# Q2D-Chunk

Perplexity released the pplx-embed-v2-context family, which distills graded chunk-level relevance from a query-aware context compression model instead of gold-chunk labels, so training targets no longer depend on how a document is chunked. The 9B model beats the strongest baseline, voyage-context-4, by 14.4 points at Answer@10 on context-bench and leads on Q2D-Chunk and ConTEB; the 0.6B model shares the 9B embedding space, so a small model can query an index built with the large one.

Perplexity released the pplx-embed-v2-context family, which distills graded chunk-level relevance from a query-aware context compression model instead of gold-chunk labels, so training targets no longer depend on how a document is chunked. The 9B model beats the strongest baseline, voyage-context-4, by 14.4 points at Answer@10 on context-bench and leads on Q2D-Chunk and ConTEB; the 0.6B model shares the 9B embedding space, so a small model can query an index built with the large one.