{"as_of":"2026-08-11T18:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e38761141e5ada5c1ddeb7dbbffb1cb5de5dfe57bca6b1cb57039e48ed66d2e","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T08:42:32.895398Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T08:42:31.149556Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.16316","snapshot_observed_at":"2026-08-03T08:42:31.149556Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.149556Z"},"links":{"cited_paper":"/paper/2601.16316","citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:03f50a93a7d607e9060207939b9acad484007f87d574ed84241fe69f6163ce9e","observation_id":"349afb61-c808-4b3a-9c7b-492612bed466","resolution":{"observed_at":"2026-08-03T08:42:31.149556Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2601.16316/citation-record","integrity":"/paper/2601.16316/integrity","json":"/paper/2601.16316/citation-record.json","paper":"/paper/2601.16316"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T08:42:31.089707Z","title":"Adapting the model for different sets of keywords requires repeating the same training procedure; therefore, it is not feasible to adapt the models on edge hardware","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.089707Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:650b0724fd98de849940e74062fd0ace406ae8f25f791c29e83428c92b539968","observation_id":"b2499bbe-abb6-4b3f-a134-5bb4227d5f46","resolution":{"observed_at":"2026-08-03T08:42:31.089707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.16316","snapshot_observed_at":"2026-08-03T08:42:31.149556Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.149556Z"},"links":{"cited_paper":"/paper/2601.16316","citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:03f50a93a7d607e9060207939b9acad484007f87d574ed84241fe69f6163ce9e","observation_id":"349afb61-c808-4b3a-9c7b-492612bed466","resolution":{"observed_at":"2026-08-03T08:42:31.149556Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-03T08:42:31.265139Z","title":"For fairness, we adapt BC-ResNet to the FS- KWS setting by replacing the final classifier to produce 64- dimensional embeddings compatible with our KD setup","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.265139Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:bf8b3cdf9be05dbf55d586bb0e607362ffcd9b293b09e3948a871fcc4f5100af","observation_id":"9674851e-29bf-4c48-9e28-95d9c6ef9e36","resolution":{"observed_at":"2026-08-03T08:42:31.265139Z","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":"2026-08-03T08:42:31.380011Z","title":"EdgeSpot delivers higher low-FAR accuracy than BC-ResNet on both MSWC and cross-domain GSC with minimal additional on-device cost","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.380011Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:d6fca3aa11e58fce0a9989166c3aa9ea753314c9f5b3ebeb5e48dd274aad22f3","observation_id":"cd30724e-0481-4890-bbe8-d3943045448d","resolution":{"observed_at":"2026-08-03T08:42:31.380011Z","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":"2026-08-03T08:42:31.491163Z","title":"Few-shot keyword spotting in any language,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.491163Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:3755a50f0448158d57757e2e537d0de002a1c11f923bfdda8182d9e6f404724d","observation_id":"d66cae02-6aa1-4fa6-8808-cc286089488e","resolution":{"observed_at":"2026-08-03T08:42:31.491163Z","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":"2026-08-03T08:42:31.659331Z","title":"Fully unsupervised training of few-shot keyword spotting,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.659331Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:d69d3548fbce245b75e8392a2705d5f370ce47c40ace5e2138b35d0ca7f0cbcb","observation_id":"40fab8eb-052a-4672-b5cc-c0873a58aaba","resolution":{"observed_at":"2026-08-03T08:42:31.659331Z","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":"2026-08-03T08:42:31.773554Z","title":"On-device cus- tomization of tiny deep learning models for keyword spotting with few examples,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.773554Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:15a0344ca5817582fdb64f90df236fa2a1ec11bd02dd04f6b30ba054134a4407","observation_id":"cda673f7-6fbe-43de-9270-3332735b979b","resolution":{"observed_at":"2026-08-03T08:42:31.773554Z","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":"2026-08-03T08:42:31.894390Z","title":"Few-shot open- set learning for on-device customization of keyword spotting systems,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:31.894390Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:8c183872ba862ec204c4bced024c8afec0fc30cc1259ec64c67279953655d5dd","observation_id":"5a553898-8329-4a66-a463-bf82b277d06b","resolution":{"observed_at":"2026-08-03T08:42:31.894390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-03T08:42:32.007703Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.007703Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:88902dfecaab0305d05ab1a1bd9f306c6dbc71167d4cf06ab5c2c603f67b5b83","observation_id":"79a032b0-6799-43ee-a66c-690df5606393","resolution":{"observed_at":"2026-08-03T08:42:32.007703Z","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":"2026-08-03T08:42:32.069390Z","title":"Enhancing few- shot keyword spotting performance through pre-trained self-supervised speech models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.069390Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:153a1e8fb9d67dfc15288e46478f129ac62a595eab8a55c36f2dcc4bf1f77c4c","observation_id":"cdab9cd6-f947-4188-9b43-eb06ab2fbb76","resolution":{"observed_at":"2026-08-03T08:42:32.069390Z","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":"2026-08-03T08:42:32.165523Z","title":"Match- boxNet: 1D time-channel separable convolutional neu- ral network architecture for speech commands recogni- tion,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.165523Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:ae3dce59cea5485a8bc4f4a097fa4e2e81534415cf76e0ccc43d379076607e87","observation_id":"f8aba5f5-959c-4ad9-9211-8dd92ca0844b","resolution":{"observed_at":"2026-08-03T08:42:32.165523Z","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":"2026-08-03T08:42:32.220720Z","title":"Depthwise