{"as_of":"2026-08-07T18:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e5cb2663a0cfe8e2b876970d0350c5e14f38f9e232340da7dadcb3ab74b2ab22","coverage":[{"denominator":138,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:29:05.314870Z","state":"measured"},{"denominator":103,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":103,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T01:29:58.474810Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"cited_work":{"arxiv_id":"2506.24124","doi":"10.48550/arxiv.2506.24124","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.24124","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2506.24124 , year=","venue":"ArXiv.org","work_id":"b13601c4-5891-43bd-9914-51d6546973b7","year":2025},"citing_paper":{"arxiv_id":"2605.25943","last_updated":"2026-05-25T15:21:06Z","snapshot_observed_at":"2026-07-06T23:35:50.787324Z","submitted_at":"2026-05-25T15:21:06Z","title":"STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T22:17:53.624667Z"},"links":{"cited_paper":"/paper/2506.24124","citing_paper":"/paper/2605.25943"},"observation_digest":"sha256:6b1d10f1ec0e86c6c7561d9253f588734e028b091c99d1de33d5d5906c906890","observation_id":"afd912c9-50f1-41cc-b294-7df8c4ad6a5e","resolution":{"observed_at":"2026-06-29T22:24:00.656941Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"cited_work":{"arxiv_id":"2506.24124","doi":"10.48550/arxiv.2506.24124","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.24124","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2506.24124 , year=","venue":"ArXiv.org","work_id":"b13601c4-5891-43bd-9914-51d6546973b7","year":2025},"citing_paper":{"arxiv_id":"2606.18986","last_updated":"2026-06-17T12:07:23Z","snapshot_observed_at":"2026-08-06T07:20:38.551006Z","submitted_at":"2026-06-17T12:07:23Z","title":"Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-06-26T20:42:50.385435Z"},"links":{"cited_paper":"/paper/2506.24124","citing_paper":"/paper/2606.18986"},"observation_digest":"sha256:bd798a64cfb3b2ab572004bb56fc2dca027ee4fad1adff261005a172b61edbf1","observation_id":"5c954d86-2670-4dce-bdaf-02a390bd3724","resolution":{"observed_at":"2026-07-04T01:09:18.666026Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"cited_work":{"arxiv_id":"2506.24124","doi":"10.48550/arxiv.2506.24124","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.24124","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2506.24124 , year=","venue":"ArXiv.org","work_id":"b13601c4-5891-43bd-9914-51d6546973b7","year":2025},"citing_paper":{"arxiv_id":"2606.28446","last_updated":"2026-06-26T08:35:55Z","snapshot_observed_at":"2026-08-07T10:51:25.338939Z","submitted_at":"2026-06-26T08:35:55Z","title":"Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-06-30T01:29:58.474810Z"},"links":{"cited_paper":"/paper/2506.24124","citing_paper":"/paper/2606.28446"},"observation_digest":"sha256:7809cfc98d5ba9158052362eb1ce7f4c961135ab604cde6454c5748a25d20246","observation_id":"7be0cf6d-ed19-4a25-aa4a-7bcbce33dec8","resolution":{"observed_at":"2026-06-30T01:34:09.349309Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2506.24124/citation-record","integrity":"/paper/2506.24124/integrity","json":"/paper/2506.24124/citation-record.json","paper":"/paper/2506.24124"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.07815","last_updated":"2024-11-04T17:42:45Z","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:53:54Z","title":"Chronos: Learning the Language of Time Series","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07815","snapshot_observed_at":"2026-08-06T21:28:52.573642Z","title":"Maddix, Hao Wang, Michael W","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.573642Z"},"links":{"cited_paper":"/paper/2403.07815","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:c64cd8425cbd23e980289f1f496f83dbe4b77495c355a2156792bb09b48256c3","observation_id":"12aad24f-4c1e-406c-b27d-1b7eb409f167","resolution":{"observed_at":"2026-08-06T21:28:52.573642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-06T21:28:52.669344Z","title":"Layer normalization","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.669344Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:abe3813b95c493ba07dae5b2a726f7e248a8a000fbe54bd97687ff7f994337e1","observation_id":"e4672016-a5af-41c7-88cd-de48d2772c31","resolution":{"observed_at":"2026-08-06T21:28:52.669344Z","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":"2026-08-06T21:28:52.820203Z","title":"Privacy preserving generative feature transformation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.820203Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:82b4fb8dae89dc23a5921a72fc7535b9d854baeae0dfe6b397817f51abe9e522","observation_id":"ff9ee70f-bbe6-4161-929c-375b1165a6cf","resolution":{"observed_at":"2026-08-06T21:28:52.820203Z","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":"2026-08-06T21:28:52.933313Z","title":"Gorec: a generative cold-start recommendation framework","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:52.933313Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:c718f95a82f5401929500a789fb49391311bd682016403a187630fb77a127302","observation_id":"b2d385e1-2244-432c-b160-6c549fbb885a","resolution":{"observed_at":"2026-08-06T21:28:52.933313Z","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":"2026-08-06T21:28:53.085687Z","title":"Multimodality invariant learning for multimedia-based new item recommendation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:53.085687Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:e74bf58f68444ac935d14d750cd1b8951c6d988d49e299a932c54ec913b5344e","observation_id":"ad68d57d-d7fb-4549-8bea-0376225aa81f","resolution":{"observed_at":"2026-08-06T21:28:53.085687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.18204","last_updated":"2025-05-21T14:11:46Z","snapshot_observed_at":"2026-08-07T15:12:48.623730Z","submitted_at":"2025-05-21T14:11:46Z","title":"Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO$_2$ Storage","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.18204","snapshot_observed_at":"2026-08-06T21:28:53.188562Z","title":"Brownian bridge augmented surrogate simulation and injection planning for geological co _2 storage","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:53.188562Z"},"links":{"cited_paper":"/paper/2505.18204","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:85c4ebd089d7d20bdb072e5bb7559c139d3e6398fc0e6f28fcae42ac657d0006","observation_id":"0889fec0-b78d-4e49-a1a1-f61225b3fb93","resolution":{"observed_at":"2026-08-06T21:28:53.188562Z","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":"2026-08-06T21:28:53.303227Z","title":"Deep learning and time series-to-image encoding for finan- cial forecasting","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:53.303227Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:1886c36baac682355c070582314d58196eabbe347e141f226a8954ff60bd04ed","observation_id":"3ea3dc0a-3a32-4928-83f7-f4879170aa3c","resolution":{"observed_at":"2026-08-06T21:28:53.303227Z","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":"2026-08-06T21:28:53.429090Z","title":"Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:53.429090Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:c11172127d9b5cf1f9544c0a646f11998b6e73d26f850896be0da35b3fd105fd","observation_id":"7620ff6f-c6f1-484d-b88d-a3495ed39952","resolution":{"observed_at":"2026-08-06T21:28:53.429090Z","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":"2026-08-06T21:28:53.591316Z","title":"Control