{"as_of":"2026-08-13T20:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e55fdb0ba59821140b78800a57d2fc045c1988cc6adace6995a07f810b97f3eb","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:51:09.975164Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2412.11608/citation-record","integrity":"/paper/2412.11608/integrity","json":"/paper/2412.11608/citation-record.json","paper":"/paper/2412.11608"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.431950Z","title":"Inspect, understand, overcome: A survey of practical methods for ai safety,","venue":null,"work_id":"08ae7f82-a452-41d9-828e-d8073f0303c4","year":2022},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.723260Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:85fc62eaafa67fa4c6d79b4272d4b35fd3084d1bfa72238f5be1177e0f50b30c","observation_id":"a400edac-6e71-458d-9cc5-0b1b43195316","resolution":{"observed_at":"2026-08-11T14:51:10.435235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.422905Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":"2928ba06-01e6-4e67-8127-5f82405a3d44","year":2014},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.727566Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:583fac5050da6eb20914be3d958f028b5d88b48fb6bea6634fb3f31cf4ac30eb","observation_id":"2bcceaea-570b-4521-b922-e095224771b8","resolution":{"observed_at":"2026-08-11T14:51:10.426104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.413581Z","title":"Explaining and Harnessing Adversarial Examples,","venue":null,"work_id":"4dbc1f41-bb5e-41ca-b06c-7f09d4447302","year":2015},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.731121Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:e6625d5f1c18de9b6a9916d3510da061d883718eb709cc76136e812132cbba54","observation_id":"aa9c4e2a-950e-4e12-ad6d-cd3d9195c69c","resolution":{"observed_at":"2026-08-11T14:51:10.417163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.403829Z","title":"Robustness to adversarial examples through an ensemble of specialists,","venue":null,"work_id":"fa0683a9-d1d2-4fd4-a4d5-b11d735ddfd3","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.734529Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:b5d4ff7f320d3e4d07767f53eef6087cabd2ec51c325362cc6e9431bb9d88ce7","observation_id":"1db0d8d5-4d56-4d76-b309-3ee3dfb839f6","resolution":{"observed_at":"2026-08-11T14:51:10.407475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.09981","last_updated":"2019-01-28T20:12:15Z","snapshot_observed_at":"2026-07-06T07:29:36.010838Z","submitted_at":"2019-01-28T20:12:15Z","title":"Improving Adversarial Robustness of Ensembles with Diversity Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.09981","snapshot_observed_at":"2026-08-11T14:51:09.738105Z","title":"Improving adversarial robustness of ensembles with diversity training,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.738105Z"},"links":{"cited_paper":"/paper/1901.09981","citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:304656afce0fcff9276322faac68d7680515e6623f03c5240e1b6cddaa4afbd3","observation_id":"12cf5c06-83d8-4f65-8b39-c3c0e6f4eb5a","resolution":{"observed_at":"2026-08-11T14:51:09.738105Z","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":"2026-08-11T14:51:10.394729Z","title":"Improving adversarial ro- bustness via promoting ensemble diversity,","venue":null,"work_id":"4cb22e4e-1620-4af9-a04f-f188a8ea850e","year":2019},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.742149Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:a6cf15b1ffdc74785e8586850f4bdfdd67e84d3025798607559768e66299b67d","observation_id":"ec6d5803-ac6e-4705-a93f-ae915026f431","resolution":{"observed_at":"2026-08-11T14:51:10.398162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.385360Z","title":"Adaptive mixtures of local experts,","venue":null,"work_id":"1ba0ad65-996c-4f92-8058-22b6c7a2aa23","year":1991},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.745518Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:73ba27a76bb51352aa83ad9ca5c5ad466f42d317afa2b9c68a3403213f2c286c","observation_id":"02d07cbe-307f-4f2b-95eb-0f12adcf925d","resolution":{"observed_at":"2026-08-11T14:51:10.388527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.376216Z","title":"Outrageously large neural networks: The sparsely- gated mixture-of-experts layer,","venue":null,"work_id":"300a3182-6ec7-4835-bbcc-1960479e1a33","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.748421Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:5b88b2d20def165a7a2d6a0c2ee7c6464293641f564d9a8a4a1705c1948a2c36","observation_id":"18d5d054-e5f8-4e6f-9dc8-393635297632","resolution":{"observed_at":"2026-08-11T14:51:10.379705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.366847Z","title":"Sparsely- gated mixture-of-expert layers for cnn