{"as_of":"2026-08-13T10:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8540448e0dc74fe780f4c907d92950746b915e2adf36711d8a58f83f267a90b0","coverage":[{"denominator":62,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":62,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:22:04.793768Z","state":"measured"},{"denominator":67,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":67,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-01T06:28:07.670564Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"cited_work":{"arxiv_id":"2411.17309","doi":"10.48550/arxiv.2411.17309","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17309","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PIM-AI: A Novel Architecture for High- Efficiency LLM Inference,","venue":"arXiv (Cornell University)","work_id":"4186e43c-98a2-4016-9e5f-9176d8be5a2f","year":2024},"citing_paper":{"arxiv_id":"2604.07935","last_updated":"2026-07-08T13:12:46Z","snapshot_observed_at":"2026-08-11T05:32:05.167726Z","submitted_at":"2026-04-09T07:55:03Z","title":"The Hyperscale Lottery: How State-Space Models Have Sacrificed Edge Efficiency","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T17:46:11.859634Z"},"links":{"cited_paper":"/paper/2411.17309","citing_paper":"/paper/2604.07935"},"observation_digest":"sha256:66cf17d162aa39e0f866b227fee7b8ca8728ddb6c8131da8105cfd24f88f033b","observation_id":"af81e374-6ef4-4fa4-8a5f-54a701a8f4c5","resolution":{"observed_at":"2026-05-11T06:11:00.979649Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"cited_work":{"arxiv_id":"2411.17309","doi":"10.48550/arxiv.2411.17309","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17309","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PIM-AI: A Novel Architecture for High- Efficiency LLM Inference,","venue":"arXiv (Cornell University)","work_id":"4186e43c-98a2-4016-9e5f-9176d8be5a2f","year":2024},"citing_paper":{"arxiv_id":"2605.25522","last_updated":"2026-05-25T07:22:13Z","snapshot_observed_at":"2026-08-07T10:42:53.213259Z","submitted_at":"2026-05-25T07:22:13Z","title":"Co-Designing Graph-based Approximate Nearest Neighbor Search at Billion Scale for Processing-in-Memory","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-29T19:51:10.637303Z"},"links":{"cited_paper":"/paper/2411.17309","citing_paper":"/paper/2605.25522"},"observation_digest":"sha256:95810b7119ff5b0834371b6c75f74cbaf15f4d131264f35b27116f81965e2321","observation_id":"33084609-477b-4384-9bcc-18af6a435940","resolution":{"observed_at":"2026-06-29T19:53:55.467021Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"cited_work":{"arxiv_id":"2411.17309","doi":"10.48550/arxiv.2411.17309","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17309","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PIM-AI: A Novel Architecture for High- Efficiency LLM Inference,","venue":"arXiv (Cornell University)","work_id":"4186e43c-98a2-4016-9e5f-9176d8be5a2f","year":2024},"citing_paper":{"arxiv_id":"2605.25522","last_updated":"2026-05-25T07:22:13Z","snapshot_observed_at":"2026-08-07T10:42:53.213259Z","submitted_at":"2026-05-25T07:22:13Z","title":"Co-Designing Graph-based Approximate Nearest Neighbor Search at Billion Scale for Processing-in-Memory","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-29T19:51:10.637303Z"},"links":{"cited_paper":"/paper/2411.17309","citing_paper":"/paper/2605.25522"},"observation_digest":"sha256:fdaffad8121bd294e8a64fcf6a17731d51f267905aa29401cb84a58f8e9020e2","observation_id":"3d94236d-d27e-4b61-9891-1facd1175a8e","resolution":{"observed_at":"2026-06-29T19:53:54.941479Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"cited_work":{"arxiv_id":"2411.17309","doi":"10.48550/arxiv.2411.17309","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17309","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PIM-AI: A Novel Architecture for High- Efficiency LLM Inference,","venue":"arXiv (Cornell University)","work_id":"4186e43c-98a2-4016-9e5f-9176d8be5a2f","year":2024},"citing_paper":{"arxiv_id":"2606.30553","last_updated":"2026-06-30T15:48:46Z","snapshot_observed_at":"2026-08-06T15:45:16.008317Z","submitted_at":"2026-06-29T16:49:17Z","title":"COSM: A Cooperative Scheduling Framework for Concurrent PIM and CPU Execution on Mobile Devices","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-30T03:09:57.069086Z"},"links":{"cited_paper":"/paper/2411.17309","citing_paper":"/paper/2606.30553"},"observation_digest":"sha256:e2ffeff70e052a398672be41aec27b2ca859063cc47e58f64d58a471e7ce5614","observation_id":"c35b4897-1a8d-4cf0-9f5f-617a6e7d4710","resolution":{"observed_at":"2026-06-30T03:14:14.929917Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"cited_work":{"arxiv_id":"2411.17309","doi":"10.48550/arxiv.2411.17309","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17309","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PIM-AI: A Novel Architecture for High- Efficiency LLM Inference,","venue":"arXiv (Cornell University)","work_id":"4186e43c-98a2-4016-9e5f-9176d8be5a2f","year":2024},"citing_paper":{"arxiv_id":"2606.30553","last_updated":"2026-06-30T15:48:46Z","snapshot_observed_at":"2026-08-06T15:45:16.008317Z","submitted_at":"2026-06-29T16:49:17Z","title":"COSM: A Cooperative Scheduling Framework for Concurrent PIM and CPU Execution on Mobile Devices","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-01T06:28:07.670564Z"},"links":{"cited_paper":"/paper/2411.17309","citing_paper":"/paper/2606.30553"},"observation_digest":"sha256:5eb23c5485b31a1e6901812f026ba8e937399f0505ac80a566febadc62db107d","observation_id":"a0c12c67-db0c-433e-a46c-8e6237efc2eb","resolution":{"observed_at":"2026-07-01T09:35:39.897941Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.17309/citation-record","integrity":"/paper/2411.17309/integrity","json":"/paper/2411.17309/citation-record.json","paper":"/paper/2411.17309"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:22:05.969161Z","title":"A comprehensive overvi ew of large language models, 2024","venue":null,"work_id":"49e7d172-2ff3-46fa-9f6d-3b08f74e827e","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.478481Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:966652978a7f2142c17e7bfe1ac3ddc8573c7480827bf4871a4c4c63f38e6859","observation_id":"ec2ed864-d673-4722-90fa-f9d636df04ea","resolution":{"observed_at":"2026-08-12T12:22:05.974735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.949463Z","title":"A survey of large language models, 2023","venue":null,"work_id":"2a73ae8b-9acc-4b9b-b935-f467635940af","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.484696Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:6e4a4da435eb79ec907f489c555a37b7e34231af84d310b5d95cd0be60005cfd","observation_id":"7cdb41c4-29a2-4d0a-bb09-fb74eb051919","resolution":{"observed_at":"2026-08-12T12:22:05.956356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.932029Z","title":"Saddam Hossain Mukta, K aniz Fatema, Nur Mohammad Fahad, Sadman Sakib, Most Marufatul Jannat Mim, Jubaer Ahmad, Mohammed Eu nus Ali, and Sami Azam","venue":null,"work_id":"9a2dad7a-8ed0-40e2-bb6a-4885ebac4325","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.490020Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:801e6a850595cb64097cb681de4a102fbae5395cd031d574111d5393a319c3d9","observation_id":"01cbf9b5-fbac-4419-9edb-16d701342905","resolution":{"observed_at":"2026-08-12T12:22:05.938270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.914913Z","title":null,"venue":null,"work_id":"c9a24d68-0294-43b2-a78b-c2da3de37d5c","year":1951},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.496427Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:5b9ea2dd4d8bb6682ddcaf968855bc9053e1e21ceb38a4c3e78cdf6ec0735ef6","observation_id":"2f821116-6339-4df7-8422-a983f7b47dfa","resolution":{"observed_at":"2026-08-12T12:22:05.919848Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.892379Z","title":"Recurrent neural net- work based language model","venue":null,"work_id":"7c2b62b8-a130-4cb7-88e4-0c90e3027525","year":2010},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.501734Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:475bac237217306250e849a0dc3f411abc509d1f974ba9db63c957804781758b","observation_id":"2cd94a8f-5589-4d3b-b045-3b16be50ebe0","resolution":{"observed_at":"2026-08-12T12:22:05.903307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.871224Z","title":"Attention is all you need","venue":null,"work_id":"427b4c48-4907-41f2-8678-4d2877c53bcf","year":2017},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.507598Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:2300c83f8e1fb16d8fad4022da1330b392c7637ecf3004b7849bf9dbcf1fe741","observation_id":"8ee1d6f7-0fb5-492a-8dd8-bcd999fdf59f","resolution":{"observed_at":"2026-08-12T12:22:05.876612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.853707Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding, 2019","venue":null,"work_id":"d45f4105-08e9-45ac-9703-824424835143","year":2019},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.514225Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:b1f68c674818acc1dfb9487d22ed39db298d17d165c7e4c7156156aa7927ba1b","observation_id":"b4b7f7a7-198b-4b15-a930-52a562033cd6","resolution":{"observed_at":"2026-08-12T12:22:05.859662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.837247Z","title":"Improving