Liquid AI Announces Personal AI Context to Devices Powered by Snapdragon Processors - audioXpress

Liquid AI was founded just three-and-a-half years ago, spun out of MIT CSAIL, where the four founders — Ramin Hasani (CEO), Mathias Lechner (CTO), Alexander Amini (CSO), and Daniela Rus — started the company to leverage decade-long research efforts into how to maximize the amount of intelligence and capabilities from the smallest unit of compute—devices. “We want to enable physical AI. We want to enable AI agents inside physical AI. Liquid AI is a foundation-model company. We are building very efficient models that can be the core of agentic behavior. At the technology core of our company is a hardware-in-the-loop approach to design models from scratch,” explains Ramin Hasani.

“The technology that we created is called Liquid Foundation Models, or LFMs. We open-weight them. There are about 1.4 million downloads on a weekly basis on Hugging Face. The models run from 200 million parameters to 24 billion parameters. This is the fabric to enable all sorts of devices, from wearables to cars, or even PCs — bigger computers.”

“We built two sets of products around the models. We built something called a model-plus-harness, ready to get deployed on top of devices. That is product number one. Product number two is a model-development stack that allows us to take these opportunities and give OEMs, in a B2B fashion, the opportunity to customize these models plus harnesses for their applications and downstream uses.”

“Liquid Context on Snapdragon is a smart memory layer that allows you to read, compress, and write every sort of experience that you have throughout your active engagement with the device — and across devices. This context layer is going to enable AI systems to go from one device to another. That is what we are announcing today,” Hasani announced.

That context would support one or more agents in interpreting information and performing tasks. Liquid AI intends the combined memory and agent layers as a B2B platform for OEMs, enabling automotive, smartphone, PC, and wearable manufacturers to build products that use contextual, on-device AI agents to deliver more complete and personalized user experiences. Device manufacturers now have a foundation for more proactive, personalized, and trustworthy AI experiences, alongside Liquid Agent, Liquid AI’s efficient embedded agent.

Personal Context That Makes Any Agent More Useful

Useful agents need to understand what matters to the user and when help is needed. With the user’s permission, Liquid Context learns from device signals, builds an understanding of routines, preferences, and needs, and keeps that understanding current. It provides a shared context layer between the device and the user's chosen agents, including third-party agents and Liquid Agent. Context supplies the understanding; agents use it to reason, suggest next steps, and take action with permission. Personal context is built and maintained locally, with relevant context made available to connected agents according to the user’s permissions.

“Personal AI starts with understanding how you live and what you need, when you need it,” said Liquid AI CEO and co-founder Ramin Hasani. “Liquid Context builds that understanding on your device so the agents you choose can offer more relevant help and anticipate your needs. The Hexagon NPU makes this continuous, local intelligence practical. Together, we are bringing personal context to the devices people rely on every day.”

Efficient NPU execution is central to keeping this understanding current. Liquid Context is designed to run in the background, turning permitted device signals into useful context without requiring a cloud model to process every update. Agents can then use that context to recognize when help would be valuable and respond in a way that reflects the user’s priorities.

Qualcomm Technologies and Liquid AI are already exploring additional opportunities to work together to further enhance the performance of Liquid Context with Snapdragon platforms, so OEMs can offer personal context as a built-in device capability, making it easier for users to enable Liquid Context. Compatible embedded, cloud, and hybrid agents could even use that context to deliver proactive, personalized assistance.

During Qualcomm’s Snapdragon Summit 2026, Cristiano Amon highlighted the fact that Liquid AI is working with Mercedes-Benz, one of Qualcomm’s automotive customers for Snapdragon Digital. Liquid AI implemented a completely local experience enabling in-car intelligence for Mercedes-Benz.

“We see opportunities in automotive — really tangible opportunities, really transforming what can happen inside the car. We are seeing that fundamental work happen in automotive,” Ramin Hasani shared. “I don’t want to have a generic agent in my car, on my mobile phone, on my watch, or on my wearables — every kind of device that I interact with. I’d like to have a personalized experience. That personalization comes from adaptation plus enough scale,” he reinforced.

Continuous context processing and responsive agent execution both require efficient hardware. Liquid AI has optimized its Liquid Context memory layer and, separately, the LFM2.5-2.6B agentic model that powers Liquid Agent to take advantage of processing, memory access, and low latency of Snapdragon processors. The next-generation Hexagon NPU combines scalar, vector, and matrix processing with dedicated transformer hardware to accelerate these workloads. This pairing can support ongoing context updates and capable embedded agents within the power constraints of everyday devices.

OEMs building embedded or hybrid agents can also work with Liquid AI to evaluate Liquid Agent, powered by its state-of-the-art, efficient LFM2.5-2.6B agentic model and optimized for execution on the Hexagon NPU. Liquid Agent can be tailored to a manufacturer’s hardware, services, interface, and brand, and can consume Liquid Context to deliver personalized, proactive assistance. Liquid Context provides the shared context layer for that vision, preserving relevant understanding and task state with permission so different agents can pick up where the user left off. OEMs can use the Hexagon NPU and Liquid Context as a foundation for their chosen agents, and evaluate Liquid Agent when they need an efficient embedded or hybrid agent of their own.

www.qualcomm.com

www.liquid.ai