Attention-based Experience Replay Framework for Continual Learning of Agnostic T
arXiv:2609.13771v1 Announce Type: new Abstract: In this paper, I formulate a Continual Learning problem and propose a method named "Homeostatic Continual Learning" that enables an
arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to
arXiv:2610.21537v1 Announce Type: new Abstract: Biological intelligence continually adapts to new information while retaining prior skills through localized plasticity. In
arXiv:2603.18712v1 Announce Type: new Abstract: The task of multi-channel time series forecasting is ubiquitous in numerous fields such as finance, supply chain management, and
arXiv:2603.19685v1 Announce Type: new Abstract: Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile
arXiv:2603.11201v2 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.
arXiv:2607.22157v1 Announce Type: new Abstract: AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are
<!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I’ve been doing some work in the continual learning space and wanted to share an open-source framework we put together called
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