meta-learning

This paper introduces Self-Organising Digital Circuits, using a topology-masked Transformer to configure lookup tables in Boolean gates, enabling circuits to self-assemble and self-repair around hardware faults. It demonstrates near-perfect recovery from soft errors and generalization to larger circuit scales, bridging biological self-organization with digital hardware resilience.

MetaKoopman proposes a Bayesian meta-learning framework for modeling nonlinear dynamics using linear latent representations via Koopman operators, enabling closed-form updates and uncertainty quantification. It is validated on autonomous truck and trailer systems under adverse winter conditions, outperforming prior methods in prediction accuracy and robustness.

MeRLa is a meta-learned reward shaping framework for RLHF that improves alignment by learning task-specific shaping functions, achieving state-of-the-art results on multiple benchmarks with significant reductions in training instability.

This paper investigates using symbolic regression to discover explicit neural network weight-update rules that outperform standard hand-designed optimizers on small symbolic regression benchmarks, achieving an aggregate MSE reduction of 44.47% in 25 out of 30 benchmark/network combinations.

This paper introduces Stochastic Meta-Unlearning (SMU), a bilevel framework that uses VLM-level feedback to learn an unlearning-ready initialization for the language backbone, achieving better forget-retain trade-offs in multimodal unlearning.

Researchers present AIDE², a system with recursive auto-research loops that improved its own code over 100 iterations, discovering seven improvements and beating a hand-tuned agent on held-out benchmarks.

A developer built a pipeline where an RL-trained AI agent creates and submits RL training jobs for small models, rewarding the agent for better performance. The project is fully open-sourced and demonstrates transfer to held-out tasks.

SMETA-ZSL proposes a method for generalized zero-shot threat classification using semantic meta-alignment and contrastive finetuning, outperforming prior methods by 10.8 points on average across 7 benchmarks.

This paper proposes a bilevel optimization framework for Direct Preference Optimization under noisy preference labels, introducing a metadata-free meta-reweighting method that uses central-difference approximation and LoRA fine-tuning to improve alignment performance.

A discussion post questioning the definition and requirements of continual learning in AI, referencing recent statements by Dario Amodei and Demis Hassabis about its importance for AGI.

This paper introduces Meta Neural Cellular Automata (MetaNCA), a framework that learns local update rules to self-organize the weights of neural networks without backpropagation, scaling to networks of 2 million parameters on MNIST and CIFAR-100 and generalizing to unseen architectures.

Proposes Efficient Long-horizon Optimization (ELO) learning, a meta-training algorithm that reallocates compute to longer horizons and uses decoupled progressive expert supervision, improving learned optimizers' performance on long-unroll tasks and out-of-distribution generalization. ELO-Celo2 consistently outperforms AdamW and matches Muon on language modeling tasks.

An article exploring the current state of memory meta in artificial intelligence, likely discussing how memory and meta-learning are combined in modern AI systems.

Proposes a labeled-data-free meta-learning method that generates tasks by assigning soft labels from pre-trained models to unlabeled data, avoiding computationally expensive model inversion. Achieves up to 104x speedup and 8.4-36.4% accuracy improvements over state-of-the-art DFML methods.

This paper proposes PhyMRI-SR, a physics-aware MRI super-resolution method that uses Gaussian splatting and physics-constrained modeling to dynamically adapt resolution-SNR configurations, achieving state-of-the-art performance.

Introduces MetaFlow, a method that trains large language models to generate zero-shot workflows for tasks by combining supervised fine-tuning and reinforcement learning with execution feedback, achieving strong generalization to untrained tasks and operator sets.

This paper introduces a meta-learning framework for anytime-valid certified robustness that uses sequential E-processes to adaptively allocate compute, achieving a 20-fold reduction in sample complexity compared to traditional randomized smoothing while maintaining rigorous statistical guarantees.

Introduces Autodata, a method where AI agents act as data scientists to create high-quality synthetic training data, showing gains on computer science, legal, and math reasoning tasks over classical methods.

Proposes a hierarchical Bayesian framework for meta-learning in dynamical systems from multiple sparse, noisy datasets, using gradient-based MCMC with an embedded ODE solver for efficient posterior inference of shared and dataset-specific parameters.

Proposes a novel meta-learning strategy called MEDIC for open set domain generalization, which uses implicit gradient matching across domain and class splits to achieve better boundaries. Experiments show state-of-the-art performance.