MIT’s MeMo framework boosts LLM performance by 26% without retraining

MeMo's innovative approach could revolutionize AI adaptability, reducing costs and enhancing efficiency in multi-domain applications. The post MIT’s MeMo boosts LLM performance by 26% without retraining appeared first on Crypto Briefing.

AutoTTS's efficiency could significantly lower AI operational costs, impacting economic models in AI-driven sectors like crypto infrastructure. The post AutoTTS reduces token usage by 69.5% in LLM reasoning strategies appeared first on Crypto Briefing.

With $25 million investment from the Commonwealth of Massachusetts, MIT to build a new shared-use facility to serve as a statewide quantum toolbox.

“The future of AI should be accessible, available, and open to people and builders everywhere, and it should not require an absurd amount of resources only available to a handful of cloud providers,” Paolo Ardoino, CEO, Tether. About 700 million people use generative AIs like Gemini and ChatGPT weekly, but adoption is far from uniform. McKinsey’s 2025 State of AI survey found that nearly half of respondents from companies with more than $5 billion in revenue have reached the AI scaling phase, compared with just 29 percent of those from companies with less than $100 million in revenue, a gap that only widens further down the chain, locking out smaller businesses, developers, and everyday users. Retail and small businesses are limited to basic AI utilities that their facilities can power, such as text-based inference and multimedia generation, using base models. That is billions of end users, and developers locked out of full utilization and development of intelligent software due to hi

Every LLM deployment has a ceiling, a latency curve, and a unit cost. Most teams operate blindly, discovering their deployment limits only when over-provisioning exhausts their GPU budget or peak traffic causes a catastrophic failure. Three numbers matter: maximum sustained concurrency before GPU saturation, end-to-end latency at that concurrency, and cost per million tokens at... The post Industry-standard LLM benchmarks in DataRobot appeared first on DataRobot.

Researchers from NUS, MIT, and A*STAR propose MEMO, a modular framework that encodes corpus knowledge into a separate trainable MEMORY model. The post MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters appeared first on MarkTechPost.

The novel power of today’s AI is in its ability to deal with intent. This is a superpower, no doubt, but it creates a huge imperative for app developers: the need to map between the anything-is-possible large language model (LLM) and the strict capabilities of code.  Unrestrained, LLM endpoints will let your user create unicorns and leprechauns while your back end can handle only purchase orders and customer profiles. You must harness the LLM’s ability to understand intent to what the app is logically capable of, meanwhile keeping context (and therefore spend) under control. Here I’ll discuss some practical, realistic techniques for doing that today. Between what the user wants to do and what your app is capable of is you. Or, more specifically, the mediation layer you build. This layer can sit anywhere on a broad spectrum, from using incredibly lightweight inline strings to using a massive retrieval-augmented generation (RAG) system backed by a vector database. Somewhere in there is t

Nous Research releases Contrastive Neuron Attribution (CNA), a method that identifies and ablates sparse MLP neuron circuits to steer LLM behavior — no sparse autoencoder training, no weight modification, and no degradation of general capability benchmarks. The post Nous Research Releases Contrastive Neuron Attribution (CNA): Sparse MLP Circuit Steering Without SAE Training or Weight Modification appeared first on MarkTechPost.