{"as_of":"2026-08-21T01:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b8f7058b9cec800b3f377fc9cd1a543d0eabf3d803ee0d480576f4e8a4de08a9","coverage":[{"denominator":61,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":61,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-12T19:07:54.710156Z","state":"measured"},{"denominator":61,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":61,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2606.05181/citation-record","integrity":"/paper/2606.05181/integrity","json":"/paper/2606.05181/citation-record.json","paper":"/paper/2606.05181"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:310e62d2921243ffc0d38797ada4d8ec68c5ba4dfabb1747878dd766a5c537bf","observation_id":"2b44e7c0-b77b-47e4-a6b2-b879ba69478f","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"A Large Anno- tated Corpus for Learning Natural Language Inference,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:157975db609572bc115d26ac90eaa70908bc5cd9680c83fed374b59b2e32165e","observation_id":"76b769e6-cbe2-4501-b460-c8d733b5573e","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.01284","last_updated":"2026-05-23T16:17:37Z","snapshot_observed_at":"2026-08-15T07:01:37.255520Z","submitted_at":"2026-05-02T06:40:42Z","title":"Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.01284","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Chain of evidence: Pixel-level visual attribution for iterative retrieval-augmented generation.arXiv preprint arXiv:2605.01284, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2605.01284","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:8cc872d047f950cd6787857e611659bbb6875cf37cd7e38c5b1958875f6c8412","observation_id":"8997e1a8-196d-4062-b333-efe8e51c037f","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Deep Residual Learning for Image Recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:fef7e1d3ac295e2a7923c4684de697afb5fa48da96a340ce278934108b152941","observation_id":"f32d7d68-3e56-4a2d-8892-cf6be0cf7b5d","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.06038","last_updated":"2017-04-26T17:37:13Z","snapshot_observed_at":"2026-08-14T21:39:05.632809Z","submitted_at":"2016-09-20T06:59:31Z","title":"Enhanced LSTM for Natural Language Inference","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.06038","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Enhanced LSTM for Natural Language Inference,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/1609.06038","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:01d1436298bbbf20e75d5de8d53b1c6341e60bf7bd55091e28e21312780bf552","observation_id":"b2cc700a-b944-43fe-95f0-a4fdcb74ca89","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Adversarial Examples for Evaluating Reading Comprehension Systems,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:7088b64274169052a993ca56b973d0720ca44375ab42087c9de0be5569a166c0","observation_id":"68bf0b51-7d36-45c5-a747-16b1566cbdfc","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.02364","last_updated":"2018-07-08T21:22:11Z","snapshot_observed_at":"2026-08-17T19:29:27.490889Z","submitted_at":"2017-05-05T18:54:39Z","title":"Supervised Learning of Universal Sentence Representations from Natural Language Inference Data","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.02364","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Su- pervised Learning of Universal Sentence Representations from Natural Language Inference Data,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/1705.02364","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:a91c9228ecbe0f0f10fc34a3f753d00064f513431327c91b016e0549cfc7ba91","observation_id":"011c6d65-b3fd-4ed6-8369-13f5a09944d0","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1709.04348","last_updated":"2018-05-26T20:55:39Z","snapshot_observed_at":"2026-08-14T20:33:32.749950Z","submitted_at":"2017-09-13T14:22:14Z","title":"Natural Language Inference over Interaction Space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.04348","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Natural Language Inference over Interaction Space,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/1709.04348","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:830b77b29230b8240f805271a089293dd80e641af2ec7bf9bca22c9651953773","observation_id":"4f72ed1f-d9e2-4c73-b322-c3a93c6f88ef","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Learning to Compose Task-Specific Tree Structures,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:c96a88f545a4882f889834941cef34e097368bf9f6efb8e1b59c07430823531f","observation_id":"f9df0df0-304a-412d-aa1b-44d3de4eb07b","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:497cbd9f7ec04505a68b6d72a663e1f3d5889889e500dbb10359c40674db4956","observation_id":"a8b6e9af-4b73-4be0-8420-5c642b01b109","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-08-16T14:33:50.657682Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:a57764f898b267d1b289cc6ac3dc08807d0dd0fe744335744ed0c61379e615d0","observation_id":"e6a3d1db-d2fe-48b7-92a8-b43706e522da","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:76e39ba2ab6444d0e5aca2958e38dd414ac08dd0d40ed9cced77538080018448","observation_id":"3727e673-6a2b-4b2e-94b6-b590294cb7e8","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Asynchronous deep