{"as_of":"2026-08-10T20:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03ad93884382a21a7687f4811f584a00dd8139947cd969b0cec5028015df09f4","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T21:57:10.698361Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T03:20:35.021120Z","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":"2026-05-18T03:20:48.722303Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"cited_work":{"arxiv_id":"2508.07747","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.07747","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Grouped speculative decoding for autoregressive image generation.arXiv preprint arXiv:2508.07747","venue":null,"work_id":"4a73d51b-15f3-40d5-99d3-7d523d9c8183","year":2025},"citing_paper":{"arxiv_id":"2510.24211","last_updated":"2026-05-05T17:15:37Z","snapshot_observed_at":"2026-07-06T22:34:13.674208Z","submitted_at":"2025-10-28T09:26:27Z","title":"Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-18T03:20:35.021120Z"},"links":{"cited_paper":"/paper/2508.07747","citing_paper":"/paper/2510.24211"},"observation_digest":"sha256:eac662031bbb3f49fd4004b11a3db019153ad987a30626ad9e0134fc131a0a4d","observation_id":"b9af429c-2abb-4500-8544-f691dedd3d32","resolution":{"observed_at":"2026-05-18T03:20:48.726651Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"cited_work":{"arxiv_id":"2508.07747","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.07747","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Grouped speculative decoding for autoregressive image generation.arXiv preprint arXiv:2508.07747","venue":null,"work_id":"4a73d51b-15f3-40d5-99d3-7d523d9c8183","year":2025},"citing_paper":{"arxiv_id":"2605.07230","last_updated":"2026-05-08T04:32:17Z","snapshot_observed_at":"2026-08-01T04:13:43.004857Z","submitted_at":"2026-05-08T04:32:17Z","title":"CASCADE: Context-Aware Relaxation for Speculative Image Decoding","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-11T02:08:27.374066Z"},"links":{"cited_paper":"/paper/2508.07747","citing_paper":"/paper/2605.07230"},"observation_digest":"sha256:ee2a80647680925e653c9f12273c59e5cc3563d6e1e757f176507bccaaa1f5ea","observation_id":"2396c129-8417-4480-a619-9312dcab28c8","resolution":{"observed_at":"2026-05-11T03:55:55.230437Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.07747/citation-record","integrity":"/paper/2508.07747/integrity","json":"/paper/2508.07747/citation-record.json","paper":"/paper/2508.07747"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:15.548917Z","title":null,"venue":null,"work_id":"836859c2-9c51-45e2-8346-2a919651117a","year":2021},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.233730Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:dc0a956c16d7b4b182567dcc7a6e8f347a823c14d6e8a793a1c567c164bb459c","observation_id":"79a1f993-6cf9-4c42-a4c9-bee172a4b01a","resolution":{"observed_at":"2026-08-05T21:57:15.632399Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:15.392538Z","title":"Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration","venue":null,"work_id":"e10e5ca1-1f60-44d4-aab0-e9467333a440","year":2013},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.254822Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:e9950874db4a6a04e1436bbf1be197e5bcc4b97f35c1027212cc52b4ad6bb18b","observation_id":"c91fff00-1778-449d-894f-92c7f96f21ee","resolution":{"observed_at":"2026-08-05T21:57:15.454391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:15.213408Z","title":"Pixel matching network for cross-domain few- shot segmentation","venue":null,"work_id":"e4d713ab-32d8-4da3-9c76-9b81bd5046eb","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.282146Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:31e347ab8912626c1874a821dcf735054ec3235859b4c1cec5d79a2e9f43ceec","observation_id":"a161acd0-cd2e-4379-9f06-7c49e0111fc7","resolution":{"observed_at":"2026-08-05T21:57:15.298637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:15.070891Z","title":"Holistic pro- totype activation for few-shot segmentation","venue":null,"work_id":"74b64fe6-39d1-4779-bb0e-9b9f408fc13b","year":2022},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.308246Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:a61733985c4b4c69f0c8b6f83bbb9857f61318394fa3f9a4082c431dab8d0bfd","observation_id":"b19d3eb4-03eb-4e83-9fae-0f26ab99cf8a","resolution":{"observed_at":"2026-08-05T21:57:15.162787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:14.894854Z","title":"Tracking anything with decoupled video segmentation","venue":null,"work_id":"7f963db5-25c1-4e52-8592-127dde58399a","year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.335651Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:221c375f0d64d657a7a6c29c68171ca0ab0f2f314a2283c831cd387b6b70cdaa","observation_id":"63287be9-210d-43b4-9552-81d9c2d38990","resolution":{"observed_at":"2026-08-05T21:57:14.977871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06558","last_updated":"2023-05-11T04:33:08Z","snapshot_observed_at":"2026-08-03T19:49:16.800693Z","submitted_at":"2023-05-11T04:33:08Z","title":"Segment