CoGR trains LLMs to generate keywords on both the query and item side so generative retrieval drops into existing inverted-index infrastructure

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arXiv 2609.00638 (2026-09-01, 61 HF upvotes) targets a practical blocker in generative retrieval: LLMs are usually confined to query-side augmentation while a conventional retriever still does the matching, because replacing the index is too expensive. CoGR has separate generators emit a compact keyword set for queries and for items, matched directly through an inverted index, so existing keyword retrieval infrastructure keeps working. Training is supervised fine-tuning to align the keyword space, then co-evolving reinforcement learning that alternately optimizes the two generators with GRPO.