{"as_of":"2026-08-12T07:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ca1ac443706b8b4bf83b405af155daecba29e205f190c371fcc185c120d27279","coverage":[{"denominator":72,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":72,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T12:43:41.916650Z","state":"measured"},{"denominator":72,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":72,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2509.01312/citation-record","integrity":"/paper/2509.01312/integrity","json":"/paper/2509.01312/citation-record.json","paper":"/paper/2509.01312"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.05270","last_updated":"2022-07-12T02:44:40Z","snapshot_observed_at":"2026-08-07T12:27:02.844514Z","submitted_at":"2022-07-12T02:44:40Z","title":"A Survey on Table Question Answering: Recent Advances","version":1},"cited_work":{"arxiv_id":"2207.05270","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.05270","snapshot_observed_at":"2026-08-05T12:43:44.538552Z","title":"A Survey on Table Question Answering: Recent Advances","venue":"cs.CL","work_id":"925ba246-e0da-4f4e-a25e-645921534bc2","year":2022},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:32.968116Z"},"links":{"cited_paper":"/paper/2207.05270","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:459849e1d9678166a4cdedc35a69d72d77abcdcacfed8b0ed135feda03748012","observation_id":"444b94e2-9e6e-4037-aa92-84808da7ed6f","resolution":{"observed_at":"2026-08-05T12:43:44.604142Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.10548","last_updated":"2024-08-20T04:59:19Z","snapshot_observed_at":"2026-08-11T12:45:50.071996Z","submitted_at":"2024-08-20T04:59:19Z","title":"Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.10548","snapshot_observed_at":"2026-08-05T12:43:33.116130Z","title":"Available from: https: //arxiv.org/abs/2408.10548","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:33.116130Z"},"links":{"cited_paper":"/paper/2408.10548","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:31c794ac117fbc2c24bc27dd9555ac100d69a8df424dc1d86f6a223cd3748e02","observation_id":"c1e31208-1f60-4295-9cd5-d5097adc3107","resolution":{"observed_at":"2026-08-05T12:43:33.116130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.19024","last_updated":"2025-05-25T08:20:34Z","snapshot_observed_at":"2026-08-07T14:19:19.197066Z","submitted_at":"2025-05-25T08:20:34Z","title":"Learn Beneficial Noise as Graph Augmentation","version":1},"cited_work":{"arxiv_id":"2505.19024","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.19024","snapshot_observed_at":"2026-08-05T12:43:44.319074Z","title":"Learn Beneficial Noise as Graph Augmentation","venue":"cs.LG","work_id":"98b4c4a0-68d6-464c-8ef6-050b9b3916b4","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:33.233410Z"},"links":{"cited_paper":"/paper/2505.19024","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:b325e792a1ddb922bcb4dc0029d63eb6fe73c6a0b8161634397ad2cde4e86d71","observation_id":"2fd88e7c-74d5-4b26-9af2-75bcd56416e7","resolution":{"observed_at":"2026-08-05T12:43:44.412810Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v39i16.33918","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Enhance vision-language alignment with noise","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"4d096f13-cbb6-46c4-ba85-577e1d72ddbf","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:33.399295Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:5cd0591a9efaaa58c18e7a6f19b88b862342eff74f416d4d44d184e9ebbd3d5a","observation_id":"31a9848c-3795-4983-92b1-8154d85fc931","resolution":{"observed_at":"2026-08-05T12:43:42.466362Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:49.751346Z","title":"Available from: https://arxiv.org/abs/2408","venue":null,"work_id":"ba639b45-3655-45aa-ae96-33fb8a23b00a","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:33.548127Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ba95c28a8cebfa150938495f34f3772fc22d387180fd44983c298a01e2c78ff5","observation_id":"393601e8-c6b6-402c-97aa-973613798f8f","resolution":{"observed_at":"2026-08-05T12:43:49.803052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.07651","last_updated":"2025-05-28T15:07:08Z","snapshot_observed_at":"2026-08-11T18:36:49.602726Z","submitted_at":"2023-06-13T09:43:32Z","title":"Variational Positive-incentive Noise: How Noise Benefits Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.07651","snapshot_observed_at":"2026-08-05T12:43:33.667920Z","title":"Available from: https://arxiv.org/abs/2306.07651","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:33.667920Z"},"links":{"cited_paper":"/paper/2306.07651","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:77bb64aba780ebcd8f4c5c7af63559debb493c64961568721929ad9abed6d6ee","observation_id":"74cc6eb5-7c47-4c48-9bbf-a2fbbb11d975","resolution":{"observed_at":"2026-08-05T12:43:33.667920Z","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":"2026-08-05T12:43:33.834137Z","title":"Positive-Incentive Noise","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:33.834137Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:b94d9bfc0c6ebe35aa5a58a7fea1f85d3e6ddadeaf6fe45644aae5921f9e7ab3","observation_id":"11c5da2e-3674-4201-a0f1-2af4e2a2000b","resolution":{"observed_at":"2026-08-05T12:43:33.834137Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-05T12:43:49.615516Z","title":"TableLlama: Towards Open Large Generalist Mod- els for Tables","venue":null,"work_id":"adf48281-4e55-40f9-8d37-159b248094c6","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:33.967977Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:1b196e0f0952f80bc9ed75b3e423279f78d92cd408c7dcc7d95077ec03804036","observation_id":"3efa07de-00a9-475a-a811-41c629d5f215","resolution":{"observed_at":"2026-08-05T12:43:49.692356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02059","last_updated":"2024-11-07T03:32:44Z","snapshot_observed_at":"2026-08-07T15:32:17.369903Z","submitted_at":"2024-11-04T13:03:13Z","title":"TableGPT2: A Large Multimodal Model with Tabular Data Integration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02059","snapshot_observed_at":"2026-08-05T12:43:34.102906Z","title":"Available from: https://arxiv","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:34.102906Z"},"links":{"cited_paper":"/paper/2411.02059","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:d281e2bfb8f6740844f9d5ec95a96e926725eb3882511e7af123ac494b6aabcb","observation_id":"9981d427-935c-412d-a650-04b1794bcbc2","resolution":{"observed_at":"2026-08-05T12:43:34.102906Z","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":"2026-08-05T12:43:49.496009Z","title":"StructLM: Towards Building Generalist Models for Structured Knowledge Grounding","venue":null,"work_id":"557a7f51-8335-4f44-9dc1-c6d2d17b313c","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:34.238086Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ced57321284a6d36b36ea34f0144c6d3a54c4a9ea34e6b11fcf27b2f969e146b","observation_id":"c5ce9775-630d-4a80-8fbe-952d7c91d078","resolution":{"observed_at":"2026-08-05T12:43:49.561377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.19318","last_updated":"2025-02-17T13:45:00Z","snapshot_observed_at":"2026-08-11T05:09:44.584688Z","submitted_at":"2024-03-28T11:21:12Z","title":"TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.19318","snapshot_observed_at":"2026-08-05T12:43:34.361441Z","title":"Available from: https://arxiv.org/abs/2403.19318","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:34.361441Z"},"links":{"cited_paper":"/paper/2403.19318","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:3cb15ee4ac5217bdd923e0ee7a8ba3b753f6255da0bf2bc34aff2ebf7953803b","observation_id":"7a1b86a7-68ed-4e17-a3a3-565b92f4e988","resolution":{"observed_at":"2026-08-05T12:43:34.361441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12415","last_updated":"2026-05-13T02:38:34Z","snapshot_observed_at":"2026-08-07T08:06:21.812040Z","submitted_at":"2025-05-18T13:40:18Z","title":"Table-R1: Region-based Reinforcement Learning for Table Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.12415","snapshot_observed_at":"2026-08-05T12:43:34.493139Z","title":"Available from: https://arxiv","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:34.493139Z"},"links":{"cited_paper":"/paper/2505.12415","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:4af300230d79156508056e749cafa2a324db93189367e01e15758e57c14effc3","observation_id":"34cf7270-96ac-4fec-91bc-64a38cff2ebc","resolution":{"observed_at":"2026-08-05T12:43:34.493139Z","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":"2026-08-05T12:43:49.356999Z","title":"Chain-of- Table: Evolving Tables in the Reasoning Chain for Table Understanding","venue":null,"work_id":"7b9c0abf-1272-4766-a64d-c8c0c25fc74f","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:34.578581Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:cd3602f751ccf1546dc5461f732eba91023cac4b4bb40a20f8a89ee791adb1e7","observation_id":"fa6ff01a-5583-4a2d-9eeb-b8807b9a83d3","resolution":{"observed_at":"2026-08-05T12:43:49.422494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:34.737310Z","title":"Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:34.737310Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:0759af720c29d37b7fb320ee57fc3141973c9e6551cf9469f2713e9849e87121","observation_id":"abd20713-cca7-4423-be60-5e454d54db5a","resolution":{"observed_at":"2026-08-05T12:43:34.737310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19378","last_updated":"2026-04-15T03:29:38Z","snapshot_observed_at":"2026-07-06T20:29:11.710285Z","submitted_at":"2025-01-31T18:31:31Z","title":"TableMaster: A Recipe to Advance Table Understanding with Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19378","snapshot_observed_at":"2026-08-05T12:43:34.843684Z","title":"Available from: https://arxiv.org/abs/2501.19378","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:34.843684Z"},"links":{"cited_paper":"/paper/2501.19378","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:0d74173118b2c86411d53b1166f6879cbf9228b067e9eb0cc6afc37531b0b613","observation_id":"4462ffdb-8d0a-4743-ae86-f0869380ab00","resolution":{"observed_at":"2026-08-05T12:43:34.843684Z","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":"2026-08-05T12:43:49.250605Z","title":"Binding Language Models in Symbolic