{"as_of":"2026-08-18T00:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5d0c9958d0934c83235b6fcfc4cf0f3463a1f0ec90227af02298ebcf6d13c867","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":"2026-08-16T04:49:17.794944Z","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":"2026-08-17T06:30:58.91139+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/2505.00350/citation-record","integrity":"/paper/2505.00350/integrity","json":"/paper/2505.00350/citation-record.json","paper":"/paper/2505.00350"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:49:17.698389Z","title":"Safety and performance, why not both? bi-objective optimized model compression toward ai software deployment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.698389Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:e9167106f8c790ab92d16a765e67c572282f26036d6f556c22f3a2d8c707f47c","observation_id":"c141e4f8-d2cb-4165-8daa-5a4b7b78d2eb","resolution":{"observed_at":"2026-08-16T04:49:17.698389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05964","last_updated":"2024-04-07T13:03:58Z","snapshot_observed_at":"2026-08-16T14:21:28.339814Z","submitted_at":"2024-02-05T12:16:28Z","title":"A Survey on Transformer Compression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05964","snapshot_observed_at":"2026-08-16T04:49:17.705670Z","title":"A survey on transformer compression,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.705670Z"},"links":{"cited_paper":"/paper/2402.05964","citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:0bec2db74ea7ea88ad96a1682f07e0fcb672904ea6daaf0d02be3b465aab1e58","observation_id":"37c4760d-ef74-4ec4-9f3f-70a12a249ab7","resolution":{"observed_at":"2026-08-16T04:49:17.705670Z","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":"2026-08-16T04:49:18.285966Z","title":"Safety and performance, why not both? bi-objective optimized model compression against het- erogeneous attacks toward ai software deployment,","venue":null,"work_id":"bdef4ab8-f951-49bb-b70e-dcbeef50287e","year":2024},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.713222Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:35ab0756c70650f8a54719e435b69b0d58779186048de0fb502ee0dabe4d06a5","observation_id":"bbc3ded8-c9c2-4e46-9652-a97555e98259","resolution":{"observed_at":"2026-08-16T04:49:18.292008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:49:18.264971Z","title":"Optimal brain damage,","venue":null,"work_id":"7eb9a646-9d5b-41dc-b085-3399ed0bc196","year":1990},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.726703Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:269ee59594a1e21db823392a81617d74069c312a4ffd4e10c85714ac8ae1602c","observation_id":"c925e1c7-d82c-4e44-a8da-23b42890dc2b","resolution":{"observed_at":"2026-08-16T04:49:18.270750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:49:18.234001Z","title":"Channel pruning for accelerating very deep neural networks,","venue":null,"work_id":"8a51c2aa-6520-4dcb-96b3-898e53af44fb","year":2017},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.734628Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:171f71c62ea59c086c1243a038c48c3d10f661f93bdc50ab48d4585785ce3721","observation_id":"bbc0c179-2156-4f52-a0a8-7eb3832760de","resolution":{"observed_at":"2026-08-16T04:49:18.245375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-14T04:51:04.817737Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-16T04:49:17.741119Z","title":"Estimating or propagating gradients through stochastic neurons,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.741119Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:1a9174747fe4ada600094a20a80ab57219fd5bdf44cd3d311e94d6989d9c8721","observation_id":"3244a517-a5cb-4324-8219-40f9b2889564","resolution":{"observed_at":"2026-08-16T04:49:17.741119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07686","last_updated":"2022-06-15T17:34:07Z","snapshot_observed_at":"2026-08-16T16:52:45.213028Z","submitted_at":"2022-06-15T17:34:07Z","title":"From near-symplectic constructions to trisections of 4-manifolds","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07686","snapshot_observed_at":"2026-08-16T04:49:17.747951Z","title":"Differentiable model compression via pseudo quantization noise,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.747951Z"},"links":{"cited_paper":"/paper/2206.07686","citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:84a704b4bf387ac04ae24ee7c3aa419b87dcdce56e0d2c8f735afa492ac8ed4e","observation_id":"e35ae700-6aae-41da-a536-611404881751","resolution":{"observed_at":"2026-08-16T04:49:17.747951Z","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":"2026-08-16T04:49:18.210099Z","title":"Xnor-net: Imagenet classification using binary convolutional neural networks,","venue":null,"work_id":"901ac11f-3454-426f-80de-dfc91ab7e73b","year":2016},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.754531Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:9b858f52b1307c352cb9ca5bd703e6e46a7d37e11145dfb9e6a75bd9d0920068","observation_id":"b3c3503b-5588-446c-aa67-76b6ee35a6d0","resolution":{"observed_at":"2026-08-16T04:49:18.216825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:49:17.760471Z","title":"The mnist database of handwritten digit images for machine learning research,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.760471Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:c64dc52589330e8c8a9541e583d5569d0d3a888470857d24fb72909a2bfe8294","observation_id":"ed400d41-785d-4a92-9bc8-42264ba6e0bd","resolution":{"observed_at":"2026-08-16T04:49:17.760471Z","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":"2026-08-16T04:49:17.766418Z","title":"Grad-cam: Visual explanations from deep networks via gradient-based localization,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.766418Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:b2dec480a38898ba2d84abc951508500d26ae96230322e8085210dea68d8b2ac","observation_id":"077ac4b2-7920-4fca-9465-6715a874cde6","resolution":{"observed_at":"2026-08-16T04:49:17.766418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.02719","last_updated":"2026-04-07T06:07:49Z","snapshot_observed_at":"2026-08-02T14:15:42.685513Z","submitted_at":"2023-07-06T01:57:37Z","title":"Understanding Uncertainty Sampling via Equivalent Loss","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.02719","snapshot_observed_at":"2026-08-16T04:49:17.771976Z","title":"Understanding uncertainty sampling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.771976Z"},"links":{"cited_paper":"/paper/2307.02719","citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:2bab088262c2ea97691475d2854b74041fed7ca5a87432f73547ea5e81ab7342","observation_id":"ff846369-d553-4773-bb68-bab5320ca6cc","resolution":{"observed_at":"2026-08-16T04:49:17.771976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13142","last_updated":"2023-01-31T10:28:52Z","snapshot_observed_at":"2026-08-16T15:59:04.312869Z","submitted_at":"2023-01-30T18:22:28Z","title":"Self-Compressing Neural Networks","version":2},"cited_work":{"arxiv_id":"2301.13142","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.13142","snapshot_observed_at":"2026-08-16T04:49:17.893858Z","title":"Self-Compressing Neural Networks","venue":"cs.LG","work_id":"3d1fae10-4322-4b3d-8929-a396856c6264","year":2023},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.777172Z"},"links":{"cited_paper":"/paper/2301.13142","citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:966f68ef04af18233b2bf1263e38c143466a12f0c9c4e10fd5b711fd7d3fd355","observation_id":"78584c3d-cce4-4cc7-b433-ee3c963e261a","resolution":{"observed_at":"2026-08-16T04:49:17.902123Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:49:18.168775Z","title":"Understanding straight-through estimator in training activation quantized neural nets,","venue":null,"work_id":"22f93426-7754-4267-8e0f-08ee28b43c88","year":null},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.783372Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:7325729e2ee709f057903267d7e83e1d07a02ee9088263918eee0502009e6a61","observation_id":"3e969d57-068c-4362-ae17-3e0f9c9a54c6","resolution":{"observed_at":"2026-08-16T04:49:18.175336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:49:18.140739Z","title":"On the size of convolutional neural networks and generalization performance,","venue":null,"work_id":"118590b3-98c1-4b97-b422-616441bb6720","year":2016},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.794944Z"},"links":{"citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:281831fc408a26f6902c0faf17a75a7ccc798b769cf28c2e163d4a56661754ce","observation_id":"13d41ca8-7922-4259-a747-06b71d981a56","resolution":{"observed_at":"2026-08-16T04:49:18.153407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.05662","last_updated":"2019-09-25T14:33:44Z","snapshot_observed_at":"2026-08-14T17:02:42.244835Z","submitted_at":"2019-03-13T18:23:43Z","title":"Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.05662","snapshot_observed_at":"2026-08-16T04:49:17.789157Z","title":"Available: https://arxiv.org/abs/1903.05662","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T04:49:17.789157Z"},"links":{"cited_paper":"/paper/1903.05662","citing_paper":"/paper/2505.00350"},"observation_digest":"sha256:b5c53396cb4787caaf02c0b6798e2a52525e96b1efc796612edd91d3074733b2","observation_id":"e6ef5d13-7a7a-4aea-b607-fece5728d620","resolution":{"observed_at":"2026-08-16T04:49:17.789157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.00350","last_updated":"2025-05-01T06:50:30Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T14:31:15.588907Z","submitted_at":"2025-05-01T06:50:30Z","title":"Optimizing Deep Neural Networks using Safety-Guided Self Compression"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":6},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2505.00350."}