2025-11-21
Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem
Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after training. The paradigm, called Nested Learning, reframes a model and its training not as a single process, but as a system of nested, multi-level optimization problems. The researchers argue that this approach can unlock more expressive learning algorithms, leading to better in-context learning and memory.
To prove their concept, the researchers used Nested Learning to develop a new model, called Hope. Initial experiments show that it has superior performance on language modeling, continual learning, and long-context reas [...]
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2025-11-28
Anthropic says it solved the long-running AI agent problem with a new multi-session Claude SDK
Agent memory remains a problem that enterprises want to fix, as agents forget some instructions or conversations the longer they run. Anthropic believes it has solved this issue for its Claude Agent [...]
2025-10-08
New memory framework builds AI agents that can handle the real world's unpredictability
Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a m [...]
2025-12-04
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs
For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning [...]
2025-10-24
Thinking Machines challenges OpenAI's AI scaling strategy: 'First superintelligence will be a superhuman learner'
While the world's leading artificial intelligence companies race to build ever-larger models, betting billions that scale alone will unlock artificial general intelligence, a researcher at one of [...]
2025-10-13
Self-improving language models are becoming reality with MIT's updated SEAL technique
Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing a technique that allows large language models (LLMs) — like those underp [...]
2025-11-02
Moving past speculation: How deterministic CPUs deliver predictable AI performance
For more than three decades, modern CPUs have relied on speculative execution to keep pipelines full. When it emerged in the 1990s, speculation was hailed as a breakthrough — just as pipelining and [...]