Languages","venue":null,"work_id":"c67887ba-770f-44be-a26b-54f63ccef723","year":2023},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.034987Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:198168d2fb03b60644cf6c6be87d7e3946bec864c981f453d237cc93923845dc","observation_id":"87d7e77e-bd4a-4dee-8aea-7da0002164c3","resolution":{"observed_at":"2026-08-05T12:43:49.295234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:49.134399Z","title":"Lever: Learning to verify language-to-code generation with execution","venue":null,"work_id":"7a4b8a3d-2c7b-4eb1-8884-cf843f48aa18","year":2023},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.163465Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:0b8840d87c325ff5d68580b341d4fe43940749622662ca5b8bf765d4565f24d6","observation_id":"0f6783da-b42a-49c4-8857-ae6603557907","resolution":{"observed_at":"2026-08-05T12:43:49.177856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:49.021017Z","title":"TableRAG: Million-Token Table Understanding with Language Models","venue":null,"work_id":"46ce1c93-1a95-4309-a8d5-451b92774732","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.306102Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:9634bec6ad5158f107c941d60499b7eee82deb8176bb83fc7665316db577e319","observation_id":"8281d948-02ea-4b2f-9696-5326f9de2555","resolution":{"observed_at":"2026-08-05T12:43:49.069848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:48.805478Z","title":"Lost in the Middle: How Language Models Use Long Contexts","venue":null,"work_id":"899736f4-9aa3-44c8-bc18-d404f3e0cac6","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.567699Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:8e2f2064a58a086604ef8540afadc0185995d07787c118fc96324738464ed6ae","observation_id":"162f594a-9d5c-4e5b-b829-d79e609410b7","resolution":{"observed_at":"2026-08-05T12:43:48.843607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:48.697159Z","title":"MAC-SQL: A Multi- Agent Collaborative Framework for Text-to-SQL","venue":null,"work_id":"ccd68fbe-0da2-4fac-bf8b-a698e1419041","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.693336Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ca30be0a7a12aa6dac50f0fd624736fb15a13cf413d5a1d819faaafba726343b","observation_id":"45a0bd76-88b6-4379-808b-0e0aed1b3ea9","resolution":{"observed_at":"2026-08-05T12:43:48.742160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:48.581000Z","title":"You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL","venue":null,"work_id":"f0315fd1-1543-47c0-a8a4-fe3cef1029cc","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.841765Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:155cc5d5967e10f07329eed1888eba76090de805998f23de1871c396bf0718aa","observation_id":"39faf162-cfaa-458b-b0e4-4027f7a47715","resolution":{"observed_at":"2026-08-05T12:43:48.634600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:48.467094Z","title":"T5-SR: A Unified Seq-to- Seq Decoding Strategy for Semantic Parsing","venue":null,"work_id":"df0ceb25-a142-4bc5-a443-3075eee0fb83","year":2023},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.972183Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:12aa26bfe6476609929281a4ff3602d08a1adcf34d1bf1d5c5556a353fc3fd1d","observation_id":"0fd238d3-50e2-4ea4-9c8e-ff7bcb0d8f62","resolution":{"observed_at":"2026-08-05T12:43:48.527296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.12588","last_updated":"2023-10-23T01:27:38Z","snapshot_observed_at":"2026-08-02T13:06:11.850456Z","submitted_at":"2022-11-22T21:06:00Z","title":"Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.12588","snapshot_observed_at":"2026-08-05T12:43:36.116212Z","title":"Available from: https://arxiv.org/abs/2211.12588","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:36.116212Z"},"links":{"cited_paper":"/paper/2211.12588","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ecd25835560397f39362206475ab43379c5bcea6f20f9e83c9de755125be80e7","observation_id":"0bf71adf-4990-46fc-ae0f-1947a88a9a29","resolution":{"observed_at":"2026-08-05T12:43:36.116212Z","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":"2026-08-05T12:43:48.360321Z","title":"React: Synergizing reasoning and acting in language models","venue":null,"work_id":"8b85f49b-6d18-450f-b834-c7545de882c4","year":2023},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:36.296447Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:044e35841c325b80fa23230268fccd19c498fa07464fc054cdc769e989927d5c","observation_id":"dd7be423-c133-40c2-bf68-e2abdda170a0","resolution":{"observed_at":"2026-08-05T12:43:48.409819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.00965","last_updated":"2016-01-21T02:46:25Z","snapshot_observed_at":"2026-07-06T04:38:34.979359Z","submitted_at":"2015-12-03T06:46:27Z","title":"Neural Enquirer: Learning to Query Tables with Natural Language","version":2},"cited_work":{"arxiv_id":"1512.00965","doi":null,"metadata_source":"pith","pith_arxiv_id":"1512.00965","snapshot_observed_at":"2026-08-05T12:43:43.883727Z","title":"Neural Enquirer: Learning to Query Tables with Natural Language","venue":"cs.AI","work_id":"3e8c0775-84af-4bb8-9520-03b4f7ae4a73","year":2015},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:36.465936Z"},"links":{"cited_paper":"/paper/1512.00965","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:4aebbcd6e9d8268cfaa1cf63c8010948311710378881d0dc95ea6ecbf2d842f5","observation_id":"eeaacfe6-c581-4a05-85fb-836683aa2522","resolution":{"observed_at":"2026-08-05T12:43:43.973057Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:48.237837Z","title":"Scalable