{"as_of":"2026-08-12T13:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:489c9c75558f78811d7dce53df14709765adec4e1526e6f9ea867c1089631b2e","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T22:21:54.402884Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:07:40.140731Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T12:39:49.219978Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-08-06T22:04:34.964920Z","title":"arXiv:2502.04563 [cs.LG] https://arxiv.org/abs/2502.04563 Dan Hendrycks and Kevin Gimpel","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.22818","last_updated":"2025-06-28T08:42:01Z","snapshot_observed_at":"2026-08-10T15:44:48.511903Z","submitted_at":"2025-06-28T08:42:01Z","title":"TriADA: Massively Parallel Trilinear Matrix-by-Tensor Multiply-Add Algorithm and Device Architecture for the Acceleration of 3D Discrete Transformations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:04:34.964920Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2506.22818"},"observation_digest":"sha256:fec9edf768354828440e5fbe06b428e1e076d02047ff408a56d1311fb24d1601","observation_id":"ff374140-96f0-4959-b4d6-62e81ed3eb2e","resolution":{"observed_at":"2026-08-06T22:04:34.964920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-08-07T05:07:40.140731Z","title":"WaferLLM: A wafer-scale LLM inference system,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.00004","last_updated":"2025-07-10T17:08:40Z","snapshot_observed_at":"2026-08-10T12:28:57.343429Z","submitted_at":"2025-06-10T14:47:48Z","title":"A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search","version":2},"reference_index":156,"source":"pdf_text","source_observed_at":"2026-08-07T05:07:40.140731Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2507.00004"},"observation_digest":"sha256:dfce43328da2b7bc2f5f4b249764e214f1a31e2c49796b64d184c692644fa1d1","observation_id":"286877e8-cfc0-4a97-9286-4b127b755e8c","resolution":{"observed_at":"2026-08-07T05:07:40.140731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-08-06T17:13:36.837844Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.11506","last_updated":"2025-09-08T14:03:34Z","snapshot_observed_at":"2026-08-08T23:13:35.142650Z","submitted_at":"2025-07-15T17:21:31Z","title":"ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:13:36.837844Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2507.11506"},"observation_digest":"sha256:17e061dbee3430c005e822283351fb014f64d0c8c8325dafdff1fbf2a9cb4806","observation_id":"e4e01058-9d02-427d-bac0-7691930b8048","resolution":{"observed_at":"2026-08-06T17:13:36.837844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-08-05T16:18:06.814541Z","title":"Waferllm: A wafer-scale LLM inference system","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.18850","last_updated":"2025-08-26T09:29:23Z","snapshot_observed_at":"2026-08-08T23:12:33.597472Z","submitted_at":"2025-08-26T09:29:23Z","title":"ClusterFusion: Expanding Operator Fusion Scope for LLM Inference via Cluster-Level Collective Primitive","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T16:18:06.814541Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2508.18850"},"observation_digest":"sha256:858795436d71c25f6bb3b440ac83d92e20af9b363bc1e45a4e2d624bd74174eb","observation_id":"11a428dd-11fb-4344-855c-5420538801e3","resolution":{"observed_at":"2026-08-05T16:18:06.814541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":"2502.04563","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-07-04T12:39:49.219978Z","title":"arXiv preprint arXiv:2502.04563 (2025)","venue":null,"work_id":"7159ee61-6219-4b7e-892c-1ef836220b5b","year":2025},"citing_paper":{"arxiv_id":"2604.24203","last_updated":"2026-04-27T09:07:15Z","snapshot_observed_at":"2026-08-12T13:30:59.237572Z","submitted_at":"2026-04-27T09:07:15Z","title":"Agentic Witnessing: Pragmatic and Scalable TEE-Enabled Privacy-Preserving Auditing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-08T02:57:11.715370Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2604.24203"},"observation_digest":"sha256:0ad74bdf97a72e8bb1dd94d5bdeda3a548b335ae5b2c01aca441d3f4a1e9b086","observation_id":"3a570285-7e65-4e33-9fd7-962efce7241f","resolution":{"observed_at":"2026-05-11T22:21:31.806071Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":"2502.04563","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-07-04T12:39:49.219978Z","title":"arXiv preprint arXiv:2502.04563 (2025)","venue":null,"work_id":"7159ee61-6219-4b7e-892c-1ef836220b5b","year":2025},"citing_paper":{"arxiv_id":"2606.22968","last_updated":"2026-06-22T07:51:15Z","snapshot_observed_at":"2026-08-02T07:47:32.272471Z","submitted_at":"2026-06-22T07:51:15Z","title":"MOCAP: Wafer-Scale-Chip-Oriented Memory-Orchestrated Chunked Pipelining Framework for Prefill-Only LLM Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T06:26:59.984686Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2606.22968"},"observation_digest":"sha256:de13e9545d0771b4c6dd182342138810395183cd304a970cdc05d203d88b3018","observation_id":"602676aa-5bc4-4c26-8bcc-5f0d44667e6a","resolution":{"observed_at":"2026-07-04T12:39:49.221490Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":"2502.04563","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-07-04T12:39:49.219978Z","title":"arXiv preprint arXiv:2502.04563 (2025)","venue":null,"work_id":"7159ee61-6219-4b7e-892c-1ef836220b5b","year":2025},"citing_paper":{"arxiv_id":"2606.28754","last_updated":"2026-07-06T08:22:51Z","snapshot_observed_at":"2026-07-12T11:19:43.619026Z","submitted_at":"2026-06-27T06:02:23Z","title":"SHIFT: Dynamic Compute Relocation Framework for Communication-Aware Chiplet-Based Systems","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T08:57:23.580353Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2606.28754"},"observation_digest":"sha256:8db5a67f6f80b2530556f31de7db3e2473884ff2fbf72362fb26cb4941a421ff","observation_id":"abcb77bf-4119-43d9-8063-6be776ac7911","resolution":{"observed_at":"2026-06-30T09:04:32.876266Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04563","snapshot_observed_at":"2026-07-12T11:19:44.400939Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.28754","last_updated":"2026-07-06T08:22:51Z","snapshot_observed_at":"2026-07-12T11:19:43.619026Z","submitted_at":"2026-06-27T06:02:23Z","title":"SHIFT: Dynamic Compute Relocation Framework for Communication-Aware Chiplet-Based Systems","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-12T11:19:44.400939Z"},"links":{"cited_paper":"/paper/2502.04563","citing_paper":"/paper/2606.28754"},"observation_digest":"sha256:d51f752f3618bd5475cf5f12f6ee0767225c05762935ce84f1683026dd737222","observation_id":"e9cdecb2-3a00-45fa-bb60-e2ba77924a45","resolution":{"observed_at":"2026-07-12T11:19:44.400939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.04563/citation-record","integrity":"/paper/2502.04563/integrity","json":"/paper/2502.04563/citation-record.json","paper":"/paper/2502.04563"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.267981Z","title":"Abadi, P","venue":null,"work_id":"509d8b79-9236-489b-b9c9-c704101b3256","year":2016},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.140958Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:329a70b9f79512fff7a5b23bb01f9e2f39562715ceee38251c39bb9984ea47ac","observation_id":"25d8dd53-25ba-43ec-a7d4-be3170ff06a2","resolution":{"observed_at":"2026-08-08T22:21:55.272941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.252540Z","title":"AMD optimizes EPYC mem- ory with NUMA","venue":null,"work_id":"c0bbb10c-aa61-4c41-a61d-0a5f00001e9a","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.146156Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:00ea1e812a0e493917578ab15994adfd193cf610fe511cf12f2bab6cd0c6c36f","observation_id":"c0470d58-a380-4d5e-8ee8-315f51b48d1b","resolution":{"observed_at":"2026-08-08T22:21:55.257422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-08T22:21:54.150702Z","title":"Gqa: Training generalized multi-query transformer models from multi-head checkpoints, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.150702Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:db188371441a19e7b74499e564d9d89c30432fa44db48c5246d057cec217c38c","observation_id":"d06c896b-3398-4e5e-a8e7-746a266e8586","resolution":{"observed_at":"2026-08-08T22:21:54.150702Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.227109Z","title":"AMD XDNA adaptive architecture, 2023","venue":null,"work_id":"aa1c71d2-463d-4b81-837f-378551cf599b","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.155440Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:bd0263320fa1f2b3e41c6e71b0579a76ee8226c4b1d63da76a675fc3a98ed8a0","observation_id":"7eff9cfd-e659-4b94-8b1e-4066cd92f195","resolution":{"observed_at":"2026-08-08T22:21:55.231836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-08T22:21:54.160164Z","title":"Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Nee- lakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.160164Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:4b51ce65eb08cc147cb657db8613301a3296e4a3ee3cc96b8aa9d62117211f44","observation_id":"742007e0-5522-46e4-996e-3ce97166d261","resolution":{"observed_at":"2026-08-08T22:21:54.160164Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.212220Z","title":"A