{"as_of":"2026-08-16T12:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b3d2d23d5c30480e46a78b40a2fb9a4832f4ce0fde02d79bbc0ad02c13d405fc","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:02:26.735754Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.06769/citation-record","integrity":"/paper/1908.06769/integrity","json":"/paper/1908.06769/citation-record.json","paper":"/paper/1908.06769"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:02:27.330768Z","title":"Learning tasks from a single demonstration,","venue":null,"work_id":"b5494f1d-3bd9-404c-9fe1-96448a4d59a6","year":1997},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.561953Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:fc87552ad724ced4c89b3deb616f4b8bc72903b21215939477d0dda67c8d20b0","observation_id":"8e86063b-9c48-4456-9ec8-ff31b7583099","resolution":{"observed_at":"2026-08-14T13:02:27.336238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.313703Z","title":"One-Shot Imitation Learning,","venue":null,"work_id":"36e2a24b-789e-4478-8ef8-f3e2d6ad7870","year":2017},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.582235Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:55914ae79dc30ac8cff4d2442947015395ba8d0e812b04f6ae7f3987e1a44d35","observation_id":"97641a00-5edd-4af3-a32d-1854cade4c05","resolution":{"observed_at":"2026-08-14T13:02:27.318811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.296205Z","title":"One-shot visual imitation learning via meta-learning,","venue":null,"work_id":"e23d2896-9eac-4415-bc2e-377a450bd542","year":2017},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.587897Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:ef7ea13396d7821279d3bcff6db14c8247a9357e68ad2f7e1b086c6eca516eae","observation_id":"1c66d7db-8920-4573-a799-f68657503856","resolution":{"observed_at":"2026-08-14T13:02:27.301737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.280723Z","title":"Neural task graphs: Generalizing to unseen tasks from a single video demonstration,","venue":null,"work_id":"160262d0-3f41-4362-8e39-c54b2f9f61f1","year":2019},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.594017Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:a5eaf54f3c3dba5d012778c25e684b2ace92727c8df44ba958889155410142ad","observation_id":"c68e4cd0-00b0-47c8-b136-9a52ecca532c","resolution":{"observed_at":"2026-08-14T13:02:27.285359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.265216Z","title":"Zero-shot visual imitation,","venue":null,"work_id":"56fcca0a-a516-41eb-8487-65a15f5cd74e","year":2018},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.599573Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:92564f1db9472d75d903d9670f62b37e41b62172fd21397bd30d6e179f24003d","observation_id":"8ea61ab4-332e-4cbf-a71f-ad28b9e752b7","resolution":{"observed_at":"2026-08-14T13:02:27.269595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.249799Z","title":"Neural task programming: Learning to generalize across hierarchical tasks,","venue":null,"work_id":"dd5c7786-ec9b-4014-ae3d-f2aca0a75bb5","year":2018},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.604740Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:8a0ced0d69ecc396169c313bf1bf2170dc5217bd8c899edeef4c0fc287300b28","observation_id":"69ffc648-e6f5-41f4-944a-2fac059e467f","resolution":{"observed_at":"2026-08-14T13:02:27.254732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.231471Z","title":"One-shot imitation from observing humans via domain-adaptive meta-learning,","venue":null,"work_id":"b3e870ee-7260-4d03-95df-e81c59bc4fb2","year":2018},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.610060Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:b5e79f696d4b66d1eed5c7088a876c0f9ea32d512598e809a82cfc70787f7704","observation_id":"5600ea0e-8329-40d9-b8b7-efcaba8709a6","resolution":{"observed_at":"2026-08-14T13:02:27.237673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.213050Z","title":"Matching networks for one shot learning,","venue":null,"work_id":"50889f16-d72a-42d6-b26c-93ce1066db08","year":2016},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.614745Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:d35b6f18afec88d6061c8d051682d16c52e6a0b396f99a2e2dd760b9f0305fe5","observation_id":"164dc8e6-248b-42f0-8f57-f0507cd685e9","resolution":{"observed_at":"2026-08-14T13:02:27.218812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.189287Z","title":"Ghallab, D","venue":null,"work_id":"703d6079-f757-42cc-8da3-dc75ad673d8e","year":2004},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.618983Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:12d173305905ae56d17a87a708c40874c7f875efad7080ed89a28580d2cc270c","observation_id":"f0b556db-d811-4f90-96b7-c5de9ea17efc","resolution":{"observed_at":"2026-08-14T13:02:27.194817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.173295Z","title":"Symbol