>> Reasoning Infrastructure <<
We build AI systems that reason like scientists do—by recognizing and navigating the deep structure of thought itself.
> The Observation
A physicist solving a quantum problem uses different cognitive moves than a biologist isolating a genetic signal. But zoom out, and both follow the same deep reasoning architecture—patterns like 'establish baseline, isolate variable, test perturbation.' These structures are domain-independent. We call them reasoning archetypes.
These structures — Named "Multi-Regime Causal Fork", "Confound-Control Funnel", "Intervention Ladder" for example— are independent of domain. They are deep reasoning archetypes.
> The Problem
Current AI operates on content—facts, tokens, patterns. It can't see reasoning structure. So it can't ask 'what cognitive process would solve this?' It guesses at tokens instead of executing a plan. This is why AI fails at complex, multi-step problems that require holding a coherent strategy.
> What We Build
Core Infrastructure: Systems that decompose problems into cognitive process graphs—explicit maps of reasoning structure with typed relationships (motivates, controls_for, reveals, contrasts_with).
This enables:
* Domain-independent ontologies of reasoning patterns extracted from successful scientific work
* Multi-agent architectures where specialized models operate on reasoning topology, not just content
* Navigation of solution spaces via structural similarity to proven reasoning paths
* Stable long-horizon reasoning: explicit structure enables controllable autonomous operation over hours, days, or weeks
* Foundation for physical AI: the same approach of extracting low-level cognitive primitives can be applied to robotic control in complex environments
> Why Now
Frontier models are finally powerful enough to execute complex reasoning—but they lack the architecture to organize it. The gap between model capability and system intelligence is widening. We're building the missing layer: explicit reasoning infrastructure that transforms capable models into coherent reasoners.
> The Thesis
The next substrate of AI is not larger models or more data. It is explicit representation of reasoning structure — graphs of cognitive operations with typed edges (motivates, controls_for, reveals, contrasts_with) that can be extracted, compared, composed, and traversed.
A system that recognizes 'this has the topology of a Confound-Control Funnel' and executes the corresponding moves—establish control, isolate variable, introduce refinement—doesn't just predict better tokens. It actually reasons. That's not an incremental improvement. It's a different kind of intelligence.
We combine:
* Knowledge graphs with fixed cognitive operation vocabularies (OBSERVE, CONTROL_CONFOUND, CAUSAL_INTERVENE, DETECT_QUALITATIVE_DIVERGENCE, etc.)
* Semantic embeddings for content-level similarity
* Graph neural networks for structural pattern matching
* Multi-agent orchestration where agents specialize in different reasoning tiers
* Test-time search over reasoning topologies, not just token sequences
The output is not "an answer" but a reasoning trace with explicit structure — a cognitive process graph that can be validated, critiqued, and refined at the level of its logical architecture.
Self-Organizing Graph Reasoning Evolves into a Critical State [2025]
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks [2025]
Dynamic Semantic Networks for Creative Thinking [2025]
AlphaEvolve: Coding Agent for Advanced Algorithms [DeepMind 2025]
FunSearch: New Discoveries in Mathematical Sciences [DeepMind 2023]
> Status
Building. Reach out.
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