charts in financial applications: An overview","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:53.591316Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:e4617cc1bed168ee4aef13a02b900c80dc18bf017ec792a61efecb088491a9bf","observation_id":"cb1d611f-4fea-4843-b258-3e9ee3ccafad","resolution":{"observed_at":"2026-08-06T21:28:53.591316Z","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":"2026-08-06T21:28:53.719457Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:53.719457Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:acbd2de80f811b95d15a8da7aeb8d298cb3bc3d2f3dd96577b290fe45223d884","observation_id":"a5f2d7b9-7b50-4880-bcd4-d23756c7c686","resolution":{"observed_at":"2026-08-06T21:28:53.719457Z","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":"2026-08-06T21:28:53.898252Z","title":"Time series forecasting for healthcare diagnosis and prognostics with the focus on cardiovascular diseases","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:53.898252Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:c6839c77178fa1516c9b1e954f98787407feb5787baf0c7e823d4d92197ff2e4","observation_id":"48333e52-ec0f-4a8a-9809-899cd7f2d88f","resolution":{"observed_at":"2026-08-06T21:28:53.898252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16083","last_updated":"2024-05-25T06:26:02Z","snapshot_observed_at":"2026-07-06T18:19:42.946629Z","submitted_at":"2024-05-25T06:26:02Z","title":"From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals","version":1},"cited_work":{"arxiv_id":"2405.16083","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.16083","snapshot_observed_at":"2026-08-06T21:29:13.800219Z","title":"From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals","venue":"cs.LG","work_id":"f53019f9-0e5c-4fe3-86d9-31662c83fa20","year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:54.021165Z"},"links":{"cited_paper":"/paper/2405.16083","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:32612d11c61b346bbf6120ab38fcb3bbb4494e2983389e9586b7d74e7c331dd7","observation_id":"c9cbc451-dea2-4878-a69d-1f5034779d70","resolution":{"observed_at":"2026-08-06T21:29:13.831468Z","resolver_source":"local_arxiv","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":"2026-08-06T21:28:54.170478Z","title":"Lightts: Lightweight time series classification with adaptive ensemble distillation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:54.170478Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:bafcd43f661c1cb8cf64b082df6ec10d2bd8756dec5e90e5a80be4f157f70946","observation_id":"3aea84ee-749e-4558-82b7-d8de20d84d55","resolution":{"observed_at":"2026-08-06T21:28:54.170478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.10362","last_updated":"2022-07-21T08:43:51Z","snapshot_observed_at":"2026-07-06T13:33:43.048614Z","submitted_at":"2022-07-21T08:43:51Z","title":"LocVTP: Video-Text Pre-training for Temporal Localization","version":1},"cited_work":{"arxiv_id":"2207.10362","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.10362","snapshot_observed_at":"2026-08-06T21:29:13.601719Z","title":"LocVTP: Video-Text Pre-training for Temporal Localization","venue":"cs.CV","work_id":"5ede4ca7-04f5-4199-98c3-3d60a5dcebd2","year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:54.312848Z"},"links":{"cited_paper":"/paper/2207.10362","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:3a9fd892d35384855e1cc30bd298fd9abe8c767f9b3dfee007989b004cc3d814","observation_id":"2ce06a8f-22ac-4381-9d57-6b96aaa98ed3","resolution":{"observed_at":"2026-08-06T21:29:13.744649Z","resolver_source":"local_arxiv","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":"2026-08-06T21:28:54.438374Z","title":"Nhits: Neural hierarchical interpolation for time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:54.438374Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:ca5538f31ed277724f061c5a883ac81591e0371b9178752817941932c9b830fe","observation_id":"fa67aee7-565e-495a-9fe2-7ae42c9ee75e","resolution":{"observed_at":"2026-08-06T21:28:54.438374Z","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":"2026-08-06T21:28:54.633314Z","title":"Multi- model approach for stock price prediction and trading recommendations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:54.633314Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:3db2c10107842275b58009538deb8f0aad30700ec358641f4b7f4788cbc7e66a","observation_id":"66be7787-6e16-469f-84fd-7187b163156b","resolution":{"observed_at":"2026-08-06T21:28:54.633314Z","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":"2026-08-06T21:28:54.924122Z","title":"Financial time series forecasting with multi-modality graph neural network","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:54.924122Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:4e71a95f684db49f9786b5c23d2fe1163af797b20277634cf0c467003671a3d4","observation_id":"f4199341-2d8e-45ab-b958-1587667f075a","resolution":{"observed_at":"2026-08-06T21:28:54.924122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08424","last_updated":"2024-04-04T16:24:19Z","snapshot_observed_at":"2026-08-06T08:23:57.165934Z","submitted_at":"2023-04-17T16:46:48Z","title":"Long-term Forecasting with TiDE: Time-series Dense Encoder","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08424","snapshot_observed_at":"2026-08-06T21:28:55.063158Z","title":"Long-term forecasting with tide: Time-series dense encoder","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.063158Z"},"links":{"cited_paper":"/paper/2304.08424","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:1c44b2c5108c67b7e2b399d23e4fde12515fee9d14254e9e15bcee61812cbb7a","observation_id":"965b7b11-eece-4c84-b22e-5c464ee84d34","resolution":{"observed_at":"2026-08-06T21:28:55.063158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02475","last_updated":"2024-06-07T07:46:26Z","snapshot_observed_at":"2026-08-01T20:24:08.204721Z","submitted_at":"2024-02-04T13:10:51Z","title":"TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02475","snapshot_observed_at":"2026-08-06T21:28:55.194907Z","title":"Timesiam: A pre-training framework for siamese time-series modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.194907Z"},"links":{"cited_paper":"/paper/2402.02475","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:bcea787329c8f5aee23eb150692c6d72a4a7cfb824f460c6e290cfbdb3f5be6a","observation_id":"1498ee0f-abdc-460f-b88e-4a487efcc00d","resolution":{"observed_at":"2026-08-06T21:28:55.194907Z","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":"2026-08-06T21:28:55.292212Z","title":"Weakly supervised video representation learning with unaligned text for sequential videos","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.292212Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:2b8fc5612a99b688205044c679364a8aed28f445096a1b564273844ccde12302","observation_id":"6605f5ff-d14f-44bc-a0d4-b2498d82caf4","resolution":{"observed_at":"2026-08-06T21:28:55.292212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-06T21:28:55.471004Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.471004Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:af3995800b7dc389a74fa876933363ad92c53d080ed8a91b96dc86970ae6cadd","observation_id":"8558c4ef-66fc-4f02-83bf-3fe25524dd33","resolution":{"observed_at":"2026-08-06T21:28:55.471004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16556","last_updated":"2024-04-24T17:37:52Z","snapshot_observed_at":"2026-07-06T15:33:42.279377Z","submitted_at":"2023-05-26T00:50:09Z","title":"LANISTR: Multimodal Learning from Structured and Unstructured Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16556","snapshot_observed_at":"2026-08-06T21:28:55.619007Z","title":"Lanistr: Multimodal learning from structured and unstructured data","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.619007Z"},"links":{"cited_paper":"/paper/2305.16556","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:9ca16e1917be71459f5d6f8b55cc29171837cd98148f88120a590366604f6d52","observation_id":"a551c215-e2d7-4598-8764-5cd2377f00f4","resolution":{"observed_at":"2026-08-06T21:28:55.619007Z","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":"2026-08-06T21:28:55.745469Z","title":"Unsupervised scalable repre- sentation learning for multivariate time series","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.745469Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:b78440ec4abee7e0fa09dbe8e4ce48fb76abb32a4ad92502ca1198cc79190a2d","observation_id":"f8e67e57-8267-40b8-941d-9cf3c8b00189","resolution":{"observed_at":"2026-08-06T21:28:55.745469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.15076","last_updated":"2025-05-21T03:49:24Z","snapshot_observed_at":"2026-08-07T15:22:29.590135Z","submitted_at":"2025-05-21T03:49:24Z","title":"Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories","version":1},"cited_work":{"arxiv_id":"2505.15076","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.15076","snapshot_observed_at":"2026-08-06T21:29:12.875192Z","title":"Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories","venue":"cs.LG","work_id":"43b9cdaf-8af5-4948-ab38-7da32f569e4a","year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.860154Z"},"links":{"cited_paper":"/paper/2505.15076","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:f3fdf3189e52aebb67ac0cb394e41dd8858a4768d87bd7fd64e12726b65c5c43","observation_id":"d3b0f1d2-bdd1-4d87-9c78-c8024e18cb72","resolution":{"observed_at":"2026-08-06T21:29:13.008545Z","resolver_source":"local_arxiv","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":{"arxiv_id":"2505.15152","last_updated":"2025-05-21T06:18:42Z","snapshot_observed_at":"2026-08-07T15:20:50.085904Z","submitted_at":"2025-05-21T06:18:42Z","title":"Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.15152","snapshot_observed_at":"2026-08-06T21:28:55.930827Z","title":"Sculpting features from noise: Reward-guided hierarchical diffusion for task-optimal feature transformation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:55.930827Z"},"links":{"cited_paper":"/paper/2505.15152","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:684d129c87c0db16c804794b5e98aee14196a42a2f9aabb3ee08a1de7c3319d7","observation_id":"2402a515-8451-4756-a82f-890d49f7306e","resolution":{"observed_at":"2026-08-06T21:28:55.930827Z","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":"2026-08-06T21:28:56.052319Z","title":"Evolutionary large language model for automated feature transformation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.052319Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:5a0cdd064271b089813a42be098f18a38fc122e63e3c5b7f5663158974abde3d","observation_id":"a3dec871-0c2a-4322-a69f-a47baacbfa58","resolution":{"observed_at":"2026-08-06T21:28:56.052319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21304","last_updated":"2025-04-30T04:26:03Z","snapshot_observed_at":"2026-08-07T15:57:55.449740Z","submitted_at":"2025-04-30T04:26:03Z","title":"Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming","version":1},"cited_work":{"arxiv_id":"2504.21304","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.21304","snapshot_observed_at":"2026-08-06T21:29:12.645705Z","title":"Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teaming","venue":"cs.LG","work_id":"4798feae-4239-44a4-9f8f-47e453952fe8","year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.143876Z"},"links":{"cited_paper":"/paper/2504.21304","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:dfe823e9c9a806a3c65813dd5e25859928d6fade406d6fb7834e1b32db254699","observation_id":"dc277e5c-1e83-4425-a8bb-106ec7f42b40","resolution":{"observed_at":"2026-08-06T21:29:12.715332Z","resolver_source":"local_arxiv","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":"2026-08-06T21:28:56.260332Z","title":"Neuro-symbolic embedding for short and effective feature selection via autoregressive generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.260332Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:545c287fcc4f99ba7b7ef5d9eecc2940dd86f13f276fdbdccdd842b2ba45afc7","observation_id":"357861e3-64f2-4712-ad87-6460106ed578","resolution":{"observed_at":"2026-08-06T21:28:56.260332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03885","last_updated":"2024-10-10T15:37:45Z","snapshot_observed_at":"2026-08-05T08:29:54.807725Z","submitted_at":"2024-02-06T10:48:46Z","title":"MOMENT: A Family of Open Time-series Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03885","snapshot_observed_at":"2026-08-06T21:28:56.368581Z","title":"Moment: a family of open time-series foundation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.368581Z"},"links":{"cited_paper":"/paper/2402.03885","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:95baec5a4bc38dbf4b2fd3855ac10ff93c2203be461f665e795bac8788bf1780","observation_id":"ff69d990-fea8-4712-a214-5936477b5c6f","resolution":{"observed_at":"2026-08-06T21:28:56.368581Z","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":"2026-08-06T21:28:56.457907Z","title":"Large language models are zero-shot time series forecasters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.457907Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:5f124620abc36d8d08287bcb07b342d867db50d5c272bae1656e23998644300c","observation_id":"3a5dc60a-625c-468c-8e54-5c264a936fc2","resolution":{"observed_at":"2026-08-06T21:28:56.457907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-06T21:28:56.621717Z","title":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.621717Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:215b02dc4217b64cd6a48067df48fe3c1a43647d084515542da8b36ba1656160","observation_id":"dd212c19-eb43-465f-94af-a2fd243970d1","resolution":{"observed_at":"2026-08-06T21:28:56.621717Z","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":"2026-08-06T21:28:56.782411Z","title":"Audioclip: Extending clip to image, text and audio","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.782411Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:807801d0fdb3e132a8b27a1554ddd90c259ca20badf217983640fd19e1f5d91e","observation_id":"e0554412-4d71-469c-ac49-7eb65ca4e259","resolution":{"observed_at":"2026-08-06T21:28:56.782411Z","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":"2026-08-06T21:28:56.881025Z","title":"Temporal alignment networks for long- term video","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:56.881025Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:064e4e8461140f26846313c96ec8f2ddcdc0afa90330fa5ec4d7093de0cdba58","observation_id":"93236bc6-6cba-4335-ab44-958f13b2417b","resolution":{"observed_at":"2026-08-06T21:28:56.881025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14608","last_updated":"2024-09-16T02:54:50Z","snapshot_observed_at":"2026-08-04T09:07:42.158421Z","submitted_at":"2024-03-21T17:55:50Z","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14608","snapshot_observed_at":"2026-08-06T21:28:57.009353Z","title":"Parameter-efficient fine-tuning for large models: A comprehensive survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.009353Z"},"links":{"cited_paper":"/paper/2403.14608","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:fa8c00a31b0a4fe047b125e8ad0f574dee1e1be16d55c04ffa756dff7dd2ed9e","observation_id":"745fe211-ea90-42fa-b86a-b6f6ad5b3695","resolution":{"observed_at":"2026-08-06T21:28:57.009353Z","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":"2026-08-06T21:28:57.075192Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.075192Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:9e1e67d7e109e2732183699f1cc706a8dde8bd3721bd64e755a16f44a673174c","observation_id":"81dba392-922a-4938-a472-5a701c5f2bcd","resolution":{"observed_at":"2026-08-06T21:28:57.075192Z","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":"2026-08-06T21:28:57.204592Z","title":"Double correction framework for denoising recommendation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.204592Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:b94ccaafb138561085ab3ff92e7971f3d012755bd007847dce5ebc95bc671f3a","observation_id":"3035ca14-c920-40dc-a34f-541a7b0f77b4","resolution":{"observed_at":"2026-08-06T21:28:57.204592Z","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":"2026-08-06T21:28:57.354424Z","title":"Long short-term memory","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.354424Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:a7e97923e2a401914329c357e6b02144507556211055ab57156b42cefea80d62","observation_id":"4a538c54-e9e8-492e-98d0-c9320fdadd15","resolution":{"observed_at":"2026-08-06T21:28:57.354424Z","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":"2026-08-06T21:28:57.425806Z","title":"Transrac: Encoding multi-scale temporal correlation with transformers for repetitive action counting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.425806Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:9e401eb55e6f6d56506f5e873d596ea98e0363ad54628a7b7966a17b0369b204","observation_id":"23724760-8b4d-4a06-956c-f6d1a145cea9","resolution":{"observed_at":"2026-08-06T21:28:57.425806Z","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":"2026-08-06T21:28:57.540299Z","title":"Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.540299Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:51185402c6bdce64006f56cc0626cb9c26c180a7555d9b49ca3b49dac2ae1fe0","observation_id":"df2c1207-45b9-465a-8f84-55e5febb6d0e","resolution":{"observed_at":"2026-08-06T21:28:57.540299Z","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":"2026-08-06T21:28:57.622463Z","title":"Gpt4mts: Prompt-based large language model for multimodal time-series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.622463Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:9161f2e453d3143af3ebdfcd66244ba00fea5c57dc83518ef629c7bf6a33fbfb","observation_id":"6c5fca9c-7645-485b-84fc-7d85708a9101","resolution":{"observed_at":"2026-08-06T21:28:57.622463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01728","last_updated":"2024-01-29T06:27:53Z","snapshot_observed_at":"2026-08-04T04:31:27.172482Z","submitted_at":"2023-10-03T01:31:25Z","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01728","snapshot_observed_at":"2026-08-06T21:28:57.826466Z","title":"Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.826466Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:88205606c7bfae78ce5c39c261848865e2b334a771bce72d8e483313970dd1c1","observation_id":"09c60e33-9d1b-4142-b086-c2ad391f4f9a","resolution":{"observed_at":"2026-08-06T21:28:57.826466Z","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":"2026-08-06T21:28:57.925912Z","title":"Position: What can large language models tell us about time series analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:57.925912Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:b0f7208fd9865aca744117f198f435a95839ba5d1ddf0172323a9ad6a362fa3e","observation_id":"ebcc2524-0b47-4275-a195-439642c21a63","resolution":{"observed_at":"2026-08-06T21:28:57.925912Z","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":"2026-08-06T21:28:58.046606Z","title":"Ai in healthcare: time-series forecasting using statistical, neural, and ensemble architectures","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.046606Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:2a20d6c2cb1f7a3c8c23a77a82c0d79482146efb4380560b4174c0289a8f0f93","observation_id":"cc426686-a8be-4e6d-ad4b-72fc1a8a1171","resolution":{"observed_at":"2026-08-06T21:28:58.046606Z","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":"2026-08-06T21:28:58.175064Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.175064Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:6d6849e307cb7508099ce67f2849fffe1ebe00d68ff32cab9a87bc57984198e3","observation_id":"cd0a5b4b-5790-4b3b-b2a2-47b0999c8f43","resolution":{"observed_at":"2026-08-06T21:28:58.175064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04451","last_updated":"2020-02-18T16:01:18Z","snapshot_observed_at":"2026-07-06T08:50:12.690900Z","submitted_at":"2020-01-13T18:38:28Z","title":"Reformer: The Efficient Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04451","snapshot_observed_at":"2026-08-06T21:28:58.297086Z","title":"Reformer: The efficient transformer","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.297086Z"},"links":{"cited_paper":"/paper/2001.04451","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:713fb1e544684defc5acd11116b0fdba08453a779f4cfb245930f21c59ec1d21","observation_id":"0549d233-f528-47fe-ab19-7c8a69a14404","resolution":{"observed_at":"2026-08-06T21:28:58.297086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01165","last_updated":"2024-08-10T12:03:44Z","snapshot_observed_at":"2026-07-06T17:54:00.048961Z","submitted_at":"2024-04-01T15:14:07Z","title":"LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01165","snapshot_observed_at":"2026-08-06T21:28:58.424037Z","title":"Lite: Modeling environmental ecosystems with multimodal large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.424037Z"},"links":{"cited_paper":"/paper/2404.01165","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:f03f79faacaf19f2082eaf95e03c580f099981206b27dd5483b9ba3d25bf2761","observation_id":"60be177e-e1d4-4a3b-853f-55cb20291c3e","resolution":{"observed_at":"2026-08-06T21:28:58.424037Z","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":"2026-08-06T21:28:58.573179Z","title":"Sehf: A summary- enhanced hierarchical framework