arXiv AI
Jul 10
Architecture Generalization with MetaNCA
arXiv:2607. 07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information.
Self-Organising Digital Circuits
arXiv:2608. 02606v1 Announce Type: new Abstract: Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes.
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arXiv Machine Learning
Jul 17
One-Shot Generative Design for Disordered Metamaterials via Self-Organizing Neural Cellular Automata
arXiv:2607. 14475v1 Announce Type: cross Abstract: Disordered metamaterials feature microstructures with inherent randomness and irregularity, enabling them to achieve broader property coverage and superior performance unavailable in their regular counterparts.
arXiv Machine Learning
Aug 10
BDD2Seq: Enabling Scalable Reversible-Circuit Synthesis via Graph-to-Sequence Learning
arXiv:2511. 08315v2 Announce Type: replace-cross Abstract: Binary Decision Diagrams (BDDs) are instrumental in many electronic design automation (EDA) tasks thanks to their compact representation of Boolean functions.
arXiv Machine Learning
Jul 27
Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning
arXiv:2607. 21971v1 Announce Type: new Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains.
arXiv Machine Learning
2d ago
It's Much Easier for Neural Networks to learn Game of Life Dynamics with the Right Activation Function: Polynomial Kolmogorov-Arnold Networks
arXiv:2606. 23587v2 Announce Type: replace Abstract: Previous work has found a gap between the scale of neural networks that reliably learn Conway's Game of Life, and minimal networks capable of representing the classic cellular automaton with hard-coded parameter values.
arXiv AI
Jun 17
TRACE: Learning to Compute on Circuit Graphs
arXiv:2509. 21886v3 Announce Type: replace Abstract: Learning to compute, the ability to model the functional behavior of a circuit graph, is a fundamental challenge for graph representation learning.
arXiv Machine Learning
Jun 30
MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability
arXiv:2605. 26343v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to explain a model's behaviour by finding its circuit: the sparse subgraph of the model's computation that is causally responsible for it.
arXiv Machine Learning
Jul 27
On the Depth Scalability of Logic Gate Networks
arXiv:2607. 21633v1 Announce Type: new Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean circuits, existing LGNs do not reliably benefit from increased depth.
arXiv AI
Jul 7
From Arithmetic to Logic: The Resilience of Logic and Lookup-Based Neural Networks Under Parameter Bit-Flips
arXiv:2603. 22770v2 Announce Type: replace-cross Abstract: The deployment of deep neural networks (DNNs) in safety-critical edge environments necessitates robustness against hardware-induced bit-flip errors.
arXiv AI
Jul 3
Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
arXiv:2607. 01595v1 Announce Type: new Abstract: As the scale and complexity of cloud-based AI systems continue to escalate, ensuring service reliability through rapid fault detection and adaptive recovery has become a critical challenge.
arXiv Machine Learning
Jun 2
Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits
arXiv:2602. 03970v3 Announce Type: replace-cross Abstract: We study the statistical behavior of reasoning probes in a stylized model of iterative computation inspired by neural algorithmic reasoning.
arXiv AI
Aug 12
SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents
arXiv:2608. 10157v1 Announce Type: new Abstract: Self-improving agents seek to reduce the human engineering effort behind AI systems by enabling them to evolve and self-improve their performance over time.