Database-Driven KGs can help Text-to-SQL","venue":null,"work_id":"d296701a-e187-46cc-8d30-89694ce82801","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:36.639277Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ec274902effc48ceaa569b9352d1cd83d8907039ab82da8d111992147db0eb22","observation_id":"cf874df3-af0b-427a-b464-fdb43cfb318b","resolution":{"observed_at":"2026-08-05T12:43:48.296177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:48.116691Z","title":"UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to- SQL","venue":null,"work_id":"2740d255-40bc-46d8-8e37-ba8fd490640c","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:36.803504Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:d39109614a3cae45b3be38484645404552677f5bfbc7f3344544a597e3834e5a","observation_id":"9bc4b2f3-2eaf-4486-93fd-161137808b8c","resolution":{"observed_at":"2026-08-05T12:43:48.166576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:47.981750Z","title":"MR-SQL: Multi-Level Retrieval Enhances Inference for LLM in Text-to-SQL","venue":null,"work_id":"1361afac-9652-456a-9f43-fd0e3d089a53","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:36.923906Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:b94cdf2ea3a611c3593e0c5ed8a58981d18e8990c3ea12a46f8bcf69ef6c5b25","observation_id":"18487014-3442-4fd8-930d-10ce502b7811","resolution":{"observed_at":"2026-08-05T12:43:48.036767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:47.836890Z","title":"LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network","venue":null,"work_id":"00ec13b8-7b4d-4edc-aa12-3269c724133e","year":2020},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.067290Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:6e25768e5aea661010e56aa18d4b8a72b2139db6a0c012e002cf9aa8722c5330","observation_id":"c01c5686-1a14-467f-9c4b-82c826d8c955","resolution":{"observed_at":"2026-08-05T12:43:47.900316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.08887","last_updated":"2019-02-02T23:53:18Z","snapshot_observed_at":"2026-07-06T07:03:52.901427Z","submitted_at":"2018-09-24T13:03:13Z","title":"Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.08887","snapshot_observed_at":"2026-08-05T12:43:37.243730Z","title":"Available from: https://arxiv.org/abs/1809.08887","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.243730Z"},"links":{"cited_paper":"/paper/1809.08887","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:f9b19696479be913ab87607a580c3f6b362c13c3fc34ce225a8074ca01d4deee","observation_id":"6d1a123f-90e3-4a52-bbb9-aebd4b3dbb45","resolution":{"observed_at":"2026-08-05T12:43:37.243730Z","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":"2026-08-05T12:43:47.693560Z","title":"Available from: https://arxiv.org/abs/2407","venue":null,"work_id":"14342655-aed1-4645-922b-cd576da28f15","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.393395Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:564733d431d92b5dbb4431548d8d58f974e9074c959ed5203fcb870aeb079dcf","observation_id":"ab5e4bc9-fdf1-408c-87e4-ff548512adb9","resolution":{"observed_at":"2026-08-05T12:43:47.754322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:47.552138Z","title":"Sentence Segmentation and Punctuation for Ancient Books Based on Supervised In-context Training","venue":null,"work_id":"86e72672-f084-470c-a771-503c35956dd3","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.538083Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:969d445192524d68ad38f26591775147142447048ace1284bd11c10784e90b35","observation_id":"5ec8ee35-a30e-4ffb-81c0-582ef8f080d3","resolution":{"observed_at":"2026-08-05T12:43:47.614623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-05T12:43:37.678369Z","title":"Available from: https://arxiv.org/abs/2201.11903","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.678369Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:29a43f040393de4c5dac923820d18c385a4246eb8d5d503abad3d64eca333919","observation_id":"bc0831a8-fce4-46a0-bf57-a0afd69bcfeb","resolution":{"observed_at":"2026-08-05T12:43:37.678369Z","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":"2026-08-05T12:43:47.416070Z","title":"Lemur: Log parsing with entropy sampling and chain-of-thought merging","venue":null,"work_id":"0ebc6772-6fc8-4ad1-9fce-a0c44aa9aa78","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.781412Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:3dee663b2ee5811e049b4115837e1aeea1dec3ad58a2fd2608cf7f744553128f","observation_id":"375c79ac-834c-4811-a322-4337684ca39e","resolution":{"observed_at":"2026-08-05T12:43:47.479987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.10625","last_updated":"2023-04-16T22:08:08Z","snapshot_observed_at":"2026-08-06T09:00:42.886249Z","submitted_at":"2022-05-21T15:34:53Z","title":"Least-to-Most Prompting Enables Complex Reasoning in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.10625","snapshot_observed_at":"2026-08-05T12:43:37.888905Z","title":"Available from: https://arxiv.org/abs/2205.10625","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.888905Z"},"links":{"cited_paper":"/paper/2205.10625","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:79026170c9d16b7167d1e6619c9a4c3c1d6f1da4e55a09abfc78f0db0a28223c","observation_id":"3ba6f8ef-6558-45a5-945f-59b9bc053715","resolution":{"observed_at":"2026-08-05T12:43:37.888905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.procs.2023.08.150","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"icsBERTs: Optimizing Pre-trained Language Models in Intelligent Customer Service","venue":"Procedia Computer Science","work_id":"f9414fc8-373c-46a2-9042-6b86b5522a70","year":2023},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:37.993504Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:401c15031d91a2644d8cd6b97d27341bd9d17c18e9fa265718dfbc54c2f3db8f","observation_id":"56e3640a-3f2c-42f5-a03c-175430c5c0c5","resolution":{"observed_at":"2026-08-05T12:43:42.250478Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:47.267557Z","title":"icsPLMs: Exploring Pre-trained Language Mod- els in Intelligent Customer Service (Student Abstract)","venue":null,"work_id":"62f26d91-71a1-4f02-8a4c-ff3105c7b50a","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.124022Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:f3f4c36bffc8c1c2b6059a63ada6c57cdc5fca42d0a3e4ddf66a7e33535bb899","observation_id":"415da0ab-1a60-4c28-8fea-b8ee95ece6c5","resolution":{"observed_at":"2026-08-05T12:43:47.341213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:47.123933Z","title":"Sentiment Analysis Technologies in AliMe - An Intelligent Assistant for E-commerce","venue":null,"work_id":"5098542f-b76d-47b1-964a-35e980fca684","year":2020},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.217775Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:38eb213118b58958b9297aebd39fcae1c98ec8a2a6fad7b39b171f891fdfa24b","observation_id":"37b45cfb-b82e-4955-a526-62e9a34395e2","resolution":{"observed_at":"2026-08-05T12:43:47.178571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:38.325995Z","title":"Available from: https: //arxiv.org/abs/2302.13971","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.325995Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ff9b3de5c24223cac55f35c668575f4d82279d3764b3e679b2c91d729390e3c9","observation_id":"1212b545-ede9-4d77-a1a0-77cd34631273","resolution":{"observed_at":"2026-08-05T12:43:38.325995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16671","last_updated":"2024-10-07T14:44:44Z","snapshot_observed_at":"2026-08-06T05:52:53.688010Z","submitted_at":"2024-02-26T15:47:01Z","title":"StructLM: Towards Building Generalist Models for Structured Knowledge Grounding","version":7},"cited_work":{"arxiv_id":"2402.16671","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.16671","snapshot_observed_at":"2026-08-05T12:43:43.512247Z","title":"StructLM: Towards Building Generalist Models for Structured Knowledge Grounding","venue":"cs.CL","work_id":"8810560e-339e-4ec7-abf3-be6b57dc4dc5","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.469173Z"},"links":{"cited_paper":"/paper/2402.16671","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:3fab19e5850bda81425f2a0a6a69b4fc68885757978a1ff8759e5c0e3eac5656","observation_id":"77b381e8-0018-4080-8cde-eaf515687316","resolution":{"observed_at":"2026-08-05T12:43:43.631400Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14361","last_updated":"2024-04-12T18:27:34Z","snapshot_observed_at":"2026-08-12T01:02:01.301513Z","submitted_at":"2024-02-22T08:01:01Z","title":"OpenTab: Advancing Large Language Models as Open-domain Table Reasoners","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14361","snapshot_observed_at":"2026-08-05T12:43:38.569990Z","title":"Available from: https://arxiv.org/abs/2402.14361","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.569990Z"},"links":{"cited_paper":"/paper/2402.14361","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:420565e5779e2e400f436cccb9eeac637c3bcbc272fac4ee9cef920944d1ac43","observation_id":"9c736e01-ac00-42bc-9cdf-9ad5599aa88c","resolution":{"observed_at":"2026-08-05T12:43:38.569990Z","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":"2026-08-05T12:43:46.981298Z","title":"Ai flow at the network edge","venue":null,"work_id":"5f5de0ad-ef0e-469f-a943-e205982364cc","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.675551Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:a66085eab75a06d6868f831a8f13691d39a67334a3530b53fb44dfae27560a2a","observation_id":"7d65e42c-3feb-4e38-a651-f76853e5aae9","resolution":{"observed_at":"2026-08-05T12:43:47.051757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:46.841564Z","title":"AI Flow: Perspectives, Scenarios, and Approaches","venue":null,"work_id":"afe758cc-8226-4b7f-bcc7-d4803c4d6ab2","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.830858Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:50cb9089ce1213dfa916a34af2dc2d45e23488ce6e9c255cbf9a86962d3dcf80","observation_id":"2e675167-3da6-4c69-9ca7-c9643a90c499","resolution":{"observed_at":"2026-08-05T12:43:46.908124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:46.687032Z","title":"Question Answering over Tabular Data with DataBench: A Large-Scale Empir- ical Evaluation of