and Track Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06558","snapshot_observed_at":"2026-08-05T21:57:09.363139Z","title":"Segment and track anything","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.363139Z"},"links":{"cited_paper":"/paper/2305.06558","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:5414d18a1ca23bb5e59c5f22ebce6b564c3d596f4c2cf572ad188c39bbb3bb24","observation_id":"e23316d3-68b9-4291-b465-fdcda0340cb3","resolution":{"observed_at":"2026-08-05T21:57:09.363139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03368","last_updated":"2019-03-29T17:36:27Z","snapshot_observed_at":"2026-08-07T00:39:52.073704Z","submitted_at":"2019-02-09T04:18:10Z","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03368","snapshot_observed_at":"2026-08-05T21:57:09.398349Z","title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the interna- tional skin imaging collaboration (isic)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.398349Z"},"links":{"cited_paper":"/paper/1902.03368","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:582ff7fc1e9870ab35cbf7cefe1eb38a6213e5b887c05b6436962fc17826b7ca","observation_id":"be4b872d-5b53-4ba9-b504-173375eab01a","resolution":{"observed_at":"2026-08-05T21:57:09.398349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:14.715617Z","title":"Deepglobe 2018: A challenge to parse the earth through satellite images","venue":null,"work_id":"b2260756-1917-4c2d-897d-f2f175790b0f","year":2018},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.427027Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:ad50250382097a134bcbe40f1133410e19fdb1795296f88f0e590751585da688","observation_id":"e1b3ac87-74e6-4692-8784-55b4416e4e8b","resolution":{"observed_at":"2026-08-05T21:57:14.802658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:14.521300Z","title":"Self- support few-shot semantic segmentation","venue":null,"work_id":"11537d3d-a667-4650-afe3-642f3da85525","year":2022},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.463670Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:e19126b15cc7a6111d2ca859856fe7e4bbfd036fed1fd9128d6ad22934fcc7b8","observation_id":"de02e574-2534-4bcb-8a7a-c04334735c57","resolution":{"observed_at":"2026-08-05T21:57:14.625947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:14.320737Z","title":"Apseg: Auto-prompt network for cross-domain few-shot semantic segmentation","venue":null,"work_id":"59648a86-0751-4fb3-8c9c-2553abca2907","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.498047Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:d014a63267fe0475cf2998d343e1a1dde7d388e05f7c03be6ae11f85a6ed86e8","observation_id":"d2d7ba77-6f0e-4807-9f53-036f8b1b4413","resolution":{"observed_at":"2026-08-05T21:57:14.414930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:14.054943Z","title":"Adapt before comparison: A new perspective on cross-domain few-shot segmentation","venue":null,"work_id":"3f6a87be-d5a3-4130-9af6-dd3306b8dfa4","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.523130Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:088d1188e95065ff42b4cb0236166766900e7a3faf8b614b6fbfee523fe23eb6","observation_id":"f825f2d5-ae14-4b6d-b74d-cc548c671b73","resolution":{"observed_at":"2026-08-05T21:57:14.192867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:09.556184Z","title":"Denoising diffu- sion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.556184Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:656e4c6ee34242b0439410271e03a9b180f9717b331fd8969ca0b592dd206c37","observation_id":"853419f0-58b1-44c9-be60-62e4fe730a5f","resolution":{"observed_at":"2026-08-05T21:57:09.556184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-07T07:43:16.294957Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-05T21:57:09.576266Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.576266Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:62c96da158689e202e5d78839c705ec88be519e849e7c5180962e5acc0f07164","observation_id":"75c53cef-2f19-4761-9ce0-afe710ca1506","resolution":{"observed_at":"2026-08-05T21:57:09.576266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:13.861940Z","title":"Tfmq-dm: Temporal feature maintenance quantization for diffusion models","venue":null,"work_id":"e990f3ed-bd10-41e8-ac95-29c1d5adf05d","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.612581Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:af6437c5ca577461947b03dff2d6e9737a7b3ad8a9debec38855dd0fd50f8d40","observation_id":"f4fc6546-fe4f-4a7a-8426-f95bd9d6dfd2","resolution":{"observed_at":"2026-08-05T21:57:13.940538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:13.727779Z","title":"Automatic