interpretability,","venue":null,"work_id":"ec28de4b-636b-49d6-9cc9-88c4f5ff92a1","year":2023},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.751282Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:912f3c22ba1ba31e6f8ecf024f0c0d00b255cdbfeea704ad5c69d6794fb2872f","observation_id":"f2bc47e1-749b-4e95-b13d-05b170ca62f5","resolution":{"observed_at":"2026-08-11T14:51:10.370087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.358317Z","title":"Deep mixture of experts via shallow embedding,","venue":null,"work_id":"fa312718-2e53-4085-a90a-c64b06aafa73","year":2019},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.754170Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:664d9625e86ad11f3921714f55feb9bacba8e95f9062bbfaf9ada460571020f8","observation_id":"b071a2c8-417e-4e7b-a1bb-637f7e9703ac","resolution":{"observed_at":"2026-08-11T14:51:10.361502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.350090Z","title":"On the adversarial robustness of mixture of experts,","venue":null,"work_id":"bbbca699-3855-43d2-9d81-e475a112dd51","year":2022},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.756849Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:699ebdc414ebe0bd52510d7a195fbffe0d0dd097a4e859ca63ecee6ec1671975","observation_id":"8fe470c1-d70f-48d0-bc46-11cc97b82a24","resolution":{"observed_at":"2026-08-11T14:51:10.353002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.340353Z","title":"Robust mixture-of-expert training for convolu- tional neural networks,","venue":null,"work_id":"bf7e8d9e-42f5-49af-be17-9892d735133e","year":2023},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.759919Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:2293c59ceae52212de58996cc589057f5b51bf6770777af4b4671df1d718fb65","observation_id":"abace388-f447-436f-8d07-9a0045daf564","resolution":{"observed_at":"2026-08-11T14:51:10.343946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.330695Z","title":"Using mixture of expert models to gain insights into semantic segmentation,","venue":null,"work_id":"3305717b-21d2-4476-9c39-0318eb0a1b75","year":2020},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.763057Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:c1c7e3d550eb0951be0613f8460218913403b1bf800f554d71fff36b398b6093","observation_id":"6feb7bca-56f8-4a5a-9167-8167eeb534ca","resolution":{"observed_at":"2026-08-11T14:51:10.334182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.321012Z","title":"Evaluating mixture-of- experts architectures for network aggregation,","venue":null,"work_id":"d7c974c4-209c-4b8d-98e5-f7f3c0675b00","year":2022},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.766073Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:5d9c74963d4b95c81ccecf27e78fac6ced3ebc00ead5db5674e62ccbea59ad7d","observation_id":"22b6f030-b72c-41dd-ae0d-937b2d534278","resolution":{"observed_at":"2026-08-11T14:51:10.324422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.310999Z","title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,","venue":null,"work_id":"5e2a3c17-ffbf-470d-b971-561a2a6406eb","year":2022},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.768890Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:071595da88e2b2f9320a73b1759c5885c591a12bf68b7ca067dd8f7a1c2eae4e","observation_id":"156225cd-ae99-4825-a9d6-ce7c5230b712","resolution":{"observed_at":"2026-08-11T14:51:10.314656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.300878Z","title":"Deepspeed-moe: Advancing mixture-of- experts inference and training to power next-generation AI scale,","venue":null,"work_id":"9086c905-f658-438c-b7d0-a2c5b008837d","year":2022},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.771727Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:2e33dcb95c037d53d8bc9815804131b3b47255b08ff0e33f4f347c1f4738e815","observation_id":"4efdb8cd-bd3e-460d-9c20-0b51926bda48","resolution":{"observed_at":"2026-08-11T14:51:10.304475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.290508Z","title":"Network of experts for large-scale image categorization,","venue":null,"work_id":"e3a85e0f-dc8e-4ca1-8f0a-f254ccc8056e","year":2016},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.774560Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:0fff1a93c2189ee5f458bab0bd3b7a19b9e2b5b0cc8547297c3f0aab303e9ee3","observation_id":"b7ed9c80-04d6-480d-86d3-8ec7ef03311a","resolution":{"observed_at":"2026-08-11T14:51:10.294446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.280563Z","title":"Adversarial examples for semantic image segmentation,","venue":null,"work_id":"25083d99-f193-47fb-be16-f1b8153a700e","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.777577Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:e9cb0de6f8237429f08aef58fc00ba5e5d19346c9641572c61b3d86157b59d02","observation_id":"ae73bc2d-fcc9-4f7d-a319-3c380abdde2d","resolution":{"observed_at":"2026-08-11T14:51:10.283954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.270271Z","title":"Adversarial examples in the