{"as_of":"2026-08-14T09:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8ca39d7741af29e2e9057c3c65c18bd8cc2e27f96c5d36278246964b61d6e719","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:05:49.208874Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.17164/citation-record","integrity":"/paper/2501.17164/integrity","json":"/paper/2501.17164/citation-record.json","paper":"/paper/2501.17164"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/mcom.002.2400106","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.231630Z","title":"LLMind: Orchestrating AI and IoT with LLM for complex task execution,","venue":null,"work_id":"a844bd54-44e0-419b-a097-3fe13de72322","year":2024},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.173820Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:6399b9b80f7dc0bc7d3d6bce644954b2e56d080dbd6edcfc3482ecd449a39e25","observation_id":"cdff9141-3718-4054-8b71-26f86792f03c","resolution":{"observed_at":"2026-08-11T14:05:49.236540Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.34702","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.359549Z","title":"Efficient prompting for LLM-based generative Internet of Things,","venue":null,"work_id":"89fdfd78-eed9-4bf8-8448-4b7701358321","year":2024},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.177238Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:7508a40a77989272cb8413812593b353b20145ba6f2d0b803733133b39e0218c","observation_id":"62a8a615-1869-49fa-b73e-8342acc51bb8","resolution":{"observed_at":"2026-08-11T14:05:49.364464Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.435965Z","title":"Understanding performance implications of LLM inference on CPUs,","venue":null,"work_id":"152cd43d-fe04-41da-8734-e8a036fb74e1","year":2024},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.179845Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:259b02e6021478c40d87a2c997e988de10dac0b85cd724ea494fe89069266cc7","observation_id":"0166e4a0-6e6b-46d3-be2a-f8a36d47a6d1","resolution":{"observed_at":"2026-08-11T14:05:49.439122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.182186Z","title":"Survey on knowledge distillation for large language models: Methods, 7 evaluation, and application,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.182186Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:5b201e610dc91c863844b4c8babe0d71b12291f96cfb6e7b380d34717be3db82","observation_id":"efcda705-7f29-48a0-bb92-932d94fc3668","resolution":{"observed_at":"2026-08-11T14:05:49.182186Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.184804Z","title":"Split learning over wireless networks: Parallel design and resource management,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.184804Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:418a5963af0b066237245656b6c38be5215685df7a7aea93bec8805a007b2bfa","observation_id":"960c1a65-887d-41a9-a1f0-dddc5e5227d7","resolution":{"observed_at":"2026-08-11T14:05:49.184804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12351","last_updated":"2024-02-23T19:22:58Z","snapshot_observed_at":"2026-08-13T05:20:42.459383Z","submitted_at":"2023-11-21T04:59:17Z","title":"Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12351","snapshot_observed_at":"2026-08-11T14:05:49.187406Z","title":"Advancing transformer architecture in long-context large language models: A comprehensive survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.187406Z"},"links":{"cited_paper":"/paper/2311.12351","citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:666f06c32fde0a95e17227a963cd99ac8cf2faec7a62e5ec7dad08a48c8924f9","observation_id":"0024d9a7-1f02-4a44-8697-cd67e1fea754","resolution":{"observed_at":"2026-08-11T14:05:49.187406Z","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":"2026-08-11T14:05:49.425096Z","title":"TouchPose: Hand pose prediction, depth estimation, and touch classification from capacitive images,","venue":null,"work_id":"792a57cd-2f14-4235-aa8b-e5a34d61928e","year":2021},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.190383Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:4826894bc92cd898d99385470f349158a82647f05c8f273ea4166b5fe6f158ac","observation_id":"1425f724-666a-4d51-a11a-701c7479b77e","resolution":{"observed_at":"2026-08-11T14:05:49.427527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.418568Z","title":"A survey on model compression and acceleration for pretrained language