{"as_of":"2026-08-20T19:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2369b0cf958909a34d4411f5d646d6515d41a720b9c1d60c515c58734c1468f7","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:55:20.934513Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T11:03:15.127030Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-11T05:38:38.069872Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-12T11:03:15.127030Z","title":"A Combined Encoder and Transformer Approach for Coherent and High- Quality Text Generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.18622","last_updated":"2024-11-27T18:59:50Z","snapshot_observed_at":"2026-08-19T21:50:02.947584Z","submitted_at":"2024-11-27T18:59:50Z","title":"Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T11:03:15.127030Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2411.18622"},"observation_digest":"sha256:ebd0ff4e30a1464971731dc165db571d7d5fa78b04aa6de64a7432320e7ccc40","observation_id":"469a01b1-68d3-4443-b605-2313819d6f40","resolution":{"observed_at":"2026-08-12T11:03:15.127030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-11T23:46:09.676987Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02211","last_updated":"2024-12-03T07:04:10Z","snapshot_observed_at":"2026-08-16T13:46:41.022499Z","submitted_at":"2024-12-03T07:04:10Z","title":"An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:46:09.676987Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2412.02211"},"observation_digest":"sha256:3e27606af0d7e64d7207aea2adfa9ff25f36f1aefbcc86cc0ef7d9200383c42e","observation_id":"d4de66fa-744b-467e-9cfc-1104b66b1e74","resolution":{"observed_at":"2026-08-11T23:46:09.676987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-11T22:50:44.128565Z","title":"A Combined Encoder and Transformer Approach for Coherent and High- QualityTextGeneration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03097","last_updated":"2024-12-04T07:50:27Z","snapshot_observed_at":"2026-08-18T11:13:39.633150Z","submitted_at":"2024-12-04T07:50:27Z","title":"Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T22:50:44.128565Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2412.03097"},"observation_digest":"sha256:319df7b34316b504239fa9c4f30d0f6ff33be4670880a3dedf1eb018e6b693ae","observation_id":"7201d1e6-2e37-41a6-98fa-22d479df27cb","resolution":{"observed_at":"2026-08-11T22:50:44.128565Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-11T22:47:27.524882Z","title":"A Combined Encoder and Transformer Approach for Coherent and High - Quality Text Generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03606","last_updated":"2024-12-04T08:15:27Z","snapshot_observed_at":"2026-08-18T18:20:47.395690Z","submitted_at":"2024-12-04T08:15:27Z","title":"Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T22:47:27.524882Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2412.03606"},"observation_digest":"sha256:73a6c7028c71542b1621ba77e9583ef476b5fe6321c60925017a8863e8cecac4","observation_id":"fc995a51-d77c-48f4-ad84-616e11f9319b","resolution":{"observed_at":"2026-08-11T22:47:27.524882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-11T18:05:02.138175Z","title":"A Combined Encoder and Transformer Approach for Coherent and High- Quality Text Generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08255","last_updated":"2024-12-11T10:06:57Z","snapshot_observed_at":"2026-08-18T08:17:02.974303Z","submitted_at":"2024-12-11T10:06:57Z","title":"Accurate Medical Named Entity Recognition Through Specialized NLP Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T18:05:02.138175Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2412.08255"},"observation_digest":"sha256:7db409b60685ef4af188ddf4a5bcddcd5bb7ad661914b4bf6e6e09f0b6e27def","observation_id":"d25a60be-de7e-491c-b36f-f62baf5d2393","resolution":{"observed_at":"2026-08-11T18:05:02.138175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-11T10:18:14.583271Z","title":"A CombinedEncoderandTransformerApproachforCoherentandHigh- QualityTextGeneration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16837","last_updated":"2024-12-22T03:06:48Z","snapshot_observed_at":"2026-08-20T16:03:37.824967Z","submitted_at":"2024-12-22T03:06:48Z","title":"Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:18:14.583271Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2412.16837"},"observation_digest":"sha256:ffa69af1ddef99a804448d75cd0ca7d5584a4b3ec9df1e8fa29f8c643bb513e6","observation_id":"3e2b25c4-786d-40fe-b7ac-826770cc1b70","resolution":{"observed_at":"2026-08-11T10:18:14.583271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":"2411.12157","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-11T05:38:38.069872Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","venue":"cs.CL","work_id":"1e1b013f-f766-4983-aedd-40208b13a706","year":2024},"citing_paper":{"arxiv_id":"2412.17314","last_updated":"2024-12-23T06:14:15Z","snapshot_observed_at":"2026-08-18T09:16:59.690543Z","submitted_at":"2024-12-23T06:14:15Z","title":"Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T05:38:37.630653Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2412.17314"},"observation_digest":"sha256:4c417fe1a47ae9acab9cd3ec7e9e3975bfafcb2db7f06bd0d2ec65ec7c79539f","observation_id":"30e39831-037d-4c91-8635-2bce0cc44efa","resolution":{"observed_at":"2026-08-11T05:38:38.074748Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.12157","snapshot_observed_at":"2026-08-11T14:55:48.082422Z","title":"A Combined Encoder and Transformer Approach for Coherent and High- Quality Text Generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.14745","last_updated":"2024-12-16T06:37:09Z","snapshot_observed_at":"2026-08-18T20:21:32.052634Z","submitted_at":"2024-12-16T06:37:09Z","title":"AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:55:48.082422Z"},"links":{"cited_paper":"/paper/2411.12157","citing_paper":"/paper/2501.14745"},"observation_digest":"sha256:1b9675032e88cae90c59ae742c77aa94bac935c6684e960b47faa04bf57a57c5","observation_id":"92f5dd74-fee5-4960-8ce0-a8dad51b54ae","resolution":{"observed_at":"2026-08-11T14:55:48.082422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2411.12157/citation-record","integrity":"/paper/2411.12157/integrity","json":"/paper/2411.12157/citation-record.json","paper":"/paper/2411.12157"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:55:20.826601Z","title":"Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs),","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.826601Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:b99ac88d00dea7fc7009e26938af97558601442fe10517764cc15b3e7cead14c","observation_id":"adeb0a9a-9952-4a9c-a773-322880609f11","resolution":{"observed_at":"2026-08-12T17:55:20.826601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13099","last_updated":"2024-10-17T00:05:05Z","snapshot_observed_at":"2026-08-16T13:47:28.483576Z","submitted_at":"2024-10-17T00:05:05Z","title":"Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13099","snapshot_observed_at":"2026-08-12T17:55:20.832421Z","title":"Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.832421Z"},"links":{"cited_paper":"/paper/2410.13099","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:a6a48082d05ced138f5b5451cc51ce8553e443fb1579dc958b94fd2135b94b65","observation_id":"fbb3894c-c761-48dc-84f6-c42b8990f027","resolution":{"observed_at":"2026-08-12T17:55:20.832421Z","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":"2026-08-12T17:55:21.440469Z","title":"Improving AutoML for LLMs via Knowledge-Based Meta-Learning,","venue":null,"work_id":"8e8d91aa-b625-4129-805a-02b119c5ad13","year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.837497Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:b91edbfba1e77f9d0f8ee886f2411d5adc2f0f773c798a5212956d4056e542bf","observation_id":"6771078b-39ae-4ff3-8bc5-3dd731f6ea08","resolution":{"observed_at":"2026-08-12T17:55:21.446129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.06326","last_updated":"2024-11-10T01:26:39Z","snapshot_observed_at":"2026-08-19T11:13:34.741115Z","submitted_at":"2024-11-10T01:26:39Z","title":"Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.06326","snapshot_observed_at":"2026-08-12T17:55:20.842454Z","title":"Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.842454Z"},"links":{"cited_paper":"/paper/2411.06326","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:aa179ec89473f0db8ab435f32283f4e8293aad4a6723a7f559ade2607ba86735","observation_id":"68e65857-fe9d-4126-89fd-ee0d06914841","resolution":{"observed_at":"2026-08-12T17:55:20.842454Z","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":"2026-08-12T17:55:21.423960Z","title":"Financial Risk Analysis Using Integrated Data and Transformer-Based Deep Learning","venue":null,"work_id":"69b9bed7-72f4-4971-bb83-0e74d82529ba","year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.847954Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:5719cdfa10a1d706be595c29fb7df1987d15a6e240bdfc693444d4f7301bc995","observation_id":"0a4aa4e4-b494-41b5-a36f-443e2e42b46c","resolution":{"observed_at":"2026-08-12T17:55:21.429313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.19211","last_updated":"2024-10-24T23:43:50Z","snapshot_observed_at":"2026-08-20T14:42:43.834088Z","submitted_at":"2024-10-24T23:43:50Z","title":"Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.19211","snapshot_observed_at":"2026-08-12T17:55:20.852458Z","title":"Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.852458Z"},"links":{"cited_paper":"/paper/2410.19211","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:d30c24f4964627e89b3f1afddd07ff6d14608b8948c3115ce6105c28d4c0c53f","observation_id":"f16b0962-5c0e-48d4-bae2-16fa77fe3e4b","resolution":{"observed_at":"2026-08-12T17:55:20.852458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24046","last_updated":"2024-10-31T15:42:24Z","snapshot_observed_at":"2026-08-20T14:28:03.555145Z","submitted_at":"2024-10-31T15:42:24Z","title":"Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.24046","snapshot_observed_at":"2026-08-12T17:55:20.857605Z","title":"Deep Learning with HM- VGG: AI Strategies for Multi-modal Image Analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.857605Z"},"links":{"cited_paper":"/paper/2410.24046","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:7568fb2188057f0f4492d46c8a64a54c3930659c3c72ed44e88e4f9b71812fdc","observation_id":"d5c04e35-31bc-4105-a38f-467014e20170","resolution":{"observed_at":"2026-08-12T17:55:20.857605Z","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":"2026-08-12T17:55:21.408659Z","title":"Medical Image Segmentation with Bilateral Spatial Attention and Transfer Learning","venue":null,"work_id":"969b01fc-c78f-4852-b089-df4942d9c9ec","year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.861920Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:92af3f96280f34715d436681d18df5bed18e9b61356c3fa754a932d174048cb9","observation_id":"2128ca6b-a6ca-4419-a21c-d205c0ec2bf2","resolution":{"observed_at":"2026-08-12T17:55:21.413719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12259","last_updated":"2024-10-16T05:58:08Z","snapshot_observed_at":"2026-08-17T18:54:20.681480Z","submitted_at":"2024-10-16T05:58:08Z","title":"Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12259","snapshot_observed_at":"2026-08-12T17:55:20.866365Z","title":"Optimizing YOLOv5s Object Detection through Knowledge Distillation Algorithm","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.866365Z"},"links":{"cited_paper":"/paper/2410.12259","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:66eaa21b8e3997794eb41f5768af768ed47aa0d38d4ad3b5424407c045b95f1e","observation_id":"f79acf9d-6a98-4291-8d29-fec30722f3fa","resolution":{"observed_at":"2026-08-12T17:55:20.866365Z","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":"2026-08-12T17:55:21.393030Z","title":"Research on Intelligent System of Medical Image Recognition and Disease Diagnosis Based on Big Data","venue":null,"work_id":"2d1cac51-85a2-4bd1-86a3-7c63931cbe0a","year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.870463Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:2c08c0f0b7e419de3128c39b0c5b4aaa7a76fe4a5b7d0aa6e34de8fb994437e4","observation_id":"8d026d08-aa54-411e-9711-ee360c330306","resolution":{"observed_at":"2026-08-12T17:55:21.398038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04283","last_updated":"2024-10-05T20:49:05Z","snapshot_observed_at":"2026-08-20T06:31:22.032050Z","submitted_at":"2024-10-05T20:49:05Z","title":"Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04283","snapshot_observed_at":"2026-08-12T17:55:20.874631Z","title":"Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.874631Z"},"links":{"cited_paper":"/paper/2410.04283","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:6ad7911627697c81be52711f91eb5aed73fe351a833ebd0c66709508c9236dad","observation_id":"8be1b810-02f0-447c-8a8f-f46c106f239a","resolution":{"observed_at":"2026-08-12T17:55:20.874631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08553","last_updated":"2024-10-11T06:05:10Z","snapshot_observed_at":"2026-08-16T13:10:27.070112Z","submitted_at":"2024-10-11T06:05:10Z","title":"Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08553","snapshot_observed_at":"2026-08-12T17:55:20.879173Z","title":"Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.879173Z"},"links":{"cited_paper":"/paper/2410.08553","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:d1b5a1e73a84f649201eaa5d481968efb5dbe63349a7a94905139582daf83f56","observation_id":"a5d92255-aa36-4572-b43f-e1ea78551128","resolution":{"observed_at":"2026-08-12T17:55:20.879173Z","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":"2026-08-12T17:55:20.883555Z","title":"Survival prediction across diverse cancer types using neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.883555Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:dfd67b89ee33b70c9e3207ef06efe9e806f0156542493aaf4bdbaec572f4b7f7","observation_id":"a949828b-90e5-47fd-859d-ed557e2963b8","resolution":{"observed_at":"2026-08-12T17:55:20.883555Z","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":"2026-08-12T17:55:20.888022Z","title":"Transformers in Opinion Mining: Addressing Semantic Complexity and Model Challenges in NLP,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.888022Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:f07cdff0bf2ae18cfd684490c5d444ad13a06012ca52c264acca8efa798eaf08","observation_id":"2638ce6d-2ef8-4524-a389-f1a1b577c884","resolution":{"observed_at":"2026-08-12T17:55:20.888022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20792","last_updated":"2024-10-28T07:17:45Z","snapshot_observed_at":"2026-08-19T17:18:15.087751Z","submitted_at":"2024-10-28T07:17:45Z","title":"Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20792","snapshot_observed_at":"2026-08-12T17:55:20.892715Z","title":"Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.892715Z"},"links":{"cited_paper":"/paper/2410.20792","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:5f7b9231e51e6d5a98c566465a102d67d9c976162d99368fd35655db01ffc508","observation_id":"fab398e1-0327-4d49-ac92-c89d4941f582","resolution":{"observed_at":"2026-08-12T17:55:20.892715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.17617","last_updated":"2024-10-23T07:14:37Z","snapshot_observed_at":"2026-08-20T12:32:11.661052Z","submitted_at":"2024-10-23T07:14:37Z","title":"Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.17617","snapshot_observed_at":"2026-08-12T17:55:20.897215Z","title":"Self- Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.897215Z"},"links":{"cited_paper":"/paper/2410.17617","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:c83d554d235bf303944edda9acbb547d92c2cce72d612fd39183c843190010a8","observation_id":"402154be-8de9-4b30-8f82-e2510428a050","resolution":{"observed_at":"2026-08-12T17:55:20.897215Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.15026","last_updated":"2024-10-19T07:49:21Z","snapshot_observed_at":"2026-08-20T00:42:12.505224Z","submitted_at":"2024-10-19T07:49:21Z","title":"A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.15026","snapshot_observed_at":"2026-08-12T17:55:20.901732Z","title":"A Recommendation Model Utilizing Separation Embedding and Self- Attention for Feature Mining,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.901732Z"},"links":{"cited_paper":"/paper/2410.15026","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:9b759203d3946ff7ab27c79b4961aecaf66fa4158f610e561eae82a6b78b0618","observation_id":"c3d496e4-1776-42e9-8659-86f5591d759e","resolution":{"observed_at":"2026-08-12T17:55:20.901732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.17586","last_updated":"2024-10-23T06:20:37Z","snapshot_observed_at":"2026-08-17T04:40:26.516371Z","submitted_at":"2024-10-23T06:20:37Z","title":"Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.17586","snapshot_observed_at":"2026-08-12T17:55:20.906325Z","title":"Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.906325Z"},"links":{"cited_paper":"/paper/2410.17586","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