meta-learning

CoRe-Stack+ is a meta-learning pipeline that improves deep stacking generalization by filtering redundancies and enhancing calibration, achieving better accuracy and efficiency on vision benchmarks.

This paper proposes a lightweight plastic-memory framework for graph few-shot class-incremental learning, which uses an evolving micro-clustering structure and meta-learning to balance knowledge retention and adaptability to new classes with limited data.

This paper introduces Self-Meta-Evolve, a hierarchical framework that personalizes prompts for each user in enterprise information extraction tasks, improving performance through continuous refinement based on interaction feedback.

This paper proposes using graph hypernetworks to explicitly represent relationships in PDEs for amortizing physics-informed neural networks, showing improved accuracy in solving coupled systems.

ALPINE introduces an ultra-lightweight spatial-relational architecture for few-shot image classification that achieves accuracy gains with fewer parameters, faster convergence, and better robustness compared to baselines like Prototypical Networks and MAML.

This paper introduces CLAW, a method that uses hypernetworks to generate low-rank adapters for world models, enabling efficient online adaptation in model-based reinforcement learning with limited test-time data.

This paper develops NTK-KIP, MetaQuill, and MetaQuill-KIP algorithms to improve neural field reconstruction from sparse observations, making NTK-driven neural fields non-linear and meta-learnable for efficient few-shot adaptation.

The paper demonstrates that prompt-space meta-learning for personalizing frozen large language models does not transfer across users, as the meta-validation objective is statistically invariant to user-support correspondence, leading to no significant improvement over seed prompts or controls.

Trained a small transformer model from scratch to achieve 44% accuracy on the ARC-AGI-1 benchmark for only 67 cents, demonstrating improvements in speed, accuracy, and cost-effectiveness over previous methods.

This paper applies meta-learning and pretraining to improve neural stimulation response modeling, reducing catastrophic forecast failures and enhancing prediction accuracy in non-human primate studies.

This paper introduces Dynamic Influence Weighting (DIW), a knowledge distillation method that improves single-IMU activity recognition by dynamically weighting teacher targets from multiple IMUs during training, achieving significant performance gains.

The paper explores whether any neural network can be redesigned to train effectively with gradient descent, proving a universality result that for any network, there exists an extension that reproduces given weights and outputs via gradient descent.

iFuzz-Meta is an interpretable fuzzy learning framework that combines top-down knowledge integration with bottom-up data-driven adaptation to enhance transparency and generalization in neural models.

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.