language understanding by generative pre-training","venue":null,"work_id":"1f38d43c-8af5-4453-9680-cb838e33d1c5","year":2018},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.519951Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:9bf59b1046d8496378e86de5b0746f6a3107dc0b9e418d14cfe5ccd0f35bec69","observation_id":"ad03c6a8-d628-49db-b77c-d0f15f98cd23","resolution":{"observed_at":"2026-08-12T12:22:05.842184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.820300Z","title":"Exploring the limits of transfer learnin g with a uniﬁed text-to-text transformer","venue":null,"work_id":"884c446a-5dce-4e44-830e-30c660a22bb6","year":2020},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.524881Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:957596593dd5aa868a8ce67c76b77965efad8058924ba7e6b647ad10aa23bafa","observation_id":"58c67ba7-272b-4db8-b0fe-8db904a66f5c","resolution":{"observed_at":"2026-08-12T12:22:05.825475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.803233Z","title":"Bart: Denoising sequence-to-s equence pre-training for natural language generation, translation, and comprehension, 2019","venue":null,"work_id":"213d67ea-48c1-4bd2-8410-9427606714b8","year":2019},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.530215Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:a671a516322333f0fe0aedcb43eea9f08e6f31d7df2edc29d96dc1d2b71ffa96","observation_id":"9959e50a-0876-45ea-9afa-8c1200c8b7a2","resolution":{"observed_at":"2026-08-12T12:22:05.808688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.783476Z","title":"Language models are few-shot learners","venue":null,"work_id":"13d27de4-cc11-4d33-948c-85cc5c2125c6","year":1901},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.535107Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:cdadd1721e6e8b10bae01658258723ab23e8c072c994ba5c7a626fd9de4455ba","observation_id":"0f0da0d2-f1d6-4ecb-83af-0c0b12c8d03c","resolution":{"observed_at":"2026-08-12T12:22:05.791731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.766685Z","title":null,"venue":null,"work_id":"28f35343-5b67-4c9f-b8af-6ba13af7b8cc","year":2021},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.540449Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:455f6894b2a4a3fc36d93170de969790c028844130dba074e6f84318f41e2051","observation_id":"b89764bf-6c00-43c6-b345-043c1236a1f7","resolution":{"observed_at":"2026-08-12T12:22:05.771576Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.748338Z","title":"Webgpt: Browser-assisted question-answering with human feedback, 2022","venue":null,"work_id":"8335fb0d-c2b7-4775-b2a8-d354365e9765","year":2022},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.546122Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:0a96cca8f878acab9175a0ba769eb62219788e32efde86f5e56ad72a33fb0957","observation_id":"37350396-3204-490a-92c7-4e74ae6fd3ea","resolution":{"observed_at":"2026-08-12T12:22:05.753744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.730693Z","title":"Llama: Open and efﬁcient foundation l anguage models, 2023","venue":null,"work_id":"9c04410c-d823-4368-acc8-3281aee1f013","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.550920Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:c366e1a69857b370a169025dd892c0ede9a80b7ff5b0da0d186b4177e72b5a55","observation_id":"2c879a45-0831-4332-a1ad-2d923f862514","resolution":{"observed_at":"2026-08-12T12:22:05.736753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.712386Z","title":"Llama 2: Open foundation and ﬁne-tuned chat models, 2023","venue":null,"work_id":"944e4d00-c20f-46c7-b764-6730d79fb6bb","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.556118Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:35e4e17fa2f70c5572092ee6993d7fa1a511796423f7ccbbb32bf748a0f3496c","observation_id":"6b55f7ed-02fc-467e-8a67-e6b43b1510c8","resolution":{"observed_at":"2026-08-12T12:22:05.718782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.694339Z","title":null,"venue":null,"work_id":"9e002460-165a-435f-893b-a38581a82c89","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.561303Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:3938f06dc3869f32a34b2cd26a7ed9076d722b9014153e8e343ce2645dfb2802","observation_id":"3bf8726d-6506-4670-97b8-6ed10b8da332","resolution":{"observed_at":"2026-08-12T12:22:05.699520Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.677852Z","title":"Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shak- eri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, J onathan