{"as_of":"2026-08-20T08:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9f1ef28daf5399ce52d7f547f77cce60c4339af19344ca2d77cd01c893b35bae","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":29,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:32:33.723134Z","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":"2026-07-03T00:47:29.746719Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-12T05:52:21.786879Z","title":"arXiv preprint arXiv:2001.00326 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.19819","last_updated":"2024-11-29T16:27:55Z","snapshot_observed_at":"2026-08-17T16:24:59.101809Z","submitted_at":"2024-11-29T16:27:55Z","title":"GradAlign for Training-free Model Performance Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T05:52:21.786879Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2411.19819"},"observation_digest":"sha256:3bc57d8d046fd02cabf688aeec01d31848eec1496b5ba521686dd5dead425f8f","observation_id":"f8602af1-88ed-4ad4-a56a-4d8fd7fa6435","resolution":{"observed_at":"2026-08-12T05:52:21.786879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-12T04:28:12.415416Z","title":"://arxiv.org/abs/2001.00326","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01420","last_updated":"2024-12-19T15:51:33Z","snapshot_observed_at":"2026-08-17T13:32:21.814730Z","submitted_at":"2024-12-02T12:00:27Z","title":"Task Adaptation of Reinforcement Learning-based NAS Agents through Transfer Learning","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T04:28:12.415416Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2412.01420"},"observation_digest":"sha256:1fd987bbae2cafa80c725fbcc676362b5b712e37f04d9d0ea931f72da6f43a99","observation_id":"6cddc742-b7d2-4c7b-8360-582beb5ea4a7","resolution":{"observed_at":"2026-08-12T04:28:12.415416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-11T22:14:08.924732Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.03718","last_updated":"2025-02-20T13:31:09Z","snapshot_observed_at":"2026-08-18T12:18:08.160909Z","submitted_at":"2024-12-04T21:14:18Z","title":"ParetoFlow: Guided Flows in Multi-Objective Optimization","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T22:14:08.924732Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2412.03718"},"observation_digest":"sha256:08a3d71474bddcf3b5bac9419fce944b340b47ac99599545330b6ff96d5a2d92","observation_id":"18fff644-dac5-4127-b8d3-26ff700b50f7","resolution":{"observed_at":"2026-08-11T22:14:08.924732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-11T15:45:48.166949Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.10723","last_updated":"2024-12-14T07:42:56Z","snapshot_observed_at":"2026-08-17T09:50:10.002517Z","submitted_at":"2024-12-14T07:42:56Z","title":"HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T15:45:48.166949Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2412.10723"},"observation_digest":"sha256:7e51930844804a9843313759d79b6c7d5a3878b730222c4218a470d63f175894","observation_id":"0f4050f2-6557-4ca9-bcd1-7f6b88bc9215","resolution":{"observed_at":"2026-08-11T15:45:48.166949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-11T11:23:48.355832Z","title":"Tcnn: Temporal convolutional neural network for real-time speech enhancement in the time domain,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.15554","last_updated":"2025-01-19T02:54:49Z","snapshot_observed_at":"2026-08-18T22:19:25.589425Z","submitted_at":"2024-12-20T04:28:02Z","title":"Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation","version":3},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-11T11:23:48.355832Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2412.15554"},"observation_digest":"sha256:f5df472fd24cb71413b2a3e2950834b0e7fcf148e2b23039ee52fd51862fbf84","observation_id":"db8d3b98-df54-4957-813e-42c6aea2d7ac","resolution":{"observed_at":"2026-08-11T11:23:48.355832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-11T00:52:22.834643Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.19206","last_updated":"2024-12-26T13:07:03Z","snapshot_observed_at":"2026-08-19T04:15:42.776374Z","submitted_at":"2024-12-26T13:07:03Z","title":"NADER: Neural Architecture Design via Multi-Agent