tuberculosis screening using chest radio- graphs","venue":null,"work_id":"30198794-3a1e-4486-b365-fe9e6fca6363","year":2013},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.647904Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:8be6e04a7f0db665137ae873c222da7ff7b8b06b16798d2a31a78875e1f8f373","observation_id":"db1e7950-5ed6-460e-a1d0-a19ce4a294ca","resolution":{"observed_at":"2026-08-05T21:57:13.797171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:13.526827Z","title":"Segment any- thing","venue":null,"work_id":"b533bf57-3165-40f6-a37e-47335cd2d643","year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.676465Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:7a3d5411162749d9d428bc591f6a3ac45f40a0193fa7998e0effb25733877ff9","observation_id":"ae8a681e-b6ff-423d-b990-3a21f8f665da","resolution":{"observed_at":"2026-08-05T21:57:13.613249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:13.332219Z","title":"Learning what not to segment: A new perspective on few- shot segmentation","venue":null,"work_id":"c5829f86-1697-41fb-a5b9-bc20f8b6740b","year":2022},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.693630Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:7532f0e736b1fb0703b38e35ed5569d288bf4d511f0bb76db968e2b92c479db2","observation_id":"3bd8508e-285f-4432-ac48-35c406a08189","resolution":{"observed_at":"2026-08-05T21:57:13.427134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:13.172156Z","title":"Base and meta: A new perspective on few-shot segmentation","venue":null,"work_id":"7c7d5505-dfbb-42a7-9d30-fbaecca0e09f","year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.714242Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:33bb946c1b5e1fd618ef632ff1085823249f950752b8142937688995b7829e51","observation_id":"23976b36-c0b8-48ae-81d6-3543a120a2e0","resolution":{"observed_at":"2026-08-05T21:57:13.216745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.969471Z","title":"Cross-domain few-shot se- mantic segmentation","venue":null,"work_id":"9cdc32b4-7fae-41d6-b35b-1193b352a681","year":2022},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.742342Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:a1cb535f90d3d26c99da47a6fb2f49dd8ff78c8981dcb17fecefc7192e520ca4","observation_id":"209e1e3a-039a-452c-a84e-63b3704f347a","resolution":{"observed_at":"2026-08-05T21:57:13.051581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.800868Z","title":"Adaptive prototype learning and allocation for few-shot segmentation","venue":null,"work_id":"2a657f34-c057-47f6-adda-6888440cf541","year":2021},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.756628Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:f641674d2d0c746f199a87b210722d6d1466fae58c806edef161684bbd3c9409","observation_id":"76f065d3-39ae-4c7c-b2a8-e8d8837f62fa","resolution":{"observed_at":"2026-08-05T21:57:12.878493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.603537Z","title":"Fss-1000: A 1000-class dataset for few- shot segmentation","venue":null,"work_id":"2271d5f1-3018-439a-a324-8c4bb7a6b6d4","year":2020},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.786939Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:80a758746be7d5018662317b93144de807051720c8a48d289127e4737bdf786b","observation_id":"5719341b-4270-45de-9a3f-c2ac3a6e8fb6","resolution":{"observed_at":"2026-08-05T21:57:12.687757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.392004Z","title":"Inter- mediate prototype mining transformer for few-shot semantic segmentation","venue":null,"work_id":"4c18f639-06b0-4f95-ab96-a3c3d30790db","year":2022},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.805565Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:cb6fa133819f2db474d74bb16edfbf721b215881f3544f9470c72ea8815c44ed","observation_id":"168d458d-2c14-44e5-8d12-f50eddaf5ace","resolution":{"observed_at":"2026-08-05T21:57:12.488417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.311155Z","title":"Simpler is better: Few-shot semantic seg- mentation with classifier weight transformer","venue":null,"work_id":"597c3b89-9597-4c1c-b388-b2e626e7d049","year":2021},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.832193Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:f4cf1a140397960116afeed1a31f1aca19634a1c8b2cafabd558471c8b03ee68","observation_id":"b8ebce6e-f7b5-4c58-b561-e3a545311e2c","resolution":{"observed_at":"2026-08-05T21:57:12.335546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.255715Z","title":"Segment anything 9 model for medical image analysis: an experimental