{"as_of":"2026-08-21T09:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:77bc988111e1065d1fedbc7d8fe553db34601bcf1c13dc1c1b36a2beea6a2a89","coverage":[{"denominator":91,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":91,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:32:08.180370Z","state":"measured"},{"denominator":91,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":91,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2501.01367/citation-record","integrity":"/paper/2501.01367/integrity","json":"/paper/2501.01367/citation-record.json","paper":"/paper/2501.01367"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.809507Z","title":"Algorithms for inverse reinforcement learn- ing","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.809507Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:9c622f04a153b47bbaae092b43073de447a87c4aaeeef6273cfbe7cfd20b14dc","observation_id":"5b377e51-ed7d-43fe-9a72-a608058facb1","resolution":{"observed_at":"2026-08-10T22:32:07.809507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.814544Z","title":"Apprenticeship learning via inverse rein- forcement learning,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.814544Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:8753caeeb858a56edbb28c61cbde36bf2bc67d9f3df8d69c3763e19d43182e36","observation_id":"d71c570e-b55b-4bd5-a860-c9fd55e7830f","resolution":{"observed_at":"2026-08-10T22:32:07.814544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.818877Z","title":"Aligning human and robot representations,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.818877Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:2d69126df730abfc870bfa08984466c16f505e4aac118d718568ecdf3e9c56c8","observation_id":"98bf758a-620f-4386-8101-380cfc71ea06","resolution":{"observed_at":"2026-08-10T22:32:07.818877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.823242Z","title":"Sirl: Similarity-based implicit representation learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.823242Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:1b4f10183de5f6dc7f8a761040342e234a2049d05aff6e188b4c74d647ba6d13","observation_id":"2c17f877-42f7-4bf5-b910-007b72eb144e","resolution":{"observed_at":"2026-08-10T22:32:07.823242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.05091","last_updated":"2021-06-09T14:10:50Z","snapshot_observed_at":"2026-08-18T07:40:57.268095Z","submitted_at":"2021-06-09T14:10:50Z","title":"PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.05091","snapshot_observed_at":"2026-08-10T22:32:07.827427Z","title":"Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre- training,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.827427Z"},"links":{"cited_paper":"/paper/2106.05091","citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:e4cbbcfe2dd55352dd2e1259cf94a4c0bd4af221506e424e8826b78f47f34ba3","observation_id":"1b7aed65-bde0-4ff5-a0e4-8cd86c6104e6","resolution":{"observed_at":"2026-08-10T22:32:07.827427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.832316Z","title":"Representation matters: Offline pretraining for sequential decision making,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.832316Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:304ce6f3ff8353de48736d726179426a01d07e40431174d8fb77b8286711ce5a","observation_id":"3e214beb-4128-4326-a64d-f925dad35541","resolution":{"observed_at":"2026-08-10T22:32:07.832316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.837056Z","title":"The rosid tool: Empowering users to design multimodal signals for human- robot collaboration,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.837056Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:73c42da7136cc0e41cce50e666bf03900dae2d640ee12388f0068ac7e9de3dec","observation_id":"d3ed6c9f-6848-4adc-bd49-3038afbbca87","resolution":{"observed_at":"2026-08-10T22:32:07.837056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.04365","last_updated":"2019-10-10T04:52:46Z","snapshot_observed_at":"2026-08-21T08:44:15.828185Z","submitted_at":"2019-10-10T04:52:46Z","title":"Asking Easy Questions: A User-Friendly Approach to Active Reward Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.04365","snapshot_observed_at":"2026-08-10T22:32:07.841345Z","title":"Asking easy questions: A user-friendly approach to active reward