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.
Online Task Adaptation via Self-Organisation
The paper proposes a method for task adaptation that eliminates the need for gradient computation during adaptation. Using a Neural Cellular Automaton, the authors train recurrent dynamics and memory read/write operations via backpropagation, then fix the slow model parameters. Online adaptation is achieved solely through local memory updates driven by prediction errors, enabling significant performance gains on new classification tasks with a single support set pass.
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arXiv AI
Jun 4
Scaling Self-Evolving Agents via Parametric Memory
arXiv:2606. 04536v1 Announce Type: new Abstract: Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model parameters frozen throughout a rollout.
arXiv AI
Jul 22
From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning
arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.
arXiv AI
Jul 16
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
arXiv AI
Aug 18
AutoMem: A Text-Gradient Recursive Self-Improvement Framework for Automated Memory Architectures Search
arXiv:2608. 14621v1 Announce Type: cross Abstract: Long-term memory is increasingly central to LLM agents, yet memory design remains a highly coupled architecture problem: what to encode, how to store it, how to retrieve it, and how to manage it can vary substantially across tasks and backbone models.
arXiv AI
Jul 3
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
arXiv:2607. 02460v1 Announce Type: cross Abstract: Post-training large language models (LLMs) without real-world interaction feedback or human-labeled supervision remains challenging, particularly in specialized domains where expert annotations are costly to obtain.
arXiv Machine Learning
Aug 18
Metaplasticity as adaptive gradient preconditioning for incremental learning
arXiv:2608. 14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses.
Hugging Face Trending Papers
Jul 15
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.
arXiv AI
Jun 16
Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks
arXiv:2410. 11687v3 Announce Type: replace-cross Abstract: Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient descent.
arXiv AI
Sep 21
Neural Cellular Automata Learn General Features in their Hidden Channels
Neural Cellular Automata (NCAs) are shown to learn general, scale‑invariant topological primitives in their hidden channels, which can be transferred from a teacher to a student model for few‑shot learning. The study introduces a transfer‑learning mechanism that injects pretrained hidden states into a student, improving early optimization and outperforming recurrent and feed‑forward baselines on MNIST benchmarks with only ~9,800 parameters. Mechanistic analysis reveals that hidden channels decouple feature extraction from classification, converging to mutually orthogonal states that absorb morphological complexity.
arXiv Machine Learning
Jun 17
Continual Self-Improvement with Lightweight Experiential Latent Memories
arXiv:2606. 17803v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning traces generated during this process.
arXiv AI
Jun 3
Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
arXiv:2606. 03979v1 Announce Type: cross Abstract: The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs).