{"as_of":"2026-08-14T20:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4120a1331a068e13b2e9935556c37ecb4784e8f666eee789ce8e3250bf0dd4f9","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:19:34.793339Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T10:19:59.801796Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.17376","last_updated":"2025-04-24T08:50:01Z","snapshot_observed_at":"2026-08-10T05:50:37.485708Z","submitted_at":"2025-04-24T08:50:01Z","title":"On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.17376","snapshot_observed_at":"2026-08-07T10:19:34.793339Z","title":"On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.05748","last_updated":"2025-06-06T05:18:54Z","snapshot_observed_at":"2026-08-07T21:55:18.527116Z","submitted_at":"2025-06-06T05:18:54Z","title":"Efficient Online RFT with Plug-and-Play LLM Judges: Unlocking State-of-the-Art Performance","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:34.793339Z"},"links":{"cited_paper":"/paper/2504.17376","citing_paper":"/paper/2506.05748"},"observation_digest":"sha256:f46ef65e82073dca15e9f9b4da67c8316592a639f5924a9fe57e8958eed91785","observation_id":"74ae2aea-c188-4db3-8f8c-e0aa6aeca16a","resolution":{"observed_at":"2026-08-07T10:19:34.793339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.17376","last_updated":"2025-04-24T08:50:01Z","snapshot_observed_at":"2026-08-10T05:50:37.485708Z","submitted_at":"2025-04-24T08:50:01Z","title":"On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.17376","snapshot_observed_at":"2026-08-07T04:27:40.326761Z","title":"Xiang, R","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.12094","last_updated":"2025-06-12T09:51:06Z","snapshot_observed_at":"2026-08-14T12:29:54.788875Z","submitted_at":"2025-06-12T09:51:06Z","title":"Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T04:27:40.326761Z"},"links":{"cited_paper":"/paper/2504.17376","citing_paper":"/paper/2506.12094"},"observation_digest":"sha256:70195a6a70d399f9ccf708459db861bae5405c2d1a89ab008b7e0643a3a8d6a0","observation_id":"ac2182a6-f225-4de6-ac58-b77e2f50b9a7","resolution":{"observed_at":"2026-08-07T04:27:40.326761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.17376","last_updated":"2025-04-24T08:50:01Z","snapshot_observed_at":"2026-08-10T05:50:37.485708Z","submitted_at":"2025-04-24T08:50:01Z","title":"On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.17376","snapshot_observed_at":"2026-08-05T19:30:24.782948Z","title":"On-device qwen2","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.12473","last_updated":"2025-08-17T19:13:27Z","snapshot_observed_at":"2026-08-13T10:18:27.198693Z","submitted_at":"2025-08-17T19:13:27Z","title":"Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T19:30:24.782948Z"},"links":{"cited_paper":"/paper/2504.17376","citing_paper":"/paper/2508.12473"},"observation_digest":"sha256:484885593ab229f995d7ba9d9735b0ca3d916751bb22ba31670cd556aede27cc","observation_id":"e0d4e713-8796-4367-b8b0-ce209f4ff207","resolution":{"observed_at":"2026-08-05T19:30:24.782948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.17376","last_updated":"2025-04-24T08:50:01Z","snapshot_observed_at":"2026-08-10T05:50:37.485708Z","submitted_at":"2025-04-24T08:50:01Z","title":"On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration","version":1},"cited_work":{"arxiv_id":"2504.17376","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.17376","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On-device qwen2. 5: Efficient llm inference with model compression and hardware acceleration","venue":null,"work_id":"48dd4e3c-58c8-428d-8e8a-b3b108f6e147","year":2025},"citing_paper":{"arxiv_id":"2605.16269","last_updated":"2026-03-31T15:09:26Z","snapshot_observed_at":"2026-07-06T23:27:24.957966Z","submitted_at":"2026-03-31T15:09:26Z","title":"Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-21T10:17:29.611062Z"},"links":{"cited_paper":"/paper/2504.17376","citing_paper":"/paper/2605.16269"},"observation_digest":"sha256:c449a4aed7dd06e3bede22d2fab0dede089c79411afdd2ce653e48c5e817a2a9","observation_id":"f37627fc-5c4a-4578-85be-7c0c441e8b0e","resolution":{"observed_at":"2026-05-21T10:19:59.804670Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2504.17376/citation-record","integrity":"/paper/2504.17376/integrity","json":"/paper/2504.17376/citation-record.json","paper":"/paper/2504.17376"},"outbound":[],"paper":{"arxiv_id":"2504.17376","last_updated":"2025-04-24T08:50:01Z","latest_version":1,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-10T05:50:37.485708Z","submitted_at":"2025-04-24T08:50:01Z","title":"On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.17376."}