Organization
10 Interestana reports mention Liquid AI, newest first. Each is summarised from the original reporting and links back to its publisher.
Liquid AI has released two open-weight multimodal models, d1-3B and d1-omni-600M, as part of its d1 decision model family. These models are engineered to provide calibrated, typed answers in a single forward pass, producing zero output tokens, which distinguishes them…
Liquid AI has released d1, a novel decision model designed for structured decision-making rather than text generation. This model accepts context and a set of typed questions, returning calibrated probabilities across a predefined set of outcomes in a single API…
Liquid AI has announced the release of LFM2.5-VL-3B-DSpark, an experimental speculative decoding draft model designed to accelerate the inference process for its LFM2.5-VL-3B vision-language model. This new component adds approximately 280 million parameters to the existing model and aims…
Liquid AI released Pipette this week, an open-source platform for benchmarking foundation models on edge devices. This initiative was developed in partnership with Artificial Analysis, which served as an independent methodology validator.
Liquid AI has released DSpark draft model checkpoints for three models within its LFM2.5 family: LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B. These "drafter" models introduce a speculative decoding path to existing target models, significantly accelerating the token generation process.
Liquid AI released LFM2.5-VL-3B yesterday, a 3.1 billion-parameter vision-language model engineered for on-device deployment. This model is capable of reading digital screens across mobile, web, and desktop interfaces. It can also ground objects to specific coordinates, parse complex documents…
Liquid AI released LFM2.5-2.6B, an agentic large language model designed to run entirely on-device, enabling local planning, tool calling, and multi-step task execution across devices such as phones, laptops, PCs, and robots.
Liquid AI has released two open-weight bidirectional encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, which are designed to efficiently process long contexts of up to 8,192 tokens, particularly on central processing units (CPUs) rather than graphics processing units (GPUs).
Liquid AI released Antidoom this week, an open-source method designed to address a failure mode in AI reasoning models known as "doom loops." A doom loop occurs when a model repeatedly emits the same sequence of tokens, continuing…