Now that we're about to see a flood of proven math theorems in the coming months, we finally get an answer to whether math has any effect on "the real world". My guess is no π€·
this + mathematically competent ai will lead to a cambrian explosion in materials and physical designs. making physical systems programmable at exactly the moment where programming becomes automated. wild
Can we compile matter - for instance, a pine cone - and derive new active materials, end-to-end from observation to manufacturing? If physical systems can be formalized as composable mathematics, we can point AI that has been shown to resolve long-open mathematical problems at matter itself. Our new work turns bioinspired engineering from analogy into formal compilation: biology and mechanics become explicit, checkable, and executable, so AI reasoning can produce physical designs.
This is the first end-to-end demonstration in which a formally compositional multiscale model is carried from a biological hierarchy, through engineered design and fabrication specification, to executable manufacturing code - and then to a physically tested artifact.
Background:
Humans have long been inspired by biology to advance technology, but this has usually been an ad hoc process rather than a mathematically rigorous one. Natural materials such as pinecones achieve adaptive behavior through mechanisms organized across many scales. Engineering typically translates those mechanisms by analogy: identify a biological principle, build something inspired by it, and validate each new design as a separate case. This can produce remarkable results, but the knowledge does not readily compound. Instead, we represent each scale as a dynamical module with explicit states, stimuli, governing laws, and interfaces. Every scale-to-scale map must preserve the stimulus - response dynamics: evolve the fine-scale system and then map upward, or map upward first and then evolve. The two paths must agree. Because this condition is preserved under composition, locally valid interfaces remain consistent when assembled into the full hierarchy.
We then carry that structure into an engineered system, translate the target behavior into a verified fabrication specification, and compile it into G-code: the toolpaths, deposition sequence, temperatures, speeds, and other commands executed by a 3D printer. The intermediate translations are explicit, checkable, and executable rather than completed through an ad hoc handoff.
The formal guarantee is that given valid local models and interfaces, their composition remains valid. Whether those models and manufacturing assumptions accurately capture physical reality remains an empirical question. That is why we fabricated and tested the results.
We generated four actuator classes by crossing two stimuli - humidity and heat - with two responses: bending and twisting. The fourth, thermal twisting, required no new pipeline and no separate derivation within the framework. It emerged by composing a thermal stimulus module already validated in one case with a twisting module validated in another. The generated G-code produced the intended motion without manual redesign, and all four predictions fell within one experimental standard deviation of the measured response.
Why this matters:
1β£For AI in science, this provides a physics-aware type system against which generative proposals can be checked - and rejected at the interface - before expensive simulation, fabrication, or experiment. It is roughly analogous to proof checking, but for the composition of physical mechanisms.
2β£For engineering, the accessible design space can scale with a library of validated components rather than with the number of individually derived cases.
3β£The mathematics, category theory, carries all the way into a physical object on a print bed. This points toward scientific knowledge as executable infrastructure: models that are not only described in papers, but typed, composable, verifiable, and able to compile into experiments.
Excellent work led by my student @leemmarom with @SkylarTibbits & @GioeleZardini.
Paper published in J. Mech. Phys. Solids along with code, Grasshopper scripts, and manufacturing G-code below.
Statement on behalf of UEFA and its 55 National AssociationStatement on behalf of UEFA and its 55 National AssociationStatement on behalf of UEFA and its 55 National AssociationStatement on behalf of UEFA and its 55 National Associations
@EpsilonTheory he might well be, but heβs also increasing the likelihood of it coming about, and itβs an outcome that will lead to a further deterioration of the wests competitive position v china
Remember when the Americans built the open internet, exported tonnes of culture, and China had to build a wall?
In 2026, China are building open models, will export tonnes of culture, and Americans are building a wall to keep opensource models out
Fate loves irony
Correct me if I'm wrong, but isn't this really just showing:
*In a trained autoregressive network, there is a subspace of internal activations that is especially aligned with future verbal output and downstream computation.*
Isn't this expected given how LLMs are trained? In fact, I suspect you could find this is many other task-optimized architectures, not just Transformers/Claudeβit would be good to try this on RNNs, MLPs, CNNs, etc.
people really understand so little economics that they do not see how this is in the interest of those affected. If implicit non-wage costs fall the clearing wage increases, and at that income level you can self-insure the income volatility that results
As an employee in Germany who would be affected by this, I am so happy that this is happening and that my employer can fire me at-will. This feels like the start of a flywheel.
1. I do think β¬180,000 is too high a number as threshold. It should be β¬100,000. This will allow FAANG for eg: to hire more freely in Germany [which they don't today partly because it's super hard to fire people]. This in turn will rise all boats and increase competition for talent at the 100-160k levels. It'll also help startups. [I guess they will get here - 180k is just a starting point].
2. I think this needs to be coupled with weakening the Betriebsrat (Works Council) and Collective Bargaining Agreements - they are IMO the bane of German industry. Will be a tougher sell given how entrenched they are, but hopefully to incentivize FAANG-type employers, we defang these [lol]
3. This should ideally be coupled with a merit-based immigration stance (eg: the Danish / Dutch type tax rebates etc.] so that exceptionally skilled people consider Europe a viable destination (given how awesome Blue Card is vis-a-vis the H1B nightmare)
Merz absolutely lacks charisma but dude knows how economics works, lol.
I think itβs inarguably true that the number of new ideas created by llms (are there any?) is very low relative to the surface level intelligence they display. As in, if people showed similar abilities, weβd expect significant creative contributions as well