Anti-forgetting representation learning method reduces the weight aggregation interference on model memory and augments the ...
Sutton believes Reinforcement Learning is the Path to to Intelligence via Experience. Sutton defines intelligence as the computational part of the ability to achieve goals. It is rooted in a stream of ...
Continual learning in neural networks addresses the challenge of adapting to new information accumulated over time while retaining previously acquired knowledge. A central obstacle to this process is ...
What if the so-called “AI bubble” isn’t a bubble at all? Imagine a world where artificial intelligence doesn’t just plateau or implode under the weight of its own hype but instead grows smarter, more ...
2025 saw a tripling of continual learning LLM papers according to arXiv trends. This is driven by foundation model scale and multimodal extensions. However, no flagship AI released models (GPT-5, Grok ...
By allowing models to actively update their weights during inference, Test-Time Training (TTT) creates a "compressed memory" ...
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