study","venue":null,"work_id":"5a2b9d4c-6cf5-48bb-a08f-d7aedae836f6","year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.868506Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:19010aa8623fa0f5ca6cd5c1502513dad1c02551614cdece62d2570b6432851d","observation_id":"4964a65a-29d0-4c39-9b16-c920dcf25bf2","resolution":{"observed_at":"2026-08-05T21:57:12.275253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.190353Z","title":"Hypercorre- lation squeeze for few-shot segmentation","venue":null,"work_id":"e392a4f7-6aff-4776-9e1e-299ab987f5ca","year":2021},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.904589Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:a85a7c9f70f32bed52236e3d2937863667d6ddc08f7e9f8637dba66690e1d4dc","observation_id":"f1a6a696-97ac-44fc-8080-4647bb41b95e","resolution":{"observed_at":"2026-08-05T21:57:12.222903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.115096Z","title":"Feature weighting and boosting for few-shot segmentation","venue":null,"work_id":"7649289a-a1ac-48a3-8ac6-29b66004f4ab","year":2019},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.941001Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:01036a9263238c2853e7173e23e634aa781a0884f9dca350713bf9bf29f6800f","observation_id":"2d6a8efa-cee3-414d-ab05-5580bf805411","resolution":{"observed_at":"2026-08-05T21:57:12.138563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:12.033045Z","title":"Cross-domain few-shot segmentation via iterative support-query correspon- dence mining","venue":null,"work_id":"1c8310f2-7c33-4965-9ab9-5309b69edac2","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:09.969202Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:1754073911e1acfbf96807ac35d7b0e407eb748ca0f2a31b1b818c6e43b287e2","observation_id":"a0f76b74-b7f4-4bbf-b5e8-87bfade6dd24","resolution":{"observed_at":"2026-08-05T21:57:12.083916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:10.003093Z","title":"A threshold selection method from gray-level histograms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.003093Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:c9c81e427cdb36555cd8a8fb62fd1be38e4c06eb0fd36e603d7ef573c3007c90","observation_id":"fe39a7b8-27b7-45e6-8cd4-ff22d751c249","resolution":{"observed_at":"2026-08-05T21:57:10.003093Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.900080Z","title":"Hierarchical dense cor- relation distillation for few-shot segmentation","venue":null,"work_id":"53a524b2-7b2c-4976-b934-3a4af8936c70","year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.031100Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:98f132790829ef4eca47c45a911e4f30ad5fcffd457d8d2850836b13c4fea91b","observation_id":"9efc5343-493c-4ff9-aacc-bd94f50a1be9","resolution":{"observed_at":"2026-08-05T21:57:11.962798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01197","last_updated":"2023-12-03T23:57:43Z","snapshot_observed_at":"2026-07-06T15:49:47.586513Z","submitted_at":"2023-07-03T17:58:01Z","title":"Segment Anything Meets Point Tracking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01197","snapshot_observed_at":"2026-08-05T21:57:10.065809Z","title":"Segment anything meets point tracking","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.065809Z"},"links":{"cited_paper":"/paper/2307.01197","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:8022c1736424ef4d87cd2ac5335f82d16146ec40cfafeb771cdfbedaee940a31","observation_id":"c27731c8-b689-452c-af05-fea3135ce67a","resolution":{"observed_at":"2026-08-05T21:57:10.065809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-05T21:57:10.095131Z","title":"Sam 2: Segment anything in images and videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.095131Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:e0ed7187752ea8f43835a4fce49e20593ed6d14e1ac5b31e3bde96d4f34b87c8","observation_id":"6f07c74e-f8d4-4470-8c3f-95a2b298029a","resolution":{"observed_at":"2026-08-05T21:57:10.095131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1709.03410","last_updated":"2017-09-11T14:34:58Z","snapshot_observed_at":"2026-08-10T08:37:57.906502Z","submitted_at":"2017-09-11T14:34:58Z","title":"One-Shot Learning for Semantic Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.03410","snapshot_observed_at":"2026-08-05T21:57:10.129865Z","title":"One-shot learning for semantic segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.129865Z"},"links":{"cited_paper":"/paper/1709.03410","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:8ce2412f832ea4324b15eb3370892763811de63d747b2ccd6b13253dfd435bc1","observation_id":"5070fe8e-eae5-4bf9-b7a8-cfa32cefabdd","resolution":{"observed_at":"2026-08-05T21:57:10.129865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-05T21:57:10.156314Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.156314Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:b3295721680f2304b7c8b182ba94cec7db8613d749e14f97341409c033948d85","observation_id":"67f6e030-3d86-4f64-8860-a608b0f9ee13","resolution":{"observed_at":"2026-08-05T21:57:10.156314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-05T21:57:10.192683Z","title":"Score-based