:a834670eb117aa9f450a3cc0cff05a965b898f8e5cbe1ada31ca6055f20ab93b","observation_id":"8974bf5d-0545-40ae-827b-d104b15c6fec","resolution":{"observed_at":"2026-08-12T17:55:20.906325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14327","last_updated":"2024-10-08T07:14:04Z","snapshot_observed_at":"2026-08-16T21:32:23.861498Z","submitted_at":"2024-09-22T06:27:07Z","title":"Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.14327","snapshot_observed_at":"2026-08-12T17:55:20.910721Z","title":"Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.910721Z"},"links":{"cited_paper":"/paper/2409.14327","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:7d55a935b06fcb0cdd0b87323bd7f65cf919b1b5a76c09c7945e026d2715761e","observation_id":"c605ffed-beb3-47ec-8ec8-0361341a9e2c","resolution":{"observed_at":"2026-08-12T17:55:20.910721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05346","last_updated":"2024-11-08T05:58:09Z","snapshot_observed_at":"2026-08-17T20:02:25.218955Z","submitted_at":"2024-11-08T05:58:09Z","title":"Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05346","snapshot_observed_at":"2026-08-12T17:55:20.915476Z","title":"Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.915476Z"},"links":{"cited_paper":"/paper/2411.05346","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:35e5cbaa918b97710aa5a23625c0d6b238324968ee4d435da55d7516a01b4f3d","observation_id":"69199d38-8e1f-46e8-a0e3-9ae257f3df34","resolution":{"observed_at":"2026-08-12T17:55:20.915476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.06374","last_updated":"2024-11-10T06:46:44Z","snapshot_observed_at":"2026-08-19T17:11:22.908894Z","submitted_at":"2024-11-10T06:46:44Z","title":"Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.06374","snapshot_observed_at":"2026-08-12T17:55:20.920236Z","title":"Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.920236Z"},"links":{"cited_paper":"/paper/2411.06374","citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:c1b14f3dca7b51b3a8fad3997992d5eac551d031decc10d1c7f49e3bbd71e7ee","observation_id":"6186fa86-658a-4b77-afff-37dfd12b2872","resolution":{"observed_at":"2026-08-12T17:55:20.920236Z","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":"2026-08-12T17:55:21.358004Z","title":"Leveraging Deep Learning Techniques for Enhanced Analysis of Medical Textual Data,","venue":null,"work_id":"0428fc3f-7ea6-4ea4-b218-74798fe3d6da","year":2024},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.925026Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:5169fd8b580fc1d2b1a7bc30fbdffe7ff568e8f23f575927027701b8993a553e","observation_id":"3bd6fc0b-80c5-47f8-957c-4443c289c2c6","resolution":{"observed_at":"2026-08-12T17:55:21.363169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T17:55:21.341865Z","title":"Investigation of creating accessibility linked data based on publicly available accessibility datasets,","venue":null,"work_id":"d923a44d-3776-49ec-95b8-634ce14e7966","year":2023},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.929798Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:342e7f4c55b086e5cc09518a9d5578f80b1c6c6fb258c9fda5818007f75e7a66","observation_id":"6e13126e-2f6e-4127-82a2-6c99a1926731","resolution":{"observed_at":"2026-08-12T17:55:21.347324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-12T17:55:21.324621Z","title":"Comparative Analysis of Summarization Methods for Skin Care Product Reviews: A Study on BERT, BART, and T5 Models,","venue":null,"work_id":"c6a28f0e-6f7f-4a90-bf7e-bb4a88066d7a","year":2023},"citing_paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T17:55:20.934513Z"},"links":{"citing_paper":"/paper/2411.12157"},"observation_digest":"sha256:c6681e3e6a04d7c569619f503c844402ae8e7f0f387bb5729f434a8dad5089ee","observation_id":"9447f350-386d-4a09-9386-9de67af49e53","resolution":{"observed_at":"2026-08-12T17:55:21.331013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.12157","last_updated":"2024-11-19T01:41:56Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-19T16:08:56.856234Z","submitted_at":"2024-11-19T01:41:56Z","title":"A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":24},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 8 inbound Pith citation observations for arXiv:2411.12157."}