cellular computer to implement the kalman filter algorithm","venue":null,"work_id":"a0d535bf-0fb2-4856-98a7-7e2c7e10fe73","year":1969},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.165762Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:cd8a4fc3875d083212bc916a5364bc0351767b8ec6fb23516a92d7c9c4aab0e7","observation_id":"4a586aa1-3995-4491-9c92-c6b7a35089e3","resolution":{"observed_at":"2026-08-08T22:21:55.217042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.197680Z","title":"GEMM with collective operations","venue":null,"work_id":"820d6f6a-cc63-406f-946d-d22653cb3e4b","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.170940Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:2ffbab69638853a34df8745f3d1bc049f7ed5c20cd2ea74c145060df28350c17","observation_id":"1f87e73b-9c25-4847-a0a0-88c36b568547","resolution":{"observed_at":"2026-08-08T22:21:55.202346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.184435Z","title":"100× defect tolerance: How cerebras solved the yield problem, 2022","venue":null,"work_id":"fa1129c0-94e8-48b9-b1f3-f2bf9909fa13","year":2022},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.175371Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:8bf452a490b10291839ec6490f1ed4d70bce3078186da50205596dee349b4904","observation_id":"7fcae573-c9b7-48d5-986a-3eb957f46366","resolution":{"observed_at":"2026-08-08T22:21:55.188480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.171049Z","title":"Benchmark GEMV collectives, 2023","venue":null,"work_id":"8f842ebe-428f-4093-bcb3-acc0d043fba3","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.179810Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:4ac2b59b6bde597a6eef14164ace1978ba1750e56ca47550bb9d5eaf7a8bdca5","observation_id":"dd086f67-45ca-4832-863c-d548b1c0a75c","resolution":{"observed_at":"2026-08-08T22:21:55.175515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.157906Z","title":"Chen et al","venue":null,"work_id":"bb2aca5c-d2e4-480a-b0f2-c9dc9006ffd8","year":2018},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.184170Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:483c898e99b56b2b2299f8b73b4f5f7ce9de88c41714da08f8cba21adcfeb83e","observation_id":"93a1ac70-a6a1-4db0-bd7f-c15c37182fbd","resolution":{"observed_at":"2026-08-08T22:21:55.162009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.144660Z","title":"Dongarra, and David W","venue":null,"work_id":"5268885f-8d8f-43f4-99c2-b9a0acf32633","year":1995},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.188459Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:918c36f707af1c95c3c3d93d35b4dec58a7e15131575d92652caf0df2c03b004","observation_id":"20925e04-0168-4aeb-b218-7342da6405d6","resolution":{"observed_at":"2026-08-08T22:21:55.148644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.131149Z","title":"FlashAttention-2: Faster attention with bet- ter parallelism and work partitioning","venue":null,"work_id":"2ace100a-f88a-4e68-ba71-7722c86873fa","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.193322Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:9dfdac85b4ab9181d4175a490caa2ad792a0d83e421932aaaf82b352a6b4654a","observation_id":"ce62af5f-36c3-466d-8322-98213bff1d98","resolution":{"observed_at":"2026-08-08T22:21:55.135307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-08T22:21:54.197682Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.197682Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:a95005e383961049cd3e640ad16e6b3461dca5d192efb8bf95541903634d8ebb","observation_id":"e1116a39-a746-4da6-9d10-71a02bc6e9c8","resolution":{"observed_at":"2026-08-08T22:21:54.197682Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.118029Z","title":"SambaNova’s new AI chip and the quest for efficiency, 2023","venue":null,"work_id":"f5008fe9-37af-4c0c-8189-526ebfeefea2","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.202748Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:b688a13ce10d416a6607670105ca363532578c435cd0800f651b1a6f535e3233","observation_id":"9c896dff-558c-4cfc-887f-76577c15e453","resolution":{"observed_at":"2026-08-08T22:21:55.122223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01282","last_updated":"2024-01-05T12:41:13Z","snapshot_observed_at":"2026-08-10T11:43:00.160557Z","submitted_at":"2023-11-02T14:57:03Z","title":"FlashDecoding++: Faster Large Language Model Inference on GPUs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01282","snapshot_observed_at":"2026-08-08T22:21:54.206928Z","title":"Flashdecoding++: Faster large language model infer- ence on