{"as_of":"2026-08-12T04:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bdfa49f89f606fdd81dda162abde7d9abaee96914b48e8d2b1b17207d5839c06","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T06:02:33.456917Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2412.16956/citation-record","integrity":"/paper/2412.16956/integrity","json":"/paper/2412.16956/citation-record.json","paper":"/paper/2412.16956"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T06:02:33.221213Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.221213Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:2212e2af404afe27c174ed6e2ad69ce37faf5d87957750cf6c1a38f8db518855","observation_id":"3bfb9588-3736-471c-a7d9-810529d7981e","resolution":{"observed_at":"2026-08-11T06:02:33.221213Z","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":"2026-08-11T06:02:34.183786Z","title":"Learning transferable visual models from natural language supervi- sion,","venue":null,"work_id":"4ffad53a-2463-4df4-b062-ca15b5c6eca2","year":2021},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.227181Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:74ecde1471469c4ba022fa9e7fb02179b68a4cde54faf39812bf3af04552ba5f","observation_id":"442a3f6a-7188-44b0-ad77-701517cdb9fd","resolution":{"observed_at":"2026-08-11T06:02:34.189003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.168145Z","title":"High- resolution image synthesis with latent diffusion models,","venue":null,"work_id":"abb8e535-f7a4-4954-9d24-322c7e44502d","year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.232083Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:1662c2c16510b4753a38798a17ed59aea62a2cd70f5584ed5e5df75d0e56c0e8","observation_id":"46887a49-4c42-4801-b4e9-c7d937f7313a","resolution":{"observed_at":"2026-08-11T06:02:34.173485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.153295Z","title":"Dual cross- attention learning for fine-grained visual categorization and object re- identification,","venue":null,"work_id":"e0e0b576-aa9a-4a43-9fea-4a130ff3b3a9","year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.237347Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:4e582d1d30f19f15f58e6a9d0c66cbf5eb16a44189ab6e3a39ae4759b4dafe02","observation_id":"40152de4-07e8-4af1-84b7-4194e6318880","resolution":{"observed_at":"2026-08-11T06:02:34.158312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.138125Z","title":"Distribution-aware data expansion with diffusion models,","venue":null,"work_id":"2d8c4765-9402-4d4d-a0d8-5b164ca599ef","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.243157Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:2d946051e41b125a3e4300201de92f133e9923bd57a61a1fd1141c1fabaa70d6","observation_id":"50c9ff26-5912-4e00-aa00-720e42956830","resolution":{"observed_at":"2026-08-11T06:02:34.143181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.122781Z","title":"Dip-go: A diffusion pruner via few-step gradient optimization,","venue":null,"work_id":"961a3d1a-6892-46c2-bdc7-6db1802449bb","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.249709Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:37ef0983da80a7ab43c3b66b161000fe4e7d5341cc7e1e116d39b92ce54258c0","observation_id":"14113b3a-6b95-474c-86cf-f0072a6988af","resolution":{"observed_at":"2026-08-11T06:02:34.127592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.107972Z","title":"Visual prompt tuning,","venue":null,"work_id":"21c92ad2-50f2-4471-97d0-2b37f2ffc3eb","year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.255716Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:c5312bd3c4fc206e9420cd42581be55ec5aae378988497acd5e9c31247bca99d","observation_id":"69fa2f2f-2f50-4c2d-a795-c150b904dd7d","resolution":{"observed_at":"2026-08-11T06:02:34.112789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.092614Z","title":"Sensitivity-aware visual parameter-efficient fine-tuning,","venue":null,"work_id":"b5253e6b-c9d6-44a3-b2db-7b4e3bfc5282","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.260399Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:d0cacba58ba66b48638c05c81b81f5689254ed3272859047acb1d0e2034d80b5","observation_id":"2f3386be-9a9a-4d8e-b17e-310325481e84","resolution":{"observed_at":"2026-08-11T06:02:34.097663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.04673","last_updated":"2022-06-14T12:15:55Z","snapshot_observed_at":"2026-08-11T03:48:11.885296Z","submitted_at":"2022-06-09T17:59:58Z","title":"Neural