{"as_of":"2026-08-22T02:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a2f1726395c2771cee181d5d044f2b00c9c4cda9dd91a4dda06c1a687f669f4","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T14:11:24.646931Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:42:38.994773Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-11T01:57:50.965515Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"cited_work":{"arxiv_id":"2605.28302","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.28302","snapshot_observed_at":"2026-07-11T01:57:50.965515Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","venue":"cs.LG","work_id":"1903bc95-8a5a-4896-b395-dd6407fea2d1","year":2026},"citing_paper":{"arxiv_id":"2607.05876","last_updated":"2026-07-08T02:47:41Z","snapshot_observed_at":"2026-08-20T02:52:02.866492Z","submitted_at":"2026-07-07T06:11:54Z","title":"Think Before You Grid-Search: Floor-First Triage for LLM Serving","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-08T22:38:12.637901Z"},"links":{"cited_paper":"/paper/2605.28302","citing_paper":"/paper/2607.05876"},"observation_digest":"sha256:1363b7061644c0f52772dd94606ea1d96a9140aa2a66e98c8c861b42aa640b92","observation_id":"b4250cbf-f3e2-4ed9-946d-6dd6324ac619","resolution":{"observed_at":"2026-07-08T22:45:40.117758Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"cited_work":{"arxiv_id":"2605.28302","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.28302","snapshot_observed_at":"2026-07-11T01:57:50.965515Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","venue":"cs.LG","work_id":"1903bc95-8a5a-4896-b395-dd6407fea2d1","year":2026},"citing_paper":{"arxiv_id":"2607.05876","last_updated":"2026-07-08T02:47:41Z","snapshot_observed_at":"2026-08-20T02:52:02.866492Z","submitted_at":"2026-07-07T06:11:54Z","title":"Think Before You Grid-Search: Floor-First Triage for LLM Serving","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-11T01:55:09.658053Z"},"links":{"cited_paper":"/paper/2605.28302","citing_paper":"/paper/2607.05876"},"observation_digest":"sha256:9853c4d81fdfa88d527139e1d3e557ae4494277ebb3b89db46fb1bb8d567a1e9","observation_id":"85f7eb06-7a9b-4bf2-b48e-7ca6860e30a4","resolution":{"observed_at":"2026-07-11T01:57:50.993804Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.28302","snapshot_observed_at":"2026-08-15T14:42:38.994773Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.04502","last_updated":"2026-08-05T06:39:57Z","snapshot_observed_at":"2026-08-18T06:53:03.104505Z","submitted_at":"2026-08-05T06:39:57Z","title":"AFD-Ledger: Deployment Provisioning for Attention--FFN Disaggregation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T14:42:38.994773Z"},"links":{"cited_paper":"/paper/2605.28302","citing_paper":"/paper/2608.04502"},"observation_digest":"sha256:d435dc0df15e8d11bc3394bc26758694d488bff017f76248da3614b88de15625","observation_id":"14580223-7cfd-4f10-8900-4bf0fd3148dc","resolution":{"observed_at":"2026-08-15T14:42:38.994773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2605.28302/citation-record","integrity":"/paper/2605.28302/integrity","json":"/paper/2605.28302/citation-record.json","paper":"/paper/2605.28302"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.16369","last_updated":"2023-08-31T00:03:02Z","snapshot_observed_at":"2026-08-15T04:42:28.752204Z","submitted_at":"2023-08-31T00:03:02Z","title":"SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills","version":1},"cited_work":{"arxiv_id":"2308.16369","doi":"10.48550/arxiv.2308.16369","metadata_source":"pith","pith_arxiv_id":"2308.16369","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills","venue":"cs.LG","work_id":"3dbdd757-ca01-436f-acfd-12ffcd6f64c6","year":2023},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"cited_paper":"/paper/2308.16369","citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:e9e6708df7aad663f08a9f53eeb1bf6c2e4d0e2e97816e65930e95c5dee19e3a","observation_id":"eed0d6e2-6f1f-4ac5-abb2-b116cc224555","resolution":{"observed_at":"2026-06-29T14:13:29.987690Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05465","last_updated":"2024-05-21T05:17:29Z","snapshot_observed_at":"2026-08-17T13:20:29.532179Z","submitted_at":"2024-05-08T23:42:13Z","title":"Vidur: A Large-Scale Simulation Framework For LLM Inference","version":2},"cited_work":{"arxiv_id":"2405.05465","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.05465","snapshot_observed_at":"2026-07-11T01:57:50.732280Z","title":"Vidur: A large-scale simulation framework for llm inference","venue":"cs.LG","work_id":"c6236eea-697f-4c84-8ad3-41279c46d8ee","year":2024},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"cited_paper":"/paper/2405.05465","citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:4ef79c8d23b6ec5970ef3a1b251bf6762f986c8d8e5946111cc9f6fb0c6271c9","observation_id":"4b85e23f-3e5c-49f5-809e-1a56ba95cf1c","resolution":{"observed_at":"2026-06-29T14:13:29.990690Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.09775","last_updated":"2026-04-20T20:54:05Z","snapshot_observed_at":"2026-08-16T16:58:08.603392Z","submitted_at":"2025-04-14T00:29:49Z","title":"MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference","version":6},"cited_work":{"arxiv_id":"2504.09775","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.09775","snapshot_observed_at":"2026-06-29T14:13:29.983886Z","title":"MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference","venue":"cs.AR","work_id":"4e937698-864f-4da3-b21a-23a13ad455a3","year":2025},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"cited_paper":"/paper/2504.09775","citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:b9d504a8c689b682a22470c28ce444ffbeeb1c08d69cc6f222ff9e7a25d928f6","observation_id":"d7d3be51-3dc0-445c-be18-c542eccbef34","resolution":{"observed_at":"2026-06-29T14:13:29.985098Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04434","last_updated":"2024-06-19T06:04:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-07T15:56:43Z","title":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","version":5},"cited_work":{"arxiv_id":"2405.04434","doi":"10.1145/3593013.3594097","metadata_source":"pith","pith_arxiv_id":"2405.04434","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model","venue":"cs.CL","work_id":"1e1df141-cac8-47fd-b068-c4c96e51e331","year":2024},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"cited_paper":"/paper/2405.04434","citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:1af269fb0287511a9c010274d2af580c512fc86fec9f9eddba89c51ff625f0e4","observation_id":"1cf17043-136e-4f42-ad74-5b81a772a514","resolution":{"observed_at":"2026-06-29T14:13:29.977309Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0006.361316","doi":"10.1145/3600006.3613163","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Efficient Memory Management for Large Language Model Serving with PagedAttention , booktitle =","venue":null,"work_id":"1b10f2a9-a178-4d23-97fb-8db2354c7e6c","year":2023},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:e16a9b64cae1caa1f518a9779141c6a2655b5bb31581946f789a135edae40386","observation_id":"ace63582-4a37-4169-a2c8-4a4cad7c4011","resolution":{"observed_at":"2026-06-29T14:13:29.703478Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T14:11:24.646931Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:15e41838d470fa72deee2713916ec6cfa197a8f252bc5c3e6bbd18e8f8117115","observation_id":"5c764205-8eb9-4e84-a64a-2d0bba678963","resolution":{"observed_at":"2026-06-29T14:11:24.646931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T14:11:24.646931Z","title":"[Online; accessed 2025-04-11]","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:cd89a6ee74d84c54c87293e1810683d59ee47ae3c8507a64bed750762527fe8b","observation_id":"c212e841-e076-4b5d-addc-3e3296d96e53","resolution":{"observed_at":"2026-06-29T14:11:24.646931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T14:11:24.646931Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:7ea8a8e55ea2b74e8a2adcff3694abdf49d52c50e5a6b1f115d835c3407975b3","observation_id":"b2be4053-d887-480c-84cb-8bd442695509","resolution":{"observed_at":"2026-06-29T14:11:24.646931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T14:11:24.646931Z","title":"StepMesh: A high-performance, low-latency communication library for attention-ffn disaggregation, 2025.https://github.com/stepfun-ai/StepMesh","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:5be311fb611f359a00cd6fa2b9638a5619af669f5fe17c63cb7825cd5cdd1e97","observation_id":"d0936738-e468-426e-8201-4c02bcb7e291","resolution":{"observed_at":"2026-06-29T14:11:24.646931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.06288","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T01:57:51.045169Z","title":"AIConfigurator: Lightning-fast configuration optimization for multi-framework LLM serv- ing","venue":null,"work_id":"dc082b55-7e02-4f54-b5e4-29a3e8831d83","year":2026},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:4432503c63202fd51de35b9208a8febd4cfa9992dd8d0f50ad8c0ef02d0ac8f2","observation_id":"607b2d92-25c1-41b5-93a1-caf608414d34","resolution":{"observed_at":"2026-06-29T14:13:29.985280Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.02263","last_updated":"2025-07-26T15:29:10Z","snapshot_observed_at":"2026-08-20T22:47:04.716888Z","submitted_at":"2025-04-03T04:20:44Z","title":"MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism","version":4},"cited_work":{"arxiv_id":"2504.02263","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.02263","snapshot_observed_at":"2026-07-11T01:57:51.799355Z","title":"In19th USENIX Symposium on Operating Systems Design and Implementation (OSDI 25), pp","venue":"cs.DC","work_id":"26883f2e-5380-48d4-bfe3-a68b38b39a08","year":2025},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"cited_paper":"/paper/2504.02263","citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:a60df51e4e60f98a72af64c4e2f681cb8bccac43133b55c6e79a350470c5208f","observation_id":"b3685467-e4ef-4344-996b-a59f74b5c1de","resolution":{"observed_at":"2026-06-29T14:13:29.982788Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T14:11:24.646931Z","title":"The prototype is used for correctness and implementation guidance, while the cluster-scale results use measured backend costs and AstraSim communication modeling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:7520f814a2f2e14864e70aca09433d8ab059e85d1ddd531458009f7469212c89","observation_id":"19344009-4e15-4d56-b87a-6b608318d107","resolution":{"observed_at":"2026-06-29T14:11:24.646931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T14:11:24.646931Z","title":"In (a), KV transfers cross the node boundary; in (b), each prefill–decode pair shares a node","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T14:11:24.646931Z"},"links":{"citing_paper":"/paper/2605.28302"},"observation_digest":"sha256:58a2211c6d2093fe935ea19b56301a17311094933596138e2610b137727dcdde","observation_id":"49e46e87-8c24-4c0e-b9d4-63cda2b8e306","resolution":{"observed_at":"2026-06-29T14:11:24.646931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.28302","last_updated":"2026-05-27T10:55:57Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T11:39:33.881533Z","submitted_at":"2026-05-27T10:55:57Z","title":"How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":6,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":13},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 3 inbound Pith citation observations for arXiv:2605.28302."}