GPUs","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.206928Z"},"links":{"cited_paper":"/paper/2311.01282","citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:9e239eb02b0f7eda1deebd704421393b338a5464729f5ca1ad04f030b46c0f54","observation_id":"ddec9caf-42b6-4a52-9843-c3892b099d91","resolution":{"observed_at":"2026-08-08T22:21:54.206928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16720","last_updated":"2026-04-30T02:46:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-21T18:04:31Z","title":"OpenAI o1 System Card","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16720","snapshot_observed_at":"2026-08-08T22:21:54.211615Z","title":"Openai o1 system card","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.211615Z"},"links":{"cited_paper":"/paper/2412.16720","citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:ec0fb971dd1bb7feb7d10d757d30199dab2f76e3fa717a22fda3464d645d05f5","observation_id":"d41dc756-1749-428b-95a1-e60654c83af9","resolution":{"observed_at":"2026-08-08T22:21:54.211615Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.104410Z","title":"Tensor processing units for machine learning: An introduction","venue":null,"work_id":"451aa3fd-db26-41fa-9cbf-d9f62c9edbbe","year":2017},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.216334Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:da8dfcca2116e28c02da3c2b27aedd1df5cdc6a47497a969097e226c4bffe192","observation_id":"daba1a71-d00e-4c44-902c-76a3a09321f7","resolution":{"observed_at":"2026-08-08T22:21:55.108718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.089713Z","title":"Tenstorrent Blackhole and Metalium for standalone AI processing, 2024","venue":null,"work_id":"d11092ac-e9cb-4833-8da6-58f26479f8bf","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.220241Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:543339e56a883ebc284e436835fafd5a102f832deae4ccba2d9d66008dcf6d44","observation_id":"cf3f1b9b-1e0a-4e8d-aef5-7ba1c7500d90","resolution":{"observed_at":"2026-08-08T22:21:55.094479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.075215Z","title":"Chiplet/interposer co-design for power delivery network optimization in heterogeneous 2.5-d ICs","venue":null,"work_id":"b6171876-3adb-4f60-b3d4-c84b59e9e4cf","year":2021},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.224161Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:dfdbbb0ee303ebac7312518979a86c6c896a60bf1a9acf6fa0d539a6d56a6d57","observation_id":"1b9d7094-e21e-4fca-9992-9ed8118786d5","resolution":{"observed_at":"2026-08-08T22:21:55.080134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.060431Z","title":"Efficient memory man- agement for large language model serving with Page- dAttention","venue":null,"work_id":"37443001-d8c9-4c0e-9b4a-c1eb0f6b9bc6","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.228140Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:e0c414010efe9cbb3d084e25078ca4186ba95e2474299d4608c71fd26410cf40","observation_id":"f4c3aa38-ec3c-462c-9c3c-285aeccaf0f1","resolution":{"observed_at":"2026-08-08T22:21:55.065697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.045678Z","title":"TSMC bets big on advanced packaging,","venue":null,"work_id":"e42a1a48-9b58-4a36-8461-97a54ed15ac9","year":null},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.232084Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:52fab92728dd4466923fd15b98473120e4a13b93e1dc2baf8b6c033c73d5fb09","observation_id":"2a8a7eed-8ec5-4575-8258-75f879ef2283","resolution":{"observed_at":"2026-08-08T22:21:55.050424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.015852Z","title":"ReSA: Reconfig- urable systolic array for multiple tiny DNN tensors","venue":null,"work_id":"c910e81a-08d2-42c1-8274-ed0cc8d67909","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.241193Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:efa546b0c32e5b533dd51e93880087f9513db596dfae67bd6aef4cb9dee1e7ff","observation_id":"15ae97b3-9de6-4bc8-b5bc-f9c843ecce6b","resolution":{"observed_at":"2026-08-08T22:21:55.020742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.001732Z","title":"Gonzalez, and Ion Stoica","venue":null,"work_id":"af5944a4-2b83-40f3-a77b-0bc558f455e9","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.245086Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:e85a8eb2747c12d17540051abe9bc00c8bb73c92009d709006f9da034cf2bec7","observation_id":"938e0880-b2d4-4051-8d0a-1cde62124697","resolution":{"observed_at":"2026-08-08T22:21:55.006231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.987495Z","title":"Cerebras architecture deep dive: First look in- side the hardware/software co-design for deep