acquisi- tion for probabilistic high-level planning,","venue":null,"work_id":"c6a16b81-39fa-432d-a281-76023c74ddc0","year":2015},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.625238Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:216c2d1a17bbc938da0b34900d212c5c62090d3c684fd5af219c763c1943d344","observation_id":"61365832-2648-4aba-aa7d-36d0a2774e94","resolution":{"observed_at":"2026-08-14T13:02:27.178268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.11244","last_updated":"2019-04-26T15:01:09Z","snapshot_observed_at":"2026-08-16T12:09:23.277274Z","submitted_at":"2018-06-29T01:11:56Z","title":"One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video","version":3},"cited_work":{"arxiv_id":"1806.11244","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.11244","snapshot_observed_at":"2026-08-14T13:02:26.817971Z","title":"One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video","venue":"cs.LG","work_id":"768c715b-81ef-47fd-8e9f-53825624dc1c","year":2018},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.630715Z"},"links":{"cited_paper":"/paper/1806.11244","citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:baa9892931d82dbb6837f64cf044a1afb8d0be752e08e97eb3c5fb7019e05f89","observation_id":"03f4f449-c7ac-449d-99fd-6944565dca2c","resolution":{"observed_at":"2026-08-14T13:02:26.824069Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.156516Z","title":"Learning grounded finite-state representations from unstructured demonstrations,","venue":null,"work_id":"dc95a07a-d06c-429a-9187-5f290e24319f","year":2015},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.636669Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:9add0357c8768ccba437438603f6638675760b36aa7bf29dbe6f3774a3e27f85","observation_id":"56aa5f74-ca39-4d0f-b8aa-b76ff86baa8c","resolution":{"observed_at":"2026-08-14T13:02:27.161605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.01840","last_updated":"2018-08-10T09:07:40Z","snapshot_observed_at":"2026-08-14T19:40:09.190178Z","submitted_at":"2018-03-02T19:26:16Z","title":"TACO: Learning Task Decomposition via Temporal Alignment for Control","version":2},"cited_work":{"arxiv_id":"1803.01840","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.01840","snapshot_observed_at":"2026-08-14T13:02:26.792188Z","title":"TACO: Learning Task Decomposition via Temporal Alignment for Control","venue":"cs.LG","work_id":"40d0c36e-ae03-4216-b9d7-f87a6d294dce","year":2018},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.641259Z"},"links":{"cited_paper":"/paper/1803.01840","citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:ca02ddfc1b59ab58d54f8280e7af5d0c3300625f1415aab5ea18a80704e019dd","observation_id":"a0cde2f5-f4a6-46f8-b08c-037b9e51a4c7","resolution":{"observed_at":"2026-08-14T13:02:26.799779Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.137404Z","title":"Algorithms for sequential decision making,","venue":null,"work_id":"08d98fc3-877d-4b80-b8ce-d1699433d8c3","year":1996},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.645533Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:dda0880f07b7cb7074f776f9a81766ba8ff69b2f41a38351ee11e17403a2b4f5","observation_id":"63da458f-6b78-4cb8-9d4c-147c3ae00b1f","resolution":{"observed_at":"2026-08-14T13:02:27.144002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.649517Z","title":"Planning and acting in partially observable stochastic domains,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.649517Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:4cb2ab811a631b2e10973258fb7035d4633705c82b2720c337524811be9612c4","observation_id":"eb36f018-e975-4d8c-8594-111ef54e0163","resolution":{"observed_at":"2026-08-14T13:02:26.649517Z","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":"2026-08-14T13:02:27.108473Z","title":"Online planning algorithms for pomdps,","venue":null,"work_id":"117638fd-5f57-4682-8769-5e42aa314c78","year":2008},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.654178Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:25b112ff240880a993bc80269b55850a38146ee7f390d856de64b457cbb81837","observation_id":"5a248afe-555e-4acf-b741-2da56b5b97d5","resolution":{"observed_at":"2026-08-14T13:02:27.113406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.092852Z","title":"Ff-replan: A baseline for probabilistic planning.,","venue":null,"work_id":"218cf029-906a-4f8e-b13b-902edf2eb72c","year":2007},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.658779Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:d22a00f3b2b23aac65842db7068f3a73f5387b34c5cd41f3dd49b02c0cd1c4bd","observation_id":"92de1cd8-6af3-4eb4-906c-3a10dbee621c","resolution":{"observed_at":"2026-08-14T13:02:27.098082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.076893Z","title":"Probabilistic planning via determinization in hindsight.,","venue":null,"work_id":"2892b8d4-ad91-47e8-ab65-0c6b85f1cb33","year":2008},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.663443Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:a4513fce0347ead3bd2622e77932a2b8bd13bb8668d826c429f7ecf0acdac9b8","observation_id":"1bbfe474-c94b-41f7-96e1-98e7162f010c","resolution":{"observed_at":"2026-08-14T13:02:27.082070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.062252Z","title":"Probabilistic planning vs. replanning,","venue":null,"work_id":"c8c6c5dd-137e-4037-ac12-f22ba83197ce","year":2007},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.668330Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:e14169898e5003ccfd5a2e367b65b4a4f752ec4e65e1e158982ad2d4f25e1fee","observation_id":"5638d8a5-d16a-4db1-b5c5-8544d3929788","resolution":{"observed_at":"2026-08-14T13:02:27.066694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.046221Z","title":"Learning grounded rela- tional symbols from continuous data for abstract reasoning,","venue":null,"work_id":"29ebda37-2259-4bc7-a26a-b3ecd589a553","year":2013},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.672390Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:8da4c0d5b24151f29b5a43aa1b8cf62845ead3e0b3b366a6900ba81ee5a962ef","observation_id":"de478d78-df58-4237-acb3-964c3f5ba512","resolution":{"observed_at":"2026-08-14T13:02:27.051071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.030259Z","title":"A hybrid architecture for hierarchical reinforcement learning,","venue":null,"work_id":"495e0bc8-549e-4e7d-8c5c-1bd1fc90a9eb","year":2000},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.677075Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:1c863b399c79130da252b1790c725290ca0e92e3927cf15897742683c843cd23","observation_id":"85b07112-b747-409a-bfa4-1ea80eb59c78","resolution":{"observed_at":"2026-08-14T13:02:27.035628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:27.013788Z","title":"Learning quickly to plan quickly using modular meta-learning,","venue":null,"work_id":"bca89d46-2065-4e78-968b-4f697d698c4c","year":2019},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.681371Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:c2d244ea9a268748a5145a5231171d3ed477692f00c5adbfcb8fe3b25ea0a0fb","observation_id":"564161bc-33ad-447a-a8ff-9b538dfda68c","resolution":{"observed_at":"2026-08-14T13:02:27.019492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.997689Z","title":"Learning heuristic search via imitation,","venue":null,"work_id":"816bb814-9717-4da8-ae0b-2d71f7983504","year":2017},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.687209Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:1d8a8b006825d0f76b991c0d2cc2857dc3095ea2a4ce58ba1e2e223be1511e75","observation_id":"9713fd84-a2b2-4b2d-9592-9e2ee3115c08","resolution":{"observed_at":"2026-08-14T13:02:27.003094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.03400","last_updated":"2017-07-18T16:45:29Z","snapshot_observed_at":"2026-08-15T14:31:49.004808Z","submitted_at":"2017-03-09T18:58:03Z","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.03400","snapshot_observed_at":"2026-08-14T13:02:26.692708Z","title":"Model-Agnostic Meta- Learning for Fast Adaptation of Deep Networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.692708Z"},"links":{"cited_paper":"/paper/1703.03400","citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:f0ea73ddb065c568f0103dd96f721f7ac5cdeb9c184542f87d56d5fdd0569ba2","observation_id":"1e265f02-a507-4f21-a4f3-2bfec68d28cd","resolution":{"observed_at":"2026-08-14T13:02:26.692708Z","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":"2026-08-14T13:02:26.980448Z","title":"Geometric backtracking for combined task and motion planning in robotic