Research map / 33 signals / 6 areas
This map connects the roadmap to superintelligence with the work needed to move it forward. Use it to orient yourself, trace a mechanism, or find a tractable research direction.
Evals, control, and governance form the assurance layer. See the full roadmap →
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.
Open question Which bottleneck—evaluation quality, search efficiency, transfer, or inspectability—most limits today's compounding AI research loops?
Governance for ASI should make fast development more legible, reliable, and deployable: measurement, evaluations, incident learning, and standards that scale with capability rather than freezing progress.
Open question Which capability-linked thresholds best increase deployment confidence without unnecessarily delaying beneficial systems?
A genomics and biology benchmark from OpenAI for evaluating AI performance on complex scientific research tasks and datasets.
Open question Do gains on GeneBench-Pro predict reproducible progress on novel genomics investigations rather than benchmark-specific analysis skill?
Gemini Omni Flash is a public-preview multimodal model for high-speed video generation and conversational video editing through the Gemini API.
Open question Can conversational video generation support causally faithful simulations rather than only visually plausible outputs?
Anthropic's Claude Science is an AI workbench for scientists that integrates research tools, produces auditable artifacts, and connects to compute.
Open question Do auditable artifacts from AI science workbenches enable independent researchers to reproduce results as reliably as artifacts produced by expert human teams?
Anthropic's June 2026 model wave includes Claude Fable 5, Mythos 5 for approved defensive cyber workflows, and Claude Sonnet 5 for broad agentic use.
Open question What evaluation and access controls can determine when long-running, cyber-capable agents are safe enough for broader deployment?
OpenAI's GPT-5.6 preview introduces Sol as a next-generation frontier model, with Terra and Luna positioned as lower-cost members of the same family.
Open question How much of Sol's reported long-horizon improvement comes from base-model capability versus extra reasoning compute and subagent orchestration?
An AlphaEvolve-inspired algorithm that decomposes LLM-guided evolutionary search into initialization, local improvement, and perturbation operators.
Open question How well do evolved initialization, improvement, and perturbation operators transfer beyond the optimization problems on which they were discovered?
Google DeepMind's defense-in-depth roadmap for securing advanced AI agents even when alignment is imperfect.
Open question How effective are layered controls against capable agents that actively evade monitoring across long-horizon tasks?
OpenAI's expert-authored benchmark for evaluating how AI systems handle realistic life-science workflows and decisions.
Open question How strongly does LifeSciBench performance predict reliable assistance on real life-science decisions with incomplete evidence and expert oversight?
OpenAI and Molecule.one report a near-autonomous AI chemistry workflow in which GPT-5.4 helped improve Chan-Lam Coupling yields across tested substrates.
Open question How well do AI-proposed reaction improvements generalize to unseen substrates, laboratories, and experimental conditions with less human steering?
A 2026 orientation report on the post-AGI frontier, defining ASI as systems more intelligent and cognitively capable than large human organizations and mapping four paths from AGI to ASI.
Open question Which measurable indicators would reveal that recursive improvement or multi-agent coordination is compounding capabilities faster than continued model scaling?
A compact evidence framework for recursive self-design, mapping public systems such as DGM, STOP, Goedel Agent, and ShinkaEvolve against criteria for inspectable, feedback-directed self-modification.
Open question What reproducibility criteria distinguish sustained recursive self-design from one-shot agent optimization across tasks and codebases?
OpenAI's recommendations for independent evaluations of frontier-model capabilities and safeguards, with emphasis on harness design and validity.
Open question Which harness configurations best predict deployed agent behavior while remaining reproducible across independent evaluators?
A public framework explaining how OpenAI aligns safety and security practices with emerging frontier-AI legal requirements.
Open question Which governance thresholds and reporting practices reliably reduce frontier-model risk without becoming outdated as capabilities and laws change?
An open-source evolutionary coding agent that combines LLMs, island-based search, crossover, meta-prompting, refinement, and evaluator feedback for algorithmic discovery.
Open question How does island-based evolutionary search compare with simpler optimization methods when evaluator calls and model inference are held to the same compute budget?
A self-evolving agent framework that treats skills as long-lived, testable assets with creation, memory, management, evaluation, and refinement.
