https://unitedstatesofamericasuperintelligence.com/companies/perplexity/ Dated, sourced news items in the USASI catalog that mention Perplexity. Independent project. Not a United States government website. en-us Tue, 13 Oct 2026 12:00:00 GMT https://unitedstatesofamericasuperintelligence.com/news/perplexity-releases-pplx-embed-v2-late/ https://unitedstatesofamericasuperintelligence.com/news/perplexity-releases-pplx-embed-v2-late/ Tue, 13 Oct 2026 12:00:00 GMT release Perplexity has published pplx-embed-v2-late, a family of multimodal late-interaction (ColBERT) retrievers for text, images, and visual documents built on Qwen3.5 with bidirectional attention, in 0.6B and 9B sizes on Hugging Face under the MIT license. The model card says both models produce one 128-dimensional vector per token, share an embedding space so the 0.6B model can query an index built with the 9B model, and were distilled from an internal 18B ColBERT teacher. The card's citation dates the accompanying Perplexity blog post October 7, 2026. Perplexity also published pplx-embed-v2-context-9b-preview, a contextual embedding model for document chunks that its card labels a preview whose weights and interface may change. USASI has not tested these models. https://unitedstatesofamericasuperintelligence.com/news/perplexity-releases-pplx-decider-v1-27b/ https://unitedstatesofamericasuperintelligence.com/news/perplexity-releases-pplx-decider-v1-27b/ Tue, 13 Oct 2026 12:00:00 GMT release In October 2026 Perplexity published pplx-decider-v1-27b on Hugging Face, a decision model fine-tuned from Qwen3.8-27B that returns a selected choice with calibrated probabilities for text and image inputs; the weights are ungated and licensed under Apache 2.0. Perplexity's API changelog says its new Decisions API answers questions about text, JSON, or images with probabilities instead of generated text, using pplx-decider-v1-27b at POST /v1/decisions. A later changelog entry says pplx-decider-v1.1-27b replaces v1 as the Decisions API model; its model card says the update uses the same backbone, gets most of its gains from lifting the causal mask and training on more data, and ships as a separate decision head rather than a full-vocabulary language-model head. USASI has not tested these models.