LLMs","venue":null,"work_id":"9c0969b7-db6c-4d8e-b703-43b053384b08","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:38.937091Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:9dfa7a87aa216576aecd80a4b50de95c5ae3bd597d950299077ddd7cace76a95","observation_id":"c1ce7141-a574-4740-b341-c3e65c985562","resolution":{"observed_at":"2026-08-05T12:43:46.751462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09174","last_updated":"2025-03-18T07:13:18Z","snapshot_observed_at":"2026-08-09T09:48:05.918224Z","submitted_at":"2024-08-17T11:40:10Z","title":"TableBench: A Comprehensive and Complex Benchmark for Table Question Answering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09174","snapshot_observed_at":"2026-08-05T12:43:39.000019Z","title":"Tablebench: A compre- hensive and complex benchmark for table question answering","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.000019Z"},"links":{"cited_paper":"/paper/2408.09174","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:b2a0ef92fa07e9530b7687bbae57fcba2506f6fa4567921414fe059802d58e8d","observation_id":"cd4d1eb0-2b8a-48ec-9769-3e2d4f85aee9","resolution":{"observed_at":"2026-08-05T12:43:39.000019Z","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":"2026-08-05T12:43:46.550066Z","title":"Compositional Semantic Parsing on Semi-Structured Tables","venue":null,"work_id":"c0b05255-c469-43bd-8558-c4431a53b928","year":2015},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.126976Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:a2687c64b4c119e31a20cc9a5b1b14fbf0fcdaaed663148daf896f39b519e3a5","observation_id":"461c4c30-c2df-4174-b71b-98a478b4422e","resolution":{"observed_at":"2026-08-05T12:43:46.616494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-08-05T12:43:39.248582Z","title":"Available from: https://arxiv.org/abs/2505.09388","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.248582Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:b9903bc93ced967bc27f58dc00fa150195f0f08913bd60917bac6d5b0ce65232","observation_id":"6d4f39b8-0553-4ffa-8951-fa82d1b7ec10","resolution":{"observed_at":"2026-08-05T12:43:39.248582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-05T12:43:39.319782Z","title":"Available from: https://arxiv.org/abs/2412.15115","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.319782Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ff45ced343ec3bfde50ac55e511e4f20f8ecf45c676d7e0991bcb3abe2871e3e","observation_id":"e2616570-bf25-4254-becc-562620325522","resolution":{"observed_at":"2026-08-05T12:43:39.319782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-10T16:40:37.411115Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-05T12:43:39.444215Z","title":"Available from: https://arxiv.org/abs/2407.21783","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.444215Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:38829dd9d6180478bba2ddc45e4000e569d2c77c1fc28e6381211cef73d15860","observation_id":"4cfb79a6-b83e-4ba0-89f6-c46bb73165f3","resolution":{"observed_at":"2026-08-05T12:43:39.444215Z","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":"2026-08-05T12:43:46.408410Z","title":"Available from: https://qwenlm.github.io/blog/qwq-32b/","venue":null,"work_id":"1e6e533d-dbd9-4f64-8fb1-d8838059dd5d","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.552402Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:c33a7f61852149dc5f823b568827dde605e957f67d7cad875aae87b75226c290","observation_id":"b8c3a783-bea9-4b7d-9d32-531740de2986","resolution":{"observed_at":"2026-08-05T12:43:46.486571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:46.275255Z","title":"TeleChat: An Open- source Billingual Large Language Model","venue":null,"work_id":"a4ab64a4-5b4e-431e-a101-e0d7c389e955","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.786412Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ed1be2eb937de66c0f60b52a507ac26de67ff39bee928cf7624cc00e79cbd709","observation_id":"81d9acb0-2695-4ed0-95e5-ed8bb9b6b7bb","resolution":{"observed_at":"2026-08-05T12:43:46.335978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16645","last_updated":"2024-04-25T14:34:47Z","snapshot_observed_at":"2026-08-12T07:10:31.278344Z","submitted_at":"2024-04-25T14:34:47Z","title":"Tele-FLM Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16645","snapshot_observed_at":"2026-08-05T12:43:39.891516Z","title":"Available from: https://arxiv.org/abs/2404.16645","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:39.891516Z"},"links":{"cited_paper":"/paper/2404.16645","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:3851a9a1efa83784a2fe0220d44a0836482fee3a81f9aab2701e41eedc90d601","observation_id":"9d0dd040-1588-4c18-a6ff-4cd658c82b5e","resolution":{"observed_at":"2026-08-05T12:43:39.891516Z","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":"2026-08-05T12:43:46.151252Z","title":"TeleChat: An Open- source Billingual Large Language Model","venue":null,"work_id":"838021fa-7376-4e8f-9a34-18ba89516ddd","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.036636Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:669cac585e5bcf28d34e62f2f882d61a738b8c9b10503342a6e11bfc1f3f8949","observation_id":"e98a41c0-5624-437e-b312-d37042b9decc","resolution":{"observed_at":"2026-08-05T12:43:46.209862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.03804","last_updated":"2024-04-02T01:45:11Z","snapshot_observed_at":"2026-08-10T14:06:41.761204Z","submitted_at":"2024-01-08T10:43:19Z","title":"TeleChat Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.03804","snapshot_observed_at":"2026-08-05T12:43:40.174698Z","title":"TeleChat