for financial report sentiment analysis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.573179Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:afca95ff599e7830283afbde64fe182adcf50ecd0fcfc1d62d6d7f004c785898","observation_id":"85681163-5902-4ad8-9c2e-222e9aa2137e","resolution":{"observed_at":"2026-08-06T21:28:58.573179Z","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":"2026-08-06T21:28:58.681043Z","title":"Sade: A speaker- aware dual encoding model based on diagbert for medical triage and pre-diagnosis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.681043Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:cd7d4b1c0ba21fce09dd084bd583ca1ab68dda32be6a21e9ca0530c5ff4c5d47","observation_id":"c696f405-ae9b-4d0e-9541-322d02010e7f","resolution":{"observed_at":"2026-08-06T21:28:58.681043Z","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":"2026-08-06T21:28:58.804030Z","title":"Frozen language model helps ecg zero-shot learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.804030Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:fd03320bfb222dd6ac6f44d31daaef3e5232f4c6f227b7ab7d878ce08326ca45","observation_id":"24431474-6f97-4885-a0ff-67268a44f9be","resolution":{"observed_at":"2026-08-06T21:28:58.804030Z","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":"2026-08-06T21:28:58.931484Z","title":"Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:58.931484Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:5b8190e8ea28c1ecf2926addbbe57b84fd42589c7b4bd8151798997c3803bf75","observation_id":"b130c929-f07e-4c8a-830d-9c9e01f1e9e1","resolution":{"observed_at":"2026-08-06T21:28:58.931484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06355","last_updated":"2024-01-04T02:06:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-10T17:59:04Z","title":"VideoChat: Chat-Centric Video Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06355","snapshot_observed_at":"2026-08-06T21:28:59.047091Z","title":"Videochat: Chat-centric video understanding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.047091Z"},"links":{"cited_paper":"/paper/2305.06355","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:bbfe1b929265e427c0636f39a72125a4cd2825dcabe9ed036078ab7df531561d","observation_id":"af2f22b6-21ba-46ab-afb4-c370dc8eafbd","resolution":{"observed_at":"2026-08-06T21:28:59.047091Z","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":"2026-08-06T21:28:59.149454Z","title":"Clip-event: Connecting text and images with event structures","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.149454Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:a5f325bf4ab9c0d75dd3d3fadf70ec408d17ed493bbd93e047c770a31570f2b4","observation_id":"aa420b5e-e238-4180-8fc4-61b8cf5e5151","resolution":{"observed_at":"2026-08-06T21:28:59.149454Z","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":"2026-08-06T21:28:59.266500Z","title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.266500Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:3be5444a4ca42be71a87449517156bfc706da97a4c5acd3be6fc02ace08a2e9b","observation_id":"6b59d7f7-01f5-4599-8fef-2b37ccd6bad2","resolution":{"observed_at":"2026-08-06T21:28:59.266500Z","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":"2026-08-06T21:28:59.368840Z","title":"Deep learning models for time series forecasting: a review","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.368840Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:48b67736dfe3a8993c1d38415ec3dbfa2501dc7e9d8b340a92ce0526e4e238b8","observation_id":"b6de7484-019b-4e50-b757-498817c3b545","resolution":{"observed_at":"2026-08-06T21:28:59.368840Z","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":"2026-08-06T21:28:59.457459Z","title":"Forecasting with time series imaging","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.457459Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:88b2928793040ca75ab5472cec01f839a0b8351d6d37a2696b47f71d433aaf86","observation_id":"31a0e8ad-e176-495f-b3e0-71089a6034eb","resolution":{"observed_at":"2026-08-06T21:28:59.457459Z","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":"2026-08-06T21:28:59.558862Z","title":"Time series as images: Vision transformer for irregularly sampled time series","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.558862Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:ca3942f091937468b693301fafddec2e0ff7a04661b3b32bbaadd6656640acc6","observation_id":"da2b637c-03c0-41c8-ad19-7c9321028db5","resolution":{"observed_at":"2026-08-06T21:28:59.558862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10721","last_updated":"2026-05-17T02:25:14Z","snapshot_observed_at":"2026-08-03T04:41:00.104637Z","submitted_at":"2023-05-18T05:39:46Z","title":"Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10721","snapshot_observed_at":"2026-08-06T21:28:59.666584Z","title":"Revisiting long-term time series forecasting: An investigation on linear mapping","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.666584Z"},"links":{"cited_paper":"/paper/2305.10721","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:5628a962bd3c76a63deef3135fa42246f36ccc61613c990ffbf45fd94a93b10a","observation_id":"dd3481ad-3e57-44a6-9dc9-478e0c4fa6f2","resolution":{"observed_at":"2026-08-06T21:28:59.666584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11200","last_updated":"2023-08-22T05:23:04Z","snapshot_observed_at":"2026-07-06T16:08:53.004992Z","submitted_at":"2023-08-22T05:23:04Z","title":"SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11200","snapshot_observed_at":"2026-08-06T21:28:59.787950Z","title":"Segrnn: Segment recurrent neural network for long-term time series forecasting.arXiv preprint arXiv:2308.11200, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.787950Z"},"links":{"cited_paper":"/paper/2308.11200","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:88a48a9c12606a083054bebe49ff3dac45f2b10b1bae95f671419185b032f995","observation_id":"71d8c771-4547-453e-bd3c-b0c49dc6d522","resolution":{"observed_at":"2026-08-06T21:28:59.787950Z","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":"2026-08-06T21:28:59.941861Z","title":"Pth and the regulation of mesenchymal cells within the bone marrow niche","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T21:28:59.941861Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:05248b33d2b2ec5cea3527497f46a833529bdae68d6186347675dbee13e451de","observation_id":"df36f1cf-1232-4907-9b6b-d5a3efe2a2e0","resolution":{"observed_at":"2026-08-06T21:28:59.941861Z","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":"2026-08-06T21:29:00.082909Z","title":"Visual instruction tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.082909Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:3168ee0880624f71b71a99a179f1ef8e7ebff78a1d6a39b75bd3815a9ce32c42","observation_id":"47fb0c6e-05a6-4874-82ff-1185c8628f69","resolution":{"observed_at":"2026-08-06T21:29:00.082909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16132","last_updated":"2024-02-25T16:14:26Z","snapshot_observed_at":"2026-07-06T17:35:10.248760Z","submitted_at":"2024-02-25T16:14:26Z","title":"LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16132","snapshot_observed_at":"2026-08-06T21:29:00.230591Z","title":"Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.230591Z"},"links":{"cited_paper":"/paper/2402.16132","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:c7a10156098966277b4220ef6d8137cf43282aafe9645e41b3aa0d4c10ff8b48","observation_id":"d66ff5d9-1eb6-493b-9437-89ff3428ee81","resolution":{"observed_at":"2026-08-06T21:29:00.230591Z","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":"2026-08-06T21:29:00.409086Z","title":"Edta