H","venue":null,"work_id":"5b6f0ad1-53c1-41d0-ac67-2f9f88c3167c","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.566424Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:54753a2f7e25a7eceb9e025d5794a3252aeda9c3a8a20dc97e969ff85f701888","observation_id":"2e857cee-a669-4b93-8d92-1db8ee03356c","resolution":{"observed_at":"2026-08-12T12:22:05.683093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.661367Z","title":null,"venue":null,"work_id":"e9ef7b1e-3c5c-4373-a342-548e5d677bb3","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.571547Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:76a390ba54b4fcfd4cfeccfab04ce0021655dff8fbeeccd3dec096c3ef461f55","observation_id":"78efecaa-c42f-4b7b-a3a2-4f47430e5c50","resolution":{"observed_at":"2026-08-12T12:22:05.666431Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-12T12:22:04.576637Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.576637Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:a1c280bc9a6608de41ed4a34c0b5e09a505f03cc28cb143e2a423252aab71938","observation_id":"021b9e3e-d59e-4875-bd44-9983b2eccc1d","resolution":{"observed_at":"2026-08-12T12:22:04.576637Z","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":"2026-08-12T12:22:05.644189Z","title":null,"venue":null,"work_id":"211c67bc-31f7-4cfc-a5bb-43c0c0268ddd","year":null},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.582025Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:ceac9af74e3840e9cac063c534f2d2ca8d6c385a9e9655d2471b7aa76b39b847","observation_id":"b84aa659-acc1-4d04-9059-ad75dc245da2","resolution":{"observed_at":"2026-08-12T12:22:05.649320Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.626862Z","title":"Webster and Chunyu Kit","venue":null,"work_id":"de2172cf-d80f-4bc9-9f45-cbe1165e2534","year":1992},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.586930Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:a880cc26b5dca4034112ca1adc6d35714cb8e57943a10779a92f800d5d809228","observation_id":"a5e1773e-8849-439b-970b-a5786de139a3","resolution":{"observed_at":"2026-08-12T12:22:05.632979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.591779Z","title":"Distributed representations of words and phrases and their compositionality, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.591779Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:f5a090d226731a25df1d86aa5647cb0216c675afa3571e4aba914cd1ff8e69b8","observation_id":"1ba81683-63e0-42ce-bff3-563afefe0d86","resolution":{"observed_at":"2026-08-12T12:22:04.591779Z","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":"2026-08-12T12:22:05.595661Z","title":"Glove: Global vectors for word representation","venue":null,"work_id":"9a9100d2-fcfb-4ee8-a5f2-28e82c5f07d8","year":2014},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.596644Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:662060ea1d0ca7c91696f75156975ac2120e5c8c4117fb9e0d8ffe62ea0d1549","observation_id":"34ef9f4a-03dd-4c0a-a8ab-118a1c5a9c46","resolution":{"observed_at":"2026-08-12T12:22:05.601861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.576065Z","title":"Root mean square layer normalization","venue":null,"work_id":"543c18d2-55f5-476e-bd56-649b05d3a21c","year":2019},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.601566Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:c7cc979e5ed6b392a46ee647f717e820d1ee8fea498e27ac9e2cf730a4f57678","observation_id":"2e0578b4-fec3-4b4d-a9fb-881ff3a4c7e6","resolution":{"observed_at":"2026-08-12T12:22:05.581655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.551912Z","title":null,"venue":null,"work_id":"09da543e-2ec5-4f03-9122-fe2446568fac","year":2016},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.606451Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:c971669b0382b7f6ddd98c7bbeda47e97091c2c470f5ce6579ee0f189971768f","observation_id":"05071be5-f98e-4a91-bfe1-29cb4fef8345","resolution":{"observed_at":"2026-08-12T12:22:05.558736Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.527223Z","title":"vllm: Easy, fast, and cheap llm serving with p agedattention","venue":null,"work_id":"90738609-06a9-4e68-ac62-18e551deecd7","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.611654Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:e2324f53e714623cb39587da6b7477b35eb87e27e1f6c0fa6786d944e57559ec","observation_id":"6f75e728-95aa-43aa-b199-e68151eae8b8","resolution":{"observed_at":"2026-08-12T12:22:05.537133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.507929Z","title":"Dissecting batching effects in gpt inferen ce, 