{"as_of":"2026-08-10T01:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b74f85ef2e3d0ba305cebd404583883f78cd8d0a746cc14d31ef92963ee8a633","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T20:05:57.621012Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T01:55:09.658053Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-11T01:57:51.086130Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"cited_work":{"arxiv_id":"2508.11269","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.11269","snapshot_observed_at":"2026-07-11T01:57:51.086130Z","title":"Inference performance eval- uation for LLMs on edge devices with a novel benchmarking framework and metric","venue":"cs.PF","work_id":"4e3f2d1b-a96f-49b7-a50a-66dee40cace0","year":2025},"citing_paper":{"arxiv_id":"2605.11186","last_updated":"2026-05-11T19:50:08Z","snapshot_observed_at":"2026-07-06T23:23:02.025664Z","submitted_at":"2026-05-11T19:50:08Z","title":"CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-13T04:06:24.409303Z"},"links":{"cited_paper":"/paper/2508.11269","citing_paper":"/paper/2605.11186"},"observation_digest":"sha256:ca41d9d1fc4fba95613a6e457604ee560e79aaf3bc0856a90a50711e3d25a391","observation_id":"05089264-03e2-453c-85ae-e6d8114f2fbe","resolution":{"observed_at":"2026-05-13T04:07:13.076124Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"cited_work":{"arxiv_id":"2508.11269","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.11269","snapshot_observed_at":"2026-07-11T01:57:51.086130Z","title":"Inference performance eval- uation for LLMs on edge devices with a novel benchmarking framework and metric","venue":"cs.PF","work_id":"4e3f2d1b-a96f-49b7-a50a-66dee40cace0","year":2025},"citing_paper":{"arxiv_id":"2607.05876","last_updated":"2026-07-08T02:47:41Z","snapshot_observed_at":"2026-08-05T07:46:13.093303Z","submitted_at":"2026-07-07T06:11:54Z","title":"Think Before You Grid-Search: Floor-First Triage for LLM Serving","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-08T22:38:12.637901Z"},"links":{"cited_paper":"/paper/2508.11269","citing_paper":"/paper/2607.05876"},"observation_digest":"sha256:521230f77fffe493c6d85c2184994a94d386dbf430f53ceb151d90230cdfa8dc","observation_id":"13b88533-4d67-4fff-8396-c5c91f6487bd","resolution":{"observed_at":"2026-07-08T22:45:40.079768Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"cited_work":{"arxiv_id":"2508.11269","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.11269","snapshot_observed_at":"2026-07-11T01:57:51.086130Z","title":"Inference performance eval- uation for LLMs on edge devices with a novel benchmarking framework and metric","venue":"cs.PF","work_id":"4e3f2d1b-a96f-49b7-a50a-66dee40cace0","year":2025},"citing_paper":{"arxiv_id":"2607.05876","last_updated":"2026-07-08T02:47:41Z","snapshot_observed_at":"2026-08-05T07:46:13.093303Z","submitted_at":"2026-07-07T06:11:54Z","title":"Think Before You Grid-Search: Floor-First Triage for LLM Serving","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-11T01:55:09.658053Z"},"links":{"cited_paper":"/paper/2508.11269","citing_paper":"/paper/2607.05876"},"observation_digest":"sha256:3cb9a657497bb415a8f3c69f87a3d5ad25b8f6ad3e373bfcab93350c8e522340","observation_id":"6d2d85db-7cfb-4463-b785-3896ab79837e","resolution":{"observed_at":"2026-07-11T01:57:51.114012Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.11269/citation-record","integrity":"/paper/2508.11269/integrity","json":"/paper/2508.11269/citation-record.json","paper":"/paper/2508.11269"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:06:01.868252Z","title":"Vaswani, N","venue":null,"work_id":"aad5d78e-c579-4b39-9d39-54a0918284d8","year":2017},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.001668Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:489f86bf66b2452890d22f4fb528eb773e112dcfe996fff5ee5a91f969cb0bd8","observation_id":"9bae77a1-71c0-483c-a593-0c037772ef94","resolution":{"observed_at":"2026-08-05T20:06:01.962138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:06:01.748203Z","title":"Brown, B","venue":null,"work_id":"c5dc0262-024f-4714-a9dc-7c883219935c","year":2020},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.062872Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:c04ee40337fc7b4ceaa2edd211e77360de8a28d153327c8a3d08783aaa6d39a0","observation_id":"96f77f29-410f-4f25-9bb8-a92ca2e2fcf5","resolution":{"observed_at":"2026-08-05T20:06:01.814023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:06:01.614532Z","title":"Ouyang, J","venue":null,"work_id":"984c44f3-85ca-4c14-99c6-b477777e2872","year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.170129Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:28879af8f3a1304185447721e7bfac7ac57d04ec41af2f8c9a078bbc09deb215","observation_id":"8dccf6c3-f44c-49ce-b933-6412e6fed1cd","resolution":{"observed_at":"2026-08-05T20:06:01.679901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.10360","last_updated":"2022-03-17T11:49:55Z","snapshot_observed_at":"2026-07-06T10:51:12.871009Z","submitted_at":"2021-03-18T16:30:26Z","title":"GLM: General Language Model Pretraining with Autoregressive Blank Infilling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.10360","snapshot_observed_at":"2026-08-05T20:05:55.270317Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.270317Z"},"links":{"cited_paper":"/paper/2103.10360","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:68c011294188aa389f436ee74affe5cfd2446be81dbdd6d5c6d885d2b08c5f16","observation_id":"8fef53c5-e5d5-4b98-9de7-582cb720414b","resolution":{"observed_at":"2026-08-05T20:05:55.270317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02414","last_updated":"2023-10-25T05:22:43Z","snapshot_observed_at":"2026-08-02T04:26:53.194797Z","submitted_at":"2022-10-05T17:34:44Z","title":"GLM-130B: An Open Bilingual Pre-trained Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02414","snapshot_observed_at":"2026-08-05T20:05:55.316579Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.316579Z"},"links":{"cited_paper":"/paper/2210.02414","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:47ccc8e28f62d3c5204a0e67ddfba51140be5fea4d184bf8e59936b61ff67a8b","observation_id":"5d61cd7a-20f7-4efc-848a-8769c1d9bbf3","resolution":{"observed_at":"2026-08-05T20:05:55.316579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-05T20:05:55.382267Z","title":"Touvron, T","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.382267Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:424063b2aaf13fc38d579b0de66deeb13e86081065f627985416313e669bcdf6","observation_id":"158519bc-5207-48cf-8ba1-422161b784da","resolution":{"observed_at":"2026-08-05T20:05:55.382267Z","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":"10.47852/bonviewaia3202939","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:58.302554Z","title":"Shahriar, K","venue":null,"work_id":"30999f2c-5110-48a8-9651-108bde30c821","year":2023},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.446911Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:886700ed84856e53d4483b532be5b4200ad6408a7f38c1653b874cb7816d59b6","observation_id":"1f208a59-b529-4f93-9737-2d38836ba360","resolution":{"observed_at":"2026-08-05T20:05:58.465668Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"9219.35693","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:06:00.541411Z","title":null,"venue":null,"work_id":"db6e0ef4-9532-4f4e-b3d9-2dfd527cace5","year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.504516Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:c84ed5b7a375a89d186c0b3b3dede4ee8a2b05e54b25c15ff3afb43b26bf1a2e","observation_id":"a279ddc0-739d-4855-aa19-5c7c67ef6a17","resolution":{"observed_at":"2026-08-05T20:06:00.584585Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/9781119551713.ch3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:58.179453Z","title":"Zhang, F","venue":null,"work_id":"5515c63e-32a8-4368-abfd-efcb2222ad87","year":2020},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.592127Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:e40a3272c0056c246c3e931e112996b149466f15455bb35df644eba4c6e2727d","observation_id":"c496601d-97d8-4903-b08b-21d81420e44d","resolution":{"observed_at":"2026-08-05T20:05:58.233180Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2013.65669","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:06:00.311260Z","title":null,"venue":null,"work_id":"47eb9bf0-7a86-45e6-ae52-ea9c1938acfd","year":2013},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.641971Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:f9d35436bad74639d25fcdc49ad8504eeef1f5bacbe5cfd75b189ee7c639f2df","observation_id":"ca6de3fc-395e-4fb4-88b3-b30e3576e016","resolution":{"observed_at":"2026-08-05T20:06:00.384999Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8618.2020","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:06:00.070675Z","title":null,"venue":null,"work_id":"b91df789-31cd-458e-bdeb-487195065994","year":2020},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.724786Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:8492902e8ecd6dae10e38d3e8205988ab7634d06c346ccdb55e2ed91a8ba6393","observation_id":"05560984-3a0d-411a-8ddf-2f7ee5ef093a","resolution":{"observed_at":"2026-08-05T20:06:00.171684Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29589","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:59.798160Z","title":null,"venue":null,"work_id":"6aeca345-a1b7-49cc-b933-e0271f6d812d","year":2019},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.786076Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:b3bba520ec0572d1e5abff9b500c21aaa5dcbb49e1f1a35f3fcf8151926b9c9d","observation_id":"63c32996-0d7a-4a66-b9be-48bcc1c7ab1c","resolution":{"observed_at":"2026-08-05T20:05:59.884792Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7360.29673","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:59.507168Z","title":null,"venue":null,"work_id":"ca177d5e-e50f-4423-b9b3-01d2ec0bffdc","year":2016},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.865354Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:7f2df46513e456cd7bf4839028e0b6a5369869e6af75f76bb67e004ad88ae055","observation_id":"4641ba15-0b3d-4877-a593-1a2cb85897fb","resolution":{"observed_at":"2026-08-05T20:05:59.579192Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2021.30952","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:59.263848Z","title":null,"venue":null,"work_id":"dd04e73f-e920-496e-abe7-179dd8a636a5","year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.932000Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:cf67a48eb1727ef0f7025e375e795eb43b73f7961021019bc21f81611e5f386a","observation_id":"467b45b3-22a2-4cbf-8c32-67f0d2e1e0fc","resolution":{"observed_at":"2026-08-05T20:05:59.359007Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3444692","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:58.051739Z","title":"Varghese, N","venue":null,"work_id":"ee25ed40-997d-4e8a-b372-004f9a8d07df","year":2021},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:55.985556Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:5c4230f1af056be6506a69e5985ba5757550c496b4f1faa822a62b15f69dc375","observation_id":"250d5fb4-a44b-49c3-b4f7-e454699144f7","resolution":{"observed_at":"2026-08-05T20:05:58.117010Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.28778","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:59.046726Z","title":"Bianco, R","venue":null,"work_id":"67a85b50-2c08-487f-9cb3-9b4014d86a5c","year":2018},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.031251Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:89c472e3d4fea71a591614063ae330c3d9be5b932663dc82d2b2bcf3ec6047d9","observation_id":"6175d6d0-fd09-4f91-b986-0ccff5a9f19c","resolution":{"observed_at":"2026-08-05T20:05:59.122227Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:06:01.492962Z","title":"Coleman, D","venue":null,"work_id":"7d251b2b-7b0b-47d5-8205-b709ffd765a8","year":2017},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.114028Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:a181a989f2cac727c15fbc76857ab1ec516698dcc12d4cf2cdde24fe523a7e5f","observation_id":"0bd623d0-3e81-4e66-b604-3a679a725a75","resolution":{"observed_at":"2026-08-05T20:06:01.550910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:06:01.374108Z","title":"Kuzmin, M","venue":null,"work_id":"ddd344a2-666e-45f2-89ca-9602d5ef3578","year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.185910Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:213ac7e21fda99a742196137dda2637701b60e8baae92fd14307d304380cb2e8","observation_id":"318fe081-99b2-49ea-95e9-37f8c7e19890","resolution":{"observed_at":"2026-08-05T20:06:01.430693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.05433","last_updated":"2022-09-29T20:47:07Z","snapshot_observed_at":"2026-07-06T13:51:20.183063Z","submitted_at":"2022-09-12T17:39:55Z","title":"FP8 Formats for Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.05433","snapshot_observed_at":"2026-08-05T20:05:56.251641Z","title":"Micikevicius, D","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.251641Z"},"links":{"cited_paper":"/paper/2209.05433","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:26cfa13050af269cd944d174bba054575fd341761cc60cf9ab439c839f1ee8a2","observation_id":"0fcb3489-a28a-4da2-84f5-e73f3e652083","resolution":{"observed_at":"2026-08-05T20:05:56.251641Z","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":"2026-08-05T20:06:01.244553Z","title":"Rodriguez, E","venue":null,"work_id":"7d2c414e-d000-4f78-be95-39cca269e079","year":2018},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.339619Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:116d495b23988fd121adb038402be1672e24593349432000c10a996a8f907ff7","observation_id":"5de54629-625a-4e03-bbf4-08768e3860ab","resolution":{"observed_at":"2026-08-05T20:06:01.307042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:06:01.094014Z","title":null,"venue":null,"work_id":"32a12453-1f50-48d4-83b7-e17e29f80c8c","year":2018},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.411430Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:3ebd26df0a7ca635f59582ae0edcd1ee1c5a9c64c852cc517374dfb65b4af054","observation_id":"0a90f51a-5dea-4e9c-9b51-09e640e2fd34","resolution":{"observed_at":"2026-08-05T20:06:01.145418Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1587/transinf.2020edp7160","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:57.921848Z","title":null,"venue":null,"work_id":"324e2f61-958d-49f1-bba4-ec2e