enhances stromal cell–derived factor 1α–induced migration of dental pulp cells by up-regulating chemokine receptor 4 expression","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.409086Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:617c6b07006f620af65cdd5cc8563f28dd8b541788c43831be9d3f6f57bd835f","observation_id":"4a0ec73e-067e-414a-b936-e089c56b433a","resolution":{"observed_at":"2026-08-06T21:29:00.409086Z","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":"2026-08-06T21:29:00.503390Z","title":"Calorie restriction in mice impairs cortical but not trabecular peak bone mass by suppressing bone remodeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.503390Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:d690edd7058e5d902adf14d67279749fa473cdc21f850606e3b18dabd894eeb0","observation_id":"f079f970-d6c2-4b8b-81fa-36e8c19e0858","resolution":{"observed_at":"2026-08-06T21:29:00.503390Z","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":"2026-08-06T21:29:00.626740Z","title":"Scinet: Time series modeling and forecasting with sample convolution and interaction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.626740Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:59547b2c5cf3078398aa87f43ecc8b4b57a391af67badc158c7e58663e440050","observation_id":"a5eabf00-0e70-4447-9965-89d2782fd179","resolution":{"observed_at":"2026-08-06T21:29:00.626740Z","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":"2026-08-06T21:29:00.728346Z","title":"Focal: Contrastive learning for multimodal time- series sensing signals in factorized orthogonal latent space","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.728346Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:e1215b0c05b900d18b7e82f284a974f2154b34a537385276268d677abd0d5afc","observation_id":"9a76fdb9-5d29-4675-9ce3-80e629141c7b","resolution":{"observed_at":"2026-08-06T21:29:00.728346Z","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":"2026-08-06T21:29:00.811287Z","title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.811287Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:081d017c2a7a821e41ee7f9d164ca9732b800e6ec18a46e99e6e69c8e729cfbe","observation_id":"d2e4bba7-044d-42cc-acda-4ee820ac12ae","resolution":{"observed_at":"2026-08-06T21:29:00.811287Z","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":"2026-08-06T21:29:00.904259Z","title":"Unitime: A language-empowered unified model for cross-domain time series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:00.904259Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:6c4bf24f5c90bb28fc347f547d627d013179de18d89d3a013dcee3cb479fffbe","observation_id":"4b2c9914-8523-4ba1-8411-c6237ef1f62e","resolution":{"observed_at":"2026-08-06T21:29:00.904259Z","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":"2026-08-06T21:29:01.005278Z","title":"Non-stationary transformers: Ex- ploring the stationarity in time series forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.005278Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:f6efe149d17d647ecfc9b3bc97f800f34604ea7706613a8d1de5e95ca077306a","observation_id":"4c9ab9be-20b4-46a3-8beb-cd07e9c6b4b0","resolution":{"observed_at":"2026-08-06T21:29:01.005278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06625","last_updated":"2024-03-14T11:45:57Z","snapshot_observed_at":"2026-07-06T16:30:29.783501Z","submitted_at":"2023-10-10T13:44:09Z","title":"iTransformer: Inverted Transformers Are Effective for Time Series Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06625","snapshot_observed_at":"2026-08-06T21:29:01.134359Z","title":"itransformer: Inverted transformers are effective for time series forecasting, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.134359Z"},"links":{"cited_paper":"/paper/2310.06625","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:8fa25497e06e908bd136f0f79e3f9781d2c47c1a6f8d6d3c1e95f07b085b5ed8","observation_id":"be74caf6-ba53-476d-8b93-c2718966e95a","resolution":{"observed_at":"2026-08-06T21:29:01.134359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02370","last_updated":"2024-10-31T11:37:41Z","snapshot_observed_at":"2026-07-06T17:25:01.583656Z","submitted_at":"2024-02-04T06:59:21Z","title":"AutoTimes: Autoregressive Time Series Forecasters via Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02370","snapshot_observed_at":"2026-08-06T21:29:01.280307Z","title":"Auto- times: Autoregressive time series forecasters via large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.280307Z"},"links":{"cited_paper":"/paper/2402.02370","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:4b04e7a5d7b2bca2afdfab25a594f3ce45a7ce07fb00ae31db98ee97aa23d585","observation_id":"aacc89a5-f6e3-4e5e-adac-90cc3a3a59ec","resolution":{"observed_at":"2026-08-06T21:29:01.280307Z","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":"2026-08-06T21:29:01.404186Z","title":"Timer: Generative pre-trained transformers are large time series models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.404186Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:2fd9741106c450dc4ecc62588315884f2288805a6cecb40982fb5e3f2967be1e","observation_id":"78bc09ea-079c-4151-883d-5eca9fe8ee5a","resolution":{"observed_at":"2026-08-06T21:29:01.404186Z","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":"2026-08-06T21:29:01.514818Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.514818Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:20cac069854de2de53b734ae587480bf499ac7963f0324002da3e8cbe9d99c32","observation_id":"6315744b-487e-448f-8e59-6245bfa6ae04","resolution":{"observed_at":"2026-08-06T21:29:01.514818Z","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":"2026-08-06T21:29:01.631306Z","title":"A cnn-bilstm-am method for stock price prediction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.631306Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:0689350919678bdf9f918b3dacc1ce1d2b17703fa3bc3713de9ba81aa10de14f","observation_id":"b23abbb3-d2c6-4d74-ac0e-27b48ca99517","resolution":{"observed_at":"2026-08-06T21:29:01.631306Z","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":"2026-08-06T21:29:01.715495Z","title":"Howto100m: Learning a text-video embedding by watching hundred million narrated video clips","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.715495Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:5da64497d19bea0238cc70fb583e047055b0be7914dfdd7eb5657908e28b5dfd","observation_id":"f575aaf1-cb54-4661-a09f-f84009812730","resolution":{"observed_at":"2026-08-06T21:29:01.715495Z","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":"2026-08-06T21:29:01.824833Z","title":"Expanding language-image pretrained models for general video recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.824833Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:6bcbd15e65df5ec4210fda4d99a7d05149c7d22d62f9fc3f9c16d18f62bf0845","observation_id":"5e4334fc-5e37-4b23-87fa-3f56cd0bbb56","resolution":{"observed_at":"2026-08-06T21:29:01.824833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-07-31T22:45:35.561492Z","submitted_at":"2022-11-27T05:15:42Z","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.14730","snapshot_observed_at":"2026-08-06T21:29:01.973202Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:01.973202Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:65579043264707edca1d70347bb0aa7d42d705dd0ad9505a7c86541f6d81758d","observation_id":"a5c6dda5-0abd-44f3-a870-11a65f1f7a81","resolution":{"observed_at":"2026-08-06T21:29:01.973202Z","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":"2026-08-06T21:29:02.114667Z","title":"Representation learning with contrastive predictive coding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:02.114667Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:6c392aa943b7d584b00cd02a1ee487e52ffc3d0b20d21b09267088a6819c51c9","observation_id":"e5c409ef-0a7d-4e52-9790-1bf5715c70ae","resolution":{"observed_at":"2026-08-06T21:29:02.114667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10437","last_updated":"2020-02-20T21:08:57Z","snapshot_observed_at":"2026-08-06T09:33:52.442294Z","submitted_at":"2019-05-24T20:28:57Z","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10437","snapshot_observed_at":"2026-08-06T21:29:02.218628Z","title":"N-beats: Neural basis expansion analysis for interpretable time series forecasting","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:02.218628Z"},"links":{"cited_paper":"/paper/1905.10437","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:9a2d1a206626fbfc9b061f6ad7e90b73236081680259349a9fdc979ee8d108ad","observation_id":"66d8dbab-91c0-4d5c-af8e-9b3c745ff5cd","resolution":{"observed_at":"2026-08-06T21:29:02.218628Z","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":"2026-08-06T21:29:02.308365Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:02.308365Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:d9c8b73595e1b03edb9469b6f176e6aef77696e8b204f2f3ffc1ed09466440b0","observation_id":"2ee961b2-2bc9-4391-af25-348f23a172a0","resolution":{"observed_at":"2026-08-06T21:29:02.308365Z","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":"2026-08-06T21:29:02.411586Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:02.411586Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:1fcced2597beca63d703b81791645cce348a0e315a216927728bc585c75fdc31","observation_id":"a9d6c929-8b36-4fb5-b390-bda00216289a","resolution":{"observed_at":"2026-08-06T21:29:02.411586Z","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":"2026-08-06T21:29:02.586917Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:02.586917Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:191da8c5dcc81d0f38c26144ca6431e8377b40e61f5cfbb6f00f047add23b0b9","observation_id":"f67a7772-c1b6-4031-ba0c-3ced2c7c61a6","resolution":{"observed_at":"2026-08-06T21:29:02.586917Z","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":"2026-08-06T21:29:02.729299Z","title":"Automatic diagnosis of the 12-lead ecg using a deep neural network","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:02.729299Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:a54fd908551f619bd8897533551c0803cec02c3ad9c962aebeabee41ab6b8a31","observation_id":"f8810c08-9755-4108-a7ad-296623248da6","resolution":{"observed_at":"2026-08-06T21:29:02.729299Z","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":"2026-08-06T21:29:02.918008Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:02.918008Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:4d90bc88a1e094cd46544beea9c27fc0f26e4e8641db31840c7d6a15d3c918b9","observation_id":"bc625d19-e4bd-4c4d-8895-a6d879bf533a","resolution":{"observed_at":"2026-08-06T21:29:02.918008Z","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":"2026-08-06T21:29:03.084368Z","title":"A review of deep learning techniques for forecasting energy use in buildings","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:03.084368Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:a287f971fe8632910b7be1dfab4e0c1d0a80a05e7b4c7c24ad102f8fcee7e8ab","observation_id":"1a3a896a-5155-47dd-9c0e-f3188e70785a","resolution":{"observed_at":"2026-08-06T21:29:03.084368Z","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":"2026-08-06T21:29:03.220999Z","title":"Image- based time series forecasting: A deep convolutional neural network approach.Neural Networks, 157:39–53, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:03.220999Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:4e64c20f40eb736d994bb86796e0149763d6ff29804c54fc42fe22ace48f6dc0","observation_id":"b31f76a3-7a37-41bb-85ed-3265589db840","resolution":{"observed_at":"2026-08-06T21:29:03.220999Z","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":"2026-08-06T21:29:03.332985Z","title":"Dust: Dual swin transformer for multi- modal video and time-series modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:03.332985Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:a0f06d686f423ac4e7df6cb19afad0fefb3b76287d23c5b6c788185c3ee0e8ea","observation_id":"37aee5d3-dbca-499e-a0be-57664450a828","resolution":{"observed_at":"2026-08-06T21:29:03.332985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05743","last_updated":"2019-09-27T21:59:59Z","snapshot_observed_at":"2026-08-07T11:03:21.673722Z","submitted_at":"2019-06-13T15:03:52Z","title":"Learning Video Representations using Contrastive Bidirectional Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.05743","snapshot_observed_at":"2026-08-06T21:29:03.461180Z","title":"Learning video representa- tions using contrastive bidirectional transformer","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:03.461180Z"},"links":{"cited_paper":"/paper/1906.05743","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:e02f66514c2ce68d5f49b3ccb6acf6aefc404cb54e3d5284c299658541d46e91","observation_id":"2e281257-1145-46ff-a2bd-75c1dd0387fb","resolution":{"observed_at":"2026-08-06T21:29:03.461180Z","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":"2026-08-06T21:29:19.545978Z","title":"Videobert: A joint model for video and language representation learning","venue":null,"work_id":"59497dca-f2e0-4536-a633-c882519c5345","year":2019},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:03.627353Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:e59eb80741220a2c4b6a0d6c37f1b06a0923ece639513aee70933040645e12b0","observation_id":"2dc11e7f-c895-4848-b551-e8f1cec888c3","resolution":{"observed_at":"2026-08-06T21:29:19.700439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2308.08241","last_updated":"2024-02-22T02:03:42Z","snapshot_observed_at":"2026-08-05T22:50:40.599711Z","submitted_at":"2023-08-16T09:16:02Z","title":"TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.08241","snapshot_observed_at":"2026-08-06T21:29:03.796086Z","title":"Test: Text prototype aligned embedding to activate llm’s ability for time