2023","venue":null,"work_id":"f7313500-5848-43c7-bc1d-e0d7571ce2f6","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.616494Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:7bb10b877594274cdc761cc012c67cf1fc9afe381a4d23e5d67817c2ce3a2e54","observation_id":"9f9e9dda-47e7-45e1-b74f-f1fcfa31fe9d","resolution":{"observed_at":"2026-08-12T12:22:05.513341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.490040Z","title":"Mobilellm: Optimizing sub-billion parameter language models for on-device use ca ses, 2024","venue":null,"work_id":"83427f38-b3b5-494d-b28e-eff9595a7d09","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.621945Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:382c1b98e20cedc5bae8e701d2a17dc8923ce7fd957679a44d9eac0a0f53b47d","observation_id":"9e108453-2dcb-464a-82ca-e1f06128b168","resolution":{"observed_at":"2026-08-12T12:22:05.495627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.626809Z","title":"Octopus v2: On-device language model for super agent, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.626809Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:6f58d27b292f4477ee58b62eebb034a019f24bc92c430525fef32174d7e79d4b","observation_id":"2a88d004-292a-4952-a924-db19515621dd","resolution":{"observed_at":"2026-08-12T12:22:04.626809Z","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":"2026-08-12T12:22:05.461572Z","title":"A survey on hardware accelerato rs for large language models, 2024","venue":null,"work_id":"9c73c86a-ea5d-4f4b-b366-efc4e2e997dc","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.631779Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:3a03dddc637739221167731a66cce64b3ebc8e08e6add7f6dc69c241d89aefe6","observation_id":"ddbc00a9-b313-482b-a44c-988c663e74ff","resolution":{"observed_at":"2026-08-12T12:22:05.467549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.444299Z","title":"Ene rgy and policy considerations for deep learning in nlp, 2019","venue":null,"work_id":"e6ab47c5-a4b5-4d83-88f3-7756e92da7f6","year":2019},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.636403Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:23edcdffad95265495a892ecc654613bf1508cc12cbdc08998e075d85ea21fa9","observation_id":"ae04b50f-f2f1-40cf-8f13-3ccb7e590a56","resolution":{"observed_at":"2026-08-12T12:22:05.449579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.423064Z","title":null,"venue":null,"work_id":"2c839f03-833d-462a-bb23-3e2c45452f35","year":1995},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.641330Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:8b56ad816ab6b709fa7b036af8b0fb2aa29987842c6c90589171ba3daa726be6","observation_id":"70ebaa6c-7349-4f9d-bca3-3b020dc7d2b4","resolution":{"observed_at":"2026-08-12T12:22:05.429206Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.405142Z","title":"Mahoney, and Kurt Keutzer","venue":null,"work_id":"445cd597-3355-43a1-a907-d366bdadce6e","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.646001Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:106d7650e4f2315b0f67d2ecba096ba937e933fa30edbd969c4b1ded5f6bc215","observation_id":"68c9198f-9cb6-4c65-9356-69a2e4d1b3e5","resolution":{"observed_at":"2026-08-12T12:22:05.410165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.387244Z","title":"From wor ds to watts: Benchmarking the energy costs of large language model inference","venue":null,"work_id":"86d7999b-aea3-4e80-8b99-a474b41f5f57","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.650726Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:b4b0a62b24cff62f36afdbfa9484c57a8912aeba16a435b9c249629af8d9fc8a","observation_id":"084126a7-03c0-4906-8ff2-ca5aca5bfdbd","resolution":{"observed_at":"2026-08-12T12:22:05.392666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.369029Z","title":"Me asuring and improving the energy efﬁciency of large language models inference","venue":null,"work_id":"b6cc9ac9-6fe5-40a6-8e38-58780cbcd49c","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.656916Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:cfa48fbedddd5d9650c6a42b1504a0f9b4e52dd3c5f3d96f40349f18009e3a6d","observation_id":"401e5b96-f03b-4ed6-8b58-eed264dae6e5","resolution":{"observed_at":"2026-08-12T12:22:05.375090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.351537Z","title":"Risks and beneﬁts of large language models for the environment","venue":null,"work_id":"bf9ef396-97f4-4424-a591-6393b13cef93","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.662417Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:6b22131969653c97957be83a005093b641677c17fb55bde295c1921c5189bea6","observation_id":"34093139-1c1b-46e0-bba1-579680996b3e","resolution":{"observed_at":"2026-08-12T12:22:05.357353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.334211Z","title":"A