Evo-Memory Benchmark: How LLM Agents Learn During Deployment
21 days ago 高效码农Evo-Memory: The streaming benchmark that forces LLM agents to learn at test time, not just remember What makes an agent truly get better while it works? A self-evolving memory that can retrieve, refine and reuse strategies across a never-ending task stream—Evo-Memory measures exactly that. What problem is Evo-Memory trying to solve? Core question: “Why do most LLM agents plateau even when they store every chat log?” Short answer: Storing is not learning. Static retrieval only replays facts; it never updates the policy. In long-horizon or goal-oriented streams the same type of sub-task appears again and again, but the agent treats …
Nested Learning: A New Paradigm for Continual AI Improvement
1 months ago 高效码农Nested Learning: A New Machine Learning Paradigm for Continual Learning The past decade has witnessed remarkable advancements in the field of machine learning (ML), driven primarily by powerful neural network architectures and the algorithms used to train them. Yet, despite the impressive capabilities of large language models (LLMs), several fundamental challenges persist—particularly in the realm of continual learning. This critical capability refers to a model’s ability to actively acquire new knowledge and skills over time without forgetting what it has already learned. Why Is Continual Learning So Important for AI? When it comes to continual learning and self-improvement, the human …
From Human Memory to AI Continual Learning: How Nested Learning Solves the “Amnesia” Problem in Large Models
1 months ago 高效码农If you’ve been following machine learning’s evolution, you’ve probably noticed a strange paradox: while today’s AI systems can write poetry, debug code, and reason through complex problems, they still struggle with something a three-year-old does effortlessly—learning new things without forgetting old ones. It’s like meeting someone who can recite the entire encyclopedia but can’t remember your name five minutes after you meet. Google Research’s recent introduction of Nested Learning, presented at NeurIPS 2025, challenges this fundamental limitation. This isn’t another incremental architecture tweak. It’s a rethinking of how we understand deep learning itself, inspired by how the human brain continually …