MeMo, ML-Embed, and CLOVER: AI's New Tools

Memory models, multilingual embeddings, and autonomous driving planning advance

May 15, 2026•4 min read

ScienceToStartup Editorial

This week's AI research delivers significant leaps in how large language models manage knowledge, how AI understands diverse languages, and how autonomous vehicles plan their routes. MeMo offers a modular framework for updating LLMs without retraining. ML-Embed tackles the challenge of inclusive and efficient multilingual embeddings. CLOVER introduces a closed-loop system for end-to-end autonomous driving planning. These developments signal a maturing AI landscape, ready for more sophisticated and practical applications.

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Frequently Asked Questions

MeMo is a modular framework that allows LLMs to incorporate new knowledge by using a separate memory model, without retraining the core LLM.

By keeping the LLM parameters unchanged and storing new information in an external memory model, MeMo avoids overwriting existing knowledge.

ML-Embed provides inclusive and efficient multilingual text embeddings, addressing high computational costs and linguistic biases.

ML-Embed shows particularly strong results in low-resource languages due to its massively multilingual training dataset.

CLOVER addresses the mismatch between imitation-based training and rule-based evaluation in end-to-end autonomous driving planners.

CLOVER uses a scorer that predicts planning-metric sub-scores to rank diverse candidate trajectories generated by a model.

AsyncFC significantly reduces end-to-end task completion time for LLM function calling by enabling asynchronous execution.

No, AsyncFC is an execution-layer framework that works with existing models and unmodified function implementations.

Yes, MeMo's plug-and-play nature allows integration with both open-source and proprietary closed-source LLMs.

3D-ML is the framework behind ML-Embed, focusing on efficiency across the model lifecycle and enhanced parameter efficiency.

CLOVER achieved state-of-the-art results on NAVSIM, with 94.5 PDMS and 90.4 EPDMS.

ML-Embed uses 3D-ML and MEL to provide comprehensive efficiency across the model lifecycle, including storage and inference.

It allows multiple function calls to execute concurrently when their dependencies permit, further speeding up task completion.

They enable startups to build global products and services that cater to diverse linguistic markets without facing high costs or biases.

MeMo's core innovation is treating memory as a separate model, allowing dynamic knowledge updates for LLMs without altering their base parameters.

MeMo, ML-Embed, and CLOVER: AI's New Tools

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In today's rundown

MeMo: Memory as a Model

ML-Embed: Inclusive and Efficient Embeddings

CLOVER: Closed-Loop Value Estimation & Ranking

Community AI Usage

Trending AI Tools and AI Research

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Frequently Asked Questions

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