models,","venue":null,"work_id":"4de461aa-a0bc-460c-9a50-6c47a2c42ce3","year":2023},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.192518Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:d419e1cfce65d754c0999c892b832e0b9d8bafd864232a60705be60e53db1986","observation_id":"e8ef8840-9530-4ea7-b8e1-ee9f1cda09ae","resolution":{"observed_at":"2026-08-11T14:05:49.420879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.411834Z","title":"Parameter-efficient and student-friendly knowledge distillation,","venue":null,"work_id":"9350ed04-bb8d-4ade-a444-2abfd3e4303f","year":2024},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.194644Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:87984251721a519fcbc1c16a2d9e1dc3a5315f339de1fcd54d5f27d0b88f788f","observation_id":"765b1f01-d84e-40c2-bb89-4257b397a448","resolution":{"observed_at":"2026-08-11T14:05:49.414265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.197604Z","title":"Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.197604Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:984c400cdef95549b115b7201c575cfe4bc095c2111696b87625366d52243acb","observation_id":"bd4be3fd-1af2-4347-ad56-b2103cd65220","resolution":{"observed_at":"2026-08-11T14:05:49.197604Z","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":"2026-08-11T14:05:49.400549Z","title":"An efficient and private ECG classification system using split and semi- supervised learning,","venue":null,"work_id":"1d989b30-ae9a-4668-9d94-a9cad1016b21","year":2023},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.200249Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:b8bbcec0ab5e332f9514663a6d2a300c24d5ad82a990e6ffb651101a040236ec","observation_id":"bc1ca039-1299-47c7-9473-e7df10255fe5","resolution":{"observed_at":"2026-08-11T14:05:49.403496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.393403Z","title":"Traffic sign classification for autonomous vehicles using split and federated learning underlying 5G,","venue":null,"work_id":"fb089935-b536-4fc0-b35c-2a1c4bb40c63","year":2023},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.202304Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:4061e49e0f3db9a1caf6ea72c8a62f3c542228541c81034964580d0638acea2c","observation_id":"5b319260-a537-49ba-9f18-8824e283f0da","resolution":{"observed_at":"2026-08-11T14:05:49.396029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.386571Z","title":"Split learning of multi-modal medical image classification,","venue":null,"work_id":"3f74750d-eb58-4571-b073-898bec8c08cf","year":2024},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.204729Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:632e76525d77b435abfca8a16da2b3b6b1e542857851e26d6fb12bd8cf8b8378","observation_id":"df4d07d3-0b99-4993-b273-fde656e1344b","resolution":{"observed_at":"2026-08-11T14:05:49.389023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.379251Z","title":"NR; Physical layer procedures for data,","venue":null,"work_id":"110a5519-d54d-4146-b798-3bae01ee9e6f","year":2022},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.206852Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:66942305d3c5adb580a2f3f68648c235bd83f6810b96278d7aee231c5be1c35b","observation_id":"f1495b77-961c-47b6-8f34-459b8bd76a7a","resolution":{"observed_at":"2026-08-11T14:05:49.381953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:05:49.370476Z","title":"Accuracy-guaranteed collaborative DNN inference in industrial IoT via deep reinforcement learning,","venue":null,"work_id":"99b5fc6f-a618-4e19-9465-c6b3b690a682","year":2020},"citing_paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:05:49.208874Z"},"links":{"citing_paper":"/paper/2501.17164"},"observation_digest":"sha256:31b0b078414473e6f30d8e5995bf21e28cd214c8acd50979aa776339ef435263","observation_id":"dfd2c5b7-598c-4753-8c58-2dcdb3877925","resolution":{"observed_at":"2026-08-11T14:05:49.373370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2501.17164","last_updated":"2024-12-17T02:31:31Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T14:38:37.935369Z","submitted_at":"2024-12-17T02:31:31Z","title":"Split Knowledge Distillation for Large Models in IoT: Architecture, Challenges, and Solutions"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":9},"total_outbound_references":15},"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 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2501.17164."}