Prompt Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.04673","snapshot_observed_at":"2026-08-11T06:02:33.265007Z","title":"Neural prompt search,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.265007Z"},"links":{"cited_paper":"/paper/2206.04673","citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:9227b5ab339dc46d203b168dfcd5cf273805f4376748a0178080d7f6fede6a96","observation_id":"11151b22-fb58-45b5-8435-d0ce3cf9487f","resolution":{"observed_at":"2026-08-11T06:02:33.265007Z","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":"2026-08-11T06:02:34.077247Z","title":"E2vpt: An effective and efficient approach for visual prompt tuning,","venue":null,"work_id":"b09196dd-1b04-4d33-81d5-9941286224c5","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.270051Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:3f4a251aae7506bc5c9dbf7018a1ed661c1094e2a6b5c97d4afb2a828fd1ac9c","observation_id":"9b60d207-c985-4138-8b17-34d420463cdd","resolution":{"observed_at":"2026-08-11T06:02:34.082474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.07039","last_updated":"2022-08-09T10:40:06Z","snapshot_observed_at":"2026-08-09T04:45:09.129535Z","submitted_at":"2022-07-14T16:32:28Z","title":"Convolutional Bypasses Are Better Vision Transformer Adapters","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.07039","snapshot_observed_at":"2026-08-11T06:02:33.274732Z","title":"Convolutional bypasses are better vision transformer adapters,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.274732Z"},"links":{"cited_paper":"/paper/2207.07039","citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:96e3623a3771f3a366cdf3c03e52c39fb0c6f5f706f36f99bfacc3422a7e2aae","observation_id":"b36e0d65-08c1-47e3-a38a-21eb5a9e9eed","resolution":{"observed_at":"2026-08-11T06:02:33.274732Z","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":"2026-08-11T06:02:34.061626Z","title":"Adapt- former: Adapting vision transformers for scalable visual recognition,","venue":null,"work_id":"2509ec34-fc56-4dc6-a088-e282e2a0e907","year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.280073Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:fde69e3613dfee9d248b8d939e407806a38d5d840375013f8179c7e6039d1ebc","observation_id":"0e79d7e7-5f2f-4bbb-8a9e-4106ca3cde60","resolution":{"observed_at":"2026-08-11T06:02:34.066985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.046041Z","title":"LoRA: Low-rank adaptation of large language models,","venue":null,"work_id":"ae1b0e98-538c-4754-8e9b-a73439256c90","year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.284800Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:7e00d78e55890694a3796fe4d0d4d3dd7eb986cf824f1b9710c625313e54dfcf","observation_id":"47ffcd57-208f-4b6a-abde-e6650a8ae911","resolution":{"observed_at":"2026-08-11T06:02:34.050999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.030834Z","title":"Side-tuning: a baseline for network adaptation via additive side networks,","venue":null,"work_id":"df56f3d4-4dee-47bf-90dc-8c55b2e9bcad","year":2020},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.290497Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:1ead60a6ce8c9363c05776d178874506d92f776d94d27b69298c9509d5856cc1","observation_id":"0cafaea2-f387-4c6f-a38a-228d67ad5ce9","resolution":{"observed_at":"2026-08-11T06:02:34.035847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:34.015153Z","title":"Sct: A simple baseline for parameter-efficient fine-tuning via salient channels,","venue":null,"work_id":"1b05ae70-a948-4c68-ae15-3e1b8039fa08","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.295177Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:77e20d46f5863cb1d2b2bf2a7390cd1bcd827d8029802b2ad2a6022e45297c6e","observation_id":"7df72a8f-4335-40c6-bd3c-921c3dfda6ba","resolution":{"observed_at":"2026-08-11T06:02:34.020311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.999831Z","title":"Sa 2vp: Spatially aligned-and-adapted visual prompt,","venue":null,"work_id":"8f4daa20-bf99-46b6-9091-85907948da23","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.299984Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:7d75e82bf5cc7c90700d2163a7f84dd30fab002a8a2fce92d4fe7f40a0ce8961","observation_id":"06e1e111-315a-41f1-a294-ed8f2fe3203d","resolution":{"observed_at":"2026-08-11T06:02:34.004892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.984915Z","title":"Lion: Implicit vision prompt tuning,","venue":null,"work_id":"952e6627-eec7-47d7-a18d-fbad975bd8dc","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.304692Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:a6b1e842d0d0f34b3ae41c7e0f9879caa9fdc9bc12f16ae3fc6202345ecb4aa5","observation_id":"9373e8fb-cf07-4e24-af38-1229717c636e","resolution":{"observed_at":"2026-08-11T06:02:33.989757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.969370Z","title":"Argue: Attribute-guided prompt tuning for vision-language models,","venue":null,"work_id":"39debf7d-78ad-4a32-987a-86529142daa4","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.309711Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:fff5129d112975434a0892e674952ebbb2395cddc55172aea17adc79c27989f0","observation_id":"93a076ee-fa33-403b-b940-b66f5f0e9505","resolution":{"observed_at":"2026-08-11T06:02:33.974300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.954759Z","title":"Aapl: Adding attributes to prompt learning for vision-language models,","venue":null,"work_id":"2ba248ad-f011-4c43-81ba-ded4e7703149","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.314482Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:7b296037f379fff440a776e2cb57783e49138404ca65d55bfadb1c9fbcc2dd95","observation_id":"9ea15752-bfc9-4da8-85af-364621142827","resolution":{"observed_at":"2026-08-11T06:02:33.959632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.939481Z","title":"Tinytl: Reduce memory, not pa- rameters for efficient on-device learning,","venue":null,"work_id":"06a0ad52-fa19-48d0-94b9-61f34b85af56","year":2020},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.319266Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:0e2b00ccbdac5b9550624148a90c561476877bf3efd8f65535415383ed0093cc","observation_id":"d0897e2e-19e0-4d76-9844-a701809bbe3d","resolution":{"observed_at":"2026-08-11T06:02:33.944810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.924349Z","title":"Parameter-efficient transfer learning for nlp,","venue":null,"work_id":"95b832e4-ec1b-4c62-aa62-2d42a49cd0c4","year":2019},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.324058Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:a5567930a33a79ee57adb43f8166f0fa74b0eb695de77197e2f14bf435f0aed4","observation_id":"4a9c55bf-d0e3-4bfe-986b-fce519bdbfeb","resolution":{"observed_at":"2026-08-11T06:02:33.929603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.908949Z","title":"Tip-adapter: Training-free clip-adapter for better vision-language modeling,","venue":null,"work_id":"1e6a6768-4492-46f6-9425-0fc138a1fb46","year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.330215Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:c06a350b34e709db18ff3d4d341ce84a60971aadf3f8678aea74d620dd99d0d1","observation_id":"d4bafd1f-c76d-400b-9875-4e2e457f0850","resolution":{"observed_at":"2026-08-11T06:02:33.914174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.893795Z","title":"Revisiting the parameter efficiency of adapters from the perspective of precision redundancy,","venue":null,"work_id":"fbe7a5a1-caf8-4c4f-98f3-80d5d1cefe9d","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.335020Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:54cbf5e5fb834ff8d4e6c6f26d05f1baa7f508f153a9ea31ceccde9ba0f69219","observation_id":"39a8024e-506c-4293-adae-27a1e9b7ce7d","resolution":{"observed_at":"2026-08-11T06:02:33.898849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.878869Z","title":"Learning multiple visual domains with residual adapters,","venue":null,"work_id":"e3c2c87e-586d-4e09-b357-dce6c7d46490","year":2017},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.339828Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:41adfcff2fe0f568fd0d7aba4d415c706b1bab2dc85564978c27fd283c4cc8bc","observation_id":"63cbdab1-9b30-4b54-a5eb-5d2384a8bdc5","resolution":{"observed_at":"2026-08-11T06:02:33.884024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.07779","last_updated":"2020-10-06T10:16:39Z","snapshot_observed_at":"2026-07-06T09:38:29.271727Z","submitted_at":"2020-07-15T15:56:05Z","title":"AdapterHub: A Framework for Adapting Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.07779","snapshot_observed_at":"2026-08-11T06:02:33.344889Z","title":"Adapterhub: A framework for adapting transformers,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.344889Z"},"links":{"cited_paper":"/paper/2007.07779","citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:c216bbd2f5cc0dee1cebf561c7a6e4819ab044bff4f0e431778152ceccb3c535","observation_id":"1fc05c77-8407-42c8-a0aa-c7c9371842a5","resolution":{"observed_at":"2026-08-11T06:02:33.344889Z","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":"2026-08-11T06:02:33.863384Z","title":"Caps-adapter: Caption-based multi- modal adapter in zero-shot classification,","venue":null,"work_id":"b8cbf8a6-bd6b-4be0-9511-a9af092c386a","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.349804Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:c033dd739a20eedc7e038638ad2737c3922c276b6e731d80cf5df299ae65b467","observation_id":"b63ee76e-480e-435c-8d03-a7166060ee46","resolution":{"observed_at":"2026-08-11T06:02:33.868345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.848606Z","title":"Distribution-aware prompt tuning for vision-language models,","venue":null,"work_id":"38129a72-b264-4077-8868-e125a443f9f0","year":null},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.354906Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:c89e8e1edeef5a64185e3d2f9453ab21b15af749716a7e36afdb41e83b79426c","observation_id":"1e91cccf-116f-4600-bd13-b062c2329db1","resolution":{"observed_at":"2026-08-11T06:02:33.853598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.833221Z","title":"Understanding and improving visual prompting: A label-mapping perspective,","venue":null,"work_id":"f2007d91-243f-44b6-9f7c-e92b6b245537","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.368081Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:c94de4653f8b627d6be9d9039b3f22fb7fa406eb5aa4b49a5020f4953595f847","observation_id":"30f82720-1ab7-4fdb-8a1f-4cbfc36504b3","resolution":{"observed_at":"2026-08-11T06:02:33.838444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.817926Z","title":"Autovp: An au- tomated visual prompting framework and benchmark,","venue":null,"work_id":"93764f8a-738c-4088-8930-258c8809d2ca","year":2024},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.372898Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:a59322b815d70d7b2cdaa969b515e869e99035796037a3a12ff3908779076527","observation_id":"b8eab316-f3cb-462b-9211-7c95c545b264","resolution":{"observed_at":"2026-08-11T06:02:33.823264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08386","last_updated":"2024-08-04T10:25:50Z","snapshot_observed_at":"2026-08-01T16:26:33.552406Z","submitted_at":"2023-04-17T15:54:10Z","title":"Progressive Visual Prompt Learning with Contrastive Feature Re-formation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08386","snapshot_observed_at":"2026-08-11T06:02:33.377282Z","title":"Pro- gressive visual prompt learning with contrastive feature re-formation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.377282Z"},"links":{"cited_paper":"/paper/2304.08386","citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:d7a4bb9e6dcc298f2403461f7a5fb0508d0d256fdff34c0620e226cecc12e97a","observation_id":"bcbb9368-8555-4405-87c3-1fc0b63937c0","resolution":{"observed_at":"2026-08-11T06:02:33.377282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05393","last_updated":"2024-05-15T16:13:39Z","snapshot_observed_at":"2026-08-11T09:02:50.899336Z","submitted_at":"2023-10-09T04:16:35Z","title":"Hierarchical Side-Tuning for Vision Transformers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05393","snapshot_observed_at":"2026-08-11T06:02:33.382208Z","title":"Hierarchical side-tuning for vision transformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.382208Z"},"links":{"cited_paper":"/paper/2310.05393","citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:8e015cbce63586e2bf1911c836ae9705e80bdf981d4e1d53a0bfe57f67153b66","observation_id":"8eef9bff-d902-49ad-b923-e01c2d3c120f","resolution":{"observed_at":"2026-08-11T06:02:33.382208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17274","last_updated":"2022-06-03T17:52:04Z","snapshot_observed_at":"2026-08-10T12:13:29.624714Z","submitted_at":"2022-03-31T17:59:30Z","title":"Exploring Visual Prompts for Adapting Large-Scale Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17274","snapshot_observed_at":"2026-08-11T06:02:33.387616Z","title":"Explor- ing visual prompts for adapting large-scale models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.387616Z"},"links":{"cited_paper":"/paper/2203.17274","citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:28c6060c9151d235c9b628b1d91960db86397014c4ee855fc9e9d486c2319c6f","observation_id":"38476415-6dae-43eb-8773-bed6e9c58852","resolution":{"observed_at":"2026-08-11T06:02:33.387616Z","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":"2026-08-11T06:02:33.802605Z","title":"Diversity-aware meta visual prompting,","venue":null,"work_id":"e5d00836-0ef4-41db-86cd-977b2fea6b75","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.392651Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:ffc5cad363b09a185d0e2563e46ffd9d5738154cd5eacdef803a35d21d6ea46a","observation_id":"f3f9f079-4dfc-4ec9-b06d-5737efd5944e","resolution":{"observed_at":"2026-08-11T06:02:33.807785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.786977Z","title":"Apollo: Unified adapter and prompt learning for vision language models,","venue":null,"work_id":"953c6914-68d3-498a-9e85-5ffffde81553","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.397543Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:ea77afc6449e0f75537adbdc397ec9b828e3f564abfbc62e4bc8bdc333008170","observation_id":"7d09e14e-b3d5-4199-a69a-fea6de943232","resolution":{"observed_at":"2026-08-11T06:02:33.792479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.771027Z","title":"Dynamic focus-aware positional queries for semantic segmentation,","venue":null,"work_id":"cc3fe766-3225-49a5-8db1-eb42f92589e9","year":null},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.402682Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:87347f4128458e5a5083a51af36f80eea1e4d7926d9d0f9f360e7d4890222a41","observation_id":"5a804154-c674-4a9e-9cec-2a33896cd1e4","resolution":{"observed_at":"2026-08-11T06:02:33.775921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.754522Z","title":"Adept: Adapter-based efficient prompt tuning approach for language models,","venue":null,"work_id":"c700d033-e5c6-46e4-90b4-355190b3d830","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.407290Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:eaa43dbfc66b2af9c2363e858bf8b00075ed3603da4e5ec63c62d9eb92ee94b0","observation_id":"e0ba85c8-a811-4251-bab6-f1292d104634","resolution":{"observed_at":"2026-08-11T06:02:33.760022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.739456Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":"801e3624-2f08-4f30-a592-32b37c47e783","year":2021},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.412820Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:3fbd0a211496c4535161c8bd12535fca2465db5f6a26bd0ea7af02f55318e2c1","observation_id":"dca2173a-3d2d-4671-8fee-fba4e1736994","resolution":{"observed_at":"2026-08-11T06:02:33.744591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.722198Z","title":"Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,","venue":null,"work_id":"c88baa1d-51a1-4a1d-8d38-723c0d9918c6","year":2021},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.417574Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:2d04a8a16f12930746c960b7f2216c966b151b4746969965df45c28097ce80a0","observation_id":"7b0d159d-d1b4-4b67-a9e1-f32679408ef7","resolution":{"observed_at":"2026-08-11T06:02:33.728021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.706758Z","title":"Some methods for classification and analysis of multi- variate