Open question How can self-evolving skill libraries avoid stale, conflicting, or unsafe procedures while preserving cumulative performance gains?
A multi-agent autonomous research pipeline with debate, self-healing execution, verifiable reporting, human-in-the-loop modes, and cross-run evolution.
Open question Which human intervention points most improve research validity without erasing the speed and scale gains of an autonomous multi-agent pipeline?
Google's Gemini for Science collects experimental tools for scientific literature work, code transformation, hypothesis generation, and discovery.
Open question Do integrated Gemini for Science workflows measurably shorten end-to-end research cycles without reducing reproducibility?
Gemini 3.5 Flash became generally available alongside Managed Agents and the Antigravity Agent preview for autonomous coding, browsing, and file work.
Open question Which parts of the model-runtime-sandbox-tool stack drive reliable gains on long-horizon coding and browsing tasks?
A 2026 paper showing LLM agents autonomously designing foundation-model architectures and training scripts, with AIRA-Compose and AIRA-Design producing models and mechanisms that match or beat hand-designed baselines.
Open question Do agent-discovered architectures retain their advantages when scaled beyond one billion parameters under compute budgets comparable to frontier human-designed models?
A critique of autonomous AI-scientist systems, arguing that current designs miss tacit lab knowledge, diversity, physical feedback, and problem selection.
Open question What combination of tacit-knowledge capture, hypothesis diversity, and physical feedback would let agentic scientists outperform expert human teams on genuinely novel discoveries?
A Gemini-built multi-agent system for generating, debating, ranking, and evolving scientific hypotheses in life sciences and beyond.
Open question Does increasing test-time compute across debate, ranking, and evolution reliably improve experimentally validated hypothesis quality?
Meta AI's AIRA2 focuses on throughput, generalization, and operator limits in AI research agents for model and architecture discovery.
Open question Which combinations of asynchronous execution, validation design, and adaptive operators let AI research agents scale search without overfitting?
Self-referential agents that combine task and meta agents into an editable program, allowing the improvement process itself to be improved across domains.
Open question Can self-referential task and meta-agent improvement transfer reliably across domains where feedback is sparse or delayed?
A self-improving coding-agent system that modifies its own code, validates changes on benchmarks, and keeps a growing archive of diverse agents.
Open question Can self-modifying agent archives continue producing transferable capability gains after benchmark feedback becomes sparse, noisy, or vulnerable to overfitting?
A measurement proposal for AI R&D automation, tracking how much AI changes research labor, progress rates, oversight, and incident patterns.
Open question Which telemetry indicators provide the earliest reliable warning that AI R&D automation is outpacing oversight capacity?
A 2026 survey of AI agent architectures, orchestration patterns, deployment settings, evaluation practices, and open reliability challenges.
Open question Which evaluation protocol best predicts an agent system's reliability across long-horizon, tool-dependent workflows under changing environments and retry budgets?
A live software engineering agent that evolves its own scaffold while solving real software tasks, reporting strong SWE-bench Verified and SWE-Bench Pro results without test-time scaling.
Open question Can on-the-fly scaffold evolution improve performance on unseen software tasks without destabilizing the agent or overfitting task feedback?
A self-improving coding-agent method that guides search by estimating the improvement potential of agent descendants rather than only current benchmark score.
Open question Does descendant-based metaproductivity predict durable self-improvement beyond the benchmarks and search depths used to select it?
Google DeepMind's evolutionary coding-agent framework for scientific and algorithmic discovery, including infrastructure optimization and new algorithms for mathematical and computational problems.
Open question How reliably do evaluator-grounded evolutionary agents produce novel improvements that generalize beyond the objectives and environments used during search?
Meta's Llama 4 Scout and Maverick introduced open-weight natively multimodal mixture-of-experts models, distilled from the larger Behemoth teacher model.
Open question How far can open-weight multimodal mixture-of-experts models support inspectable agent research before capability gaps with closed frontier models dominate?
A framework for automated scientific discovery in which frontier models generate ideas, write code, run experiments, plot results, write papers, and simulate review.
Open question Which stages of the automated research loop remain the dominant bottlenecks to producing reproducible discoveries that survive expert review and independent replication?
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