{"as_of":"2026-08-09T12:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4367d73d26f29a076b8ad595b9e088f594a261355b42a13eedb08fbbe1070eb3","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T20:29:38.808044Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T05:12:22.662176Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T20:31:14.145402Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"cited_work":{"arxiv_id":"2508.10409","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.10409","snapshot_observed_at":"2026-07-01T02:17:16.486242Z","title":"Analogseeker: An open-source foundation language model for analog circuit design","venue":null,"work_id":"03c60a63-4970-46dc-806f-13ec29fc8185","year":2025},"citing_paper":{"arxiv_id":"2604.23195","last_updated":"2026-04-25T08:01:14Z","snapshot_observed_at":"2026-07-06T23:09:29.537323Z","submitted_at":"2026-04-25T08:01:14Z","title":"AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-08T08:39:19.434878Z"},"links":{"cited_paper":"/paper/2508.10409","citing_paper":"/paper/2604.23195"},"observation_digest":"sha256:a4bb5bc181faafe7faa38cd092826a3d7d244c0d94a9e98e5fbdb2658cd6f009","observation_id":"914728cc-1cdb-42df-acfb-c22407b176f9","resolution":{"observed_at":"2026-07-01T02:17:16.486242Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10409","snapshot_observed_at":"2026-08-02T05:12:22.662176Z","title":"Analogseeker: An open-source foundation language model for analog circuit design,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.14165","last_updated":"2026-07-15T05:19:35Z","snapshot_observed_at":"2026-08-02T10:40:59.594748Z","submitted_at":"2026-07-15T05:19:35Z","title":"Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T05:12:22.662176Z"},"links":{"cited_paper":"/paper/2508.10409","citing_paper":"/paper/2607.14165"},"observation_digest":"sha256:2579aadbde3e181e7566579b5a7fb0359a984d894bc53091befc7c8e4ed8c2d7","observation_id":"ff666f85-7eac-415f-a757-ddca6c0ae3f1","resolution":{"observed_at":"2026-08-02T05:12:22.662176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10409","snapshot_observed_at":"2026-08-01T14:27:14.130541Z","title":"AnalogSeeker: An Open-source Foundation Language Model for Analog Circuit Design,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18772","last_updated":"2026-07-21T06:53:09Z","snapshot_observed_at":"2026-08-09T04:18:05.685307Z","submitted_at":"2026-07-21T06:53:09Z","title":"RF-Agent: A Practical Framework for Building Language Agents for RFIC Design","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T14:27:14.130541Z"},"links":{"cited_paper":"/paper/2508.10409","citing_paper":"/paper/2607.18772"},"observation_digest":"sha256:e54d98fa9095344c6b19680e7f4f08b7a286139aa30b101ce39ca606be9763f9","observation_id":"9d59aac9-5308-4db5-8ede-aa969abd87d8","resolution":{"observed_at":"2026-08-01T14:27:14.130541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2508.10409/citation-record","integrity":"/paper/2508.10409/integrity","json":"/paper/2508.10409/citation-record.json","paper":"/paper/2508.10409"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:29:44.002853Z","title":"curveformer++:3d lane detection by curve propagation with temporal curve query and attention, 2025","venue":null,"work_id":"3bfd5d8b-8aee-44fb-a4eb-070fad6b8ecb","year":2025},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:36.442266Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:cfcec95e36695e34269aa6b6a7438d4a7b52b52c3f2728645a31f0831ccbeb49","observation_id":"57a4a391-458a-4314-9160-3ac785e8be73","resolution":{"observed_at":"2026-08-05T20:29:44.117746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:43.747112Z","title":"Persformer: 3d lane detection via perspective transformer and the openlane benchmark","venue":null,"work_id":"74916e91-40d8-42ed-add5-3c129a087b5d","year":2022},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:36.536298Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:f02e0f1444403b8bce82e8841db9c41a245f58d1df8afb109d0de7b66f6989d6","observation_id":"5f7023e5-4fde-4585-b5c6-d127b834c2ff","resolution":{"observed_at":"2026-08-05T20:29:43.855750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:43.422417Z","title":"Pixel to elevation: Learning to predict elevation maps at long range using images for autonomous offroad navigation","venue":null,"work_id":"564c58e7-57d6-456c-9d59-06825c207bcc","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:36.644108Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:69fc30d9719a3e53acaec02ad1551f5a39cfef23969c067f4fd791ee41b9ec70","observation_id":"31279e93-4975-425e-b882-d7a86841363b","resolution":{"observed_at":"2026-08-05T20:29:43.567439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:43.149938Z","title":"Towards cross-view-consistent self-supervised sur- round depth