sepa- rable convolutional resnet with squeeze-and-excitation blocks for small-footprint keyword spotting,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.220720Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:f2748dc00772270a89277b7dc2173f9599c2801be18cd16f30d8541c3d118a85","observation_id":"143919d4-5f7b-4eda-80d4-54db2c26493d","resolution":{"observed_at":"2026-08-03T08:42:32.220720Z","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":"2026-08-03T08:42:32.280208Z","title":"Temporal convolution for real- time keyword spotting on mobile devices,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.280208Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:1db51b18d97997bbb5b69c52226a7981eb470834c73eebd392c99a50ab5b41a8","observation_id":"54bb7d11-c8de-44d0-a190-94bbf7dca047","resolution":{"observed_at":"2026-08-03T08:42:32.280208Z","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":"2026-08-03T08:42:32.322050Z","title":"Broadcasted residual learning for ef- ficient keyword spotting,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.322050Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:4ed88ece2cac6294a2f8f8ab2dbb07e4a88ebd89bf43f22169f552cbe60a1bff","observation_id":"54071d6c-b6dd-47df-9ed0-d4f6628e566e","resolution":{"observed_at":"2026-08-03T08:42:32.322050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03209","last_updated":"2018-04-09T19:58:17Z","snapshot_observed_at":"2026-07-06T06:32:32.083176Z","submitted_at":"2018-04-09T19:58:17Z","title":"Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03209","snapshot_observed_at":"2026-08-03T08:42:32.381699Z","title":"Speech commands: A dataset for limited-vocabulary speech recognition,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.381699Z"},"links":{"cited_paper":"/paper/1804.03209","citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:bc1b0644a936dc4ac594aa7abbf0da7c8a616d5ca9ab419038ca1d12d986cf3f","observation_id":"493345c2-24ab-442a-a12c-7eb821362f4c","resolution":{"observed_at":"2026-08-03T08:42:32.381699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.05666","last_updated":"2016-07-19T17:17:58Z","snapshot_observed_at":"2026-08-03T13:09:34.087171Z","submitted_at":"2016-07-19T17:17:58Z","title":"Trainable Frontend For Robust and Far-Field Keyword Spotting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.05666","snapshot_observed_at":"2026-08-03T08:42:32.466188Z","title":"Trainable frontend for robust and far-field keyword spotting,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.466188Z"},"links":{"cited_paper":"/paper/1607.05666","citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:52413abf5e0b286171063e11a4b86434ae6f479d5695a9d94349ab2b8e1c0ac7","observation_id":"66fe4d76-189f-4e8c-8adc-ccc0c4f4517a","resolution":{"observed_at":"2026-08-03T08:42:32.466188Z","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":"2026-08-03T08:42:32.526610Z","title":"Multilingual spoken words corpus,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.526610Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:ac6688e89ef75dc8b36d858531884c526a12e9d964153c2ebe97f327c8a91513","observation_id":"dd79dca4-ae33-482a-b99c-c50106e176b0","resolution":{"observed_at":"2026-08-03T08:42:32.526610Z","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":"2026-08-03T08:42:32.603157Z","title":"Per-channel energy normalization: Why and how,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.603157Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:c8f8cdc0c94ab38888f5399fdfbe2bb67c39436a8aa02904270994e770470884","observation_id":"d5bfe3aa-4e8a-43f3-a485-571073bf5def","resolution":{"observed_at":"2026-08-03T08:42:32.603157Z","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":"2026-08-03T08:42:32.699417Z","title":"Efficientnetv2: Smaller models and faster training,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.699417Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:b24302670262ed430ae07d099520949b54397c81d79cde744e9bc607c2a6e7ce","observation_id":"c41de38b-233d-43db-a7a0-fd0acda20c2f","resolution":{"observed_at":"2026-08-03T08:42:32.699417Z","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":"2026-08-03T08:42:32.754903Z","title":"wav2vec 2.0: A framework for self- supervised learning of speech representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.754903Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:b70e37b492699a48c60d28eae8dfb622bad92fec41110b887d520f95ee3ffe68","observation_id":"f9014e5b-6e46-4366-885e-2e47d6baf495","resolution":{"observed_at":"2026-08-03T08:42:32.754903Z","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":"2026-08-03T08:42:32.836719Z","title":"Sub-center arcface: Boosting face recognition by large-scale noisy web faces,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.836719Z"},"links":{"citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:179a916dd1232277c55243328ef64a30ef5f33e48f1f4edbb2c73c1058f75418","observation_id":"469b23ae-6902-4198-9cc3-c7c17371260b","resolution":{"observed_at":"2026-08-03T08:42:32.836719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.08779","last_updated":"2019-12-03T18:19:07Z","snapshot_observed_at":"2026-08-09T05:21:08.018150Z","submitted_at":"2019-04-18T17:53:38Z","title":"SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.08779","snapshot_observed_at":"2026-08-03T08:42:32.895398Z","title":"Specaugment: A simple data augmentation method for automatic speech recognition,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T08:42:32.895398Z"},"links":{"cited_paper":"/paper/1904.08779","citing_paper":"/paper/2601.16316"},"observation_digest":"sha256:e6925e322d320f4410b1d8f33c994d5a4c811ec7c34043a894cb90dc6f673f37","observation_id":"1c1c9ce6-fdff-4b42-b3d6-21c2255a12c6","resolution":{"observed_at":"2026-08-03T08:42:32.895398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.16316","last_updated":"2026-01-22T20:52:50Z","latest_version":1,"primary_category":"eess.AS","snapshot_observed_at":"2026-08-09T05:21:38.419540Z","submitted_at":"2026-01-22T20:52:50Z","title":"EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":22},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2601.16316."}