7d3078a5","year":2021},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.470438Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:43c540beec154b7200985a25dc594cfe65acffc3e717a8b4d244228aa087d610","observation_id":"a84879eb-6ed3-43c0-88cd-f65798ae0201","resolution":{"observed_at":"2026-08-05T20:05:57.958969Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:05:56.559532Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.559532Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:6db8dabd9e6a83cc086fda3268e216135edb92c680e85e7372aa6943f635564c","observation_id":"38c6f5dd-736c-457b-b665-cebc0f133e81","resolution":{"observed_at":"2026-08-05T20:05:56.559532Z","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":"2020.00045","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:58.785317Z","title":null,"venue":null,"work_id":"c5bc70ee-96b3-46c5-822d-c55b58ee0ee9","year":2020},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.625867Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:dbc3d09e5a09c73e61b8f1c1c7a1e829cdac6519cc9a72d598c1fa6c9335a9ac","observation_id":"7ef26c64-5753-4e4c-8c44-322cb9e87bcd","resolution":{"observed_at":"2026-08-05T20:05:58.831268Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.04624","last_updated":"2023-08-08T23:30:20Z","snapshot_observed_at":"2026-07-06T16:04:11.116224Z","submitted_at":"2023-08-08T23:30:20Z","title":"Benchmarking LLM powered Chatbots: Methods and Metrics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.04624","snapshot_observed_at":"2026-08-05T20:05:56.713568Z","title":"Banerjee, P","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.713568Z"},"links":{"cited_paper":"/paper/2308.04624","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:30d84d8b86485505faab86a4b2cc17cfe7ceb9c9d4a8b5f865d0b6a3ecb15ba4","observation_id":"50393aab-ef14-4e4e-8ded-1339af24ddff","resolution":{"observed_at":"2026-08-05T20:05:56.713568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12241","last_updated":"2023-08-23T16:32:54Z","snapshot_observed_at":"2026-07-06T16:09:35.920101Z","submitted_at":"2023-08-23T16:32:54Z","title":"LLMRec: Benchmarking Large Language Models on Recommendation Task","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12241","snapshot_observed_at":"2026-08-05T20:05:56.773541Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.773541Z"},"links":{"cited_paper":"/paper/2308.12241","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:6f6f8f30257eb6f107663589ce566c2ac199c776facdd767758891921b475514","observation_id":"2b6d70b8-8968-4298-8a25-a3a8aaa67cfe","resolution":{"observed_at":"2026-08-05T20:05:56.773541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13848","last_updated":"2023-01-31T18:46:19Z","snapshot_observed_at":"2026-08-05T10:16:18.497606Z","submitted_at":"2023-01-31T18:46:19Z","title":"Benchmarking Large Language Models for News Summarization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.13848","snapshot_observed_at":"2026-08-05T20:05:56.847389Z","title":"Zhang, F","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.847389Z"},"links":{"cited_paper":"/paper/2301.13848","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:ef297f0041e7e12ae3a8044d53611c5fbdb96992380f78cd209baebbb8a1127a","observation_id":"77854e3e-7cf4-4d08-91ed-216b311768a7","resolution":{"observed_at":"2026-08-05T20:05:56.847389Z","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":"10.1109/mcse.2010.69","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:57.783315Z","title":null,"venue":null,"work_id":"a474ba91-cbdb-43b8-afbe-35013d199a34","year":2010},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.934150Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:ad0f077a2a8086e57f9c1cf2a856c2dd4178542dc8115b20f2fcaf1d41bc0e7f","observation_id":"a69ab616-d340-4ff6-8fdd-169e10513b8a","resolution":{"observed_at":"2026-08-05T20:05:57.855990Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:05:56.997246Z","title":"Nugteren, CLBlast: A tuned OpenCL BLAS library, in: Proceedings of the International Workshop on OpenCL, IWOCL ’18, ACM, Oxford, UK, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:56.997246Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:c1147b14ff08868f139b9b20d9686f1cef9c311773e6dbf4f6a018a7e46331d8","observation_id":"d7737b23-1a9c-49b8-a376-a73641473bbd","resolution":{"observed_at":"2026-08-05T20:05:56.997246Z","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":"10.1785/0220220241","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:05:57.686215Z","title":"Gebraad, A","venue":null,"work_id":"d7c74401-95b4-4f6e-861e-2feee8c814d4","year":2023},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.065743Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:7192048f82523c944732da8decc23da4ac9724636f37b1a56c60768367265665","observation_id":"1e6ca89c-a1