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Benchmarked parity is not a universal replacement theorem for Transformers.", "domain": "Efficient sequence modeling" }, "sources": [ { "citation": "RWKV: Reinventing RNNs for the Transformer Era", "url": "https://arxiv.org/abs/2305.13048v2" } ], "statement": "# Peng and colleagues: RWKV parallel training and recurrent sequence inference\n\nUsed receptance-weighted key-value computation to combine parallelizable sequence training with a recurrent decoding state.\n\n## Scientific scope\n\nFixed per-token state cost assumes fixed model dimensions; total sequence work still grows with length. Benchmarked parity is not a universal replacement theorem for Transformers.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nBo Peng; Eric Alcaide; Quentin Anthony; Alon Albalak; Samuel Arcadinho; Stella Biderman; Huanqi Cao; Xin Cheng; Michael Chung; Matteo Grella; Kranthi Kiran GV; Xuzheng He; Haowen Hou; Jiaju Lin; Przemysław Kazienko; Jan Kocoń; Jiaming Kong; Bartłomiej Koptyra; Hayden Lau; Krishna Sri Ipsit Mantri; Ferdinand Mom; Atsushi Saito; Guangyu Song; Xiangru Tang; Bolun Wang; Johan S. Wind; Stanisław Woźniak; Ruichong Zhang; Zhenyuan Zhang; Qihang Zhao; Peng Zhou; Qinghua Zhou; Jian Zhu; Rui-Jie Zhu\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [RWKV: Reinventing RNNs for the Transformer Era](https://arxiv.org/abs/2305.13048v2)\n", "summary": "Used receptance-weighted key-value computation to combine parallelizable sequence training with a recurrent decoding state.", "title": "Peng and colleagues: RWKV parallel training and recurrent sequence inference" }, "record_id": "urn:uuid:a0524db3-d79f-4e2f-a163-e7a08a77dba7", "version": 1, "source_ref": { "record_id": "urn:uuid:a0524db3-d79f-4e2f-a163-e7a08a77dba7", "version": 1, "content_hash": "sha256:4f6ed61f776c4abc2d0f4ddaa26e9bed0e744ae7d53640935c38fcde3f77a708" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:bb0f696a16ed8ba1ac5f31e3683927168e9adb8621eb10a7deedfd23ece9cc3e" }, "target": { "record_id": "urn:uuid:a0524db3-d79f-4e2f-a163-e7a08a77dba7", "version": 1, "content_hash": "sha256:4f6ed61f776c4abc2d0f4ddaa26e9bed0e744ae7d53640935c38fcde3f77a708" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "peng-et-al-rwkv-2023", "entry_id": "peng-et-al-rwkv-2023", "revision": 1, "title": "Peng and colleagues: RWKV parallel training and recurrent sequence inference", "context": "Efficient sequence modeling", "track": "Human-designed research method or resource", "band": "A", "modifier": "-", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Used receptance-weighted key-value computation to combine parallelizable sequence training with a recurrent decoding state.", "scope": "Used receptance-weighted key-value computation to combine parallelizable sequence training with a recurrent decoding state. Fixed per-token state cost assumes fixed model dimensions; total sequence work still grows with length. Benchmarked parity is not a universal replacement theorem for Transformers.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Fixed per-token state cost assumes fixed model dimensions; total sequence work still grows with length. 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