{"as_of":"2026-08-18T15:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:123ed11b8d0e73c88fbc363826ee16fffc8ac9afd83bad8edeb44d4c0272615d","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T18:22:48.581921Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2606.08866/citation-record","integrity":"/paper/2606.08866/integrity","json":"/paper/2606.08866/citation-record.json","paper":"/paper/2606.08866"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T18:22:48.581921Z","title":"Fully convolutional networks for semantic segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:9d007c0af2e2bacc64f7b3017e9b92040f5e7c82b6ca80fdb2a0b4e63ea40909","observation_id":"68bdacd6-977c-4efe-a615-ff8e302a359d","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","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":"2026-06-27T18:22:48.581921Z","title":"Encoder- decoder with atrous separable convolution for semantic image segmen- tation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:56d0cf0b00e4a336c2f1b242aed7386e45bc9cd5689ca2cc75017b5198b3a50a","observation_id":"0a6eaaef-4bf9-42e6-b1bd-cb5fe9a64965","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","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":"2026-06-27T18:22:48.581921Z","title":"Pyramid scene parsing network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:8702f1ef424d2092c342055288e85bc075efbd4d80f539dd1424d5f62da2ed83","observation_id":"3a72dce3-09b7-48bd-920d-7e273df316e4","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-08-17T20:47:46.242385Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":"2312.00752","doi":"10.48550/arxiv.2312.00752","metadata_source":"pith","pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","venue":"cs.LG","work_id":"4ee75248-1199-492c-a52f-6661e0f4adff","year":2023},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:5fe3976f6927a628c3058decb7a05985e854d041d01ae4251242925909666cd2","observation_id":"b57796b3-679d-45ac-ad7e-9ee67c412f2d","resolution":{"observed_at":"2026-07-02T23:17:29.338006Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-13T20:38:14.544227+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-13T20:38:14.544227+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10166","last_updated":"2024-12-29T14:57:13Z","snapshot_observed_at":"2026-08-17T14:56:56.233298Z","submitted_at":"2024-01-18T17:55:39Z","title":"VMamba: Visual State Space Model","version":4},"cited_work":{"arxiv_id":"2401.10166","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.10166","snapshot_observed_at":"2026-07-04T09:49:44.618504Z","title":"VMamba: Visual State Space Model","venue":"cs.CV","work_id":"ed658421-9e61-4b1b-b2c5-a1376e9f4b34","year":2024},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"cited_paper":"/paper/2401.10166","citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:ef49c1549a7622b8bbf04227625a1d96726cbd663540aceec704c5b689af0518","observation_id":"731029c4-dce5-4132-9c95-431e69e598a9","resolution":{"observed_at":"2026-07-02T23:17:29.340587Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09417","last_updated":"2024-11-14T02:00:33Z","snapshot_observed_at":"2026-08-14T11:12:31.002605Z","submitted_at":"2024-01-17T18:56:18Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","version":3},"cited_work":{"arxiv_id":"2401.09417","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.09417","snapshot_observed_at":"2026-07-04T19:30:07.531920Z","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","venue":"cs.CV","work_id":"bd81352e-a64f-4720-9f76-ddda0ea9af83","year":2024},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"cited_paper":"/paper/2401.09417","citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:c7e35b0c9b707541d678cc324c89f42e118b6f9970c75942ddde3529222f2e23","observation_id":"6d671d11-aa88-4391-addf-99377baf2022","resolution":{"observed_at":"2026-07-02T23:17:29.333080Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.23399","last_updated":"2026-05-04T11:59:38Z","snapshot_observed_at":"2026-08-15T15:20:23.654679Z","submitted_at":"2026-04-25T18:11:59Z","title":"Breaking the Resource Wall: Geometry-Guided Sequence Modeling for Efficient Semantic Segmentation","version":2},"cited_work":{"arxiv_id":"2604.23399","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.23399","snapshot_observed_at":"2026-07-02T23:17:29.334445Z","title":"Breaking the Resource Wall: Geometry-Guided Sequence Modeling for Efficient Semantic Segmentation","venue":"cs.CV","work_id":"f3f037a9-31ff-42c8-b5d6-b12294022619","year":2026},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"cited_paper":"/paper/2604.23399","citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:d2946a9a569ed7ed3e8a8b076a917d90ea775050640e916d4ef67b1ff1218d4b","observation_id":"46389930-abab-4d93-bf53-e838b4205d0b","resolution":{"observed_at":"2026-07-02T23:17:29.335702Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T18:22:48.581921Z","title":"Dual attention network for scene segmentation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:f4ac43d6b58c1500152b32a84c346d0fad13b0e03e4a1b95c899b5cabd585baf","observation_id":"1b70d22a-17d3-4699-a755-7958024b1a6d","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","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":"2026-06-27T18:22:48.581921Z","title":"CCNet: Criss-cross attention for semantic segmenta- tion,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:a14a03bf6c7014843593c447145be70610e694ef22da73b768b44d415ea27da1","observation_id":"d4390992-396f-43ea-b0d1-5a3546bcbb39","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","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":"2026-06-27T18:22:48.581921Z","title":"PSANet: Point-wise spatial attention network for scene parsing,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:f8dd466245a9eccb75ff56f80cfb8862bb64ba95f95f6badb7aa1f0389d1256c","observation_id":"594a48ce-c40c-4b8f-a063-ce11ef2408c3","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","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":"2026-06-27T18:22:48.581921Z","title":"Object-contextual representations for semantic segmentation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:c7dcf6860ea94211e5b287db4cfd74ce333b7754e5dff864ebc008c07320112c","observation_id":"b613413b-41cb-4842-8cb7-324c62e6d823","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","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":"2026-06-27T18:22:48.581921Z","title":"The Cityscapes dataset for semantic urban scene un- derstanding,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:235532d33673ac8d035523ca036c8e7e1fc743ffb8db5e7a437517306fc58695","observation_id":"705aca2d-0273-414a-b704-f964c421bcc9","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","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":"2026-06-27T18:22:48.581921Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-27T18:22:48.581921Z"},"links":{"citing_paper":"/paper/2606.08866"},"observation_digest":"sha256:583e98cf7a9a8126441f773b9e532d0804d8ca9a7ed3374639b08e41d76b2934","observation_id":"7271afff-a408-46bc-bfff-0cd9c4d117b2","resolution":{"observed_at":"2026-06-27T18:22:48.581921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.08866","last_updated":"2026-06-07T22:43:38Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T01:20:54.743286Z","submitted_at":"2026-06-07T22:43:38Z","title":"Generalizing Geometry-Guided Mamba as a Plug-and-Play Context Module for CNN-based Semantic Segmentation"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":13},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2606.08866."}