generative modeling through stochastic differential equa- tions","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.192683Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:9697e90f95dd15989205f4653579e131528fd47e58aff4f1543392e8937585e7","observation_id":"1abe324e-1494-4813-98b3-11dedda441b4","resolution":{"observed_at":"2026-08-05T21:57:10.192683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.777901Z","title":"Domain-rectifying adapter for cross-domain few-shot segmentation","venue":null,"work_id":"9a9722ca-bb49-4d5b-84f4-db4faec3c660","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.229407Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:8539dee8fda75a44c5932715ae183a0271feb2ae995a2373a5c4aa28b44c2b28","observation_id":"1993001d-4b0b-42b4-9ce9-c3db4f3b67cc","resolution":{"observed_at":"2026-08-05T21:57:11.823568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.674168Z","title":"Vrp-sam: Sam with visual reference prompt","venue":null,"work_id":"304d1aa0-81d8-411f-9254-44bd1d96fce5","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.265734Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:6097f79f24f7dfd5f76083beb09068d1020ab345b0b51b1810bcd64e709498f2","observation_id":"07badc3a-3e63-498f-87c5-f3e9b7d9a9b2","resolution":{"observed_at":"2026-08-05T21:57:11.718676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.580948Z","title":"Prior guided feature enrich- ment network for few-shot segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence , 44(2):1050– 1065, 2020","venue":null,"work_id":"672e0053-fe01-452f-8324-5fd026079993","year":2020},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.293296Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:909b46a36dec0f8aa2e41008ecbbd8f5c1986745041573ac1d724b98c29f317f","observation_id":"229cb221-ab4b-4da9-bcd6-b8f098a392ae","resolution":{"observed_at":"2026-08-05T21:57:11.626982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.478877Z","title":"The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions","venue":null,"work_id":"e7e8c494-d4c1-45d4-bb2b-2fb6d9febffb","year":2018},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.329815Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:80cdc637cbb2e6b6b7b9ae77aa9aa61f8baa77b647dabdf413a260f346f71c1a","observation_id":"9ffc7bd5-bf3b-4d50-8797-884226e50bd2","resolution":{"observed_at":"2026-08-05T21:57:11.509704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.392657Z","title":"Panet: Few-shot image semantic seg- mentation with prototype alignment","venue":null,"work_id":"4f23c66c-b03a-4922-b823-8595bdd00622","year":2019},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.366542Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:19f9795960513709ec7025a2c5f54bc2912514ea22754cb9937ff3f4884aa8d3","observation_id":"669ed5a0-a317-420b-8776-422e1b70360d","resolution":{"observed_at":"2026-08-05T21:57:11.433622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.319572Z","title":"Remember the differ- ence: Cross-domain few-shot semantic segmentation via meta-memory transfer","venue":null,"work_id":"bc6d9d27-00d1-40a9-a0ff-4e49ae7c9123","year":2022},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.403151Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:7434869532fc28334406d13bce24b8ea47a682d728437020fcac44bd11b5e4db","observation_id":"4297bda5-8eec-44e7-bcd5-80beea7833e7","resolution":{"observed_at":"2026-08-05T21:57:11.350205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.247002Z","title":"Rethinking the correlation in few-shot segmentation: A buoys view","venue":null,"work_id":"18e87e4b-e4e5-4229-83bd-8e691501b559","year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.430043Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:01366ca99093fddf2129e6feb334a3714f896bac06d2b7c06b2127ba6cdf4320","observation_id":"1cfb2f00-0b22-4565-8708-96fd04d92298","resolution":{"observed_at":"2026-08-05T21:57:11.277508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.179711Z","title":"Accelerating diffu- sion sampling with optimized time steps","venue":null,"work_id":"7b4f9db1-fb61-4354-8831-235c76ee4c27","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.466814Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:967a08d0e9f2ed080ba1faaf4153c4f1072e9ace7e726022991a0abd9c9cc6fd","observation_id":"fd9bfa0d-c977-468e-9053-fe423188a3bd","resolution":{"observed_at":"2026-08-05T21:57:11.215374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.148033Z","title":"Prototype mixture models for few-shot semantic