{"as_of":"2026-08-07T15:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ba035c1cd397dfddedc8b653431817a27cea0f69779343011023f638c8eed589","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T01:32:33.691163Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2607.25794/citation-record","integrity":"/paper/2607.25794/integrity","json":"/paper/2607.25794/citation-record.json","paper":"/paper/2607.25794"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T01:32:30.850883Z","title":"Large scale visual food recognition,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:30.850883Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:af500157782ce24c9e6bc7b4fef1855bd44c0bf17476fdfce379838807837d3f","observation_id":"71822307-3b9a-4fe5-9b57-976d2447c888","resolution":{"observed_at":"2026-08-01T01:32:30.850883Z","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":"10.1007/978-3-319-10599-4","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Food-101: Mining discriminative components with random forests,","venue":"Lecture notes in computer science","work_id":"7efc87a6-6728-4f09-890e-6f6642fa63f2","year":2014},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:30.928635Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:7e37a0d3e95b8faa793cc7a71df0ac7cbbb20afc2ec6c6781338e3db391a2610","observation_id":"b909d30d-0a76-4638-bf09-67ba3619bf5c","resolution":{"observed_at":"2026-08-01T01:36:19.906506Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-01T01:32:31.018574Z","title":"Dishcovery Mission II Challenge: Where VLM meets food,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.018574Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:729d415dd89859ebc49192f42570e905b143e669b2d44612fd46c3287279762c","observation_id":"3b0b58e3-1d0a-4c29-8752-b542e742649b","resolution":{"observed_at":"2026-08-01T01:32:31.018574Z","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":"2026-08-01T01:32:31.096810Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.096810Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:5ab9d3504d9adf1764b0e6ffb5826f4afa1a8acf44982166cc55127dff6232a2","observation_id":"5011870a-4062-45ec-b15f-5a45f7943446","resolution":{"observed_at":"2026-08-01T01:32:31.096810Z","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":"2026-08-01T01:32:31.209122Z","title":"Scaling up visual and vision-language representation learning with noisy text supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.209122Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:6bdcf8e6eb4941e979b0edacd73d3c0f8a68cf73f3fc5ddc09d7baee8868fe2a","observation_id":"1d4e9d48-164c-41fd-87cb-6cfa0f79bd8c","resolution":{"observed_at":"2026-08-01T01:32:31.209122Z","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":"2026-08-01T01:32:31.316247Z","title":"OpenCLIP,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.316247Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:be4349e73a3f1b413de736a1727c4e64b02c34996c2c710ce9cfa028e33c012a","observation_id":"ec4cc4b1-1195-4617-88c6-7aede3e7e100","resolution":{"observed_at":"2026-08-01T01:32:31.316247Z","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":"2026-08-01T01:32:31.413710Z","title":"Sigmoid loss for language image pre-training,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.413710Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:b5e7711754fcc5919d2275ccbeeaa3c97c7170c339641b663d92e758d60a7a7b","observation_id":"4bc8373a-9368-4d7a-9109-a9e0fb6563d3","resolution":{"observed_at":"2026-08-01T01:32:31.413710Z","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":"2026-08-01T01:32:31.524269Z","title":"Recipe1M+: A dataset for learning cross-modal embeddings for cooking recipes and food images,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.524269Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:eb4de751c6806e8cc534524457d029904275aad47cbff8e0232adc28f8f20676","observation_id":"2fb9aa6e-023d-45ea-9f50-5ea57f34cbfe","resolution":{"observed_at":"2026-08-01T01:32:31.524269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.04085","last_updated":"2020-04-14T14:57:40Z","snapshot_observed_at":"2026-08-02T11:18:37.014004Z","submitted_at":"2019-01-13T23:27:58Z","title":"Passage Re-ranking with BERT","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.04085","snapshot_observed_at":"2026-08-01T01:32:31.632993Z","title":"Passage re-ranking with BERT,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.632993Z"},"links":{"cited_paper":"/paper/1901.04085","citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:7837609148816039c0867b4f9ae9b199447ac4964d26746accef9fd31009ab31","observation_id":"4113570c-a919-4009-8331-caa84c3b06da","resolution":{"observed_at":"2026-08-01T01:32:31.632993Z","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":"2026-08-01T01:32:31.737098Z","title":"Reciprocal rank fusion outperforms Condorcet and individual rank learning methods,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.737098Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:ca14afdeecd4b758597d165b1ebf6143ff7ee8436162fbd3587eff48a4b45e31","observation_id":"50866322-c439-4de3-ac5b-987a2b194ab6","resolution":{"observed_at":"2026-08-01T01:32:31.737098Z","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":"2026-08-01T01:32:31.905207Z","title":"Is ChatGPT good at search? Investigating large language models as re-ranking agents,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:31.905207Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:446de5c5e014d61288541c4c62912fd52e19311537f9496cb1670f24426d9379","observation_id":"e08251a9-de2f-4b18-8d70-ade1ef08f85e","resolution":{"observed_at":"2026-08-01T01:32:31.905207Z","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":"2026-08-01T01:32:32.026185Z","title":"CLIP-Adapter: Better vision-language models with feature adapters,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:32.026185Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:633cd7cfa1aa1956b28ffddf54fe34ce7f3644d2fc6c4d1356c4be36355eca9e","observation_id":"ee499a87-f617-4c8f-965c-d14822c0ec41","resolution":{"observed_at":"2026-08-01T01:32:32.026185Z","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":"2026-08-01T01:32:32.233068Z","title":"LoRA: Low-rank adaptation of large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:32.233068Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:6aa7d885bec8ce5c1ad666056fbcc5c9e442ca5d1e35b4b4325917788abee1f4","observation_id":"a0e35696-986c-463d-9c07-23fc58da0d66","resolution":{"observed_at":"2026-08-01T01:32:32.233068Z","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":"2026-08-01T01:32:32.353401Z","title":"DoRA: Weight-decomposed low-rank adaptation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:32.353401Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:1196e26959aba7960c7c92fcf9788c14bd881c5404d88d728e35d6a8ef097083","observation_id":"c9e4422c-3f6b-41b8-921f-b87e4ef6b8dc","resolution":{"observed_at":"2026-08-01T01:32:32.353401Z","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":"2026-08-01T01:32:32.509324Z","title":"When and why vision-language models behave like bags-of-words, and what to do about it?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:32.509324Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:496420a3224682a9f4d51f73a763dde4694f5d8f164ad16e95a55c46f596f16a","observation_id":"8daaf14e-c364-4fc4-8826-61df1af6d98f","resolution":{"observed_at":"2026-08-01T01:32:32.509324Z","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":"2026-08-01T01:32:32.632467Z","title":"Robust fine-tuning of zero-shot models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:32.632467Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:8e1c0313259fc1581be24128ac1104282625b40a3de701f790b82f54c6b2a516","observation_id":"24415aea-59b0-4d02-a711-2fcf757b1fb2","resolution":{"observed_at":"2026-08-01T01:32:32.632467Z","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":"2026-08-01T01:32:32.802189Z","title":"Data filtering networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:32.802189Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:81a57a56aa100066f594942dbb89274b93608772ebeedf4da16c4c52623b7c00","observation_id":"d39d5fe0-49df-43cb-b1a7-e0731b559b5b","resolution":{"observed_at":"2026-08-01T01:32:32.802189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.22062","last_updated":"2025-08-01T06:40:13Z","snapshot_observed_at":"2026-08-06T12:08:20.848892Z","submitted_at":"2025-07-29T17:59:58Z","title":"Meta CLIP 2: A Worldwide Scaling Recipe","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.22062","snapshot_observed_at":"2026-08-01T01:32:32.988872Z","title":"Meta CLIP 2: A worldwide scaling recipe,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:32.988872Z"},"links":{"cited_paper":"/paper/2507.22062","citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:9d7b5844016b59700a6f63730a87b7dec5e22e9a5c3e5ff50d092a739065c362","observation_id":"6d0e6292-82af-4548-95f5-d21db5e3b9d6","resolution":{"observed_at":"2026-08-01T01:32:32.988872Z","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":"2026-08-01T01:32:33.172208Z","title":"Gemma 4 31B Instruct,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:33.172208Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:770ab23ae5276eabf4bae21cc1da9d84492c7fafdff24c373851d4ea4fcab911","observation_id":"757bf3f9-2c13-4fcc-ab7b-661a22738357","resolution":{"observed_at":"2026-08-01T01:32:33.172208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-01T01:32:33.376887Z","title":"Representation learning with contrastive predictive coding,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:33.376887Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:3beaa327970bfb268916a20fc64592244e6e0c7b06b215fa53097328fdf1a26f","observation_id":"e028e063-5105-46da-8d61-23c2e401d404","resolution":{"observed_at":"2026-08-01T01:32:33.376887Z","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":"2026-08-01T01:32:33.691163Z","title":"Hugging Face Hub,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:33.691163Z"},"links":{"citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:672d41533925d56835dfed5eb096e21bc65fdadb15d06c010fbf931822280220","observation_id":"1aea730e-f5c5-4f3f-be6b-ca7ceb03c6f2","resolution":{"observed_at":"2026-08-01T01:32:33.691163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-01T01:32:33.548750Z","title":"Available: http://dx.doi.org/10.48550/arXiv.1807.03748","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-01T01:32:33.548750Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2607.25794"},"observation_digest":"sha256:ea9d25f5a6bee020defba04e2cc9f6784d691ac48a814c95bc4b7af16d4b54f3","observation_id":"769b9aa2-e3dd-4831-ba80-cba6f28b0042","resolution":{"observed_at":"2026-08-01T01:32:33.548750Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.25794","last_updated":"2026-07-28T14:44:56Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-01T01:32:30.484864Z","submitted_at":"2026-07-28T14:44:56Z","title":"Fine-Grained Food Image Understanding via Target-Aware Data Alignment"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":22},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.25794."}