McAfee Professor of Engineering @MIT; Co-Founder & CTO at Unreasonable Labs; AI-Driven Scientific Discovery
Cambridge, MA
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AI discovered how a material just one atom thick can keep carrying load as its atomic structure begins to break, preventing catastrophic failure. This discovery is based on first-principles atomic scale reasoning integrated with biological principles, cutting across scales and providing deep insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms.
Here is what we did:
▶️ We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention.
▶️ We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model.
▶️ We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms.
What emerged is a set of physical design principles:
▶️ The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently.
▶️ Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion.
▶️ Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate.
▶️ A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge.
▶️ The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures.
The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise.
A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth.
Models building models, with physical evidence shaping recursive reasoning loops.
AI that reasons from first principles, atom by atom. Physics becomes native to the reasoning process itself, and drives a powerful self-evolving loop where intelligence and matter merge.
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This is actual footage of people walking through a protein molecule... humans scale themselves down to the atomic structure of caddisfly silk - moving through it, interacting with atoms and molecules using their own bodies, and hearing molecular motion transformed into sound in real time. It was magical: immersive, physical, hands-on. A unique way to experience and engineer matter from within - and to enter the world within worlds that normally exists far beyond our senses. Connecting scientific imaging with human perception, it expands what we think, know, and see - and changes how we experience our world. Amphibian Drift - a wonderful collaboration with Nomeda and Gediminas Urbonas, Wei Lu, Terry Kang, and Thomas Lee Harriett / Pyraloid - shown at the inaugural MIT Future Fest in the MIT.nano Immersion Lab. Thank you STUDIO.nano for making this possible.
🎹 The Elegy of Motion, Markus J. Buehler
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Markus J. Buehler retweeted
"The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise."
AI discovered how a material just one atom thick can keep carrying load as its atomic structure begins to break, preventing catastrophic failure. This discovery is based on first-principles atomic scale reasoning integrated with biological principles, cutting across scales and providing deep insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms.
Here is what we did:
▶️ We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention.
▶️ We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model.
▶️ We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms.
What emerged is a set of physical design principles:
▶️ The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently.
▶️ Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion.
▶️ Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate.
▶️ A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge.
▶️ The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures.
The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise.
A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth.
Models building models, with physical evidence shaping recursive reasoning loops.
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Markus J. Buehler retweeted
A breakthrough that demonstrates how to make anything we make better… marking the beginning of AI making Graphene material science explode.
Or as we say in the Singularity, it’s Tuesday.
AI that reasons from first principles, atom by atom. Physics becomes native to the reasoning process itself, and drives a powerful self-evolving loop where intelligence and matter merge.
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Markus J. Buehler retweeted
frontier intelligence + hard scientific problems = some of the largest venture outcomes of the next decade
AI discovered how a material just one atom thick can keep carrying load as its atomic structure begins to break, preventing catastrophic failure. This discovery is based on first-principles atomic scale reasoning integrated with biological principles, cutting across scales and providing deep insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms.
Here is what we did:
▶️ We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention.
▶️ We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model.
▶️ We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms.
What emerged is a set of physical design principles:
▶️ The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently.
▶️ Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion.
▶️ Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate.
▶️ A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge.
▶️ The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures.
The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise.
A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth.
Models building models, with physical evidence shaping recursive reasoning loops.
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Markus J. Buehler retweeted
“AI agents work together to create an executable representation of matter, use it to investigate possible responses, and return with principles that organize further experiments. The instruments and evidence persist beyond the initial investigation.
The design approach produced a library of hundreds of thousands of atomically specified graphene structures, revealing an extremely rich design space for physical reasoning by the AI, with fundamental insights into mechanisms and scaling laws extracted by the model.”
AI discovered how a material just one atom thick can keep carrying load as its atomic structure begins to break, preventing catastrophic failure. This discovery is based on first-principles atomic scale reasoning integrated with biological principles, cutting across scales and providing deep insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms.
Here is what we did:
▶️ We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention.
▶️ We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model.
▶️ We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms.
What emerged is a set of physical design principles:
▶️ The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently.
▶️ Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion.
▶️ Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate.
▶️ A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge.
▶️ The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures.
The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise.
A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth.
Models building models, with physical evidence shaping recursive reasoning loops.
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AI that reasons from first principles, atom by atom. Physics becomes native to the reasoning process itself, and drives a powerful self-evolving loop where intelligence and matter merge.
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Join us at MIT for the first ScienceClaw Hackathon... Apply at scienceclawhack.ai/
Spend a weekend at @MIT for the ScienceClaw Hackathon - Oct. 30-Nov. 1, 2026: Exploring the collective dynamics of AI for science to solve real-world science and technology problems across scales.
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Incredibly excited to share Amphibian Drift with all of you at the inaugural MIT Future Fest Oct 2-3, 2026 at the @mit_nano Immersion Lab, featuring the vinyl release of The Elegy of Motion - exploring the relentless molecular movement of an exquisite silk protein...with an immersive landscape of sound, visuals, touch, and structure.
