eyvind niklasson

@eyvindn

research @ google
self-organising systems / evolving evolution

zürich eyvind.me Joined May 2009

4.5K Following 1.4K Followers 290 Posts

Latest posts 5

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eyvind niklasson @eyvindn Oct 1

the hardware lottery is also the hardware curse.

with our team, we've been pushing the boundaries of what fully local neural cellular automata models achieve on visual reasoning.

we believe this opens the door to a swathe of efficient, robust, and scalable hardware designs.

Mayalen Etcheverry @mayalen_etc Oct 1

Can complex multi-step reasoning emerge purely from cells that only talk to immediate neighbors?

Happy to share our paper “Reasoning with Neural Cellular Automata (NCA)”, from our team at Google, Paradigms of Intelligence 🧵👇

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eyvind niklasson @eyvindn 11h

no NCAs... but super cool side quest from my colleague @PietroMiotti!!!

Pietro Miotti @PietroMiotti 11h

My AI agents found an unmapped Maya site in Mexico, using LiDAR data from a NASA dataset.

72 hours from a random thought on vacation to a reply from Mexico's national archaeology institute.

What a time to be alive…

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eyvind niklasson @eyvindn 5d

check out Kunal's latest paper! understanding swarms, self-organisation, and its advantages in agentic settings is a super exciting new (...old, but renewed) frontier!

Kunal Jha Verified account @kjha02 5d

What if a swarm of AI agents could learn to work together on its own? 🐝

No leader. No roles. No turn order. Just one shared reward.

Our new paper s³: multi-agent RL lets LLM swarms self-organize, beat best-of-N, and scale past their training size!

scalingswarms.com 🧵
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eyvind niklasson @eyvindn Sep 14

exciting work led by our student researcher @kjha02. we explore how cooperation emerges in a "computational primordial soup" with emergent+endogenous self replication. crucial for scaling such envs towards intelligence, mirroring nature's path to e.g. multicellularity & societies

Kunal Jha Verified account @kjha02 Sep 11

Can self-interested, self-improving, self-replicating agents learn to cooperate?

Our new paper, Tapes Together Strong, shows they can: when social behavior, computation, and reproduction share one energy budget, cooperation evolves from scratch.

arxiv.org/abs/2609.10817 🧵

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eyvind niklasson @eyvindn Sep 11

brilliant work from our team! led by @MariiaD_ML+@aidan_sirbu, we explore reasoning w/ somewhat atypical diffusion models.

classic sudoku solvers need memory to track possible states -> providing denoisers w/ persistent memory between steps seems key! 99.9% on sudoku extreme 🤯

Mariia Drozdova @MariiaD_ML Sep 3

1/7) What happens when we add a persistent memory state and remove timestep conditioning from a denoising model?

We obtain an anytime iterative solver!

With less than 250k-params, we obtain a 99.9% accuracy on Sudoku-Extreme and 98.3% on Maze-Unique.

arxiv.org/abs/2609.01449

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