segmentation","venue":null,"work_id":"daf6bc79-c6e2-4883-ad70-aaf186d49241","year":2020},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.491495Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:0bab538982de2a339c16724f49bfda239a931abf9b648e3adca087039c19769b","observation_id":"9eee6f70-50c5-464b-b683-bd3b7f60858c","resolution":{"observed_at":"2026-08-05T21:57:11.156407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11968","last_updated":"2023-04-28T03:21:27Z","snapshot_observed_at":"2026-08-10T06:14:13.595413Z","submitted_at":"2023-04-24T10:04:06Z","title":"Track Anything: Segment Anything Meets Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11968","snapshot_observed_at":"2026-08-05T21:57:10.519532Z","title":"Track anything: Segment anything meets videos","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.519532Z"},"links":{"cited_paper":"/paper/2304.11968","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:8ab1a2137f42515978a51dc5584312651f4ff9d50740664e3644dd67efdffd46","observation_id":"e579c1b4-5e9d-4862-84a0-bcbba4f1976e","resolution":{"observed_at":"2026-08-05T21:57:10.519532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.118645Z","title":"Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation","venue":null,"work_id":"6160c0f4-14e4-4c9a-a27a-82ddb4fe4f6a","year":null},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.548369Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:a0cb4859b0a80d95d83a9e75ee2d74933b50b47898b5d310ec921d4f0bb98620","observation_id":"5a2cb369-447c-47ae-a609-3da38bbf5ba7","resolution":{"observed_at":"2026-08-05T21:57:11.126913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:11.063956Z","title":"Canet: Class-agnostic segmentation networks with it- erative refinement and attentive few-shot learning","venue":null,"work_id":"24d04865-7bd9-4e34-bc8e-d4004a0ea7b7","year":2019},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.575617Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:1da08cfb2fa5951ae327c7eace78ca64930d8764157d4f2734f69c32b46a9d13","observation_id":"0e9c25a7-92eb-4fcd-a656-7a4f2365877e","resolution":{"observed_at":"2026-08-05T21:57:11.095156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08196","last_updated":"2023-05-19T16:33:03Z","snapshot_observed_at":"2026-08-09T17:26:24.629501Z","submitted_at":"2023-05-14T16:23:22Z","title":"A Comprehensive Survey on Segment Anything Model for Vision and Beyond","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.08196","snapshot_observed_at":"2026-08-05T21:57:10.611602Z","title":"A comprehensive survey on segment anything model for vision and beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.611602Z"},"links":{"cited_paper":"/paper/2305.08196","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:a14850356c24e364d0092f448a04959c6c5d538e959067506de835e149f7e66d","observation_id":"9340981a-21eb-493a-8e2e-235ee97c4143","resolution":{"observed_at":"2026-08-05T21:57:10.611602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:10.999365Z","title":"Few-shot segmentation via cycle-consistent trans- former","venue":null,"work_id":"2010aba4-bb62-45a6-aea5-44ac04f85093","year":2021},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.643272Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:53c9759595ddd7579b21d7b4ca11625af78b6bb12536d91f8fbd62e0cadd527d","observation_id":"a682af2c-64a4-4cc2-9129-f7f64a70bcfd","resolution":{"observed_at":"2026-08-05T21:57:11.022289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-05T21:57:10.671895Z","title":"mixup: Beyond empirical risk minimization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.671895Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:d5127c7d8ea1c8921145c1d52b1b6b470969ddd0128f4cbe6e878f533a848ad6","observation_id":"9afe68a5-a023-4413-9553-0cecbab2ae81","resolution":{"observed_at":"2026-08-05T21:57:10.671895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:57:10.928706Z","title":"Diffmorpher: Unleashing the capability of dif- fusion models for image morphing","venue":null,"work_id":"db769527-5cde-461d-aead-4ad6cec1448b","year":2024},"citing_paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T21:57:10.698361Z"},"links":{"citing_paper":"/paper/2508.07747"},"observation_digest":"sha256:3d73f13237fab7eab0a56d68aef6048743f4eb33adfb28bb7ec6b41529c02db9","observation_id":"844425a2-4298-4d44-8e99-1e527624bc0f","resolution":{"observed_at":"2026-08-05T21:57:10.958827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.07747","last_updated":"2025-08-11T08:27:57Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T15:25:53.370611Z","submitted_at":"2025-08-11T08:27:57Z","title":"Grouped Speculative Decoding for Autoregressive Image Generation"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":0,"verified_fuzzy":36},"total_outbound_references":50},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2508.07747."}