interaction network for natural language inference,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:42d3a7f4ad982a633f7a65cfbcbe35e74b31575d5ff7653c097bc24d3e1bc412","observation_id":"823467e3-3d46-49de-8b47-831832bad117","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Graph convolutional networks for text classification,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:067828360d95a4cf4b274ef6da8f1b5f508bade05006d58cb5d3c8b9cab77e68","observation_id":"5fd4be53-ef36-4cca-a01a-f7545576a3ff","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Sentence-BERT: Sentence embeddings using siamese BERT-networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:d1959c89e994e360c5ce8bb174b6dfd00d623042a045d774097810d1e4aba6aa","observation_id":"a84366b7-9f84-4336-8d8c-1a4ec4706bc9","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Semantics-Aware BERT for Language Understanding,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:d8771da112be79a649eef68dc6811829d05606cca2a7ee9742b7be2a0de33c45","observation_id":"69347c8c-379d-4117-aa08-ea2e2ba2a249","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:76a5da8307880f7a975d1369bcc01a5939162494ab5020296aa32d84a1ece233","observation_id":"9caaf864-933b-4dd3-ac29-234c07b6dc71","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"CharBERT: Character-Aware Pre-Trained Language Model,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:bc78fade9be8944adcc14f18c78f9cebf0b96425c5acf1d26e0a93275e398f94","observation_id":"c381ccd6-9a9f-4f0e-b901-2097b3843246","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Heterogeneous Graph Transformer for Graph-to-Text Generation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:a2e9f884c3e331b187b2815998658530a821e3c0ac72e8a827ca31643863521f","observation_id":"7afa314b-e6cf-4cbc-b37d-b87fb2ec8f89","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Graph neural networks for natural language processing: A survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:1e1f78b43d950da0c132db7f5dc65bd084dbdbdd0e5a2b4aa275d539eadf9587","observation_id":"f5eed396-c495-4643-a0f1-b9fe7bf81413","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.14010","last_updated":"2026-04-15T15:55:38Z","snapshot_observed_at":"2026-08-06T20:07:02.345938Z","submitted_at":"2026-04-15T15:55:38Z","title":"Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.14010","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Parameter importance is not static: Evolving parameter isolation for supervised fine-tuning.arXiv preprint arXiv:2604.14010, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2604.14010","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:7947d4f3403cceff035926115c440f265beaff6d1f3ee712128d5fb24eb05ef3","observation_id":"a0acb7f1-5047-4640-884c-a2120130fd8f","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.11365","last_updated":"2026-04-13T12:05:34Z","snapshot_observed_at":"2026-08-19T07:01:17.364170Z","submitted_at":"2026-04-13T12:05:34Z","title":"Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.11365","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Learning from contrasts: Synthesizing reasoning paths from diverse search trajectories.arXiv preprint arXiv:2604.11365, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2604.11365","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:43bf517c29291158ff207e2af6dfc7bf4cc5a2a11ca4b1a531bf3dcb545d9740","observation_id":"d6b25e44-68a3-4d25-854d-c427030cdc10","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Artificial Intelligence and Law: Risks, Research, and Limits,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:248cc05ab997b7dc3cb464d82c6a6389259894ad24efb8a189976ad64ac21f2d","observation_id":"d60bd144-796c-442e-868f-7b25dbb27a3e","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Connecting the dots: Document-level relation extraction with edge-oriented graphs,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:d674eb2a4cc5cb65b8f6ffba9cc277b0147d1a9130c4016cf5ca13958d6a77e4","observation_id":"72ff764d-3a56-4b70-82ec-a19cd7868577","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Enhanced Attentive Convolutional Neural Networks for Sentence Pair Modeling,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:c4fddf1126f46e7b1dfb666dd74a96845cb70fb4ababfbf37fea63ef92521370","observation_id":"f6eeb0bf-519e-4f41-9034-af504119bf98","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Using Prior Knowledge to Guide BERT’s Attention in Semantic Textual Matching Tasks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:d28614f7e1d7a4f2450747016b0088fcac67b2684f582803f2e5ce4f48738736","observation_id":"d0e366c4-407c-46c9-a16b-1810c295c0fb","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:3dd6e794bc2a2ceafa23a9382e517d80e63123f2091b4bd7cd1a07d71bc951c3","observation_id":"57eed6e6-4be5-41fd-9482-3ad8689f2c86","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Explainaboard: An Explainable Leaderboard for NLP,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:0af8468543bca87f8ea7f68ed80ba8e62d0d0876249476d004aaeded3bbe35ad","observation_id":"c28aef29-8867-4c8d-8d27-b7ce5049f574","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.05218","last_updated":"2025-09-08T03:13:38Z","snapshot_observed_at":"2026-08-14T23:22:55.529770Z","submitted_at":"2025-09-05T16:20:48Z","title":"HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.05218","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Hope: Hyperbolic rotary positional encoding for stable long-range dependency modeling in large language models.arXiv preprint arXiv:2509.05218, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2509.05218","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:33066a3be74baf61cda06883d9be383cf14531cb2055000b30957d1e29831de0","observation_id":"227d4ddc-db31-48e8-8917-277af0b640f6","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Cqg: A simple and effective controlled generation framework for multi-hop question generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:29bbddb8f0c2d24ff075cd54943404f5db9829bbc726112a0975bed89081f3da","observation_id":"04e7bb19-c836-483d-86ec-2e73d29acef1","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Decorl: Decoupling reasoning chains via parallel sub-step generation and cascaded reinforcement for interpretable and scalable rlhf","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:372a96e03753f0d4de4b05324204ca07b56cdfaeaeba34a646541979b1c1389a","observation_id":"50126aab-f325-49ed-bf95-758af5f75980","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Transferring from formal newswire domain with hypernet for twitter pos tagging","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:4e8227449d825759254d0292fe9cd08eb7305ea0ae8a809e694aeddd665eeded","observation_id":"fa449b41-d248-412d-acab-feb8f462dcfb","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07220","last_updated":"2024-12-10T06:18:07Z","snapshot_observed_at":"2026-08-18T06:38:08.754452Z","submitted_at":"2024-12-10T06:18:07Z","title":"Comateformer: Combined Attention Transformer for Semantic Sentence Matching","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07220","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Comateformer: Combined at- tention transformer for semantic sentence matching.arXiv preprint arXiv:2412.07220, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2412.07220","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:bb254b9919764a14c22db78d61ec6a13afe1ce4351528f853fba3d9e88687b7e","observation_id":"7e252795-b67a-47b6-8388-b87cb06276d1","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"When safety becomes a vulnerability: Exploiting llm alignment homogeneity for transferable blocking in rag.arXiv preprint arXiv:2603.03919, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:d968573b96eaf17d6edfee97b92924b9e87f50f4cdcb72d803646b71f43d85fa","observation_id":"ebe3cf42-d5b1-47d4-87dd-8b69fcfcb32c","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Local and global: Text matching via syntax graph calibration","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:a88ddd61689833d6bd443ca25b9816cfebbb720c570e448f8d845d33e1bebea8","observation_id":"9812bcaf-1d95-4361-a28e-ebb6ea999f91","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.31025","last_updated":"2026-05-29T08:57:06Z","snapshot_observed_at":"2026-08-20T14:58:22.130098Z","submitted_at":"2026-05-29T08:57:06Z","title":"TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.31025","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"TRACE: Discovering task-specific parameter via adaptation-aware prob- ing for continual fine-tuning.arXiv preprint arXiv:2605.31025, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2605.31025","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:6f6edf6d4aaeab3965e63023bcfe5ab49b027b33b42e9976bb9f8e7320da9dc6","observation_id":"eed1f58d-3819-49cc-969b-54dc7bc732c4","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Adaptive multi-attention network incorporating answer information for duplicate question detection","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:a399c55ec05aa62d7d2de7e899f1cb06a557b09ecc10c58edbaf0f175658c821","observation_id":"228f18f6-0484-4158-839d-dce90948b780","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Who stole your data? a method for detecting unauthorized rag theft.arXiv preprint arXiv:2510.07728, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:f761c33c45f08d0d2418c9ffc487602cbb67368bb336b090df9bb50cfce5daf1","observation_id":"1cf972b0-e7e9-442b-ab4a-336c19442bf3","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Dpi: Exploiting parameter heterogeneity for interference-free fine-tuning.arXiv preprint arXiv:2601.17777, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:0c8541ce82986d71bb1658545f48271a45a2218521965a559100efb9de2da2d4","observation_id":"9244888e-28cc-441d-9c17-2b6b378843ad","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Structural reward model: Enhancing interpretability, efficiency, and scalability in reward modeling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:4afb55032c82cdf862a61613c9e1b0508aa01b2f99a57283c7d52b9bf6e77b58","observation_id":"67b0b80e-5890-4192-aa9b-d70b81e07f12","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12434","last_updated":"2024-05-21T01:19:52Z","snapshot_observed_at":"2026-08-16T22:00:05.974139Z","submitted_at":"2024-05-21T01:19:52Z","title":"Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12434","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Resolving word vagueness with scenario-guided adapter for natural language inference.arXiv preprint