Technical Report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.174698Z"},"links":{"cited_paper":"/paper/2401.03804","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:37380c91e82abc2030647babb29f431f7f967d431525443fc3ae52a7849d0741","observation_id":"80d22d2f-6bb3-4290-9ea0-35cfe37c9514","resolution":{"observed_at":"2026-08-05T12:43:40.174698Z","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":"2026-08-05T12:43:46.023936Z","title":"Available from: https://arxiv.org/abs/2507","venue":null,"work_id":"0872d9b0-b9b7-44fd-963f-11cbec5a594b","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.309345Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:d68a23f6ebbc2346c5a53ababbe35ecc49ce5503df9399b7a46ae69e9831c9ad","observation_id":"526062f2-2ea2-4955-bd47-f4bf5b627556","resolution":{"observed_at":"2026-08-05T12:43:46.091693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04272","last_updated":"2026-04-13T04:41:02Z","snapshot_observed_at":"2026-07-06T20:02:17.096487Z","submitted_at":"2024-12-05T15:54:16Z","title":"PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04272","snapshot_observed_at":"2026-08-05T12:43:40.431799Z","title":"Available from: https://arxiv.org/abs/2412.04272","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.431799Z"},"links":{"cited_paper":"/paper/2412.04272","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:e2fc4efdc914b55f54770a477e16af0d63f02d7196ca407931f028a9ea65e244","observation_id":"89d84351-25f7-4b67-af9f-559c7675658c","resolution":{"observed_at":"2026-08-05T12:43:40.431799Z","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":"2026-08-05T12:43:45.859589Z","title":"TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition","venue":null,"work_id":"1e085ba5-8376-4b18-8a07-4bbb93faae10","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.500881Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:4416133e030e1b728b2456a234b41bcae92c3d34de468deaabbd83034297b977","observation_id":"3d048681-0300-4be4-a73d-bf6635454320","resolution":{"observed_at":"2026-08-05T12:43:45.942901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14215","last_updated":"2024-12-05T06:02:59Z","snapshot_observed_at":"2026-08-10T12:15:06.049528Z","submitted_at":"2024-04-22T14:31:28Z","title":"Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14215","snapshot_observed_at":"2026-08-05T12:43:40.693458Z","title":"Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.693458Z"},"links":{"cited_paper":"/paper/2404.14215","citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:617a6e37398891b4561af6e7aa7c3640880205d9abdc994a635289075543421c","observation_id":"1f7f64c7-19f9-43aa-bb7e-13628e1592b3","resolution":{"observed_at":"2026-08-05T12:43:40.693458Z","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":"2026-08-05T12:43:45.725861Z","title":"5725–5737","venue":null,"work_id":"3396a868-084f-4c17-8be9-207b084bb6c9","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.586517Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:a9a52552e6523e45e9f8ee17d6b8593bacd288666458172e58d69043c7b5ef69","observation_id":"4c569aec-5f3b-4360-8722-f33c4fe9f3f0","resolution":{"observed_at":"2026-08-05T12:43:45.784167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:45.426018Z","title":"Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data","venue":null,"work_id":"e697ba42-b084-4f2e-b274-16d9dd39880d","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.933041Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:c666bfeef00d6c68e6d923390be91e74b8aaff7c231a875988d583ebeb40fb6f","observation_id":"16156e27-35b2-4681-919e-6fe78e5e4209","resolution":{"observed_at":"2026-08-05T12:43:45.474066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:45.554024Z","title":"A Survey of Table Reasoning with Large Language Models","venue":null,"work_id":"2b607638-5994-483b-83c8-3bd8406e14b1","year":2024},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:40.798397Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:7aa141b0bc1eb0323cace9d143c6886d8307a78543201973fef018d8b28da0b5","observation_id":"9a342a26-fee0-4ce6-9f03-ea56f149794d","resolution":{"observed_at":"2026-08-05T12:43:45.638170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:45.120099Z","title":"TaPERA: Enhancing Faithfulness and Inter- pretability in Long-Form Table QA by Content Planning and Execution-based Reasoning","venue":null,"work_id":"15339c8b-00c7-4cba-b2b3-30dd5758892d","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.137265Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:cb43b9caca071ebbb1474b93c05069078a8ee4f5cf9561a8e56e1fb010868fec","observation_id":"9aa82dae-471f-4a3a-9687-9eed9772150a","resolution":{"observed_at":"2026-08-05T12:43:45.196039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:45.297190Z","title":"TeleAI at SemEval-2025 Task 8: Advancing Table Reasoning Framework with Large Language