observations,","venue":null,"work_id":"7fcb949c-a882-45cf-b96a-a1135ef25edd","year":null},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.422274Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:431ca334995b9e795d1cc8bfcd6867b1ab28e8cda6dc6f029b2d871695f7e192","observation_id":"49d1621b-d6b4-4ec9-89dd-fbdee810205a","resolution":{"observed_at":"2026-08-11T06:02:33.711944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.04867","last_updated":"2020-02-21T13:36:15Z","snapshot_observed_at":"2026-08-09T06:48:42.729935Z","submitted_at":"2019-10-01T17:06:29Z","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.04867","snapshot_observed_at":"2026-08-11T06:02:33.427453Z","title":"A large-scale study of representation learning with the visual task adaptation benchmark,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.427453Z"},"links":{"cited_paper":"/paper/1910.04867","citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:e45066d42c76acfb377a252f557b811641739d6e820a71e39f106637b7d12592","observation_id":"c7581f7f-93fb-4d80-b1dd-c1380c4b611b","resolution":{"observed_at":"2026-08-11T06:02:33.427453Z","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":"2026-08-11T06:02:33.689380Z","title":"Fixing weight decay regularization in adam,","venue":null,"work_id":"675bd4d3-4987-4b76-ae97-0fe4a39f70a6","year":2018},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.432775Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:335cb0797b3616518c7108f02e7cf251fc112e0af2e181e2a734a7fd380c505d","observation_id":"a2ad7aa8-8e83-439f-bd69-451fe952535a","resolution":{"observed_at":"2026-08-11T06:02:33.695128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.671267Z","title":"Diversity-aware meta visual prompting,","venue":null,"work_id":"b2e29844-e6b3-447e-8239-27f687694c8f","year":2023},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.437508Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:83abf1d6fe20e14cf28fedff182cd89989281d993ddc307363e072adbaf7b017","observation_id":"85d33aa0-fa3e-4ac6-9797-cf83f017fb8d","resolution":{"observed_at":"2026-08-11T06:02:33.676626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.656030Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":"5ea20c60-4ee1-4777-bb3d-3708dd75a571","year":2009},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.442524Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:989e8830b3f660b50f175799b27ed25ea1e764d5feb11ab0b84120f4e80c8cb3","observation_id":"47ec2f35-066d-4e38-84db-ebbd7d05cdc2","resolution":{"observed_at":"2026-08-11T06:02:33.660933Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.639756Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":"aac1d77b-de76-4185-8142-55fa15d7635d","year":2009},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.447456Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:acc7d0d2ecfc8c70ff55c8daf4012d83311bff0ffacfe0dd87778135e1b22ef4","observation_id":"00628907-1ad7-4c98-876f-bc77a07e026d","resolution":{"observed_at":"2026-08-11T06:02:33.644836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.622584Z","title":"Sun database: Large-scale scene recognition from abbey to zoo,","venue":null,"work_id":"b8349b03-1b8e-45e6-9279-fc22e6561761","year":2010},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.452233Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:83f952bc26df07a9524812f5dcd978cfdabdad50699d0755b74e5313803dbe0d","observation_id":"788cfeac-d60b-4844-9b52-9c7155923631","resolution":{"observed_at":"2026-08-11T06:02:33.628519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-11T06:02:33.604972Z","title":"Cats and dogs,","venue":null,"work_id":"4590504a-0d1b-478d-ac9d-88b05ddb639b","year":2012},"citing_paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T06:02:33.456917Z"},"links":{"citing_paper":"/paper/2412.16956"},"observation_digest":"sha256:21ec98b2a0d55cf2cef3b188e5d77c4ed4239243030890456ee1504961125a6d","observation_id":"1c7f5bb0-7ba7-4cb5-88c4-cbe84c23ea9d","resolution":{"observed_at":"2026-08-11T06:02:33.611273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16956","last_updated":"2024-12-24T09:07:26Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T05:54:29.731196Z","submitted_at":"2024-12-22T10:28:52Z","title":"Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":46},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2412.16956."}