Collaboration","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T00:52:22.834643Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2412.19206"},"observation_digest":"sha256:47c79927606e2a6d6629cad40ede8966d4b1ebadac91a424702fb3c1c901b3d5","observation_id":"2b9a4f6a-1d9f-4d0a-a15b-ea42788a2c73","resolution":{"observed_at":"2026-08-11T00:52:22.834643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-10T14:46:38.668281Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.15014","last_updated":"2025-01-28T20:29:44Z","snapshot_observed_at":"2026-08-15T08:26:57.013948Z","submitted_at":"2025-01-25T01:37:03Z","title":"On Accelerating Edge AI: Optimizing Resource-Constrained Environments","version":2},"reference_index":136,"source":"arxiv_source","source_observed_at":"2026-08-10T14:46:38.668281Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2501.15014"},"observation_digest":"sha256:49ebda71c34de5661beb8de374c229c0be1f2cadde7e2a97a5ce103ca3b792b9","observation_id":"f00be33f-3b7f-4810-af8e-97525632f1c4","resolution":{"observed_at":"2026-08-10T14:46:38.668281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-08T20:49:36.199029Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2502.04975","last_updated":"2025-02-07T14:48:28Z","snapshot_observed_at":"2026-08-18T02:14:42.207438Z","submitted_at":"2025-02-07T14:48:28Z","title":"Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T20:49:36.199029Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2502.04975"},"observation_digest":"sha256:238366543af425d76355a44392c8dc2485dcc5a6fa9eec0ea94350feb1ecc5ac","observation_id":"5c8abed3-4699-4915-8bb3-9d3b5e805d55","resolution":{"observed_at":"2026-08-08T20:49:36.199029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-16T04:32:33.723134Z","title":"and Yang, Y","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.01468","last_updated":"2025-05-02T05:59:21Z","snapshot_observed_at":"2026-08-18T03:07:11.064552Z","submitted_at":"2025-05-02T05:59:21Z","title":"One Search Fits All: Pareto-Optimal Eco-Friendly Model Selection","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-16T04:32:33.723134Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2505.01468"},"observation_digest":"sha256:a06f9e1902246796cf71d7106168fd5179ff1dca22bca405204ae4d2cf9d9eb0","observation_id":"00bf2578-2059-46cb-9158-dee9d9800117","resolution":{"observed_at":"2026-08-16T04:32:33.723134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-15T22:26:06.748507Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.07300","last_updated":"2025-05-12T07:44:52Z","snapshot_observed_at":"2026-08-20T02:07:47.704053Z","submitted_at":"2025-05-12T07:44:52Z","title":"L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T22:26:06.748507Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2505.07300"},"observation_digest":"sha256:664d2b2241b3672b316253c549b0d2e6f7c2b91239a661542b88111df5b0a21f","observation_id":"5c752391-f990-4805-8f25-cf645e1496a8","resolution":{"observed_at":"2026-08-15T22:26:06.748507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-15T20:35:08.777789Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.12523","last_updated":"2025-05-18T19:17:03Z","snapshot_observed_at":"2026-08-18T23:31:27.894288Z","submitted_at":"2025-05-18T19:17:03Z","title":"Energy-Aware Deep Learning on Resource-Constrained Hardware","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:35:08.777789Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2505.12523"},"observation_digest":"sha256:4e2a6e2f7ba3982e9002a21f32b3122c3e21bdc2991d3f61b68522d502f0b28a","observation_id":"e4ccbb57-ada8-4175-b719-34bb5c3f076d","resolution":{"observed_at":"2026-08-15T20:35:08.777789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-07T14:54:26.958219Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.17254","last_updated":"2025-05-22T20:05:20Z","snapshot_observed_at":"2026-08-16T13:35:24.151225Z","submitted_at":"2025-05-22T20:05:20Z","title":"Approach to Finding a Robust Deep Learning Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:54:26.958219Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2505.17254"},"observation_digest":"sha256:8312b664f44416b119188cb6c33c207e667b45534ba7fdef3bc6007d55557d9d","observation_id":"38cc5536-8d4f-41ef-8d44-2fcd39b967e4","resolution":{"observed_at":"2026-08-07T14:54:26.958219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-07T14:05:32.664999Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search.arXiv preprint arXiv:2001.00326,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.20221","last_updated":"2025-05-26T17:03:22Z","snapshot_observed_at":"2026-08-17T14:46:25.061453Z","submitted_at":"2025-05-26T17:03:22Z","title":"Gradient