ba-433f-af5e-ec660fd72a9d","resolution":{"observed_at":"2026-08-05T20:05:57.726370Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.09557","last_updated":"2024-04-01T06:09:41Z","snapshot_observed_at":"2026-07-06T13:22:35.494601Z","submitted_at":"2022-06-20T03:48:17Z","title":"LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09557","snapshot_observed_at":"2026-08-05T20:05:57.150331Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.150331Z"},"links":{"cited_paper":"/paper/2206.09557","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:b8dce263954ef73cbfdf93d69f516dd55b23572b54d059fb5fb6ac6c842a092a","observation_id":"bbb99788-89e8-4c14-871c-d812d8e0286e","resolution":{"observed_at":"2026-08-05T20:05:57.150331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.17323","last_updated":"2023-03-22T13:10:47Z","snapshot_observed_at":"2026-08-07T08:38:54.025062Z","submitted_at":"2022-10-31T13:42:40Z","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.17323","snapshot_observed_at":"2026-08-05T20:05:57.229767Z","title":"Frantar, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.229767Z"},"links":{"cited_paper":"/paper/2210.17323","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:acc46d528e426a1e38e77459a968d5809a9898669827f5ddb7abcf52720e4f4f","observation_id":"5ce29606-fc79-4210-b1bb-06a683abf5fc","resolution":{"observed_at":"2026-08-05T20:05:57.229767Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.07339","last_updated":"2022-11-10T18:14:31Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-08-15T17:08:50Z","title":"LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.07339","snapshot_observed_at":"2026-08-05T20:05:57.318565Z","title":"Dettmers, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.318565Z"},"links":{"cited_paper":"/paper/2208.07339","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:9d11a3c3d0cf447912b7b673c75548b069f31f5a55138cc8bf87d740bcdb70f4","observation_id":"236f72f6-978c-4337-b9ca-9a91c9c7f68c","resolution":{"observed_at":"2026-08-05T20:05:57.318565Z","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":"2026-08-05T20:06:00.985453Z","title":null,"venue":null,"work_id":"4ec687c6-1219-458f-a3b4-155e14537c41","year":2019},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.382306Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:44084bb8d5eddf8b2cc42bf2f72413b2d94f6b12620a656b08ac5b1344fb9b3d","observation_id":"4fa0a500-24c5-49f2-82bf-1b3cef5443ed","resolution":{"observed_at":"2026-08-05T20:06:01.031012Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:06:00.838519Z","title":"Banner, Y","venue":null,"work_id":"e27382b1-e560-4e99-a2f0-459611577752","year":2019},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.493137Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:47990837724f310f53d262600a57c9b995f18797fee941eaa46532083edc3567","observation_id":"98f79e35-1f90-4ce3-bf3d-8f70e23be6f2","resolution":{"observed_at":"2026-08-05T20:06:00.895185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00502","last_updated":"2023-12-07T12:16:42Z","snapshot_observed_at":"2026-07-06T16:41:39.124080Z","submitted_at":"2023-11-01T13:08:50Z","title":"Efficient LLM Inference on CPUs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00502","snapshot_observed_at":"2026-08-05T20:05:57.550624Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.550624Z"},"links":{"cited_paper":"/paper/2311.00502","citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:6038c0fcef8f0d1b8436a1b40365f187259a4a3e0e85f70efe473c788ecbd024","observation_id":"14aecba7-3b88-4f69-ba96-79c87f35e852","resolution":{"observed_at":"2026-08-05T20:05:57.550624Z","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":"2026-08-05T20:06:00.684296Z","title":"Huyen, Evaluation metrics for language modeling, The Gradient (2019)","venue":null,"work_id":"40a4b3ca-f8ba-443a-b163-5204fcd5b095","year":2019},"citing_paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T20:05:57.621012Z"},"links":{"citing_paper":"/paper/2508.11269"},"observation_digest":"sha256:fd6743c4b1a7fe1cc2adf493727f250749761e344d37aea0eb596795ff291ac3","observation_id":"66c69ea8-ba93-4365-80ff-de6b573f562f","resolution":{"observed_at":"2026-08-05T20:06:00.765994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.11269","last_updated":"2025-08-15T07:08:46Z","latest_version":1,"primary_category":"cs.PF","snapshot_observed_at":"2026-08-06T23:15:22.967250Z","submitted_at":"2025-08-15T07:08:46Z","title":"Inference performance evaluation for LLMs on edge devices with a novel benchmarking framework and metric"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":6,"parse_uncertain":0,"unresolved":15,"verified_exact":8,"verified_fuzzy":8},"total_outbound_references":37},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 3 inbound Pith citation observations for arXiv:2508.11269."}