systems,","venue":null,"work_id":"c11f416c-b315-4279-b62d-56ac82bbde1e","year":2017},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.697840Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:8d8ea728beff0623566fd664caa53ed45d6f7776ac935d1bfdbcc25fac9e966c","observation_id":"d802bfed-230e-46d0-b1f5-f4ccf6ab0a08","resolution":{"observed_at":"2026-08-14T13:02:26.985603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.957614Z","title":"Asymov: A planner that deals with intricate symbolic and geometric problems,","venue":null,"work_id":"1644da22-a4a1-45fb-b99a-9d816cb0d08a","year":2005},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.701949Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:39e9dd6da75f0b10ea48a31686bddbb709064a7027b3832e2950f1a70b819539","observation_id":"e66af3d2-481c-4b19-87cf-cf68f936183a","resolution":{"observed_at":"2026-08-14T13:02:26.963700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.940394Z","title":"Hierarchical planning in the now,","venue":null,"work_id":"4ee9fdca-c76b-4bd0-bf46-74acc43f48bc","year":2010},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.706366Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:92dda5dae5c25f0ad1ef99fc0224dd0bf2e8265160866405ff71635ddeb3e79d","observation_id":"fd020300-8eb0-46e3-9b5e-bdee7126419d","resolution":{"observed_at":"2026-08-14T13:02:26.945632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.924204Z","title":"From low-level tra- jectory demonstrations to symbolic actions for planning,","venue":null,"work_id":"b7d01059-3036-41e3-9750-de2e091f9bbe","year":2012},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.711098Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:f5994bde3af1f5473274a27defa5d7e4a538a2fb48c0488ba66add842bec4204","observation_id":"cccfe399-f74f-4f12-ad1b-a58e6cfd1ecf","resolution":{"observed_at":"2026-08-14T13:02:26.930065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.908173Z","title":"Manipulation planning using learned symbolic state abstractions,","venue":null,"work_id":"47bc89c5-ac43-4ad4-8962-71c9d1ff3d68","year":2014},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.717254Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:4dcdf3ae3e62f40770be3d1904d1e86d4eb1573372497d31610a7d08c69ac24d","observation_id":"8e245ea0-5056-4efb-98b7-8adc501130a3","resolution":{"observed_at":"2026-08-14T13:02:26.913226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.889813Z","title":"Learning spatial relations from functional simulation,","venue":null,"work_id":"636b1059-cc25-4eaa-8ce1-61943beb09b6","year":2011},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.722134Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:62b0035160ff9b233096deeca86c7f144e4ba32404bb670620a889d6d46389ad","observation_id":"f53071e1-a5a1-40df-94be-bc5e35985eff","resolution":{"observed_at":"2026-08-14T13:02:26.896325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.871285Z","title":"Neural module networks,","venue":null,"work_id":"299bb8bc-df5e-4fd8-9ce4-bb1b38400f49","year":2016},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.727079Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:24e9940aee1710aac17bd5745735bec2eb049f9ebd04767a6214b48fe166a621","observation_id":"801adf60-f6f9-4773-8aff-fc3f3883b7d2","resolution":{"observed_at":"2026-08-14T13:02:26.876587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.853494Z","title":"Nervenet: Learning structured policy with graph neural networks,","venue":null,"work_id":"c889f333-e228-44ff-8997-d23176f7a471","year":2018},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.731183Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:b954c9acf16d079579181cbc85db4c0ba9aabac0434cf6e49be29a8b4d7d665e","observation_id":"0a1b6b04-4156-43d0-b8ae-59d325f1a299","resolution":{"observed_at":"2026-08-14T13:02:26.858819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:02:26.836173Z","title":"Markov logic networks,","venue":null,"work_id":"cde27712-fdd4-44bb-a623-44c1e6d92c86","year":2006},"citing_paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T13:02:26.735754Z"},"links":{"citing_paper":"/paper/1908.06769"},"observation_digest":"sha256:1434e6dee5a58bb6e857090322605c0678bd84dc4e4638a5ba57f44bf1bfd904","observation_id":"acf37e3a-9d30-4b95-8cc5-575899b2b253","resolution":{"observed_at":"2026-08-14T13:02:26.840833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.06769","last_updated":"2019-11-05T02:58:19Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-15T14:32:35.869451Z","submitted_at":"2019-08-16T16:28:12Z","title":"Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":2,"verified_fuzzy":29},"total_outbound_references":33},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.06769."}