We are made of motion that cannot stop. Please join us for Amphibian Drift, an immersive exhibition and XR experience as part of MIT Future Fest, jointly with Nomeda & Gediminas Urbonas/Urbonas Studio, at the MIT.nano Immersion Lab. You will experience the relentless nanoscopic movement of molecular mechanics of an extraordinarily intricate caddisfly silk protein, from within an immersive landscape of sound, visuals, touch, and structure is created.
In a world beyond what our senses can yet perceive, Amphibian Drift displays the exquisite order sustained without the possibility of choosing otherwise, an elegy of motion that will still comfort you as the molecular scale becomes strangely personal. Something of that encounter will follow you back into ordinary life: a deeper intimacy with matter, and a different feeling for what it means to endure.
October 2–3, 2026 11am-5pm
MIT.nano Immersion Lab, 12-3207, 60 Vassar Street, Cambridge, MA
Registration details in reply.
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So excited that our lab is partnering with @e14fund and other organizations for the ScienceClaw Hackathon
Spend a weekend at @MIT for the ScienceClaw Hackathon - Oct. 30-Nov. 1, 2026: Exploring the collective dynamics of AI for science to solve real-world science and technology problems across scales.
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Spend a weekend at @MIT for the ScienceClaw Hackathon - Oct. 30-Nov. 1, 2026: Exploring the collective dynamics of AI for science to solve real-world science and technology problems across scales.
Join us at the MIT Media Lab for the ScienceClaw Hackathon, building the internet of agents for science. AI is becoming a collaborator in the real world - designing materials, creating instruments, running experiments, and connecting its capabilities with those of other agents. AI adapts as a problem unfolds - revising its reasoning, learning how to collaborate, and assembling scattered pieces of knowledge, evidence, and raw capability into solutions to some of the hardest challenges in science, technology, and innovation.
Teams will connect AI agents, models, simulations, robots, cloud labs, and scientific tools into functioning systems to tackle problems in protein design, robotics, materials, manufacturing, experimental science, and beyond.
The challenge is to turn ideas into tested results - and demonstrate how agents collaborating across teams and disciplines can accomplish more together. Explore how agents can specialize, challenge one another’s assumptions, learn from failed experiments, and combine their expertise to solve harder problems. Show how collaboration changes what your system can discover, design, or build.
One agent's discovery becomes another's starting point. A tool built by one team enables an experiment by another. Connect your team of AIs, share a capability someone else needs, and build on what others have learned, produced, built. The ambition is an internet of agents through which scientific knowledge, tools, and capabilities can grow, evolve and be utilized across teams and institutions.
🗓️ When: October 30-November 1, 2026
📍 Where: MIT Media Lab
Bring your expertise, your tools, and a problem worth solving - or find one. Help build AI that can contribute to science through what it can discover, create, and make work.
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Markus J. Buehler retweeted
Incredibile. AI swarms spontaneously grow scale-free topologies with a few dominant hubs, the Barabási–Albert physics of preferential attachment!
Local interactions + rich-get-richer produce the long-tailed degree distribution and information brokers with no central planner.
This self-organization without a central planner is the same class of emergent phenomenon seen in many physical systems like phase transitions, self-organized criticality, and spontaneous symmetry breaking where purely local rules generate global structure, information brokers, and efficient long-range integration.
Fascinating AI swarm dynamics: a few agents spontaneously emerge as highly connected hubs, while most remain locally connected. The swarm develops a strongly heterogeneous interaction topology with a long-tailed degree distribution - an emergent organizational structure arising from initially decentralized local interactions. There is no central planner assigning roles; the swarm builds its own coordination architecture, with information brokers and increasingly global integration emerging from local behavior.
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This is a first! The beginning of some very interesting possibilities - your self-driving robot (Tesla) integrated with your team of agents that work with other parts of your ecosystem
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U.S. News just ranked MIT #1 among National Universities for the first time, after several years at #2. A great testament to the amazing students, staff, and faculty who make this place so special. It’s a magical place, and we are fortunate to call it home and share our work with the world.
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Markus J. Buehler retweeted
incredible, the future is most certainly here.
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.
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Markus J. Buehler retweeted
Two fascinating studies.
As a biologist, I get goosebumps thinking about this, even though I had anticipated something of the sort.
Especially stigmergy, the division of labor, and an environment that preserves the traces and results of past activities strongly remind me of certain biological systems found in ecosystems. One agent creates something; another encounters it, modifies it, and thereby opens up new possibilities for those who follow. The environment becomes part of the collective memory. 1 / 2
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.
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The day matter learned to hear
I made a vinyl record from some of my music - taking compositions that had mostly existed as digital recordings and turning them into a physical object. Incredible to hear the music encoded in grooves and played back mechanically in analog form. If you want to listen, the playlist is linked below.
About the music:
▶️ The pieces move between piano, electronic and experimental composition.
▶️ The compositions draw on ideas ranging from proteins and biology to topology, fracture and more abstract sound worlds.
Pieces include:
Deep Aria - Protein Antibody in E minor - Koto of Topology - Counterpoint Through Fracture, and others
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Fascinating AI swarm dynamics: a few agents spontaneously emerge as highly connected hubs, while most remain locally connected. The swarm develops a strongly heterogeneous interaction topology with a long-tailed degree distribution - an emergent organizational structure arising from initially decentralized local interactions. There is no central planner assigning roles; the swarm builds its own coordination architecture, with information brokers and increasingly global integration emerging from local behavior.
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.
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