physical world,","venue":null,"work_id":"2aa92c87-d938-43d1-aa52-120ecd3cc291","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.780536Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:5c6151fb17d85d0da79dbbfae9eb3c649be31d59d3cddb0cebf6bc06e5f972d0","observation_id":"4c00e2fb-f50e-4b7c-8e60-22d1ca5f64d7","resolution":{"observed_at":"2026-08-11T14:51:10.273912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.259182Z","title":"On the robustness of semantic segmentation models to adversarial attacks,","venue":null,"work_id":"cea63dff-a9d6-4b13-a01c-24303f99222e","year":2018},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.783511Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:97e22b6f492f6b76e0f0cd9585f6bfa0d3092f60595c29a7d41c14b9bca5f0e7","observation_id":"e2ef69d0-71b1-4acb-92cb-f27963aea0e9","resolution":{"observed_at":"2026-08-11T14:51:10.262788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.249981Z","title":"The pascal visual object classes (VOC) challenge,","venue":null,"work_id":"e126c4fc-992a-45c7-9e3f-ab8989f7780c","year":2010},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.786401Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:9d7de6371c3317933260f7fa1a3dcdd2f83d81b66bcd9f67ad4f8646821f5a05","observation_id":"55a9449a-a9cd-4d55-be1d-b289c3404cdc","resolution":{"observed_at":"2026-08-11T14:51:10.253252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.240491Z","title":"The cityscapes dataset for semantic urban scene understanding,","venue":null,"work_id":"e8496bf0-a70d-439c-a35f-5613f41360e9","year":2016},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.789614Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:c131dca3df8ca637c36d644b795488d613d435071b47c5d71c7faa711d4ea0c4","observation_id":"b6724c73-1b4b-4d2e-a4f6-937092a6145d","resolution":{"observed_at":"2026-08-11T14:51:10.244186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.231338Z","title":"Universal adversar- ial perturbations against semantic image segmentation,","venue":null,"work_id":"4ba871cf-26b2-4221-89f3-113b40ede614","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.792895Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:b2e28888b0f70680a5b61b9e1591d3bf563b4c6dff44e6dd913f38780d126c7d","observation_id":"023335ac-f593-4b98-978a-17f6438508c2","resolution":{"observed_at":"2026-08-11T14:51:10.234536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.11368","last_updated":"2021-11-22T17:26:21Z","snapshot_observed_at":"2026-08-13T18:16:35.436100Z","submitted_at":"2021-11-22T17:26:21Z","title":"Adversarial Examples on Segmentation Models Can be Easy to Transfer","version":1},"cited_work":{"arxiv_id":"2111.11368","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.11368","snapshot_observed_at":"2026-08-11T14:51:10.070247Z","title":"Adversarial Examples on Segmentation Models Can be Easy to Transfer","venue":"cs.CV","work_id":"d23781d0-25c0-4dee-8795-56a30da91e41","year":2021},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.796140Z"},"links":{"cited_paper":"/paper/2111.11368","citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:1d564164d55135ddd4a30616400d6d4243ac1791b3216177801040406d984f5c","observation_id":"8f5a878d-8f57-427b-9003-9e37e311ce98","resolution":{"observed_at":"2026-08-11T14:51:10.074335Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.222555Z","title":"Pyramid scene parsing network,","venue":null,"work_id":"04608940-0bf5-466c-b517-4110d450498e","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.799722Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:cd27ee5abf30e9b0646d30d22704ad3a7c72e9127c03e01c87d5a73e5e95b782","observation_id":"b55f5012-46d9-4ed4-ac9a-8201df93ce05","resolution":{"observed_at":"2026-08-11T14:51:10.225529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-07T13:44:53.690521Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-11T14:51:09.802978Z","title":"Rethinking atrous convolution for semantic image segmentation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.802978Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:67455745c50fc9e556aff96910032903e1bc5a75aa9aaff9e90ea32b65e5180f","observation_id":"68cb89d1-0e6c-481b-9f05-bc0b981decdf","resolution":{"observed_at":"2026-08-11T14:51:09.802978Z","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":"2026-08-11T14:51:09.806611Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.806611Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:554fb4dcb9bf4cbd7a8d4f1996adb74538462f70dec0b14e88304594268911a6","observation_id":"d6242a86-fca9-405c-9978-255fdf1996ea","resolution":{"observed_at":"2026-08-11T14:51:09.806611Z","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":"2026-08-11T14:51:10.207544Z","title":"Deepfool: A simple and accurate method to fool deep neural