short survey of viewing large languag e models in legal aspect, 2023","venue":null,"work_id":"2f44372c-0aa3-4504-83cc-2c6fad48b462","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.667481Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:191c6b2996fb62887b29a7c7d651277dcf77929bdb3896c69052028c04811003","observation_id":"135cf3a2-c618-4053-b74b-3318178b39b5","resolution":{"observed_at":"2026-08-12T12:22:05.339485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.316268Z","title":"What does it mean for a language model to preserve privacy? In Proceedings of the 2022 ACM conference on fairness, accountability, and transparency, pages 2280–2292, 2022","venue":null,"work_id":"93bdb90d-2431-4ba1-8443-d22e2d52d3d0","year":2022},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.672762Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:fbfb8c619b06eece05fefe163eddf97026f774faf278a062c90725c2884fd0b7","observation_id":"391a95d7-9599-480a-9a9d-db49df27cf13","resolution":{"observed_at":"2026-08-12T12:22:05.322171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.298645Z","title":"Y ou are what you write: Preserving privacy in the era of large language models, 2022","venue":null,"work_id":"e421bcb1-157f-41ba-b4d2-9feb69bd0336","year":2022},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.677567Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:a948f2fe7b7c9d4b5af4c8a0640389a7a394c5c668a08fb4a3a6d6c8b66a0ff0","observation_id":"0b75a610-c378-4972-9867-61fa8a3c0023","resolution":{"observed_at":"2026-08-12T12:22:05.304921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.279150Z","title":"Deli ver high performance ml inference with aws inferentia","venue":null,"work_id":"aa9db393-3df7-4fbe-9063-9707ccb9854b","year":2019},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.682320Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:c64a021fcaae2d98ebb5c152e57b0b93faa7f73119ef138489c78180c1505c35","observation_id":"2326507b-179d-42af-aa61-2a9bcafe802e","resolution":{"observed_at":"2026-08-12T12:22:05.285540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.261168Z","title":"Mm1: Methods, analysis & insights from multimo dal llm pre-training, 2024","venue":null,"work_id":"6c66784c-d11c-4cae-b189-245fc99f59c1","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.687110Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:2c82fc8fe2ab4b33db96f86adaba6cb74dce053be4dbb8d1c4a1a83689796153","observation_id":"9c83a7b9-1261-451f-8a98-389e0c3f9c36","resolution":{"observed_at":"2026-08-12T12:22:05.266413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.244394Z","title":"SmoothQuant: Accurate and efﬁcient post-training quantization for large language mo dels","venue":null,"work_id":"834e87ff-e1f6-43c0-aafc-7f8ada012007","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.693099Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:e677002582a01d29daf6d3e7add99b9b700b847caf392fa88acb13324a8af75b","observation_id":"7bd2a334-5f1d-4d49-884e-ba05c4f45c0e","resolution":{"observed_at":"2026-08-12T12:22:05.249324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.226533Z","title":"Compression of generative pre-trained language models via quantizatio n, 2022","venue":null,"work_id":"768374d7-2765-4d18-bef1-ac54e344a471","year":2022},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.698101Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:b47770027bd7a8cb8e374532a8efc9f0cbf2bc1a08f97e7c163b910ff208e0e6","observation_id":"98d44f72-f20a-41c3-9f8d-53f922b4c328","resolution":{"observed_at":"2026-08-12T12:22:05.232371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.205536Z","title":"Mahoney, and Kurt Keutzer","venue":null,"work_id":"64b48991-29a2-4300-8da3-30e9f2f44eb2","year":2021},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.703891Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:16ae774bc5d3e48e95ee23310d3072b894c5dd0cbb27bc06dcbf11ee859a1f5b","observation_id":"7c857d5b-d3ca-47ea-a961-6322d3ad5fe3","resolution":{"observed_at":"2026-08-12T12:22:05.214957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.187096Z","title":"Onebit: Towards extremely low-bit large language models, 2 024","venue":null,"work_id":"8e851ea4-ac83-47c9-9152-5d28e0eb1db1","year":null},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.709035Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:2f2c4f61bc5e6cda3241e07ab736387760b373f77f5c1cfb1940c9051437201d","observation_id":"7cd873ac-af68-43eb-bda9-d5ae4b9fc840","resolution":{"observed_at":"2026-08-12T12:22:05.192944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.169408Z","title":"The