Flow Matching for Learning Update Dynamics in Neural Network Training","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:05:32.664999Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2505.20221"},"observation_digest":"sha256:2328f81cbebfbd3fae44680a1099f0dd65a9266f80c716e7094934dccf3c4680","observation_id":"a5f228a7-9655-467b-89c1-12f52929a55f","resolution":{"observed_at":"2026-08-07T14:05:32.664999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-07T05:32:13.143382Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search.arXiv preprint arXiv:2001.00326, 2020","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.07735","last_updated":"2025-06-09T13:20:02Z","snapshot_observed_at":"2026-08-18T00:18:05.578823Z","submitted_at":"2025-06-09T13:20:02Z","title":"Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:32:13.143382Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2506.07735"},"observation_digest":"sha256:a04383003783e797df19e28983b49942a1a876113a2fcf70efcba326e6726997","observation_id":"13f670aa-d25b-4ca9-9950-2da5ea26d1b4","resolution":{"observed_at":"2026-08-07T05:32:13.143382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-06T23:13:15.293756Z","title":"and Yang, Y","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.19384","last_updated":"2025-06-24T07:20:16Z","snapshot_observed_at":"2026-08-13T19:22:39.816186Z","submitted_at":"2025-06-24T07:20:16Z","title":"Deep Electromagnetic Structure Design Under Limited Evaluation Budgets","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T23:13:15.293756Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2506.19384"},"observation_digest":"sha256:03dba9a154aadbebc63a4d74e1bb8a85f27822cbd2d92f3b752fffdd1ae435ec","observation_id":"68067605-71ed-4b19-932a-3416f0ebbe69","resolution":{"observed_at":"2026-08-06T23:13:15.293756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-06T21:11:58.717299Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.00880","last_updated":"2025-07-01T15:46:18Z","snapshot_observed_at":"2026-08-20T05:11:40.371081Z","submitted_at":"2025-07-01T15:46:18Z","title":"NN-Former: Rethinking Graph Structure in Neural Architecture Representation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:11:58.717299Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2507.00880"},"observation_digest":"sha256:e7f6fcf56adec0c2fd47fc6a55ba0fb8ff38678d878561e1696f7cdd815a6307","observation_id":"b16e3f2c-cd57-42be-99de-981ccd765ed1","resolution":{"observed_at":"2026-08-06T21:11:58.717299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-06T13:31:42.536729Z","title":"NAS-Bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.20592","last_updated":"2025-07-28T08:02:31Z","snapshot_observed_at":"2026-08-16T06:01:45.387070Z","submitted_at":"2025-07-28T08:02:31Z","title":"PhaseNAS: Language-Model Driven Architecture Search with Dynamic Phase Adaptation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T13:31:42.536729Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2507.20592"},"observation_digest":"sha256:9ee5c25a32d0048227d6a2eaead868f557b3fc1d82a9c226490ac35d2e60fc17","observation_id":"03034d57-468b-47e2-af1c-74cfe1f017bc","resolution":{"observed_at":"2026-08-06T13:31:42.536729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-06T10:50:47.312654Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.23437","last_updated":"2025-08-01T09:38:03Z","snapshot_observed_at":"2026-08-18T08:52:02.680899Z","submitted_at":"2025-07-31T11:16:46Z","title":"Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T10:50:47.312654Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2507.23437"},"observation_digest":"sha256:09d3e7c72179f894d02915c171e4d85b7d8c407ae08302fb3a22c05058739535","observation_id":"939de935-2e3f-4cf6-9348-ae9eeb59646a","resolution":{"observed_at":"2026-08-06T10:50:47.312654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2001.00326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-03T00:47:29.746719Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search","venue":null,"work_id":"630ce5d0-3891-4143-a94a-5b37d32522be","year":2001},"citing_paper":{"arxiv_id":"2510.14235","last_updated":"2026-02-03T02:30:05Z","snapshot_observed_at":"2026-08-12T20:14:22.026190Z","submitted_at":"2025-10-16T02:27:07Z","title":"Spiking Neural Network Architecture Search: A Survey","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-18T07:00:27.719109Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2510.14235"},"observation_digest":"sha256:b4cbe3bc74a3c3cb321db98b1211d4f8d189f55e399d095a61e1e39bb6b38aaf","observation_id":"95feb137-0ff5-4a3e-8743-61e855efed94","resolution":{"observed_at":"2026-05-18T07:01:01.333488Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2001.00326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-03T00:47:29.746719Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search","venue":null,"work_id":"630ce5d0-3891-4143-a94a-5b37d32522be","year":2001},"citing_paper":{"arxiv_id":"2605.04057","last_updated":"2026-06-30T03:07:24Z","snapshot_observed_at":"2026-08-12T15:07:26.610644Z","submitted_at":"2026-04-10T07:40:23Z","title":"Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T17:39:02.693259Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2605.04057"},"observation_digest":"sha256:ce6b9fdddcde1acf27749c729608b683abaf07b1b14efbbe24996e5be4e7da86","observation_id":"f2f431d9-d863-4cdb-ad03-1d0b6df5547e","resolution":{"observed_at":"2026-05-11T06:25:58.453733Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2001.00326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-03T00:47:29.746719Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search","venue":null,"work_id":"630ce5d0-3891-4143-a94a-5b37d32522be","year":2001},"citing_paper":{"arxiv_id":"2605.06187","last_updated":"2026-05-07T13:03:19Z","snapshot_observed_at":"2026-08-13T05:29:29.701481Z","submitted_at":"2026-05-07T13:03:19Z","title":"In-Context Black-Box Optimization with Unreliable Feedback","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-08T13:40:53.271150Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2605.06187"},"observation_digest":"sha256:0a4a414eac299e0178ddb5964cb7a1c8813659c4f92e670d755489be4ae501b3","observation_id":"b70980eb-2c5c-4ddf-b876-391f9281dcee","resolution":{"observed_at":"2026-05-11T18:51:07.405276Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2001.00326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-03T00:47:29.746719Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search","venue":null,"work_id":"630ce5d0-3891-4143-a94a-5b37d32522be","year":2001},"citing_paper":{"arxiv_id":"2605.30019","last_updated":"2026-05-29T07:41:37Z","snapshot_observed_at":"2026-08-14T08:05:43.824845Z","submitted_at":"2026-05-28T14:41:43Z","title":"elasticAI.explorer: Towards a Unified End-to-End Framework for Hardware-Aware Neural Architecture Search","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T00:15:01.723690Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2605.30019"},"observation_digest":"sha256:76d73e86137d8629b6b8ae375120c33946efb2be6dc320bb7ab185ae31ff5b58","observation_id":"25bdb834-d1cb-41b0-8072-ab5b23fcb7d6","resolution":{"observed_at":"2026-06-29T00:22:51.664538Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2001.00326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-03T00:47:29.746719Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search","venue":null,"work_id":"630ce5d0-3891-4143-a94a-5b37d32522be","year":2001},"citing_paper":{"arxiv_id":"2606.05139","last_updated":"2026-06-03T17:48:31Z","snapshot_observed_at":"2026-08-15T12:36:55.060826Z","submitted_at":"2026-06-03T17:48:31Z","title":"BBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-06-28T06:52:09.587293Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2606.05139"},"observation_digest":"sha256:06439f413b51a210380f75d243c1d32a874d065051f3b574dffb59119505ca6e","observation_id":"82cdd94b-6d90-4b73-9322-dfd08d02adc8","resolution":{"observed_at":"2026-07-02T07:36:44.986861Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2001.00326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-03T00:47:29.746719Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search","venue":null,"work_id":"630ce5d0-3891-4143-a94a-5b37d32522be","year":2001},"citing_paper":{"arxiv_id":"2606.10068","last_updated":"2026-06-08T18:42:00Z","snapshot_observed_at":"2026-08-07T18:03:10.261596Z","submitted_at":"2026-06-08T18:42:00Z","title":"Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-27T17:04:45.496413Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2606.10068"},"observation_digest":"sha256:b992821e7a4a9b8e2be12d41e2b31e09ec20c31b1f849332925a12e42901274b","observation_id":"01c01e09-5f28-49e9-b47d-24bd7f4861bb","resolution":{"observed_at":"2026-07-03T00:47:29.748961Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":"2001.00326","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-03T00:47:29.746719Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search","venue":null,"work_id":"630ce5d0-3891-4143-a94a-5b37d32522be","year":2001},"citing_paper":{"arxiv_id":"2606.29582","last_updated":"2026-06-28T19:54:20Z","snapshot_observed_at":"2026-08-06T03:28:56.736718Z","submitted_at":"2026-06-28T19:54:20Z","title":"Bilevel