{"as_of":"2026-08-17T19:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:750ed8a7267d9772a24d95cfa89bb006227145b1502ca9928232f42eb078df20","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:21:18.560751Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.13175/citation-record","integrity":"/paper/2505.13175/integrity","json":"/paper/2505.13175/citation-record.json","paper":"/paper/2505.13175"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:21:18.389414Z","title":"Frozen in time: A joint video and image encoder for end-to-end retrieval","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.389414Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:9bc23862781dd285297649f9219a7e16c69f6b31044630fefa63bd49462ce375","observation_id":"bb298253-acf4-4b50-b891-4702f1d49afc","resolution":{"observed_at":"2026-08-15T20:21:18.389414Z","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":"2026-08-15T20:21:19.076229Z","title":"Multimodal machine learn- ing: A survey and taxonomy","venue":null,"work_id":"523e4df6-4c82-45a1-8e8f-2eed7076526d","year":2018},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.393786Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:2ec99c682b7d200cee2c578405f09f8b990a97a4cd56ed197bceb4b2207d9e49","observation_id":"7d98f2b8-64d8-462e-85fb-07ea45865fdf","resolution":{"observed_at":"2026-08-15T20:21:19.080581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-15T20:21:18.397882Z","title":"On the opportunities and risks of foundation models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.397882Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:42242c6e1f37f11a01be88c8906eb4685c2c980a52934c14a9254c7026f34e4c","observation_id":"867fed0f-f181-4c4d-9020-34477875db11","resolution":{"observed_at":"2026-08-15T20:21:18.397882Z","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":"2026-08-15T20:21:18.402676Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.402676Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:b2d5fe04c3e9bd9c20aec364685dd47d713de08ee0e4f0b75ed248879b76a97c","observation_id":"3928af47-775d-4408-adf4-fadfdcbc5bb6","resolution":{"observed_at":"2026-08-15T20:21:18.402676Z","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":"2026-08-15T20:21:19.055006Z","title":"Nhits: Neural hierarchical interpolation for time series fore- casting","venue":null,"work_id":"7e9917af-1794-4857-ac62-ec8325ea287c","year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.407958Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:1a49aea866429b01fbca2197fe14aa6e3565f57cfbda697e1e528e76d54b6529","observation_id":"9d9b3559-d88d-48b8-9e59-15135c88f225","resolution":{"observed_at":"2026-08-15T20:21:19.059692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:21:19.042037Z","title":"Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms","venue":null,"work_id":"c3c6474b-bf41-4fe5-ba11-c0d9ef6a2e02","year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.412113Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:4bbf58b52b68ce4b688f8310cd2cd10106e1e58104ea78bd4701e149dc191f64","observation_id":"31ae64c1-419e-46e2-a8c1-229aa1a5f472","resolution":{"observed_at":"2026-08-15T20:21:19.046356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:21:18.416399Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.416399Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:4ecf745470b4b1be808f64afc02131d6db8ff639903c5319877d43b58f9c058f","observation_id":"f17305ef-57df-48c6-b97e-69220fdf46d9","resolution":{"observed_at":"2026-08-15T20:21:18.416399Z","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":"2026-08-15T20:21:18.420156Z","title":"Audioclip: Extending clip to image, text and audio","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.420156Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:d6c6b246e88028c8ef789a38914a1bec81a564deb367c8674b61e031ca47cbac","observation_id":"155bf0c0-15c8-4001-90d0-377e6cdf4c0f","resolution":{"observed_at":"2026-08-15T20:21:18.420156Z","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":"2026-08-15T20:21:18.423855Z","title":"Context-alignment: Activating and enhancing llm capabilities in time series","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.423855Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:5f79958f7ee9451382d3dba3f150793a1cc069fbbe27b298fe202429a09bc866","observation_id":"0def9d55-b98b-4ee0-8a47-e4a7e08400d2","resolution":{"observed_at":"2026-08-15T20:21:18.423855Z","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":"2026-08-15T20:21:18.427913Z","title":"Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.427913Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:506c0fab6839fd5b8844168fe7d330d7cd52d9fba579cfff1566d63c94fafbf4","observation_id":"9be2b5e2-66db-4c78-b168-b08bac6be67b","resolution":{"observed_at":"2026-08-15T20:21:18.427913Z","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":"2026-08-15T20:21:18.431557Z","title":"Scaling