Models","venue":null,"work_id":"0aa7425d-d9a0-4c3c-a0f2-39eb5039719f","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.032982Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:9c732d7e4a91fb766a168abd020a7b488ddfff7f0cb5e2de02a96502a076de39","observation_id":"5955afa0-bf24-4729-9277-e8191c479557","resolution":{"observed_at":"2026-08-05T12:43:45.345365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:44.824824Z","title":"LLMSR@XLLM25: A Language Model-Based Pipeline for Structured Reasoning Data Construction","venue":null,"work_id":"df1c88f5-9eac-4631-8a1c-5d9005c1b8b9","year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.396825Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:0b885563c842496f135a666087af26d4db87728f6930ddc3e547166e35eeaa70","observation_id":"b659b081-51fe-439e-b691-4b0b280c7071","resolution":{"observed_at":"2026-08-05T12:43:44.870531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:44.947545Z","title":null,"venue":null,"work_id":"35d5f723-3e9d-4d76-b448-db2f45ffee8c","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.236531Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:eda10850208994f2f42d8b06dfc4dc94a7b068e02e30e8aaca89f3d371d04e2c","observation_id":"032769b0-4a32-47eb-bd40-d910d60eb6a4","resolution":{"observed_at":"2026-08-05T12:43:45.022441Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:41.281048Z","title":"Enhancing math reasoning ability of large language models via computation logic graphs","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.281048Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:bb68fad02db694ae9b3ac3306d89b4a8910dc9f9c395f261f4c97c2b91c27ef5","observation_id":"e5dc702a-3c89-47f7-aecb-f9ddc78a152a","resolution":{"observed_at":"2026-08-05T12:43:41.281048Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-05T12:43:44.691624Z","title":"Human Carrying Status in Visual Surveillance","venue":null,"work_id":"9f858eaa-5b37-4ae2-904e-f60212f2ee2f","year":2006},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.680564Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:d4455a854bd76d67b555234e24eca9a6c2772f1595ca62a16a3e01c32b1ad297","observation_id":"9b7730f9-6481-413b-acf7-efb731ca50cc","resolution":{"observed_at":"2026-08-05T12:43:44.762092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:41.488583Z","title":"Deep neural networks with Elastic Rectified Linear Units for object recognition","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.488583Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:fd311fb8d807742bfc0869587259f3f06f04234e4da8e9c69c7d84643ad0f549","observation_id":"6e237086-93aa-42b9-a207-e5196992fb36","resolution":{"observed_at":"2026-08-05T12:43:41.488583Z","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":"2014.23771","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T12:43:43.125782Z","title":"Learning to Rank for Blind Image Quality Assessment","venue":null,"work_id":"c5113ae2-2157-4c21-b16a-4efa035fed94","year":2015},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.548797Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:c42d1dfe1263fd7c443eda4fd7674b7a7351db89b38ffb5a16881ab9bab9a40f","observation_id":"e22d1ee4-9058-4245-8b1c-1cede839b635","resolution":{"observed_at":"2026-08-05T12:43:43.168556Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2015.24044","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T12:43:42.923210Z","title":"Two-Stage Learning to Pre- dict Human Eye Fixations via SDAEs","venue":null,"work_id":"4808418f-1b9f-45f3-94f8-de5d76306014","year":2016},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.804518Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:115edecf1a4e03dd80451b85606a33c4c0b9164154245156b6149686e156defa","observation_id":"45d273e4-4f69-43cc-a793-7fa27b489aa4","resolution":{"observed_at":"2026-08-05T12:43:42.987892Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2008.20028","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T12:43:42.659115Z","title":"Bayesian Tensor Approach for 3-D Face Modeling","venue":null,"work_id":"e8a1bf1d-a891-494d-a4ed-43d0b0015fc9","year":2008},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:41.916650Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:ac462b4373174c886c48cdf1334610037dd13bb496514b3a2b1daecdeba4a2ee","observation_id":"21e4a8c6-09b8-48fc-b74b-8796a486cc3b","resolution":{"observed_at":"2026-08-05T12:43:42.720456Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T12:43:48.903042Z","title":null,"venue":null,"work_id":"a67ad13b-e374-4436-99f2-0e604846d5ce","year":null},"citing_paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-05T12:43:35.434517Z"},"links":{"citing_paper":"/paper/2509.01312"},"observation_digest":"sha256:52331ece493664ebeeff992a0a52d3d8188f681d70ec1daec3174e7161db6b03","observation_id":"c704bbff-00be-4d8a-b769-362a84ca8ff1","resolution":{"observed_at":"2026-08-05T12:43:48.956249Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.01312","last_updated":"2025-09-01T09:53:01Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-11T08:58:49.868787Z","submitted_at":"2025-09-01T09:53:01Z","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering"},"reference_resolution":{"displayed":72,"state_counts":{"malformed_identifier":2,"metadata_mismatch":7,"parse_uncertain":0,"unresolved":24,"verified_exact":2,"verified_fuzzy":37},"total_outbound_references":72},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2509.01312."}