meta-learning

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.

This paper presents Connect the Dots (CoD), a framework for training LLMs via reinforcement learning to develop meta-capabilities for long-lifecycle agents, enabling continuous learning and cross-domain generalization.

Proposes ReGrad, a paradigm that treats gradients as retrievable units of knowledge for continual post-training, avoiding cumulative weight drift by storing document-specific gradients in a Gradient Bank and retrieving query-relevant gradients for temporary weight adaptation.

This paper argues that recent claims that neural networks have solved Fodor and Pylyshyn's systematicity challenge are premature. The authors show that the meta-learning for compositionality model fails to generalize out-of-distribution and behaves unsystematically even on in-distribution problems, concluding the challenge remains unmet.

Introduces WIZARD, a weight-space meta-learning framework that generates task-specific LoRA parameters for frozen VLA policies from language instructions and demonstration videos, enabling efficient task adaptation without fine-tuning.

This paper proposes a three-stage diagnostic framework to identify why offline model selectors fail to beat the best single model, applying it to dropout prediction on edX clickstream data. The study finds that the bottleneck is local representational ambiguity rather than learner choice or distribution shift, recommending state redesign or new data collection over further algorithm tuning.

SePO (Self-Evolving Prompt Optimization) proposes a self-referential prompt agent that optimizes both task agents' system prompts and its own system prompt through an evolutionary search, outperforming Manual-CoT, TextGrad, and MetaSPO across five benchmarks including AIME'25, ARC-AGI-1, and GPQA.

R-APS (Reflective Adversarial Pareto Search) is a novel method for constrained design tasks that addresses three structural failures in LLM-based agentic systems—error propagation, robustness evaluation, and knowledge invalidation—through reasoning-mode decomposition across three timescales, requiring no fine-tuning. Evaluated on planar mechanism synthesis, it achieves 3.5x tighter robustness certificates, 46% faster iterations-to-first-admission, and 2.1x Chamfer-distance reduction over baselines.