up visual and vision-language representation learning with noisy text supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.431557Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:6b9df31bd20278cc87144426bb08b5754baeb1c94e8aaa78555f931ee695cd26","observation_id":"80946cb3-7e84-44aa-9cd0-2c083fd64deb","resolution":{"observed_at":"2026-08-15T20:21:18.431557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03182","last_updated":"2024-02-05T16:46:35Z","snapshot_observed_at":"2026-08-16T14:21:21.465222Z","submitted_at":"2024-02-05T16:46:35Z","title":"Empowering Time Series Analysis with Large Language Models: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03182","snapshot_observed_at":"2026-08-15T20:21:18.435248Z","title":"Empowering time series analysis with large language models: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.435248Z"},"links":{"cited_paper":"/paper/2402.03182","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:89827edfc03e9ea6e022e312ead42995db67034adcceb89a28e38078c29e9a15","observation_id":"3ce9faec-c34f-4281-b466-465797e66e36","resolution":{"observed_at":"2026-08-15T20:21:18.435248Z","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":"2026-08-16T11:06:17.042878Z","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":"2026-08-15T20:21:18.439810Z","title":"Time-llm: Time series forecasting by reprogramming large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.439810Z"},"links":{"cited_paper":"/paper/2310.01728","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:67978fff6c960c7ba0b46bc127047395dc510e24f2d699f5f599c3d2da783a07","observation_id":"044a06f8-26f9-46d9-ae69-4f6e445e0beb","resolution":{"observed_at":"2026-08-15T20:21:18.439810Z","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":"2026-08-15T20:21:18.444364Z","title":"Large language models are zero-shot reasoners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.444364Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:461c3c355c96ca987cafdd840c26f3342154a65932f83ce20470e13875cbc931","observation_id":"506d3e47-13bb-4119-88a2-7cf286abcfda","resolution":{"observed_at":"2026-08-15T20:21:18.444364Z","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":"2026-08-15T20:21:18.448119Z","title":"Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.448119Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:d707bd97f37dde87ac53d43aa2c7ddc94af9b1c230575885fa9b359710bfe7dd","observation_id":"a125451e-4e3f-47af-b222-66117210b65e","resolution":{"observed_at":"2026-08-15T20:21:18.448119Z","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":"2026-08-15T20:21:18.983609Z","title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","venue":null,"work_id":"3aa924f9-decd-4efc-a01b-d16bcd76786e","year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.451789Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:05b81eaa06a9d63512700ebc97e698453256af194c7732ac9916aa23c9b1d845","observation_id":"49255957-be53-41cf-903f-c25314a76a7c","resolution":{"observed_at":"2026-08-15T20:21:18.988260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:21:18.970921Z","title":"Timecma: Towards llm-empowered multivariate time series forecasting via cross-modality alignment","venue":null,"work_id":"59647cbe-c5ab-4e45-be0b-f3d87dddb78b","year":2025},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.455459Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:2e195b819f0f297e8e89052f8e65fb467553561ca32895828b8b62d1fcae4db5","observation_id":"22399c53-db56-4fd4-ae36-df235a96a507","resolution":{"observed_at":"2026-08-15T20:21:18.975075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:21:18.958651Z","title":"Calf: Aligning llms for time series forecasting via cross-modal fine-tuning","venue":null,"work_id":"b559a62e-17e7-4370-b164-b7ae642454b4","year":2025},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.459058Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:ee2335a3125a7383c2f83fa300347761bff5bc5f627bed112f67055594301ff0","observation_id":"c7822a7e-701a-47df-a114-ff4482e9af3e","resolution":{"observed_at":"2026-08-15T20:21:18.962733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06625","last_updated":"2024-03-14T11:45:57Z","snapshot_observed_at":"2026-08-14T20:15:49.960714Z","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":"2026-08-15T20:21:18.462840Z","title":"itransformer: Inverted transformers are effective for time series forecasting","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.462840Z"},"links":{"cited_paper":"/paper/2310.06625","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:15ab1a16d43101073ffba5d20228115d791db92248520f5a4a02633a35066977","observation_id":"958659ff-cb4b-448e-a6c3-d45d390f7549","resolution":{"observed_at":"2026-08-15T20:21:18.462840Z","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":"2026-08-15T20:21:18.945793Z","title":"Non-stationary transformers: Exploring the stationarity in time series forecasting","venue":null,"work_id":"d180eed6-0fb2-431d-b28f-4c64503a9f40","year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.466924Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:d2ab5e1c7fb481a9fd51e6087b81640ac247121575056bd250f37da4e81278f8","observation_id":"f9f9a3fd-13fb-4de7-9ec7-b44ea737df68","resolution":{"observed_at":"2026-08-15T20:21:18.949977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:21:18.932519Z","title":"The m4 competition: Results, findings, conclusion and way forward","venue":null,"work_id":"5be2c8d0-99b1-47da-bac4-144b5ad506a5","year":2018},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.470766Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:247b542e51405a14d3a49c2e27c1f1ae932b1c86364d5d29fdc0e5d3bc7d6421","observation_id":"025d1793-d8d5-42e7-8fb7-5741f9414413","resolution":{"observed_at":"2026-08-15T20:21:18.936645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:21:18.918554Z","title":"Maximum entropy markov models for information extraction and segmentation","venue":null,"work_id":"2943921d-7bc6-497e-bfe4-fea6f4ff89e1","year":2000},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.474188Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:65700a2764b7643b398de75d2f98c0cb57270fa809f7386025bbdccb4c960eec","observation_id":"2f41b723-7776-414c-81d9-27193bad6123","resolution":{"observed_at":"2026-08-15T20:21:18.924456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.14730","last_updated":"2023-03-05T22:11:56Z","snapshot_observed_at":"2026-08-17T01:22:50.943392Z","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":"2026-08-15T20:21:18.477840Z","title":"A time series is worth 64 words: Long-term forecasting with transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.477840Z"},"links":{"cited_paper":"/paper/2211.14730","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:31fd0f6ffc42e48852726a33922b41f563d2bc5d8537b3f77ebc8441ac7a7ff0","observation_id":"c966bcba-c478-457f-955a-e04663ae2dfe","resolution":{"observed_at":"2026-08-15T20:21:18.477840Z","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":"2026-08-14T16:26:52.986402Z","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":"2026-08-15T20:21:18.481514Z","title":"N-beats: Neural basis expansion analysis for interpretable time series forecasting","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.481514Z"},"links":{"cited_paper":"/paper/1905.10437","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:42e0b335a8a55d4c2a41e8834fdd328e2f452de3000cd36c46c200940016f1b1","observation_id":"ca81bf27-bb85-45a8-a7a2-fda2d873a1b6","resolution":{"observed_at":"2026-08-15T20:21:18.481514Z","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":"2026-08-15T20:21:18.485562Z","title":"S2ip-llm: Semantic space informed prompt learning with llm for time series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.485562Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:957a3f72fdab60947f3f2634cff5e75e6986c9c0546a795e69cf4f490b31e223","observation_id":"32dafd82-4c09-4577-a048-175965ad95c8","resolution":{"observed_at":"2026-08-15T20:21:18.485562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-15T20:21:18.489232Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.489232Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:18f5e1b2bac636e1e2d21009881e5641feaf9250d298c549edf9d68755339d49","observation_id":"1120601c-c80d-48ac-8204-31d409c45e6c","resolution":{"observed_at":"2026-08-15T20:21:18.489232Z","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":"2026-08-15T20:21:18.493337Z","title":"A tutorial on hidden markov models and selected applications in speech