networks,","venue":null,"work_id":"b166eaed-b846-4f69-aac0-66b97c7d98d3","year":2016},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.810216Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:bf1fd66259af222739949fdc39173c48cfb3b5a07df45f19e1ef4245f131a9ca","observation_id":"ef26a440-dc7d-418d-b042-b612b9aa4824","resolution":{"observed_at":"2026-08-11T14:51:10.210803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.03423","last_updated":"2018-02-08T08:48:03Z","snapshot_observed_at":"2026-07-06T05:59:06.631941Z","submitted_at":"2017-09-11T15:01:03Z","title":"Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.03423","snapshot_observed_at":"2026-08-11T14:51:09.813568Z","title":"Ensemble methods as a defense to adversarial perturbations against deep neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.813568Z"},"links":{"cited_paper":"/paper/1709.03423","citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:107e9cabe9e6949470697bda0c19485103f503dbafb3d7bd4ba4153f7a72eaf0","observation_id":"bcc40208-b6de-4d90-b17d-f6a102bdd031","resolution":{"observed_at":"2026-08-11T14:51:09.813568Z","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":"2026-08-11T14:51:10.198056Z","title":"Ensemble methods in machine learning,","venue":null,"work_id":"9e4e7755-772a-440d-b0ab-f1436d79a647","year":2000},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.817501Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:de4c9ff725680761c9f45f6e6efedc1aaec70871f7d7ea1a63604501b561fe39","observation_id":"05574165-2186-4ec2-9744-591aec326439","resolution":{"observed_at":"2026-08-11T14:51:10.201443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.187710Z","title":"Improving robustness and calibration in ensembles with diversity regularization,","venue":null,"work_id":"57ce1932-1b30-45ae-98f0-b23b0e58d15c","year":2022},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.820844Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:83bf06fca4c52fdb5ee287d68a51f11091c66b86562b53c29aac2109f98e3d44","observation_id":"a0ecb30d-8932-46eb-88c2-a3eacea7c872","resolution":{"observed_at":"2026-08-11T14:51:10.191320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.177596Z","title":"Measuring ensemble diversity and its effects on model robustness,","venue":null,"work_id":"208eb033-e99d-4c07-b74e-cadc98d14aca","year":2021},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.824162Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:62a6ec24e15f2b4c976a3817230927b5f5367b7b7b50d35d5b0bf59ba3ed499d","observation_id":"ad3a8ed4-52f4-4581-a3b5-5e4af7e63f67","resolution":{"observed_at":"2026-08-11T14:51:10.181139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.10586","last_updated":"2025-03-26T15:14:25Z","snapshot_observed_at":"2026-08-12T13:31:00.274300Z","submitted_at":"2021-04-21T15:27:07Z","title":"Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.10586","snapshot_observed_at":"2026-08-11T14:51:09.827622Z","title":"Mixture of robust experts (more): A robust denoising method towards multiple perturbations,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.827622Z"},"links":{"cited_paper":"/paper/2104.10586","citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:cf5ff1eb7202ffef69b11fabd84a571ce4fc419583f2b4df5900aa0ff6c3e724","observation_id":"72f76335-fc9f-4949-8463-80a52a85ae32","resolution":{"observed_at":"2026-08-11T14:51:09.827622Z","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":"2026-08-11T14:51:10.167054Z","title":"Synergy-of-experts: Collaborate to improve adversarial robustness,","venue":null,"work_id":"5cbb29bf-20c0-4560-86cc-1a1960a80105","year":2022},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.831187Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:bba5c7edc64a88c662b2411c922d3099a78721f762a2110c98225c2b3242d90f","observation_id":"2f10860b-e0ca-4642-ae7c-e9fdb2df5270","resolution":{"observed_at":"2026-08-11T14:51:10.170552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18787","last_updated":"2024-02-29T01:27:38Z","snapshot_observed_at":"2026-08-13T04:07:46.183455Z","submitted_at":"2024-02-29T01:27:38Z","title":"Enhancing the \"Immunity\" of Mixture-of-Experts Networks for Adversarial Defense","version":1},"cited_work":{"arxiv_id":"2402.18787","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.18787","snapshot_observed_at":"2026-08-11T14:51:10.014245Z","title":"Enhancing the \"Immunity\" of Mixture-of-Experts Networks for Adversarial Defense","venue":"cs.LG","work_id":"862a5082-e379-4696-a1ca-618a949e4283","year":2024},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.834403Z"},"links":{"cited_paper":"/paper/2402.18787","citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:684da7e873eaf2c9246e896f73ec3892131017cb59d8279f138425f2495c01cc","observation_id":"4c347e84-8591-4b96-b0e5-f04f587bb124","resolution":{"observed_at":"2026-08-11T14:51:10.019788Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.156535Z","title":"Towards deep learning models resistant