learning","venue":null,"work_id":"e3faa8a1-eb92-47ad-aab1-b8164a8683b8","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.248918Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:0431d553a06438ee82275d8ae6bec2fc96b1d69a5602fe6e013b3cf954ccf4c0","observation_id":"a3b6c9a1-8982-4f73-afb5-f040108f4d58","resolution":{"observed_at":"2026-08-08T22:21:54.992077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.973152Z","title":"Scaling deep learn- ing computation over the inter-core connected intelli- gence processor with T10","venue":null,"work_id":"982c1981-0822-4b2f-becb-d081fed09312","year":null},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.253125Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:5c470b3ffe3faf07bd288f28b6eaceee0d604482c8a16eaa195b75663d8c506e","observation_id":"3404f0b1-556f-4f6e-a040-5a5a0d357fc6","resolution":{"observed_at":"2026-08-08T22:21:54.977486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.959795Z","title":"TENET: A framework for modeling tensor dataflow based on relation-centric notation","venue":null,"work_id":"30dd3300-6e61-4879-ab14-43f78c34d745","year":2021},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.257355Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:86f8950df3060a461586e4ff485b4399e74fa322750ad44a116e2d81e79227c9","observation_id":"8c965d35-ad95-44c5-a24a-96e795d5bf0c","resolution":{"observed_at":"2026-08-08T22:21:54.963893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.945416Z","title":"Near- optimal wafer-scale reduce","venue":null,"work_id":"af37e503-acf7-4ccd-a565-ac158465d593","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.261817Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:f96add6969a5e2850d6377b715143276727947489b8f3a200be6fae0b14842c4","observation_id":"e05af19e-01ff-4c0e-9d1f-06c1182f0435","resolution":{"observed_at":"2026-08-08T22:21:54.950407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.931406Z","title":"Rammer: Enabling holistic deep learning compiler optimizations with rTasks","venue":null,"work_id":"91d3488d-a1b4-4045-a01b-6f004ac37d9b","year":2020},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.265936Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:6b0397fb9219006592939c6c77321390c88b90b793ed5e3c2ac81f92519cb4bc","observation_id":"b5aac418-d9df-4089-8594-2be3ba6f090b","resolution":{"observed_at":"2026-08-08T22:21:54.935653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.916738Z","title":"An electrical-thermal co-simulation model of chiplet hetero- geneous integration systems","venue":null,"work_id":"0650155b-f561-4919-b342-0a23f58d549b","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.270098Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:b67eb9791b8d714cceb9c4787992fcc009d7c6c439d04c742259abc96bbaeb21","observation_id":"bb14a3af-ccb0-4ffa-acc8-ee16d3ecd3ea","resolution":{"observed_at":"2026-08-08T22:21:54.921161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.902474Z","title":"Introducing MTIA: Meta’s next-generation training and inference accelerator for AI, 2024","venue":null,"work_id":"8763c3ea-dcdc-4eb0-b246-9f4411c5a2d7","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.274213Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:82b81482ff96a1f7c872482fea3fdef04d989fb47dabdbbc61e07fdcb28be778","observation_id":"0101c665-bc87-4693-9697-f61b2b2f69d5","resolution":{"observed_at":"2026-08-08T22:21:54.906816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.888919Z","title":"Azure Maia: For the era of AI from silicon to software to systems, 2023","venue":null,"work_id":"823a1c0a-5e69-4b80-89b3-9344b39c0408","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.278152Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:919b8220051026a2b5ec1f77d6a844051d48939ba2097ca2b7c812005ff0db17","observation_id":"20579df0-e4f2-4d9c-846a-2e7b51bce463","resolution":{"observed_at":"2026-08-08T22:21:54.893136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.874035Z","title":"Efficient large-scale language model training on GPU clusters using Megatron-LM","venue":null,"work_id":"238585d0-fc92-43de-9757-ab1f044d39b8","year":2021},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.282071Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:433abefb5ead7358b4bbe304d7ff664501ae9349748386003c97fffda002fe87","observation_id":"3c4cb968-8a7f-4070-aeb5-4438eb11bcdf","resolution":{"observed_at":"2026-08-08T22:21:54.878875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.858918Z","title":"Openai o3 and o4-mini system