Ivan

Co-Founder of Marvaq

Data, Machine Learning & AI for Sports Solutions

[Markus J. Buehler @ProfBuehlerMIT]

We built recursive meta-intelligence, an AI that creates its own scientific instruments, turns them into persistent worlds that an agent ecology with hundreds of AIs inhabits, and uses those worlds to discover mechanistic principles in one of the hardest classes of physical problems: how complex hierarchical materials (nested structures of matter that give rise to new function through organization) evolve and fail. The AI reasons across enormous spaces of possible physical trajectories, where every rupture changes what can happen next, and compresses those histories into principles (which humans can understand and design with) - complex chains of causal events, highly nonlinear, and intricate.

Scientific superintelligence is tangible here - machine-scale exploration opening cognitive channels into complexity that has been extremely difficult for humans to traverse directly. AI builds the spaces in which its next level of reasoning becomes possible; a representation becomes an instrument, the instrument becomes an executable world, and that world becomes the substrate for further intelligence. Intelligence then grows by constructing new spaces to think in.

The task we explored started from a seemingly simple prompt to explore a biological material system - the AI then chose the representation, mechanics and experiments, built a fracture laboratory to push materials to their limit, tested hypotheses and generated scientific conclusions. The swarm explored a combinatorial universe in which architecture controls function and every rupture changes the future state of the material.

The AI discovered a compact principle that defines how multiscale material architecture can program the evolution of failure. Material placement and geometric order determine how forces redistribute, whether damage cascades or remains distributed, and whether function survives substantial flaws. For this discovery to happen the AI had to reason across long path-dependent histories, simulate alternative futures and compress them into generative invariants (model-based causal reasoning, counterfactual simulation and temporal abstraction applied to an evolving physical world). It is incredible to witness this transition to a new form of intelligence and capability through scaling swarms.

A lot of positive will come out of this because it expands the human epistemic horizon as AI can traverse thousands of possible histories and return mechanisms compact enough for us to understand, test and build from. Intelligence compounds through its artifacts!

A few lessons we learned:

▶️ Learning and discovery are flows through spaces of possibility. Early work has shown how backpropagation flows through parameters, reinforcement learning through action and consequence, and autonomous swarms through representations, instruments and executable worlds, bringing it all together. Flows create structure; structure redirects future flows in the recursive instrument.

▶️ Nonlinear physics actually defines a larger principle, where high-dimensional dynamics generate stable invariants; invariants become effective variables; those variables become the substrate for a new level of cognition.

▶️ Recursion then becomes level creation - one possibility space compresses into a principle, and that principle opens a larger space above it.

[Ivan @ivvanled]

@sebuzdugan @Bhavani_00007 fully agree

[Ivan @ivvanled]

@chattyjralt Coincido en instrumentar desde el día uno. Lo de "después" iba por las capas del stack, no por cuándo lo implementás. Ese set mínimo (id, versión, coste con reintentos, aceptado/rechazado) es el baseline antes de sumar routing o caché, y sí, lo aplico en un proyecto de LLM Twin.

[Ivan @ivvanled]

Estuve leyendo AI Engineering, de Chip Huyen, y algo me quedó dando vueltas:

RAG, agentes y modelos son la base. Después aparecen routing, cache, guardrails y observabilidad.

[Julián @juliandeangeIis]

https://t.co/w22YgMgZ3E