Optimization for Neural Architecture Search","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-30T07:25:50.831689Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2606.29582"},"observation_digest":"sha256:06a0104323ceafc61aaccfe9231340961aaca966b0dcc095720a3b9be00bd0f2","observation_id":"e76f275c-e040-4b42-b49d-71a500940af9","resolution":{"observed_at":"2026-06-30T07:34:21.951169Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-11T12:38:26.980658Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search.arXiv preprint arXiv:2001.00326, 2020","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.04845","last_updated":"2026-07-06T09:15:19Z","snapshot_observed_at":"2026-08-11T21:53:07.859680Z","submitted_at":"2026-07-06T09:15:19Z","title":"HamQASBench: A Hamiltonian-Informed Diagnostic Benchmark for Evaluating Quantum Architecture Search","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-11T12:38:26.980658Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2607.04845"},"observation_digest":"sha256:8f9034f9237760fb24a1e83159068e3ed2526258734dd68efa68c8cd9fa44338","observation_id":"6d547fa0-d3cc-4ad6-8521-0200edc28531","resolution":{"observed_at":"2026-07-11T12:38:26.980658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-07-14T03:27:50.131307Z","title":"Nas-bench-201: Extending the scope of repro- ducible neural architecture search,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.11746","last_updated":"2026-07-13T16:05:16Z","snapshot_observed_at":"2026-08-18T13:00:36.687733Z","submitted_at":"2026-07-13T16:05:16Z","title":"HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T03:27:50.131307Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2607.11746"},"observation_digest":"sha256:0c0ee0b0aae19909a9d15b8c53d45859eecc4bd51b7573d2cff6d0db7ae9827e","observation_id":"9b129056-12c2-40df-a843-d5afb4f5db0f","resolution":{"observed_at":"2026-07-14T03:27:50.131307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-12T00:11:25.753014Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.08344","last_updated":"2026-08-08T21:52:04Z","snapshot_observed_at":"2026-08-17T21:51:53.772422Z","submitted_at":"2026-08-08T21:52:04Z","title":"PRISM: A Predictive Protocol for Permutation Optimization via Landscape Diagnostics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T00:11:25.753014Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2608.08344"},"observation_digest":"sha256:4c547e13270376bf3789112171298f0c00810be5330251ddc126610a19cc2991","observation_id":"9cb1c9a6-5d05-49c2-a79a-e5aaf22c834a","resolution":{"observed_at":"2026-08-12T00:11:25.753014Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.00326","snapshot_observed_at":"2026-08-14T14:31:26.063774Z","title":"”Nas-bench-201: Extending the scope of re- producible neural architecture search.” arXiv preprint arXiv:2001.00326 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.13293","last_updated":"2026-08-13T14:26:08Z","snapshot_observed_at":"2026-08-18T02:14:38.610187Z","submitted_at":"2026-08-13T14:26:08Z","title":"NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:31:26.063774Z"},"links":{"cited_paper":"/paper/2001.00326","citing_paper":"/paper/2608.13293"},"observation_digest":"sha256:2075cb9d69e47dc377f87af0b2fbe7191d7900a569dac1a4fc1705cd63f7dff3","observation_id":"cd249acc-123e-43f6-bd66-b206f154e888","resolution":{"observed_at":"2026-08-14T14:31:26.063774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2001.00326/citation-record","integrity":"/paper/2001.00326/integrity","json":"/paper/2001.00326/citation-record.json","paper":"/paper/2001.00326"},"outbound":[],"paper":{"arxiv_id":"2001.00326","last_updated":"2020-01-15T12:38:55Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T21:53:39.306369Z","submitted_at":"2020-01-02T05:28:26Z","title":"NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2001.00326."}