learning,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.841345Z"},"links":{"cited_paper":"/paper/1910.04365","citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:ea9e7d0ca6bffc57c9d03b5a889edd31737cbb4617113fc9ff0709adda6138d7","observation_id":"23ae3f05-894b-4ea6-879a-d60614bcb4a2","resolution":{"observed_at":"2026-08-10T22:32:07.841345Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.845765Z","title":"Active preference- based learning of reward functions,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.845765Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:46995a13e172fe6361f055f39ddc74c531b1e1d3c1e5d9a1a000862a1ffc3b50","observation_id":"0dac8917-c798-4f9f-bd6d-9822f501dbf6","resolution":{"observed_at":"2026-08-10T22:32:07.845765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.849955Z","title":"Preference-driven texture modeling through interactive generation and search,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.849955Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:8d6bb84529cbd38c3cf759a989f872cb58a87e096c331d2b3bac09fc9e4ccf44","observation_id":"c563ae19-c69e-49b9-becf-038e4f7e0e02","resolution":{"observed_at":"2026-08-10T22:32:07.849955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.854028Z","title":"Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.854028Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:86ba6714e176df4cfdb097417cb17524a3a6ce8f7b6dfee54f790968d8f75487","observation_id":"85509394-2453-4d19-990b-a483cd832eb8","resolution":{"observed_at":"2026-08-10T22:32:07.854028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.858315Z","title":"Learning multimodal rewards from rankings,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.858315Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:692100e964217e2db97160c8756bab0b3d1a3bd88ee83bdaaf4a6608ce766ce8","observation_id":"eecb5818-81ce-4ea0-a5de-d564f1e57021","resolution":{"observed_at":"2026-08-10T22:32:07.858315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.862441Z","title":"Learning from suboptimal demonstration via self-supervised reward regression,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.862441Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:77987416d7af38c8eaef4c31f02e8e4320a40471a8eb1dcb2c1b9ad3f888e87d","observation_id":"f2436f87-2620-4902-b459-9c8a5006ae79","resolution":{"observed_at":"2026-08-10T22:32:07.862441Z","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":"2026-08-10T22:32:09.215597Z","title":"An interactive framework for learning continuous actions policies based on corrective feedback,","venue":null,"work_id":"9fbf93d6-e6bf-488c-bc43-fd7cf8cff1f2","year":2019},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.866304Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:ef513015eb49363b68c644f2dbf8c85686abb52c65c2718f2b457bcc646bce66","observation_id":"309a8c03-4d1f-4541-bb1f-a4361e5481db","resolution":{"observed_at":"2026-08-10T22:32:09.220069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.201910Z","title":"Learning from physical human corrections, one feature at a time,","venue":null,"work_id":"71003880-1940-429a-8c89-ff6dafc9adf2","year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.870348Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:f4737e944d06d67f618362ec3f242b0d78051ac15297440fba6bc0f66d174fb5","observation_id":"7031a207-28d0-47f1-9aa9-8684055167da","resolution":{"observed_at":"2026-08-10T22:32:09.206540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:07.874430Z","title":"Learning human objectives from sequences of physical corrections,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.874430Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:9c7a413b85d7613c4f69be802b7d122c625c64d724f4385fb1f3ee53955a04cd","observation_id":"9abf61e2-4fe9-4eef-9f0f-7d64f1532cff","resolution":{"observed_at":"2026-08-10T22:32:07.874430Z","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":"2026-08-10T22:32:09.178335Z","title":"Active reward learning from critiques,","venue":null,"work_id":"957bb371-ea3c-4b8b-be4a-94486bb62268","year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.878415Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:f898672d4972738a16eaa064dcf22d383ba7a9ec17c950745ef19a3297f8d9ad","observation_id":"e607aa7f-0526-4b2e-a134-1df11d78f942","resolution":{"observed_at":"2026-08-10T22:32:09.182864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.164769Z","title":"Including uncertainty when learning from human