arXiv:2405.12434, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2405.12434","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:30430cb08b8383a332587e77fed3775c41b98cd05b652f262b96ae8a127527e1","observation_id":"d62ecf96-ed6c-4b00-98f4-5f4c65c0b0cf","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Time-aware multiway adaptive fusion network for temporal knowledge graph question answering","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:70fe49615943ab5e9cddd43994b810e94db3a17f868d45ae8cd5f0038256d09d","observation_id":"52b77915-adca-412b-87ab-c10e97a12dfd","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Local and global: Temporal question answering via information fusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:ea921ef0929644ef4eefe5aad386708039372992db75f481d04d24b0ea9a6a37","observation_id":"72766710-682d-43f0-a8a8-d12a620bc852","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.03352","last_updated":"2022-06-07T14:39:31Z","snapshot_observed_at":"2026-08-16T16:54:43.357102Z","submitted_at":"2022-06-07T14:39:31Z","title":"Searching for Optimal Subword Tokenization in Cross-domain NER","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.03352","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Searching for optimal subword tokenization in cross-domain ner.arXiv preprint arXiv:2206.03352, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2206.03352","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:426261cb0a6aee9d5e2b162c155338c1bff6e48c14bc3f4aa8ceb5d655bd781f","observation_id":"24eeec5a-809b-4e6a-90cf-35dd6f1e9f9b","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Adaptive curriculum strategies: Stabilizing reinforcement learning for large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:4742f2a25d87e0032dc6b3b0ada801cdcd81c02de3558dbe07bb6ec2931b43bf","observation_id":"eac87d53-6034-467b-b84b-c0b07577d8ad","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.08471","last_updated":"2023-08-24T07:13:27Z","snapshot_observed_at":"2026-08-19T11:53:46.142370Z","submitted_at":"2022-10-16T07:17:27Z","title":"Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.08471","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Improving semantic matching through dependency-enhanced pre-trained model with adaptive fusion.arXiv preprint arXiv:2210.08471, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2210.08471","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:cc247840cd083c121fc774998caceac0fdf85edf7cebd8b09980ec0d1aa2938a","observation_id":"1a42b2f3-f121-42ba-84ec-18ba28119c6b","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.01302","last_updated":"2026-05-02T07:22:24Z","snapshot_observed_at":"2026-08-13T05:15:28.429527Z","submitted_at":"2026-05-02T07:22:24Z","title":"Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.01302","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Beyond semantic relevance: Counterfactual risk minimization for robust retrieval-augmented generation.arXiv preprint arXiv:2605.01302, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2605.01302","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:5424ab0f6b856be3fa5be28b275abca18c3fc758966f6cbcccc492c5d2bf3e17","observation_id":"4739888d-f56f-4372-b996-1572db8e859c","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.20868","last_updated":"2026-04-16T17:09:04Z","snapshot_observed_at":"2026-07-06T22:43:21.411174Z","submitted_at":"2026-01-14T05:06:42Z","title":"Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.20868","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Rethinking llm- driven heuristic design: Generating efficient and specialized solvers via dynamics-aware optimization.arXiv preprint arXiv:2601.20868, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2601.20868","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:f867b6ee3fb4e9258a7c1ed0603d53cbdbc0c4b30932821e8d08f98782557f14","observation_id":"482888c6-9736-4e35-a142-e5e256b9ed5d","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03454","last_updated":"2023-04-14T07:14:17Z","snapshot_observed_at":"2026-08-16T16:26:10.039557Z","submitted_at":"2022-10-07T10:54:49Z","title":"DABERT: Dual Attention Enhanced BERT for Semantic Matching","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03454","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Dabert: Dual attention enhanced bert for semantic matching.arXiv preprint arXiv:2210.03454, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2210.03454","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:2bdf8f6f6c4f77af201cb16f1ce4d28519e08c6df85a779ab9f89b47d169f389","observation_id":"0d6ecccb-c4fe-4bc6-8a74-9aafca031264","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Not all parameters are created equal: Smart isolation boosts fine-tuning performance.arXiv preprint arXiv:2508.21741, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:c8e07ec9bc76de515586aa1f9a79d13b752e9556ef441fd3aed6ed8677b40ded","observation_id":"6edb06e4-b966-423c-b942-6e88f2342223","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.04065","last_updated":"2025-06-04T15:31:46Z","snapshot_observed_at":"2026-08-13T19:00:44.799706Z","submitted_at":"2025-06-04T15:31:46Z","title":"Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.04065","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Pro- gressive