Updated
Updated
AIIn agentic search, a GPT-OSS-120B agent using the 9B model answers 64.0% of BrowseComp+ questions correctly, 4.9 points ahead of the next ColBERT model. It also uses fewer searches than any baseline.
AIOn the Q2D-Web benchmark of about 70,000 production-derived search queries over 190M web documents, Perplexity's 9B and 0.6B models reach 74.8% and 73.6% Recall@1000. Both outperform nemotron-embed-8b, which scores 69.3%.
AIPerplexity's 0.6B and 9B models are both distilled token by token from one 18B teacher, so they share a single embedding space. A corpus indexed with the 9B model can be searched using 0.6B queries, raising ViDoRe v3 from 62.3% to 63.5% with no added query cost.
AIPerplexity's pplx-embed-v2-late retains a 128-dimensional vector for each token rather than compressing a document into one vector. It scores matches with MaxSim, pairing each query token with its closest document token, which the post presents as preserving detail in long or visually dense pages.
AIA survey of 300 data, AI, and technology executives found only 34% of organizations' agentic AI projects reach production, with legacy systems, security concerns, and missing knowledge context as main obstacles. Production leaders, who advance 61% of projects beyond pilot, show stronger semantic knowledge capabilities. Most firms plan to invest in retrieval pipelines, AI-ready APIs, retrieval-augmented generation, and knowledge graphs.
AIPerplexity says context-bench, a context-aware retrieval benchmark created and privately held by turbopuffer, has 2,099 queries and 38,894 documents. Perplexity submitted it for blind evaluation, with queries and capabilities inspired by turbopuffer customer conversations.
AIPerplexity overcomes gold-chunk supervision limits by distilling relevance from its query-aware context compression model. The model scores every document token against the query, and those scores, aggregated into chunk-level targets, train the embedder to retrieve answer and supporting chunks.
AIRetrieval systems that split long documents into chunks lose surrounding context. Contextual embedding models address this by encoding the entire document once and pooling chunk vectors afterward. They are usually trained using one gold chunk per query.