corrections,","venue":null,"work_id":"4c8196e6-136d-4bfc-97bf-9e077172a0cc","year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.882428Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:270d7ff5686f6f2dd8e22b5e91e97d185ae443cb4d00dfe79b5f9e1d71e7e003","observation_id":"636089ef-1b0f-47a6-9518-857385e4498a","resolution":{"observed_at":"2026-08-10T22:32:09.169297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.151487Z","title":"Asking the right questions: Facilitating semantic constraint specification for robot skill learning and repair,","venue":null,"work_id":"302dc659-f60c-45b8-9902-2fdc2e9022f1","year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.886456Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:879fe8be03629b0f89242442da3915efc407f82d2e0cf4f0a84ca86c38321df5","observation_id":"1a7bb38e-4c52-4495-ba78-f418fccfba82","resolution":{"observed_at":"2026-08-10T22:32:09.155777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.138169Z","title":"Planning with large language models via corrective re-prompting,","venue":null,"work_id":"213a1b9b-1325-4ddb-9856-d223fcea4ad2","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.890437Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:51024d9d9a3a1c40be327ca3bab22aa4536490fea789eb38d7da067e7e91693b","observation_id":"1319cb95-ce25-4608-be77-449ee1213255","resolution":{"observed_at":"2026-08-10T22:32:09.142458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.124692Z","title":"Grounding complex natural language commands for temporal tasks in unseen environments,","venue":null,"work_id":"2c386ddc-0a3a-4201-8f72-fcf47417b9d2","year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.894359Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:2133eb1b14e822d2797c2312e5e6cc34bf816670367932a594391d488966a6bc","observation_id":"19fc7482-4773-4b14-ad73-bd3a47551336","resolution":{"observed_at":"2026-08-10T22:32:09.129405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:07.898248Z","title":"Tidybot: Personalized robot assis- tance with large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.898248Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:4a22ef837be470d8096af56684a3fe75803e192c6609b00fc12e4417fdcd1890","observation_id":"ed8d1e39-88f2-4468-b83b-6085cebbe5d0","resolution":{"observed_at":"2026-08-10T22:32:07.898248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.902377Z","title":"The empathic framework for task learning from implicit human feedback,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.902377Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:9716d0b0e7ab4dee03148b791c2271fb1d78eea31e15e509f9774e4bbf4cdb35","observation_id":"a745b4ed-a131-4de3-a6ac-6056994d3b4f","resolution":{"observed_at":"2026-08-10T22:32:07.902377Z","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":"2026-08-10T22:32:09.094428Z","title":"On using social signals to enable flexible error-aware hri,","venue":null,"work_id":"c701aa24-8f90-47d7-90da-e1107081a1f7","year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.906550Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:4caed59eb3a5c10013c7b6ffbd30d4a04f59ee9a0d56398b94c03f29b1e05355","observation_id":"29ac1b6b-55e0-4f08-b30f-a12690dd02a2","resolution":{"observed_at":"2026-08-10T22:32:09.098726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.080108Z","title":"Human-robot cross-training: computational formulation, modeling and evaluation of a human team training strat- egy,","venue":null,"work_id":"4388617e-39cf-4117-9531-932597fa9d93","year":2013},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.910643Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:7ebf16909960eed6880d839f1ba9dfd3d2bb8f4da502e61ea3aaf2606343e979","observation_id":"1c57030b-0879-45a1-9f4f-6293db70e1c2","resolution":{"observed_at":"2026-08-10T22:32:09.085373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.066898Z","title":"Efficient model learning from joint-action demonstrations for human-robot collaborative tasks,","venue":null,"work_id":"400bcdb2-1111-43a9-857a-8e2232f2d547","year":2015},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.914667Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:f68cd91e7bc0f3d222466959fb5ef4bc65501e3fe09bcc73bc21c497c93e4645","observation_id":"7c5de638-912d-4f1b-b752-2d34ae3ef364","resolution":{"observed_at":"2026-08-10T22:32:09.071271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:07.918596Z","title":"Maximum entropy inverse reinforcement