card","venue":null,"work_id":"e2d1742f-2e02-43fe-bb2d-6fc385521a61","year":null},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.286166Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:b0eb0e0a76b8f8f7de30297c5b140126e62a5d3c3327114e455a6caaa4053ee9","observation_id":"d480844b-3c4b-439b-b772-cc2e5c572f31","resolution":{"observed_at":"2026-08-08T22:21:54.863555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.835461Z","title":"Paszke, S","venue":null,"work_id":"95b433eb-c2be-4ba9-803e-e58c42da01d4","year":2017},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.294450Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:7c995950db54aa11beec1d7bda58f8c0c1b273e353be5b9a90ff950618caade4","observation_id":"90057448-1fab-4adf-8a3b-16a570348c21","resolution":{"observed_at":"2026-08-08T22:21:54.840014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.820646Z","title":"Efficiently scal- ing transformer inference","venue":null,"work_id":"ed88bf72-d0f4-484e-97a8-b5926e2ba1a7","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.298804Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:f13cd1fdff4499572f1fd313891713f2effd4136933ec7a81323f25df44a9c41","observation_id":"d81b6e30-9f5d-41ee-80a0-c32d3033cc86","resolution":{"observed_at":"2026-08-08T22:21:54.825719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.806657Z","title":"Rock et al","venue":null,"work_id":"b0dd19bc-efae-48f8-a345-09877e31054c","year":2017},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.303421Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:195e98fe436efcbf8dc484dd8facf9bf4285ee30c5dc7e2eff5335be132d9ce5","observation_id":"d04338a6-1bb9-4568-81d7-ed81316bc1fb","resolution":{"observed_at":"2026-08-08T22:21:54.811162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.791969Z","title":"Welder: Scheduling deep learning memory access via tile-graph","venue":null,"work_id":"7be09a0b-00ca-46f3-81b0-f391c9007cc9","year":2023},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.307797Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:3770aca1dccde8d857fd5341648922e39e6d04f1770a190d30f0a2faae51b8fa","observation_id":"74b22d8f-7f75-4219-a107-04924cb34d4d","resolution":{"observed_at":"2026-08-08T22:21:54.796785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.777811Z","title":"Souri, Kaustav Banerjee, Amit Mehrotra, and Krishna C","venue":null,"work_id":"233ab8fa-8ee1-466e-b636-75e19b00ac77","year":2000},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.312229Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:36f5cfe82950210cc64e529945089b2422ff5dd323691305b7f1d721db9ce3fc","observation_id":"b10a1373-0988-4f0c-ae07-fc0fc98692fb","resolution":{"observed_at":"2026-08-08T22:21:54.782502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.763630Z","title":"Cerebras and g42 break ground on condor galaxy 3, an 8 exaflops ai supercom- puter","venue":null,"work_id":"76b175ca-9783-4efb-be98-502f407c1902","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.316773Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:e77044509fe24045cb4e555e017b47572cc0d2e93ba9e92ba122afce17f7fb8a","observation_id":"30ced605-e430-4616-8c8c-9d8435161f23","resolution":{"observed_at":"2026-08-08T22:21:54.768221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.750459Z","title":"Cerebras powers perplex- ity sonar with industry’s fastest ai inference","venue":null,"work_id":"7d466302-48ed-4b7b-b1ba-6ec47c7f688e","year":2025},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.321212Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:ff2b4584d487bf528218a1d8adc5b09d45039898f6c5d0db18d1bbda5fba64aa","observation_id":"422088e2-f153-4688-a943-b462f2629ee8","resolution":{"observed_at":"2026-08-08T22:21:54.754599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.737708Z","title":"DOJO: The microarchitecture of Tesla’s exa-scale com- puter","venue":null,"work_id":"1aa8f1b6-2f10-447c-8a3e-c78d9e63fc8e","year":2022},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.325648Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:db5138e8d2ef4bee9c2bc5e934f94b59613e6288a340e5ce614973b14dc0283d","observation_id":"b8efa9c7-ba37-4cdb-aa11-b3ef4eec0c36","resolution":{"observed_at":"2026-08-08T22:21:54.741889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.724491Z","title":null,"venue":null,"work_id":"3dd584a1-8e85-40c0-82d1-914aef0316e6","year":1997},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.330382Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:e69f05f4db1abee77e49fd2a2d98170d272363f07fccf905f1fa74da03b1a0b5","observation_id":"e17cdad2-50ef-4d0d-90fa-2bbf750dc6d8","resolution":{"observed_at":"2026-08-08T22:21:54.728796Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.711371Z","title":"Gomez, Łukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":"c428f6a0-d16c-48e3-b9e9-74a04d25e32d","year":2017},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.334809Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:95dcd5d3bfd9d40b2f9f8b8bb06f01c46c269edef4536206be21a42390cad15d","observation_id":"40c3d0d4-f478-450c-9b99-612ea00643f7","resolution":{"observed_at":"2026-08-08T22:21:54.715403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.698632Z","title":"Cerebras