continual-learning

The paper introduces Chain-of-Experience, a method for continual improvement of large language models through iterative test-time feedback, demonstrating better performance and cost efficiency across various domains.

This paper proposes a framework for evaluating agentic learning harnesses in cybersecurity without labeled benchmarks, using a teacher-student model based on the scaling hypothesis to proxy performance improvements.

This paper proposes learnable wavelet activations to combat plasticity loss in continual learning, decomposing activations into low- and high-frequency components with dynamic injection and regularization, achieving state-of-the-art results on benchmarks.

A developer claims to have built a real continual learning model prototype using LoRA to give Qwen4B instant, generalizable memory without retraining, and is inviting independent researchers to validate the mechanism.

This paper presents a layer-wise information-theoretic framework for replay-based continual learning, decomposing the generalization gap into replay-induced representation drift and optimization-dependence terms, with refinements via Wasserstein relaxation and SGLD instantiation.

AWARe is a fine-tuning method that mitigates catastrophic forgetting in multimodal large language models by selectively freezing important parameters based on activation patterns, preserving upstream capabilities while adapting to downstream tasks.

Discusses unverified claims that Ilya Sutskever's new company has achieved a paradigm shift in AI by building a model that learns and rewires itself in real time, potentially advancing AGI.

Introduces Controlled Memory Interference (CMI), a diagnostic framework for studying how LLM agent memory evolves under different memory relationships, revealing that relationship-specific interference suppresses update plasticity and that interference-aware training improves valid update distinction.

Igor Babuschkin, CEO of River AI, gave a talk at UC Berkeley about the blockers to building powerful personal AI and shared the company's research roadmap centered on continual learning.

This paper introduces Macaron-V1, an open continual learning agent-model family using Mixture-of-LoRA to compose specialist adapters on frozen base models, with recursive self-improvement and model-harness co-design.

A tweet speculates that OpenAI's Doug training run, combined with SEAL-style continual learning, could push beyond GPT-6 toward AGI.

This article presents Dwarkesh Patel's eight predictions for AI development in the era of continual learning, covering fundamental changes in safety regulation, alignment, model diversity, competitive dynamics, and business models.

This paper introduces ATLAS, a model-based continual reinforcement learning algorithm that combines Grow When Required networks with Successor Features to achieve high sample efficiency and robust adaptation to non-stationary environments, demonstrating positive backward transfer in spatial navigation tasks.

This paper introduces NeuMoSync, a novel architecture that integrates neuron-specific neuromodulatory signals into deep neural networks to improve plasticity and adaptability in continual learning, demonstrating strong performance across multiple benchmarks.

This paper surveys the evolution of continual learning from parameter-centric methods to system-level adaptation, proposing a tri-axial framework (When, How, Where) to characterize learning across pre-training, post-training, and inference stages.

This paper introduces a novel measure called relative parameter importance for task-agnostic, replay-free continual learning, enabling better balance between stability and plasticity by regularizing only parameters critical for past tasks while allowing others to update for backward knowledge transfer. The method is evaluated on class-incremental and domain-incremental text classification tasks.

This arXiv paper presents a unified LLMOps architecture for real-time, enterprise-ready LLM deployments, integrating data ingestion, continual learning, RAG, and feedback loops. It introduces components like AIPO, STAR+FAR, and SAGE to address knowledge staleness, hallucination, and latency-cost trade-offs in regulated sectors.

DSETA is a dual-stage continual learning framework for travel time prediction that separates intra-day real-time adaptation from inter-day long-term trend learning, with online A/B tests showing MAE reductions across three cities and successful deployment in DiDi's production environment.

Introduces ContinualSkillBench, a dynamic evaluation framework for in-context continual skill learning in LLM agents, showing that while sequential execution improves performance, current methods struggle to consolidate experience into robust, transferable skills.

Mind Lab claims its Macaron-V1 model surpasses GLM-5.2 in benchmarks, using five LoRA expert modules attached to GLM-5.1 with dynamic expert switching and continual learning via distilled LoRA adapters.