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  "text": "Most language models only read forward.\n\nPerplexity just open-sourced 4 models that read text in both directions.\n\nThey used a technique from image generation to retrain Qwen3 so every word can see every other word in a passage. \n\nThat changes how well a model understands meaning.\n\nThey built four models from this:\n\n1. Two sizes: 0.6B and 4B parameters\n\n2. Two types: standard search embeddings and context-aware embeddings\n\nThe context-aware version is the interesting one. \n\nIt processes an entire document at once, so each small chunk \"knows\" what the full document is about. Standard embeddings treat each chunk in isolation.\n\n> Tops benchmarks for models of similar size\n> Works in multiple languages out of the box\n> MIT licensed, free for commercial use\n\nIf you're building search over large document collections, you can now get document-level understanding without running a massive model. \n\nSmall enough to actually deploy.",
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