era of 1-bit llms: All large lang uage models are in 1.58 bits, 2024","venue":null,"work_id":"a6df9d17-f60e-471f-8321-a9a8757b02ec","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.713994Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:3263cd6b3505fa36dd25a6510765d3b3b8969506b01f321275d390ea80380ec1","observation_id":"97442a05-4323-4615-a962-a9e8855852e8","resolution":{"observed_at":"2026-08-12T12:22:05.174903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.150084Z","title":"Llm-pruner: On the structural pruning of large language mod- els","venue":null,"work_id":"beaebc6b-d094-4f14-91ba-342e8509f3aa","year":null},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.719045Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:42a24231338f55571d23f1e1c0d543b89e70e6b8132fa554af63fd99378a66cb","observation_id":"454ca70c-e3ee-440b-9391-a7fcc22f822c","resolution":{"observed_at":"2026-08-12T12:22:05.156196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.131474Z","title":"From dense to sparse: Contrastive pruning for better pre-trained lang uage model compression","venue":null,"work_id":"45710066-cbe1-4d9e-86bd-6467ef1f2cb5","year":2022},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.723777Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:ba85a7108bb7355f98efaa7ae82deab770060e73a19c05ee7e0fc598ab8394a5","observation_id":"0f579aff-c75b-4d47-8acd-1ef769fda8a1","resolution":{"observed_at":"2026-08-12T12:22:05.137016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.111122Z","title":"A s urvey on model compression for large language models, 2023","venue":null,"work_id":"f08df03b-1226-4e97-8433-7e6351176b57","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.728663Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:58b1f76d3d8475574e498d18b339acdae710a40d1b07ad72a4230e2defc78e3c","observation_id":"7971c7d1-dcb8-4bdf-bf50-f4eff84e164d","resolution":{"observed_at":"2026-08-12T12:22:05.116672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.094906Z","title":"Distill ing the knowledge in a neural network, 2015","venue":null,"work_id":"41d0b6af-1000-4da5-9cb7-9ed760bf732d","year":2015},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.733590Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:d6f9e07de894dd217e4f90e5ae89756878ec0bfb5a3cbf070b7a67ea901bf4ab","observation_id":"03ab731c-db83-4481-b2d8-fb16f09e2daf","resolution":{"observed_at":"2026-08-12T12:22:05.099988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.077109Z","title":"Knowledge distillation: A survey","venue":null,"work_id":"661579fc-a078-4db1-8d43-8840ad0a982c","year":2021},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.738393Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:62b21f46dfa9c90eb4a2f33dde52e92c697093067f46a57dd5cbf7b75e6687b9","observation_id":"439f6cc7-c116-4059-abbb-87227f76885f","resolution":{"observed_at":"2026-08-12T12:22:05.082243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.059657Z","title":"De Lima, Hamid Farzaneh, and Jeronimo Castrillon","venue":null,"work_id":"6ab27e9b-3f74-47e7-9efb-3d9664a43436","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.743633Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:b1c76b5015012e40fe0c2d4d46e27d4fbc22bd60fa6320a7a1a0b17874e3ece9","observation_id":"71280841-43d0-4ece-9a94-f49cf061451d","resolution":{"observed_at":"2026-08-12T12:22:05.065206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.036471Z","title":"A Modern Primer on Processing in Memory, pages 171–243","venue":null,"work_id":"3519eb6c-6cfb-4ba0-9183-4d12d71a06d3","year":null},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.748254Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:56d6ec65f20881372abe871c31a51d26570d7e75a8dd77f3fcc177e3c1557f33","observation_id":"63e1812a-a159-4e0d-ba55-3193a165e25e","resolution":{"observed_at":"2026-08-12T12:22:05.043911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:05.017620Z","title":"High-speed emerging memories for ai hardwar e accelerators","venue":null,"work_id":"25f68912-677f-4e28-9342-31df2f8d36d8","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.753366Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:494b26fba313c86bb9d064edd779982827a777a541ca3b448b6b362fd0ade1ea","observation_id":"c63990ee-a8b6-4a53-888b-d1ac89d0b452","resolution":{"observed_at":"2026-08-12T12:22:05.024572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.998622Z","title":"The breakthrough memory solutions for improved perfo rmance on llm