{"as_of":"2026-08-09T06:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6667a9cc26ca0ada11fbbc1c5e47fc28cdf2b93b0a60868cc73ffe56e472eb92","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T13:55:16.160002Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2607.10151/citation-record","integrity":"/paper/2607.10151/integrity","json":"/paper/2607.10151/citation-record.json","paper":"/paper/2607.10151"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T13:55:16.160002Z","title":"Improving language models by retrieving from trillions of tokens","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:26a2cc72df71cbd7d92631bc07d3e71c46fad0b2f452391c34b1ec4cddfb98ea","observation_id":"81d09d2e-3f57-4580-840c-fa4dc1dd213d","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:ed703b435b4ac390c3519caa4408a80601258c5a2cbf33f201d8f8c478f4ab1f","observation_id":"40439415-dd10-40e0-968b-4cb33e1fc707","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16130","last_updated":"2025-02-19T10:49:41Z","snapshot_observed_at":"2026-07-06T18:05:11.700127Z","submitted_at":"2024-04-24T18:38:11Z","title":"From Local to Global: A Graph RAG Approach to Query-Focused Summarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16130","snapshot_observed_at":"2026-07-14T13:55:16.160002Z","title":"From local to global: A graph RAG approach to query-focused summarization.CoRR, abs/2404.16130,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"cited_paper":"/paper/2404.16130","citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:5f2bd00e0ed15534e4315fd37d147627578c08a8077de5fefe1605cab36dd487","observation_id":"ca664ec5-8728-45d4-95c8-995786450573","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"A survey on RAG meeting llms: Towards retrieval-augmented large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:8df48d62db3287b0f17ed86d65b7831e6e0ee5be4758d93d6638a38bdd13f0fd","observation_id":"645825ed-95f7-425c-ba7b-5f258b33326c","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Lightrag: Simple and fast retrieval-augmented generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:13f33efd7c16f2e2bebe8d1074cd009627e4383a85905c871e743f1a1227624f","observation_id":"165b6956-381f-48e2-af8d-8df16725fb9e","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Turboiso: towards ultrafast and robust sub- graph isomorphism search in large graph databases","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:71295dc00172e36d8a198ca7d7b1d417ec0628b9170a1a5e9de5d57517986ae0","observation_id":"82377efa-a3cd-445e-9a45-f3f6aeaaf319","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"G-retriever: Retrieval-augmented gener- ation for textual graph understanding and question answer- ing.Advances in Neural Information Processing Systems, 37:132876–132907,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:90dbe0855853a37adb77a6b25239e1472fb5e872286304d446877e4cb3523489","observation_id":"b509c4d1-40da-4768-aa7b-13bf61cd82da","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Leveraging passage retrieval with generative mod- els for open domain question answering","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:e0189696d1c21d6b42ee3e15c51264f938096db2548cc661a38495213db745c1","observation_id":"520b4c8c-0b15-4e92-903a-3626c93cf995","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Dense passage retrieval for open-domain question answering","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:f640693a3891829417e1b266041d2aecc945ccfcd3decb9a333589d397470bf8","observation_id":"1e476eae-5bc4-46b2-8744-0955dbe67f70","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Colbert: Efficient and effective passage search via contextualized late interaction over bert","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:712a9f66004b0341fd5ed79c8764378045b8f30710b184073bc413b09541c1d0","observation_id":"72011e19-cdf4-4422-8770-bb2d06acc225","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Turboflux: A fast continuous subgraph matching system for streaming graph data","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:e37fda128a5f26e6f39243a52082ac955ce992d82272a34cbf184519391758d7","observation_id":"4100946e-f2c2-45d7-9e6d-242a4d07b15d","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Natural questions: A benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453– 466,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:db86bcd8b3b0c67019b77e2215f911b07f72c1d48e6ef2db544fe6206c7c2697","observation_id":"00da0432-05f7-448f-9a7a-8a983d34618e","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:5951fdb625b8da06de43cb3e3267a42b60e7d5b02fc0d4b4d923e1ca8094fbd9","observation_id":"9b88e152-b62b-4ee3-9333-f3e94c63dd25","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"KAG: boosting llms in professional do- mains via knowledge augmented generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:2748abe11829fe5433c7f33b5f9d5987ef4b8c01e93f661c209aad3af76e7d7e","observation_id":"37b827d7-c06c-47ce-91ca-806691d1bf87","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Bhowmick, Gao Cong, and Qing Wang","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:05068764094af91cf4aa1dc788c8db2d1fe3e485c493e7bcc32738beff89099f","observation_id":"036d5a91-4433-427f-ba69-5c2e5246088e","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Panda: a system for par- tial topology-based search on large networks.Proc","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:2915e687ea0888ee5613d32a7241b5ee2fc5821b3c4ed91c313b4bd861f6bffe","observation_id":"fff23aeb-3157-483b-9ec5-4d3450a7d0ab","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Structure guided retrieval-augmented generation for factual queries,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:e1bac7e2a4b5ffa0effc1296c00083f1cf07ce8a500db16c7d0e9b783c2f920e","observation_id":"af5d6bc4-4236-4e1f-ab37-123fc67b10a7","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18150","last_updated":"2024-06-12T03:21:15Z","snapshot_observed_at":"2026-08-05T02:38:50.168738Z","submitted_at":"2024-02-28T08:24:38Z","title":"Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18150","snapshot_observed_at":"2026-07-14T13:55:16.160002Z","title":"Unsu- pervised information refinement training of large language models for retrieval-augmented generation.arXiv preprint arXiv:2402.18150,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"cited_paper":"/paper/2402.18150","citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:68ab9411e6c4b745b35c00d340d68031642050bf1584532276d6c0e71cca1c31","observation_id":"b7eefbed-9a03-46dc-a234-443b776d8010","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"Efficient exact subgraph matching via gnn- based path dominance embedding.Proc","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:24a471735fc598a19dc55ca4fd6d0c08be49522697030022e285ae76106ce7a3","observation_id":"2fbb397c-a7f4-46fb-9658-ef424546a741","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","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":"2026-07-14T13:55:16.160002Z","title":"A comprehensive survey and experimen- tal study of subgraph matching: trends, unbiasedness, and interaction.Proceedings of the ACM on Management of Data, 2(1):1–29, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T13:55:16.160002Z"},"links":{"citing_paper":"/paper/2607.10151"},"observation_digest":"sha256:8853b31f77a9db9270deba4a4c9f177671643a4b74a43265e17c76b93a8262b7","observation_id":"f43bfee7-ef3c-4b10-8d71-f98bc5bc7efa","resolution":{"observed_at":"2026-07-14T13:55:16.160002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.10151","last_updated":"2026-07-11T06:20:38Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-06T12:01:07.529764Z","submitted_at":"2026-07-11T06:20:38Z","title":"MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":20},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.10151."}