to adversarial attacks,","venue":null,"work_id":"2cde804a-4281-4010-bfe0-df131d18f41b","year":2018},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.837904Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:330a27c59fd712c6d6a28ab32d692551a3e935b8939affc8f364e4196086d047","observation_id":"8c67d932-6298-4d53-9512-40dacb7ee726","resolution":{"observed_at":"2026-08-11T14:51:10.160289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.146366Z","title":"Adversarial risk and the dangers of evaluating against weak attacks,","venue":null,"work_id":"5225e50a-2b19-4724-811c-55d75aa996b5","year":2018},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.841063Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:6b690c8d56452fddb2cd554ba87d95519788e01bf376fc1bcc77d565448c2620","observation_id":"e6b04853-ddff-4ca8-af78-e2f7ac95eb32","resolution":{"observed_at":"2026-08-11T14:51:10.150021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.135965Z","title":"Towards evaluating the robustness of neural networks,","venue":null,"work_id":"6283adb4-5c0d-433b-8331-f22633b6298f","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.956118Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:23384462772f7d90708e7a41115c1696d832f2f214d0b9be3168575def1cf7ad","observation_id":"22cce3f0-b813-4c27-9083-71f0507d0361","resolution":{"observed_at":"2026-08-11T14:51:10.139887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:09.959665Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.959665Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:ab08bdfeba882b12785b90dcf020ff82444ef4ce3f38acabb834072a08ac78a9","observation_id":"02fff393-8a63-4352-a781-9f370f0535b1","resolution":{"observed_at":"2026-08-11T14:51:09.959665Z","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":"2026-08-11T14:51:10.120174Z","title":"Univer- sal Adversarial Perturbations,","venue":null,"work_id":"54879359-cddf-4ab0-b28e-4444042fabc1","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.962964Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:b98cc3833814d0f98fc98a119037113c6934ab3a27a8d94fda4a6f46ac3d8499","observation_id":"628ae363-2035-4438-bfc6-5b04852d1af0","resolution":{"observed_at":"2026-08-11T14:51:10.123633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.110122Z","title":"Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes,","venue":null,"work_id":"16b2e72b-9092-45ff-8795-2388e2b42fdb","year":2017},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.966268Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:6a491f29d827b7e702549021f67a365574407769c3d486bdbe374edcc971c4b8","observation_id":"98646342-b10a-4c91-bf2f-93f6dc1c01c3","resolution":{"observed_at":"2026-08-11T14:51:10.113552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.100358Z","title":"Encoder- decoder with atrous separable convolution for semantic image segmenta- tion,","venue":null,"work_id":"272da2ff-777a-45ed-89b8-d39e1d3630e8","year":2018},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.969405Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:ed2f9b115353475394a4dce45671851b04cb63c42820beb3d9c139bbcdf81cd2","observation_id":"97ad35ee-1b72-4ba7-8226-3bab47d1859e","resolution":{"observed_at":"2026-08-11T14:51:10.103756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:51:10.090610Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"21910c08-ea7f-4bd3-a7a4-7c0ed50e7379","year":2016},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.972415Z"},"links":{"citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:c3fcf4eab049d63531ef0166499a750088679dd70e5579f5e8a02dfab9049cc0","observation_id":"600b5b2b-190b-44fe-9dda-e281da0f8168","resolution":{"observed_at":"2026-08-11T14:51:10.094077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.06320","last_updated":"2026-07-27T21:25:47Z","snapshot_observed_at":"2026-08-13T13:49:08.730047Z","submitted_at":"2020-04-14T06:45:07Z","title":"A2D2: Audi Autonomous Driving Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.06320","snapshot_observed_at":"2026-08-11T14:51:09.975164Z","title":"A2D2: audi autonomous driving dataset,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T14:51:09.975164Z"},"links":{"cited_paper":"/paper/2004.06320","citing_paper":"/paper/2412.11608"},"observation_digest":"sha256:f64fc9f583a13eb5c6ce14f733acffb187902ef4f19bd9b1206316240bd62057","observation_id":"c2c577f6-13ab-4e30-9644-b56faac56f71","resolution":{"observed_at":"2026-08-11T14:51:09.975164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.11608","last_updated":"2024-12-16T09:49:59Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T13:49:43.613450Z","submitted_at":"2024-12-16T09:49:59Z","title":"Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentation"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":35},"total_outbound_references":44},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.11608."}