recognition","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.493337Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:f9b3c8d0999f4c60fa0f34ee06fab92c036ea6cf83eea03be1d138ff50df9fc9","observation_id":"fe7640ba-f686-4f63-be0a-4525fa4ee90f","resolution":{"observed_at":"2026-08-15T20:21:18.493337Z","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":"2026-08-15T20:21:18.497152Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.497152Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:194ad1f4787d8f642b676b0697bbaec8276c356db29f9faea39d42a4ac4c4798","observation_id":"878a8ace-129e-4a44-8af0-1c8b51dc01ef","resolution":{"observed_at":"2026-08-15T20:21:18.497152Z","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":"2026-08-15T20:21:18.501214Z","title":"Improving language understanding by generative pre-training","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.501214Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:8614222ae326bd51e9465fd640bdc2c3c76cd3e8d13bdfc279845c0ce0d2fe46","observation_id":"bf0c0254-6160-42e0-9ff6-968ec497426c","resolution":{"observed_at":"2026-08-15T20:21:18.501214Z","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":"2026-08-15T20:21:18.504986Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.504986Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:6f27a86b3d5649c7ae24670ae56cf0dc161e3b2d08177016e48f9f4af4630ac7","observation_id":"c140182d-788c-457b-982b-545a835b5c8d","resolution":{"observed_at":"2026-08-15T20:21:18.504986Z","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":"2026-08-15T20:21:18.508461Z","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":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.508461Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:a67d330c0f497d22260e99fb8aa16b640b0ec6de6b600b412e46067fc884ac00","observation_id":"3fd47715-b1f1-4626-8282-7d1de0f5e832","resolution":{"observed_at":"2026-08-15T20:21:18.508461Z","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":"2026-08-15T20:21:18.860421Z","title":"Cross-modal fine-tuning: Align then refine","venue":null,"work_id":"e80c83d8-b22a-40b8-b391-beaa323e345d","year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.512244Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:63d7fbe707586b1356ca4e93ab3632008f3286a6a97a856bd3f8fdf87de01e1e","observation_id":"6b23b2ea-9c68-4e46-a5ad-c577cc602132","resolution":{"observed_at":"2026-08-15T20:21:18.864638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08241","last_updated":"2024-02-22T02:03:42Z","snapshot_observed_at":"2026-08-16T15:08:09.493746Z","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":"2026-08-15T20:21:18.515910Z","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":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.515910Z"},"links":{"cited_paper":"/paper/2308.08241","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:f75805f5c8ab9ef641fe598c5faca4eabc438fb7232f574319a8b5864abba438","observation_id":"89166ff5-61c6-472f-b030-c5fdb34cdfbe","resolution":{"observed_at":"2026-08-15T20:21:18.515910Z","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":"2026-08-15T20:21:18.519813Z","title":"Semantic reconstruction of continuous language from non-invasive brain recordings","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.519813Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:07a07498dfd26e713635c5c6a21f1cf9039695125970862a66a65349083a3f48","observation_id":"5abc28d0-f3b1-46a1-81bf-be9f5f076824","resolution":{"observed_at":"2026-08-15T20:21:18.519813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-15T20:21:18.523520Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.523520Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:a8c028d9fa5bfb1c6a9beda87ec2acd1a170d2d35d8de2303d864be71bdc2532","observation_id":"6516755d-a6a0-40f1-a2ac-0dda1fd2edbd","resolution":{"observed_at":"2026-08-15T20:21:18.523520Z","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":"2026-08-15T20:21:18.527318Z","title":"Multimodal transformer for unaligned multimodal language sequences","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.527318Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:f0e806d012bf439a7355a09b3ae26926d827a50e880196a86007e855b8bca63f","observation_id":"22079dd5-ecf1-43b9-b8ce-d80e49fa5913","resolution":{"observed_at":"2026-08-15T20:21:18.527318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14616","last_updated":"2024-05-23T14:27:07Z","snapshot_observed_at":"2026-08-16T13:50:10.731222Z","submitted_at":"2024-05-23T14:27:07Z","title":"TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14616","snapshot_observed_at":"2026-08-15T20:21:18.531205Z","title":"Timemixer: Decomposable multiscale mixing for time series forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.531205Z"},"links":{"cited_paper":"/paper/2405.14616","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:40301a395c94e4e45fb375a84be4a1ec1e276cc7b63646d6b81bd56c53eee6f4","observation_id":"e83c0933-0cbb-40ff-8ef5-debaca4eb9fa","resolution":{"observed_at":"2026-08-15T20:21:18.531205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02186","last_updated":"2023-04-12T02:34:03Z","snapshot_observed_at":"2026-08-17T15:00:57.936069Z","submitted_at":"2022-10-05T12:19:51Z","title":"TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02186","snapshot_observed_at":"2026-08-15T20:21:18.535106Z","title":"Times- net: Temporal 2d-variation modeling for general time series analysis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.535106Z"},"links":{"cited_paper":"/paper/2210.02186","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:60c566237190fa7cbc9032f780e93f06c5e856998f86ab2af857a2dd55fe6d0a","observation_id":"0d3c7203-5ae0-415b-b9da-3d77f56b4279","resolution":{"observed_at":"2026-08-15T20:21:18.535106Z","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":"2026-08-15T20:21:18.539189Z","title":"Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.539189Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:57b346f0f1de2b1ae50c3aa67a3935b67958aaa210349c00bd8970269b88a0cf","observation_id":"a5a6a78b-d68c-4747-8b5d-9a7a594f059e","resolution":{"observed_at":"2026-08-15T20:21:18.539189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01917","last_updated":"2022-06-14T00:48:04Z","snapshot_observed_at":"2026-08-13T15:12:42.567441Z","submitted_at":"2022-05-04T07:01:14Z","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01917","snapshot_observed_at":"2026-08-15T20:21:18.543560Z","title":"Coca: Contrastive captioners are image-text foundation models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.543560Z"},"links":{"cited_paper":"/paper/2205.01917","citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:403b9c0974c36b13ccd912e15e7b61c542c07b21203565d532f8fece4d9e4ec8","observation_id":"180ce0b1-a0e2-46a6-8a6b-97cdd5bb45b1","resolution":{"observed_at":"2026-08-15T20:21:18.543560Z","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":"2026-08-15T20:21:18.548572Z","title":"Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.548572Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:2228272e429a4e106d3b26ca4aae68fad5a8b2e03acd545eda4d52f5751dbe07","observation_id":"34318f01-0190-4dd7-9f81-cf3b6a105b94","resolution":{"observed_at":"2026-08-15T20:21:18.548572Z","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":"2026-08-15T20:21:18.552617Z","title":"A transformer-based framework for multivariate time series representation learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.552617Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:e5e4eda734b9f16f3524caaa86ea6ef824b4a0bac4dca09ffb5cef9c8a3dbc7a","observation_id":"4ce5900c-1654-4a64-85cf-a1c5359adbf2","resolution":{"observed_at":"2026-08-15T20:21:18.552617Z","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":"2026-08-15T20:21:18.557011Z","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.557011Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:37f908d1a66e41637e85b0819754dc349eaae0da23bdd944215b5d2c4e86a82e","observation_id":"f1a4618f-af90-4b9f-b5da-a2f22881dca9","resolution":{"observed_at":"2026-08-15T20:21:18.557011Z","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":"2026-08-15T20:21:18.795108Z","title":"One fits all: Power general time series analysis by pretrained lm","venue":null,"work_id":"3e5db8c4-12bb-48a2-88fe-4fa53198c154","year":2023},"citing_paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:18.560751Z"},"links":{"citing_paper":"/paper/2505.13175"},"observation_digest":"sha256:badfa798e9b697a288baa1405832d0bb6f8e89b7cd2fbdfde621e0042e6564d3","observation_id":"592889c4-1c7d-42cc-a084-4bf6da879adc","resolution":{"observed_at":"2026-08-15T20:21:18.801293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.13175","last_updated":"2025-05-19T14:30:41Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-15T20:15:57.066138Z","submitted_at":"2025-05-19T14:30:41Z","title":"Enhancing LLMs for Time Series Forecasting via Structure-Guided Cross-Modal Alignment"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":33,"verified_exact":0,"verified_fuzzy":11},"total_outbound_references":44},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.13175."}