mastery: Customized curriculum learning with guided prompt- ing for mathematical reasoning.arXiv preprint arXiv:2506.04065, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2506.04065","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:519bd9c4818e1321b15caf64e925c03673dd22c51a9a1338598a31e7fa6b6ed5","observation_id":"49f7204f-3d94-4c12-bf6b-fbaa660f5708","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Breaking size barrier: Enhancing reasoning for large-size table question answering","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:6aeea43125dee7924e01aa94dcb746be19ac1d59552ca384e452240ca6b610ec","observation_id":"c7364aa9-18bf-4f0c-9d3e-8f36ddf5337e","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Mmtablebench: A multi-level multimodal benchmark for reasoning and layout complexity in table qa","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:3a37f96f90d1da90b32971afbef44f48b112e6b8c9a03581fc22cfd4b93a88a5","observation_id":"f9d881d1-b1f6-4c8e-a687-7785723a10f0","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Tablebench: A comprehensive and complex benchmark for table ques- tion answering","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:8cc7c35efb51df4e7ab15f7ff01e9a0210c9c175185a3844e565b86c561e4df3","observation_id":"30a2661a-311d-4a5f-a5c2-f64f9dafa418","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Unleashing potential of evidence in knowledge-intensive dialogue generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:8bb7354c1fb9aca20f86d6d4a80ca0e35ce4edbed516d8a42f803a9bb5c5baeb","observation_id":"b9e09c8c-d2b7-4064-a617-ac594a877488","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Question calibration and multi-hop modeling for temporal question answering","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:fb25ecb5da084bade2ead8c027c51ea4eaf4a7191c41f9bbaed7353a9c893b11","observation_id":"d08142ef-6653-4453-8f08-d83f15c0cccf","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Dual path modeling for semantic matching by perceiving subtle conflicts","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:0c57b60132343e15514c85cc534834d746b94a4b81a91a3a5376d6fcb17c7d30","observation_id":"e5a99dff-35da-4fcd-b234-e8fa929b685e","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.03013","last_updated":"2022-11-06T02:59:27Z","snapshot_observed_at":"2026-08-17T12:36:04.464467Z","submitted_at":"2022-11-06T02:59:27Z","title":"Robust Lottery Tickets for Pre-trained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.03013","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Robust lottery tickets for pre-trained language models.arXiv preprint arXiv:2211.03013, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2211.03013","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:ca4a5f6cd58f30541a0dc057f934588cb678047cbca8f55dbcaaa7891e44c870","observation_id":"338ad014-d7d3-479d-9c46-f24a5ea390d5","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","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":"2026-07-12T19:07:54.710156Z","title":"Parameter importance is not static: Evolving parameter isolation for supervised fine-tuning, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:19e4d00580dfb4be6869256102b402de4298071b267bb3ba09ada55b0c79e432","observation_id":"28c7eeda-b3da-4893-bfb6-e8b71a96f70f","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.10072","last_updated":"2026-05-03T16:04:49Z","snapshot_observed_at":"2026-08-18T00:47:51.174250Z","submitted_at":"2026-04-11T07:35:08Z","title":"Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.10072","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Reason only when needed: Efficient generative reward modeling via model-internal uncertainty.arXiv preprint arXiv:2604.10072, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2604.10072","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:6699439f2e56f839a46d80810180badffc09c36deb1a1284b6285a5485d4fd94","observation_id":"7655dc5d-87bb-4aac-bdb8-72e0a3a1ed53","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.10079","last_updated":"2026-04-24T13:14:41Z","snapshot_observed_at":"2026-08-03T01:45:24.087989Z","submitted_at":"2026-04-11T07:55:32Z","title":"Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.10079","snapshot_observed_at":"2026-07-12T19:07:54.710156Z","title":"Why supervised fine-tuning fails to learn: A systematic study of incomplete learning in large language models.arXiv preprint arXiv:2604.10079, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-12T19:07:54.710156Z"},"links":{"cited_paper":"/paper/2604.10079","citing_paper":"/paper/2606.05181"},"observation_digest":"sha256:fd2be728f0ddf6b40ef3ce36ec48f3240164c38eb5776807be9fd3d4824ffdb9","observation_id":"aa44e228-1657-46cf-bf9f-6317b8d1d4c3","resolution":{"observed_at":"2026-07-12T19:07:54.710156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.05181","last_updated":"2026-06-22T08:05:27Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-17T03:34:10.025962Z","submitted_at":"2026-04-18T16:19:32Z","title":"Multi-Granularity Reasoning for Natural Language Inference"},"reference_resolution":{"displayed":61,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":61,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":61},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2606.05181."}