inference","venue":null,"work_id":"2ca2d149-b08e-4e19-8819-5ad9480436f8","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.758120Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:328711a93834ea0d38e00a4afa8902cf5830f357e4ecf07f0b1ec3a5c5042fe1","observation_id":"a240b566-1cf7-4912-8c5d-5e765c8d0464","resolution":{"observed_at":"2026-08-12T12:22:05.005213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.975560Z","title":"Oliveira, and Onur Mutlu","venue":null,"work_id":"410aea6b-2cce-44f9-b783-7ff5232f15e5","year":2021},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.763341Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:7da886ce43fc432a93fee4dbd05ebe0fd6f934190d7851b7baf6baddeaecb711","observation_id":"5bdf3035-69fa-413a-864f-38ad766bca71","resolution":{"observed_at":"2026-08-12T12:22:04.981952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.957433Z","title":"Energy efﬁciency impa ct of processing in memory: A comprehensive review of workloads on the upmem architecture","venue":null,"work_id":"7f6a124e-25a5-486f-9fa7-0f17449b3497","year":2023},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.767996Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:d21baf2a548cf2c632f3d0e7a7e3261a9576eb061a7f2a5fa321af24fead1dbc","observation_id":"434583d4-6302-40fe-ad3c-80b4121bf811","resolution":{"observed_at":"2026-08-12T12:22:04.963238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.940904Z","title":"Technical report, Qualcomm, 2024","venue":null,"work_id":"3b277d20-65d7-4837-bc66-d8dbb5b387e9","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.772859Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:d0d547eecd0106e561be6821afb036a625a674a0e5bce897f4c78e4a85c4e528","observation_id":"f01ef35c-cdd6-42ab-a09b-e5ee369c8e45","resolution":{"observed_at":"2026-08-12T12:22:04.945984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.03771","last_updated":"2020-07-14T03:42:34Z","snapshot_observed_at":"2026-07-06T08:27:58.343233Z","submitted_at":"2019-10-09T03:23:22Z","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.03771","snapshot_observed_at":"2026-08-12T12:22:04.777569Z","title":"Huggingface’s transformers: State-of-the-art natural language processing","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.777569Z"},"links":{"cited_paper":"/paper/1910.03771","citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:e5152cf3bac785b4194883c9283b1c8bebb43345660f6aaf07cbb0abaabbd366","observation_id":"4625f2ac-3218-4515-a7ba-cf0c113b9fd8","resolution":{"observed_at":"2026-08-12T12:22:04.777569Z","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":"2026-08-12T12:22:04.923668Z","title":"Accessed: 2024-07-11","venue":null,"work_id":"00f46750-e29a-4481-81d7-0ceafa588a98","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.783813Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:dff953b467fe04ae6eaa2b1e4f5d642f85464c9a1e885116da9d9b8394a6f70e","observation_id":"c551978f-5160-48cf-bd94-d259895def6c","resolution":{"observed_at":"2026-08-12T12:22:04.928678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.904920Z","title":"Introducing Apple’s On-Device and Server Found ation Models","venue":null,"work_id":"fcb8b14b-4262-48bd-894d-d0ad783fd6ce","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.788924Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:2d23173695ba7a1fef0cef5f4381ee31ad9db8252f70e936f6f9ab7c3cb70967","observation_id":"210ed5ae-eaa8-421c-924d-1efdbeb6eff2","resolution":{"observed_at":"2026-08-12T12:22:04.911018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T12:22:04.879588Z","title":"Achieving High Mixtral 8x7B Performance with N VIDIA H100 Tensor Core GPUs and TensorRT- LLM","venue":null,"work_id":"3906bcc1-cc13-4916-9b94-41f74f4c250d","year":2024},"citing_paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T12:22:04.793768Z"},"links":{"citing_paper":"/paper/2411.17309"},"observation_digest":"sha256:68ec7b5b9113c77d2180f3f1a1779e1cbe71d45e8c702a9c71758dc79f7464d0","observation_id":"e52482d3-145e-4d4d-ac38-58cfb605fccd","resolution":{"observed_at":"2026-08-12T12:22:04.889189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.17309","last_updated":"2024-11-26T10:54:19Z","latest_version":1,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-13T05:39:12.380207Z","submitted_at":"2024-11-26T10:54:19Z","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference"},"reference_resolution":{"displayed":62,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":51},"total_outbound_references":62},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 5 inbound Pith citation observations for arXiv:2411.17309."}