series","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:03.796086Z"},"links":{"cited_paper":"/paper/2308.08241","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:6f8720dca74c969e4b9ff7abde0396f3ddda1ff8237179c78756b51d7189f417","observation_id":"abda9d6e-5505-4bd9-8d7f-2c4a62896087","resolution":{"observed_at":"2026-08-06T21:29:03.796086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.06031","last_updated":"2023-03-02T09:05:43Z","snapshot_observed_at":"2026-08-01T22:41:21.854568Z","submitted_at":"2022-10-12T09:08:27Z","title":"Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.06031","snapshot_observed_at":"2026-08-06T21:29:03.936579Z","title":"Long-form video-language pre-training with multimodal temporal contrastive learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:03.936579Z"},"links":{"cited_paper":"/paper/2210.06031","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:5366e96f79b3d1869df47780844aca7852d8f06220835786bcd614a69a3b3048","observation_id":"5c8101c3-3abd-4b70-8500-7ed013e5dc5e","resolution":{"observed_at":"2026-08-06T21:29:03.936579Z","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":"2026-08-06T21:29:19.309376Z","title":"Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024","venue":null,"work_id":"6d17bb05-bccc-4bae-9be7-572302435a89","year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:04.042255Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:ddbde3cd3e96598d685269359c0480b5656b45ce6a3ca1f6ab21a3c429a92a61","observation_id":"7b2ee3d7-90fb-4978-ac46-bbd58fe0f971","resolution":{"observed_at":"2026-08-06T21:29:19.417664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:29:19.112767Z","title":"Are language models actually useful for time series forecasting? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024","venue":null,"work_id":"57479207-d8fd-4ec5-a42d-a9a4a446a3b5","year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:04.148304Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:e2be2322bd3e4c42efacc6f8f16dc74be4c8cb418c2b8a694d83fc98ca02e676","observation_id":"a3147d8f-832b-427e-b3f3-8afbb9bde3d3","resolution":{"observed_at":"2026-08-06T21:29:19.196750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-06T21:29:04.295397Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:04.295397Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:becae8ffb8d930eecc10bba56b5467ef924d3581d9efb625aa5279ec5ce59c9d","observation_id":"e831c829-605f-4219-991a-3d502d35445b","resolution":{"observed_at":"2026-08-06T21:29:04.295397Z","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":"2026-08-06T21:29:04.435893Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:04.435893Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:9e2c24a579cae6a22f02173bb50180c7159ddba153f002fb67303b2b118495c6","observation_id":"167d606e-03eb-457b-952c-9774ae0356d7","resolution":{"observed_at":"2026-08-06T21:29:04.435893Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.10555","last_updated":"2025-01-17T21:05:09Z","snapshot_observed_at":"2026-07-06T20:22:41.032443Z","submitted_at":"2025-01-17T21:05:09Z","title":"Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.10555","snapshot_observed_at":"2026-08-06T21:29:04.583770Z","title":"Towards data-centric ai: A com- prehensive survey of traditional, reinforcement, and generative approaches for tabular data transformation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:04.583770Z"},"links":{"cited_paper":"/paper/2501.10555","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:a4e68ef3164cd4acbdff5d48c86064ca48d2a2b2f74535e4d0b05b61cb9155fb","observation_id":"a2726999-8d48-4c29-a8c9-2386cdd8ecf8","resolution":{"observed_at":"2026-08-06T21:29:04.583770Z","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":"2026-08-06T21:29:04.698553Z","title":"Micn: Multi-scale local and global context modeling for long-term series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:04.698553Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:14677e8fcc5e00caad27a5c6ac6f9c768290567c9a323218a230df241ec90094","observation_id":"66a86caf-e597-4e13-b649-582afd9a3f95","resolution":{"observed_at":"2026-08-06T21:29:04.698553Z","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":"2026-08-06T21:29:18.873157Z","title":"Long-short temporal contrastive learning of video transformers","venue":null,"work_id":"df325e12-d48f-42df-a826-7d653d527ae4","year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:04.825726Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:5afe92d404f21cc43c8a95d60f16c2c6f131b6b74bb7bd56e200f44fcae36c28","observation_id":"8e26c3b6-34a2-465d-8a82-efc4eac27530","resolution":{"observed_at":"2026-08-06T21:29:18.967420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2109.08472","last_updated":"2021-09-17T11:21:34Z","snapshot_observed_at":"2026-07-06T11:48:42.000083Z","submitted_at":"2021-09-17T11:21:34Z","title":"ActionCLIP: A New Paradigm for Video Action Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.08472","snapshot_observed_at":"2026-08-06T21:29:05.004317Z","title":"Actionclip: A new paradigm for video action recognition","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:05.004317Z"},"links":{"cited_paper":"/paper/2109.08472","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:4841c7e2791a45f705049a6a9496faf541e799ce2d4a5b1a9f7e2b5e2a6e64fc","observation_id":"5e0caaa7-7186-43ea-a47c-e988f75f6f33","resolution":{"observed_at":"2026-08-06T21:29:05.004317Z","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":"2026-08-06T21:29:18.598391Z","title":"A hierarchal bert structure for native speaker writing detection","venue":null,"work_id":"24bc2214-5057-4c49-92e4-2a07cab959ad","year":2022},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:05.163304Z"},"links":{"citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:a4e012f93a6217af4b6389c71bdcdbbde9a3832acf308d4946df0e41cab447ab","observation_id":"2d9357f1-380d-45bb-b419-1bd210e8b1f3","resolution":{"observed_at":"2026-08-06T21:29:18.704941Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2410.03521","last_updated":"2025-02-25T02:17:05Z","snapshot_observed_at":"2026-07-06T19:27:47.617823Z","submitted_at":"2024-09-27T00:01:32Z","title":"Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models","version":2},"cited_work":{"arxiv_id":"2410.03521","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.03521","snapshot_observed_at":"2026-08-06T21:29:12.003849Z","title":"Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models","venue":"cs.CL","work_id":"45b13f6d-32d4-4d79-b334-5108c77aa71a","year":2024},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:05.314870Z"},"links":{"cited_paper":"/paper/2410.03521","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:f1e72516a3695ffbf8ef36ce6570bff43769e0975633d139d25e391d1bd411d4","observation_id":"2ec5269b-6731-4f8a-b4c0-58c71406f8cb","resolution":{"observed_at":"2026-08-06T21:29:12.093386Z","resolver_source":"local_arxiv","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"}}],"paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T21:21:21.461120Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":90,"verified_exact":5,"verified_fuzzy":5},"total_outbound_references":138},"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 100 of 138 outbound references and 3 inbound Pith citation observations for arXiv:2506.24124."}