{"as_of":"2026-08-13T19:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3d1414ab92ab089eb4924165f4f3e3faab02238fa19d623fb5a5ebb4bcf5bd77","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T19:32:34.390988Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T00:39:16.352729Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.17072","snapshot_observed_at":"2026-08-09T19:32:34.390988Z","title":"arXiv preprint arXiv:2408.17072 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00306","last_updated":"2025-06-30T16:37:59Z","snapshot_observed_at":"2026-08-12T04:02:30.056679Z","submitted_at":"2025-02-01T04:01:18Z","title":"Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T19:32:34.390988Z"},"links":{"cited_paper":"/paper/2408.17072","citing_paper":"/paper/2502.00306"},"observation_digest":"sha256:b1c8b8c05e5b858c0e515e62aac62e74f4f1a6d9bd4dea2011673b49114d833a","observation_id":"450cab20-6770-4056-9fb1-86229daf05d2","resolution":{"observed_at":"2026-08-09T19:32:34.390988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.17072","snapshot_observed_at":"2026-08-07T14:53:18.561534Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17391","last_updated":"2025-05-23T02:01:15Z","snapshot_observed_at":"2026-08-08T00:22:36.393355Z","submitted_at":"2025-05-23T02:01:15Z","title":"Curriculum Guided Reinforcement Learning for Efficient Multi Hop Retrieval Augmented Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:53:18.561534Z"},"links":{"cited_paper":"/paper/2408.17072","citing_paper":"/paper/2505.17391"},"observation_digest":"sha256:664481a8887ca01631dbae1d83e5e3068d0a53c09e0673e7ef3639feb0ebe476","observation_id":"edf635c5-338a-4c6e-a34b-d64d52fdeecc","resolution":{"observed_at":"2026-08-07T14:53:18.561534Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.17072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.17072","snapshot_observed_at":"2026-07-04T00:39:16.352729Z","title":"Maerfw: A curriculum-based reinforcement learning framework.arXiv preprint arXiv:2408.17072","venue":null,"work_id":"600856fc-f68c-4ab7-8e30-671125b04e9f","year":2024},"citing_paper":{"arxiv_id":"2604.09666","last_updated":"2026-04-01T07:21:32Z","snapshot_observed_at":"2026-07-06T22:58:29.952947Z","submitted_at":"2026-04-01T07:21:32Z","title":"Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-13T22:35:18.954951Z"},"links":{"cited_paper":"/paper/2408.17072","citing_paper":"/paper/2604.09666"},"observation_digest":"sha256:d70fba956fedcd163020491e09a3ec5925d9d657a10062b9f3c450635042b673","observation_id":"8d8d9bc2-999a-44bd-b15d-0228d44e7235","resolution":{"observed_at":"2026-05-13T22:38:22.355780Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.17072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.17072","snapshot_observed_at":"2026-07-04T00:39:16.352729Z","title":"Maerfw: A curriculum-based reinforcement learning framework.arXiv preprint arXiv:2408.17072","venue":null,"work_id":"600856fc-f68c-4ab7-8e30-671125b04e9f","year":2024},"citing_paper":{"arxiv_id":"2605.01248","last_updated":"2026-06-08T23:39:22Z","snapshot_observed_at":"2026-08-11T06:12:59.324541Z","submitted_at":"2026-05-02T05:01:05Z","title":"$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-09T15:08:53.731480Z"},"links":{"cited_paper":"/paper/2408.17072","citing_paper":"/paper/2605.01248"},"observation_digest":"sha256:c6b7c370e1d34eb0c1a35b1b4a47f6e5252c44283fa5153d3d57431545972184","observation_id":"1c29964f-6e8b-4d9a-bc6a-12adc7a48ccc","resolution":{"observed_at":"2026-05-11T16:46:06.841997Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.17072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.17072","snapshot_observed_at":"2026-07-04T00:39:16.352729Z","title":"Maerfw: A curriculum-based reinforcement learning framework.arXiv preprint arXiv:2408.17072","venue":null,"work_id":"600856fc-f68c-4ab7-8e30-671125b04e9f","year":2024},"citing_paper":{"arxiv_id":"2605.01248","last_updated":"2026-06-08T23:39:22Z","snapshot_observed_at":"2026-08-11T06:12:59.324541Z","submitted_at":"2026-05-02T05:01:05Z","title":"$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-01T00:48:54.797750Z"},"links":{"cited_paper":"/paper/2408.17072","citing_paper":"/paper/2605.01248"},"observation_digest":"sha256:12e1d197435e34b2bfd3d655bf6d8f25cf166c081cb072aab74bae5e92acf096","observation_id":"e1aa4933-1f07-4be0-b2de-f79dd3f8fa25","resolution":{"observed_at":"2026-07-01T00:55:12.091187Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.17072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.17072","snapshot_observed_at":"2026-07-04T00:39:16.352729Z","title":"Maerfw: A curriculum-based reinforcement learning framework.arXiv preprint arXiv:2408.17072","venue":null,"work_id":"600856fc-f68c-4ab7-8e30-671125b04e9f","year":2024},"citing_paper":{"arxiv_id":"2607.00017","last_updated":"2026-07-02T04:36:04Z","snapshot_observed_at":"2026-08-02T04:51:59.336195Z","submitted_at":"2026-05-28T06:47:48Z","title":"Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-07-02T23:06:32.468189Z"},"links":{"cited_paper":"/paper/2408.17072","citing_paper":"/paper/2607.00017"},"observation_digest":"sha256:5b9251749cbac9f6f2151081b138dd724194edd1915a98770c67d4537b404569","observation_id":"fabe0df8-df57-45ad-ad22-ce356b57fd63","resolution":{"observed_at":"2026-07-02T23:07:26.354200Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","version":2},"cited_work":{"arxiv_id":"2408.17072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.17072","snapshot_observed_at":"2026-07-04T00:39:16.352729Z","title":"Maerfw: A curriculum-based reinforcement learning framework.arXiv preprint arXiv:2408.17072","venue":null,"work_id":"600856fc-f68c-4ab7-8e30-671125b04e9f","year":2024},"citing_paper":{"arxiv_id":"2607.00017","last_updated":"2026-07-02T04:36:04Z","snapshot_observed_at":"2026-08-02T04:51:59.336195Z","submitted_at":"2026-05-28T06:47:48Z","title":"Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-07-04T00:37:05.411197Z"},"links":{"cited_paper":"/paper/2408.17072","citing_paper":"/paper/2607.00017"},"observation_digest":"sha256:92696b279a5030b4f9eeda6cfce6c542366bd7cf4bc5a2f697b33e4a4e83082c","observation_id":"9118b76e-30b5-4ae2-81ff-a50140a4e937","resolution":{"observed_at":"2026-07-04T00:39:16.354487Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2408.17072/citation-record","integrity":"/paper/2408.17072/integrity","json":"/paper/2408.17072/citation-record.json","paper":"/paper/2408.17072"},"outbound":[],"paper":{"arxiv_id":"2408.17072","last_updated":"2024-12-19T13:16:40Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-12T22:54:41.589144Z","submitted_at":"2024-08-30T07:57:30Z","title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2408.17072."}