{"as_of":"2026-08-08T11:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5c8e282371571279f85d2ff484a7743411ed2321fc092740ebdd8de1f3968799","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T05:19:35.751961Z","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":13,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-08T05:19:35.751961Z","title":"LLMCad: Fast and Scalable on-device Large Language Model Inference,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.08381","last_updated":"2025-02-12T13:16:07Z","snapshot_observed_at":"2026-08-08T05:15:02.153593Z","submitted_at":"2025-02-12T13:16:07Z","title":"The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T05:19:35.751961Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2502.08381"},"observation_digest":"sha256:e25826504e9ada51b660ed881090cef9cb53582d453ffa8bc9d2702015912ac4","observation_id":"b84fa4dd-a1af-40a9-a267-37d9d60a920a","resolution":{"observed_at":"2026-08-08T05:19:35.751961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-07T15:03:08.669376Z","title":"Llmcad: Fast and scalable on-device large language model inference,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16508","last_updated":"2025-05-29T08:56:27Z","snapshot_observed_at":"2026-08-07T14:57:25.646074Z","submitted_at":"2025-05-22T10:43:00Z","title":"Edge-First Language Model Inference: Models, Metrics, and Tradeoffs","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:03:08.669376Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2505.16508"},"observation_digest":"sha256:d03dbf089c11050f16b7b0adbc51e249dd8d48413935f710dceeab7b587e1611","observation_id":"991996f3-5e6e-4046-889d-6806864ff512","resolution":{"observed_at":"2026-08-07T15:03:08.669376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-07T13:02:10.198594Z","title":"Llmcad: Fast and scalable on-device large language model inference,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23219","last_updated":"2025-06-10T01:54:59Z","snapshot_observed_at":"2026-08-08T00:09:46.174159Z","submitted_at":"2025-05-29T08:03:43Z","title":"Ghidorah: Fast LLM Inference on Edge with Speculative Decoding and Hetero-Core Parallelism","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:02:10.198594Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2505.23219"},"observation_digest":"sha256:494415a44b593e6eedc3bb94f6094b5cfbf9f524dfca80931f9b95a7bc78892d","observation_id":"4012f9de-80bc-48e5-bbbf-2660dc0bc53f","resolution":{"observed_at":"2026-08-07T13:02:10.198594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-07T00:46:10.431567Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12699","last_updated":"2025-06-19T06:30:24Z","snapshot_observed_at":"2026-08-07T00:57:05.301160Z","submitted_at":"2025-06-15T03:14:03Z","title":"SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation","version":2},"reference_index":129,"source":"pdf_text","source_observed_at":"2026-08-07T00:46:10.431567Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2506.12699"},"observation_digest":"sha256:fefef98436365b2721a80da6ba295429cbd0d1717f67701ff97eff709c8aaa14","observation_id":"f0112c21-da6c-4356-a3b6-c4449348fa7e","resolution":{"observed_at":"2026-08-07T00:46:10.431567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-06T21:16:14.815374Z","title":"Llmcad: Fast and scalable on-device large language model inference,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.00672","last_updated":"2025-07-01T11:13:56Z","snapshot_observed_at":"2026-08-07T09:02:35.437888Z","submitted_at":"2025-07-01T11:13:56Z","title":"Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T21:16:14.815374Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2507.00672"},"observation_digest":"sha256:7c4ac74181a6ad1b5c877332c13866a2508c08436d3469b7b3181d9778386d80","observation_id":"84b9f65e-e87a-4695-8004-395b4d9fd9e0","resolution":{"observed_at":"2026-08-06T21:16:14.815374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-06T20:44:17.577855Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02135","last_updated":"2025-07-02T20:47:40Z","snapshot_observed_at":"2026-08-07T22:34:13.745068Z","submitted_at":"2025-07-02T20:47:40Z","title":"Dissecting the Impact of Mobile DVFS Governors on LLM Inference Performance and Energy Efficiency","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:44:17.577855Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2507.02135"},"observation_digest":"sha256:8ae6a56a91c0dc148fd6bceb6999d9d48173d14da0db8f85fc4348e187fabba5","observation_id":"946a9f0f-bebf-4248-9d2f-047f2fac3976","resolution":{"observed_at":"2026-08-06T20:44:17.577855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-06T15:06:47.908155Z","title":"Llmcad: Fast and scalable on-device large language model inference","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.16731","last_updated":"2025-07-22T16:13:43Z","snapshot_observed_at":"2026-08-07T22:15:08.576638Z","submitted_at":"2025-07-22T16:13:43Z","title":"Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:47.908155Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2507.16731"},"observation_digest":"sha256:75feec228c72c90ce131248c3c06aa49b46c9363f296e00942fafd5e7e7a9138","observation_id":"c27dfe0c-4edb-423c-8fa7-d61f33313141","resolution":{"observed_at":"2026-08-06T15:06:47.908155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":"2309.04255","doi":"10.48550/arxiv.2309.04255","metadata_source":"arxiv_reference","pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2309.04255, 2023","venue":"arXiv (Cornell University)","work_id":"9477bff9-d8f2-4d68-999a-6f0e8af20f3d","year":2023},"citing_paper":{"arxiv_id":"2604.14403","last_updated":"2026-04-15T20:34:10Z","snapshot_observed_at":"2026-07-06T23:02:13.885476Z","submitted_at":"2026-04-15T20:34:10Z","title":"A Unified Model and Document Representation for On-Device Retrieval-Augmented Generation","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-10T11:54:09.047134Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2604.14403"},"observation_digest":"sha256:511ee4e7acb686928905ac152e42bcdabc54fb5762d96f87daabc7ee3494dbb1","observation_id":"3d8da4af-b8d6-4510-972e-098368068528","resolution":{"observed_at":"2026-05-10T11:55:20.092471Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":"2309.04255","doi":"10.48550/arxiv.2309.04255","metadata_source":"arxiv_reference","pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2309.04255, 2023","venue":"arXiv (Cornell University)","work_id":"9477bff9-d8f2-4d68-999a-6f0e8af20f3d","year":2023},"citing_paper":{"arxiv_id":"2605.06485","last_updated":"2026-06-10T14:41:46Z","snapshot_observed_at":"2026-08-02T11:04:16.432773Z","submitted_at":"2026-05-07T16:07:39Z","title":"Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-08T10:12:36.972813Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2605.06485"},"observation_digest":"sha256:0c3f68727851d5e1c10d6ffd4a2e1346d0f68c089d27da43ef695a6176d28049","observation_id":"26ed021b-5237-4a1b-825a-665c2fa70f3d","resolution":{"observed_at":"2026-05-11T20:11:08.765744Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":"2309.04255","doi":"10.48550/arxiv.2309.04255","metadata_source":"arxiv_reference","pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2309.04255, 2023","venue":"arXiv (Cornell University)","work_id":"9477bff9-d8f2-4d68-999a-6f0e8af20f3d","year":2023},"citing_paper":{"arxiv_id":"2605.06485","last_updated":"2026-06-10T14:41:46Z","snapshot_observed_at":"2026-08-02T11:04:16.432773Z","submitted_at":"2026-05-07T16:07:39Z","title":"Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-30T23:12:52.038253Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2605.06485"},"observation_digest":"sha256:bbca4a06184d1b59dc08898a3e79860c140eb82f1ea88bc836e58793ef94ae40","observation_id":"99247455-449b-4bbd-a21a-6313359a4049","resolution":{"observed_at":"2026-07-01T13:25:46.082791Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.04255","snapshot_observed_at":"2026-08-06T12:36:18.965771Z","title":"LLMCad: Fast and scalable on-device large language model inference,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.04974","last_updated":"2026-08-05T15:45:15Z","snapshot_observed_at":"2026-08-08T11:12:30.745169Z","submitted_at":"2026-08-05T15:45:15Z","title":"AsymSpec: Efficient Cloud-Edge Speculative Decoding over Asymmetric Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:36:18.965771Z"},"links":{"cited_paper":"/paper/2309.04255","citing_paper":"/paper/2608.04974"},"observation_digest":"sha256:88823ebf6d7a9ef03b20e350a5caa35ebd505319361b563e66b17ed9336c9a31","observation_id":"7803a98a-0f7d-4895-b61c-0ebdf468abeb","resolution":{"observed_at":"2026-08-06T12:36:18.965771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2309.04255/citation-record","integrity":"/paper/2309.04255/integrity","json":"/paper/2309.04255/citation-record.json","paper":"/paper/2309.04255"},"outbound":[],"paper":{"arxiv_id":"2309.04255","last_updated":"2023-09-08T10:44:19Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-07-06T16:16:07.182657Z","submitted_at":"2023-09-08T10:44:19Z","title":"LLMCad: Fast and Scalable On-device Large Language Model Inference"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2309.04255."}