estimation","venue":null,"work_id":"b037015d-5fdf-432d-8c5b-cda0353ef21d","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:36.709898Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:0a2c5e5de9fc92cb3984578ca5abf22f64dd01da5bb8fba8c2bbe66148a2d8ee","observation_id":"e7ed7525-c2a6-431a-9986-ae4ea6dbd235","resolution":{"observed_at":"2026-08-05T20:29:43.293306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:42.914446Z","title":"3d-lanenet: End-to-end 3d multiple lane detection","venue":null,"work_id":"de5392b9-fa47-4393-9f06-4c03d0555d92","year":2019},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:36.779360Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:433fad64dbe27a80ed1e82953930b823a6e1a1804fa8e444762a211865b2dd76","observation_id":"dc5b77e1-3df6-4b74-9a3c-fe317f5cce1b","resolution":{"observed_at":"2026-08-05T20:29:43.037990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:42.680227Z","title":"Bros- tow","venue":null,"work_id":"32f8e9f2-33eb-475d-86c6-02c77d6bb886","year":2017},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:36.851469Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:be638868b2bb1cfc8d526c1bade169b833426c60e1e1cd63b218c1e89a93f864","observation_id":"2d0e44e4-ecef-4a8a-95af-4d9bb220f6b8","resolution":{"observed_at":"2026-08-05T20:29:42.759669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:42.623819Z","title":null,"venue":null,"work_id":"c30af16e-58ff-4997-9d08-0dbb91db5a9f","year":null},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:36.932612Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:4d1f3af2194e6b9f10edf37b5e2075e3e848e94a5d43ccc01156007df7137ddd","observation_id":"d464df2e-a7be-4394-a2db-31546d0d3259","resolution":{"observed_at":"2026-08-05T20:29:42.650258Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:42.328997Z","title":"Gen-lanenet: A generalized and scalable approach for 3d lane detection","venue":null,"work_id":"4ee64a5c-a7d0-46c1-ac5a-f33922d34e6b","year":2020},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.027514Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:e5a4741b94b1138721e1f14046bfb2c5b51f60154f5b5952bbe4f1af4d69650b","observation_id":"9a9579d1-aea4-468a-b249-a51fede2440d","resolution":{"observed_at":"2026-08-05T20:29:42.468096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:37.097550Z","title":"Deep residual learning for image recognition, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.097550Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:e7f484daf3db7f06d3d880d5832d4517cef45d1a1ee8ca8b38ea8f960053df8e","observation_id":"61934565-ef14-479b-878d-f88c7e35cd18","resolution":{"observed_at":"2026-08-05T20:29:37.097550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02121","last_updated":"2023-11-03T06:43:32Z","snapshot_observed_at":"2026-07-06T16:42:48.073439Z","submitted_at":"2023-11-03T06:43:32Z","title":"Enhancing Monocular Height Estimation from Aerial Images with Street-view Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02121","snapshot_observed_at":"2026-08-05T20:29:37.198252Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.198252Z"},"links":{"cited_paper":"/paper/2311.02121","citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:d8e874c40358bd32a04ce6b1392b9c62f199ec19fa695baad71ed9e5bf38a399","observation_id":"3245773a-a843-46a2-8242-19f0a15f234f","resolution":{"observed_at":"2026-08-05T20:29:37.198252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.02095","last_updated":"2024-11-27T07:59:25Z","snapshot_observed_at":"2026-08-07T23:17:31.541423Z","submitted_at":"2024-09-03T17:52:03Z","title":"DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.02095","snapshot_observed_at":"2026-08-05T20:29:37.287177Z","title":"Depthcrafter: Generating consistent long depth sequences for open-world videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.287177Z"},"links":{"cited_paper":"/paper/2409.02095","citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:8a19af6baf0247eedb7ae688b156b738a87a320b0d09a18f4edea6914f8e0dcd","observation_id":"449a0a52-d00a-4d21-a8e6-30259f73c7da","resolution":{"observed_at":"2026-08-05T20:29:37.287177Z","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":"2026-08-05T20:29:42.034570Z","title":"Anchor3dlane: Learning to regress 3d anchors for monocular 3d lane detection","venue":null,"work_id":"2b262460-4fb7-4f0f-ad83-61a01d96c8a1","year":2023},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.355683Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:0bfa199e349a0fcb8d0181f0ffd6689cd0fced2ee4424f3d45463ba284e36fe4","observation_id":"a3c6bdab-46d8-49dc-82b4-6bdf45c79132","resolution":{"observed_at":"2026-08-05T20:29:42.182692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:41.705055Z","title":"Video depth without video models, 2024","venue":null,"work_id":"b28d9f38-2aa6-40f9-9a9f-ec0dccf6a5c1","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.427332Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:4817a89bb7a2abc175957c7d8c82431f9180eee30890a77d9024c8e9009bc918","observation_id":"64def8e3-1572-4e99-83b2-d31c25e196fb","resolution":{"observed_at":"2026-08-05T20:29:41.855896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:41.396808Z","title":"Repvf: A unified vector fields represen- tation for multi-task 3d perception","venue":null,"work_id":"8903ac5b-0d19-421d-b91e-dc9ab473a18f","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.518533Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:3dd0b82c8e94d05b7d4ab587bde259a42a21cca43cae3c3f28f14dc51d296183","observation_id":"b1f8486a-5211-42d7-b7c5-0a9a34f1b4c7","resolution":{"observed_at":"2026-08-05T20:29:41.480906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:41.320269Z","title":"Height estimation from single aerial images using a deep ordinal regression net- work","venue":null,"work_id":"b534c82c-c832-4843-8bbf-7845f67c152d","year":null},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.576429Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:fef6d08d2e92f6369a55991c4f597b36b2796fb667d832d504d5fd6cef6cdf79","observation_id":"f66897f6-93e9-4708-91a1-2b8895927fc0","resolution":{"observed_at":"2026-08-05T20:29:41.375670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:41.172171Z","title":"Petrv2: A unified framework for 3d perception from multi-camera images","venue":null,"work_id":"f99dbe9b-2986-4a0a-b6ab-70d547141f69","year":2023},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.635651Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:f1d30af4a58e20460876707eebef674ec14abafff006c5b603ac05fac3185031","observation_id":"d84d5940-38ee-49b1-8c0a-414a03c6d516","resolution":{"observed_at":"2026-08-05T20:29:41.247156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:41.002724Z","title":"Latr: 3d lane detection from monocular images with transformer","venue":null,"work_id":"74d62611-fa27-4ecd-b0ed-87d4176eb8ed","year":2023},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.709036Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:164f92adb0a3b8f422ecb7ce71671defdf82c84c87e7767abb3faa0836e67861","observation_id":"b2d3afbe-11fa-4126-a4ce-e790b2c320c5","resolution":{"observed_at":"2026-08-05T20:29:41.075923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:40.796854Z","title":"Gonzalez Bello, Byeongjun Kwon, and Minsu Kim","venue":null,"work_id":"751e54cf-53f6-4cce-8ba3-7637779f9397","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.827717Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:d2d0996f6d11d20d7d61d76ad673d22dc1a187e626ba43238b4bae029d45cf38","observation_id":"97d568bb-fb5f-44e3-ab41-ce54b8836bc6","resolution":{"observed_at":"2026-08-05T20:29:40.905340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.10249","last_updated":"2018-02-28T03:32:36Z","snapshot_observed_at":"2026-07-06T06:25:47.627302Z","submitted_at":"2018-02-28T03:32:36Z","title":"IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network","version":1},"cited_work":{"arxiv_id":"1802.10249","doi":null,"metadata_source":"pith","pith_arxiv_id":"1802.10249","snapshot_observed_at":"2026-08-05T20:29:39.506572Z","title":"IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network","venue":"cs.CV","work_id":"67df0ab1-d810-4211-be00-ecbeeee31f17","year":2018},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.894514Z"},"links":{"cited_paper":"/paper/1802.10249","citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:c967a701785a3266ba71a813af778bcc5d21398a19f6691121cad9edf13cc681","observation_id":"4b516c6d-f254-4e97-9e90-30c7025d7520","resolution":{"observed_at":"2026-08-05T20:29:39.605192Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:40.652581Z","title":"Heightlane: Bev heightmap guided 3d lane detection","venue":null,"work_id":"c9ab7c12-15ab-4d3e-9d31-44f5c96aeaca","year":2025},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:37.983109Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:9a4c6639b38a5b99b5d5aa7d121a31066a99c7231f8fa1cc915f31e6c7d05bcc","observation_id":"90fe3a0e-bb0c-47c5-b939-8688647d7f4e","resolution":{"observed_at":"2026-08-05T20:29:40.713402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:40.487133Z","title":"Lanecpp: Continuous 3d lane detection using physical pri- ors","venue":null,"work_id":"2bd34487-cdf5-493d-aa9d-edcad73a3e25","year":null},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.048225Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:424dbc6c56d4a4aef56a66acd1d05d9219bc19a3d79c88823542218ec323cde3","observation_id":"5a4f2177-9a9e-4dc1-9e95-3499430499e0","resolution":{"observed_at":"2026-08-05T20:29:40.568009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.01408","last_updated":"2024-11-03T02:35:17Z","snapshot_observed_at":"2026-08-03T06:34:02.659354Z","submitted_at":"2024-11-03T02:35:17Z","title":"HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning","version":1},"cited_work":{"arxiv_id":"2411.01408","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.01408","snapshot_observed_at":"2026-08-05T20:29:39.320267Z","title":"HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning","venue":"cs.CV","work_id":"bc82d74c-7743-46a9-bcc9-05089b04d59a","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.110726Z"},"links":{"cited_paper":"/paper/2411.01408","citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:c72e8d0b8f52e8900acf34525060627ee6401576edb7f42d1fe107e77c9825d6","observation_id":"fd1827c9-438d-43d3-af80-83a1036ce7f5","resolution":{"observed_at":"2026-08-05T20:29:39.398721Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:40.339597Z","title":"Bev-lanedet: An efficient 3d lane detection based on virtual camera via key-points","venue":null,"work_id":"a515ff37-8f9a-4048-8c1d-1dc895ea008a","year":2023},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.215537Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:af80549bc76e1bac1605889ea6b1142919cbfe6f7ee7ec40336d703f84284236","observation_id":"3066f99e-e4c2-41bb-bc66-aa428116e8c8","resolution":{"observed_at":"2026-08-05T20:29:40.412722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:40.203152Z","title":null,"venue":null,"work_id":"f48cdd6a-0f2d-417a-8795-81d0bdbcc7cf","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.340282Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:db27925fefa5a8139363680c2057b70dce47133022053aca69d763e50f3cc79d","observation_id":"8e2fe95d-ed02-4946-ab2d-cd7883ff0246","resolution":{"observed_at":"2026-08-05T20:29:40.251476Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:40.033185Z","title":"Real-time neural dense elevation mapping for urban terrain with uncertainty estimations","venue":null,"work_id":"0a7c9626-b0cb-4239-bf28-b61dc2dc3b4d","year":2022},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.428484Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:e1aad25d76d7cee20a2ba6aed203a5437396ca4b9421038e786940badc6b746f","observation_id":"1ab42485-42bf-4e8c-afa5-bf11af2d1347","resolution":{"observed_at":"2026-08-05T20:29:40.111569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:39.886425Z","title":"Exploiting temporal consistency for real-time video depth estimation","venue":null,"work_id":"66b65f6e-954f-48e2-a6d8-3420d80617ed","year":2019},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.517945Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:4d920fde982e444cd44335b9f44c5d8dc04d181effaee3602ebf4f26166e5015","observation_id":"a00abe24-8c77-4b78-a36d-ee50c9bbc115","resolution":{"observed_at":"2026-08-05T20:29:39.942568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01049","last_updated":"2025-01-02T04:18:40Z","snapshot_observed_at":"2026-07-06T20:15:36.280948Z","submitted_at":"2025-01-02T04:18:40Z","title":"TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo","version":1},"cited_work":{"arxiv_id":"2501.01049","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.01049","snapshot_observed_at":"2026-08-05T20:29:39.151472Z","title":"TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo","venue":"cs.CV","work_id":"5f3c41eb-a91d-480c-b058-08ed026865bc","year":2025},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.648202Z"},"links":{"cited_paper":"/paper/2501.01049","citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:7ea8b8edecc12e7a6694157dd725539a2e186139a3a8f207abbeccb605f922b4","observation_id":"ce278270-a016-4ed3-b491-31b50f33ea11","resolution":{"observed_at":"2026-08-05T20:29:39.219610Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2504.20525","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T20:29:39.005910Z","title":"Geometry-aware temporal aggregation network for monocular 3d lane detection","venue":null,"work_id":"629975dd-a7ce-4916-8adb-ed1b0585dfef","year":2025},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.725063Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:6b31ff5f74cc0928697a180f8266fff58794f5b3929f578335396210b614f1a7","observation_id":"4b7b26f1-4980-460b-b158-01ca5fb3f6a7","resolution":{"observed_at":"2026-08-05T20:29:39.060008Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-05T20:29:39.744440Z","title":"Pvalane: prior-guided 3d lane detection with view- agnostic feature alignment","venue":null,"work_id":"95cb3c22-5fdb-4320-9026-2d0560dacc6b","year":2024},"citing_paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:38.808044Z"},"links":{"citing_paper":"/paper/2508.10409"},"observation_digest":"sha256:52ed5041582a141c0a9656f3c6e53497cb64253b855127b68deaa4e0b49dd932","observation_id":"956500af-c1e6-4ab9-b3e5-77960b81d6f1","resolution":{"observed_at":"2026-08-05T20:29:39.825349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.10409","last_updated":"2026-06-30T08:41:21Z","latest_version":3,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-08T22:52:23.826233Z","submitted_at":"2025-08-14T07:32:07Z","title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":4,"verified_fuzzy":20},"total_outbound_references":29},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 3 inbound Pith citation observations for arXiv:2508.10409."}