brings instant inference to mistral le chat","venue":null,"work_id":"5381f887-5f44-46a8-a227-34a1d2b9a863","year":2025},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.339350Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:f53481266f35b7d940aca30d7d9570b06e916180806fbbd4ac32dc5a75b3dd51","observation_id":"742b4138-2316-460a-b01c-dd851f2b601b","resolution":{"observed_at":"2026-08-08T22:21:54.702790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.685596Z","title":"Ladder: Enabling efficient low- precision deep learning computing through hardware- aware tensor transformation","venue":null,"work_id":"b7ef0f9a-6cfd-443e-b92b-c3b7b7760114","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.343841Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:d440e24648a0e96dc3c0a2d76279ffaa9e922f7bcb2c267f4690f19717c794b3","observation_id":"07cfb9f5-a679-4ea9-b980-1deced62566d","resolution":{"observed_at":"2026-08-08T22:21:54.689708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.672479Z","title":"Application defined on-chip networks for heteroge- neous chiplets: An implementation perspective","venue":null,"work_id":"9e0c46f8-3898-41d6-89ca-12bf4baf68d4","year":2022},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.348251Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:f1dcfd1c9ae7875a335c4013d17c9077d290f6d3c3a4e2ae7ffebaf47d930843","observation_id":"af81a517-e45c-4079-aafd-53f9fb64d5d9","resolution":{"observed_at":"2026-08-08T22:21:54.677137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.658024Z","title":"Static random-access memory,","venue":null,"work_id":"6f88264e-0d26-405b-af57-253445b0dece","year":null},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.352727Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:7eee470b23fe3d68290b37f308571f14b7841112279364e0ebbada01d10169b7","observation_id":"379300a7-5c26-4a55-9fc8-46e539135281","resolution":{"observed_at":"2026-08-08T22:21:54.663171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.628661Z","title":"Wafer-scale integration, 2024","venue":null,"work_id":"3e90dd96-266d-4978-88dc-d7e3ca284f77","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.361889Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:6dcebba36dcdb4144aee36eae81db70f4722fe4b4a462029aaba162b2406721e","observation_id":"602e7d7c-5fc5-4814-8bed-ef523d2f193b","resolution":{"observed_at":"2026-08-08T22:21:54.633328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.613719Z","title":"LoongServe: Efficiently serving long-context large language models with elas- tic sequence parallelism","venue":null,"work_id":"89697753-55b4-4003-a016-1c18f6ec63ee","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.366455Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:1e16c1d67be9c3cc2aed2db22a6ffa00a0f3c3a4e290838bfa5fac85a8684738","observation_id":"95bd58e5-84c1-4e3c-97f3-311bf62a7bb4","resolution":{"observed_at":"2026-08-08T22:21:54.618819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.598776Z","title":"DIS- TAL: the distributed tensor algebra compiler","venue":null,"work_id":"091fe972-ed14-4292-9bb9-8a83627bcb9f","year":2022},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.371071Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:7f0be6f03c27f35b04603f0e79163aec172754ce961a5e434a5969f13b63beff","observation_id":"9886b644-2fa8-4fa8-8684-38addff6a143","resolution":{"observed_at":"2026-08-08T22:21:54.603662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.583467Z","title":"Zhao et al","venue":null,"work_id":"5b942459-0ec8-431b-a664-653203d8f6a3","year":2020},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.375549Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:9bf2df3d1b6b73612e1629cd85dcda3a5695daf0e684592d452f7e6e12872846","observation_id":"65ffef5b-a088-48a2-8594-b09bd93fdf4b","resolution":{"observed_at":"2026-08-08T22:21:54.588499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.566996Z","title":"Alpa: Automating inter-and intra-operator parallelism for dis- tributed deep