separately for defensive cybersecurity workflows through Project Glasswing, with invitation-only access.
The market is segmenting by capability, risk, and workflow. Long-running agents, coding agents, and restricted cybersecurity models are now distinct deployment categories rather than abstract benchmark tiers.
Source trail: Anthropic posted about Fable and Mythos access on X.
]]>max reasoning effort for Sol
and an ultra mode that uses subagents for complex work.
This is a frontier-model drop where the interesting feature is not just answer quality. It is the shift toward models that budget deeper reasoning, coordinate subagents, and operate inside tool-heavy workflows. Those are exactly the ingredients that make AI R&D automation compound.
Source trail: OpenAI also announced the preview on X.
]]>The paper reports new state-of-the-art packings for multiple hexagon-in-hexagon cases, a stronger lower bound for the second autocorrelation inequality, and improvements for many spherical-code instances.
The big lesson is cognitive load management. If we want LLM-guided search to scale, we may need decomposed operator libraries rather than heroic single-shot program synthesis.
]]>The roadmap combines sandboxing, endpoint security, prompt-injection resistance, model alignment, monitoring, prevention, and response. It maps security protocols to capability levels, including detection evasion and attack execution.
Self-improving and tool-using agents will need permissioning, monitoring, and graduated access. A control roadmap is one way to let capability scale while keeping deployment incremental and auditable.
]]>Frontier systems are increasingly evaluated as agents rather than chat models. Life-science deployment needs benchmarks that measure evidence handling, analysis, design, validation, translation, and scientific communication across realistic artifacts.
If AI systems begin accelerating biology, the bottleneck becomes trustworthy measurement. Benchmarks like LifeSciBench help separate fluent scientific language from systems that can support actual research decisions.
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