venturebeat
MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26%

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a framework from researchers at multiple universities, encodes new knowledge into a dedicated smaller memory model that operates separately from the main LLM.The modular architecture works with both open- and closed-source models and sidesteps the complexity of RAG pipelines and full model retraining.Experiments show that MeMo handles complex queries reliably even when retrieval pipelines are noisy. It avoids the catastrophic forgetting associated with direct fine-tuning and provides a cost-effective pathway for continuous knowledge updates.The challenge of updating LLM memoryLarge language mod [...]

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venturebeat
MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%

Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]

Match Score: 265.49

venturebeat
OpenAI report reveals a 6x productivity gap between AI power users and everyone else

The tools are available to everyone. The subscription is company-wide. The training sessions have been held. And yet, in offices from Wall Street to Silicon Valley, a stark divide is opening between w [...]

Match Score: 83.50

venturebeat
Attention ISN'T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique

When the transformer architecture was introduced in 2017 in the now seminal Google paper "Attention Is All You Need," it became an instant cornerstone of modern artificial intelligence. Ever [...]

Match Score: 76.27

venturebeat
Phi-4 proves that a 'data-first' SFT methodology is the new differentiator

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]

Match Score: 72.72

venturebeat
Under the hood of AI agents: A technical guide to the next frontier of gen AI

Agents are the trendiest topic in AI today — and with good reason. Taking gen AI out of the protected sandbox of the chat interface and allowing it to act directly on the world represents a leap for [...]

Match Score: 68.56

venturebeat
A weekend ‘vibe code’ hack by Andrej Karpathy quietly sketches the missing layer of enterprise AI orchestration

This weekend, Andrej Karpathy, the former director of AI at Tesla and a founding member of OpenAI, decided he wanted to read a book. But he did not want to read it alone. He wanted to read it accompan [...]

Match Score: 67.16

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Karpathy shares 'LLM Knowledge Base' architecture that bypasses RAG with an evolving markdown library maintained by AI

AI vibe coders have yet another reason to thank Andrej Karpathy, the coiner of the term. The former Director of AI at Tesla and co-founder of OpenAI, now running his own independent AI project, recent [...]

Match Score: 65.82

venturebeat
MIT’s new ‘recursive’ framework lets LLMs process 10 million tokens without context rot

Recursive language models (RLMs) are an inference technique developed by researchers at MIT CSAIL that treat long prompts as an external environment to the model. Instead of forcing the entire prompt [...]

Match Score: 65.14

venturebeat
Researchers find that retraining only small parts of AI models can cut costs and prevent forgetting

Enterprises often find that when they fine-tune models, one effective approach to making a large language model (LLM) fit for purpose and grounded in data is to have the model lose some of its abiliti [...]

Match Score: 64.97