learning","venue":null,"work_id":"5673927a-a3cb-4535-addd-0ca4ce6c1fc7","year":2022},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.380046Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:d1a7d6a0e6a80ab6b1b6fc75f93dd7a0c0800145afbe41897e5dae3d073c4810","observation_id":"236a53b9-f82b-4e9c-808d-cdf3dd650f2c","resolution":{"observed_at":"2026-08-08T22:21:54.571727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.552013Z","title":"Sglang: Efficient execution of structured language model programs","venue":null,"work_id":"f8c2d99f-402a-4d3f-987f-d7c43c0370f6","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.384445Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:330fc8d2cd0bd87a7cfdf6352f62accef22152c2e2c7ca3f7ddc055a15124ae8","observation_id":"f80f57b5-2bf4-4127-b992-3ad075767420","resolution":{"observed_at":"2026-08-08T22:21:54.556246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.538721Z","title":"FlexTensor: An automatic schedule exploration and optimization framework for tensor com- putation on heterogeneous system","venue":null,"work_id":"e3922d3a-fd29-4c3f-9f2c-b07bae974851","year":2020},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.389298Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:28431c77c658f8c8219bc08286139c02989e791de89a195094750ba203b77359","observation_id":"b6798210-4e09-4008-bd73-09b5bee2e050","resolution":{"observed_at":"2026-08-08T22:21:54.542960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.524841Z","title":"Dist- Serve: Disaggregating prefill and decoding for goodput- optimized large language model serving","venue":null,"work_id":"96329d1b-4226-4c56-b4a1-f0a4c1f48ad7","year":null},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.393963Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:fb6563da0af87a6fb2b4fd4a11c8bfa38acc57535484f62ae25b9991247f03b0","observation_id":"176952f1-a9c6-49f3-a494-d069cd3feace","resolution":{"observed_at":"2026-08-08T22:21:54.529170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.511143Z","title":"Exploring TensorRT to improve real-time inference for deep learning","venue":null,"work_id":"47b0cbf5-2690-45b0-8c74-054806c73780","year":2022},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.398542Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:46c4be3e473d2b634762be10e4fcd5779747b08136279145cf67c5b2fe07a63d","observation_id":"030b42b6-6bc5-488c-b103-2c12a9405591","resolution":{"observed_at":"2026-08-08T22:21:54.515207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.494537Z","title":"ROLLER: Fast and efficient tensor compilation for deep learning","venue":null,"work_id":"802c7c69-6cf3-4ee9-bfa7-698bfff5b972","year":2022},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.402884Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:e5847c936a3a70e12f3cbd0be66de5bfc948a538727cbd443bc5b05e9c63a320","observation_id":"7f35b1d5-92db-43a3-bc23-f1fc7adcb85b","resolution":{"observed_at":"2026-08-08T22:21:54.501039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:55.031172Z","title":null,"venue":null,"work_id":"568c8223-89fc-4b8a-b5a2-333442bf5ead","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.237298Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:df4a96bec3e94136382b9e6a6f7c3e19ebce1e1a938034bd4efc3afd70ff53ec","observation_id":"fd737c22-2cc1-4ee6-9899-f7ab408cc806","resolution":{"observed_at":"2026-08-08T22:21:55.035858Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:54.643177Z","title":null,"venue":null,"work_id":"3ff5d664-a27a-4b29-96bb-6d3dfcc55409","year":2024},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.357471Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:8bab0180a160a2e9a42eb07a4ecb59eddcde0e0b91abc33ff15cc46d2d272aec","observation_id":"0db404a6-c439-4996-8afc-9de7e5e0b5d9","resolution":{"observed_at":"2026-08-08T22:21:54.647903Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-08T22:21:54.290170Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale","version":3},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-08T22:21:54.290170Z"},"links":{"citing_paper":"/paper/2502.04563"},"observation_digest":"sha256:fcaf22aefb378acb195ad4e49652c66320e51a02695714f836440493a63237e3","observation_id":"6ded79cf-5a9e-4430-8a3e-3cebcfdfdde6","resolution":{"observed_at":"2026-08-08T22:21:54.290170Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.04563","last_updated":"2025-05-30T12:10:19Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T15:44:47.462753Z","submitted_at":"2025-02-06T23:32:19Z","title":"WaferLLM: Large Language Model Inference at Wafer Scale"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":3,"unresolved":6,"verified_exact":0,"verified_fuzzy":51},"total_outbound_references":60},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 8 inbound Pith citation observations for arXiv:2502.04563."}