learning","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.918596Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:a2139e15dbaef2335bbe7008cf709b5f051b8ffcb9b8edaee0eb3f446ce5a49e","observation_id":"2f4be17e-8866-4f17-9da2-dafc88ed5745","resolution":{"observed_at":"2026-08-10T22:32:07.918596Z","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":"2026-08-10T22:32:09.044736Z","title":"Learning from imperfect demonstrations from agents with varying dynamics,","venue":null,"work_id":"6d198999-2d65-45a6-8349-902608b9001e","year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.922825Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:79cec64f3b78f16c552124988dd28b2b53ae2b7020d30550e1a13003d300db1e","observation_id":"a1f81e20-d338-4d22-95be-5a6d8f2c42f6","resolution":{"observed_at":"2026-08-10T22:32:09.049226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:09.030541Z","title":"Inferring non-stationary human preferences for human-agent teams,","venue":null,"work_id":"c659da01-274c-4a5b-83ec-612350d8fefd","year":2020},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.926746Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:d4756251befdbe3dec72c77017218ddfc65ae7c7bb4651cc43d0eb11f77f893c","observation_id":"acbb9441-cceb-44b9-9e70-fcad321f1662","resolution":{"observed_at":"2026-08-10T22:32:09.035299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:07.930722Z","title":"Inquire: Interactive querying for user- aware informative reasoning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.930722Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:1534df284deaacee07708056506f8b360b1aa3509a72fab0fa556941facc77f8","observation_id":"03f89f5a-0af9-48b0-ade2-a6a99aa7f430","resolution":{"observed_at":"2026-08-10T22:32:07.930722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.934693Z","title":"Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.934693Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:d5832260d2f981cea553653ba7d2508cecfd5b9a6941a13c3f7703ed0ab726f5","observation_id":"0e75b3ca-7047-49b8-b11c-77c091a81fbd","resolution":{"observed_at":"2026-08-10T22:32:07.934693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.938566Z","title":"Understanding the relationship between interactions and outcomes in human-in-the-loop machine learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.938566Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:7df1455fa4a6e7a4310776f30008a01a79bca12d69e2b4cb5107d5733e2e2c7d","observation_id":"5b005b9d-3290-4917-a65f-09a08bfc0ecf","resolution":{"observed_at":"2026-08-10T22:32:07.938566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.942516Z","title":"Inverse reward design,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.942516Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:3e8f97883d3816e971df3be2c906588355afabad04a3e6064e1aa015cf34c847","observation_id":"5cc0718a-7dd2-483f-a0be-ecee96d8f89d","resolution":{"observed_at":"2026-08-10T22:32:07.942516Z","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":"2026-08-10T22:32:08.982040Z","title":"Modeling variation in human feedback with user inputs: An exploratory methodology,","venue":null,"work_id":"a476bc70-636b-48cb-853f-d1c9fe2d4093","year":2024},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.946616Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:8091b2dfef1487aa90b2770b366ffc11cc6a5a8951074f2e83620e6fc9866f1f","observation_id":"9ba577cb-e3fa-48b0-a59a-89f61cdf06a8","resolution":{"observed_at":"2026-08-10T22:32:08.986431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:07.950543Z","title":"Safe imitation learning via fast bayesian reward inference from preferences,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.950543Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:06887a13a26aacf6a9b89b5a3d11b6e0488c35133e7810997c92d267b0233812","observation_id":"121d1910-be52-4f7d-8748-4e0f57448b0f","resolution":{"observed_at":"2026-08-10T22:32:07.950543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:07.954660Z","title":"Curl: Contrastive unsupervised representations for reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.954660Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:1de4890ca189c36f44d9b9525569761e003aee59123d26a541e20e40830df6f0","observation_id":"f3265cc4-88a8-4654-976d-e5c69f522611","resolution":{"observed_at":"2026-08-10T22:32:07.954660Z","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":"2026-08-10T22:32:08.949572Z","title":"Recurrent world models facilitate policy evolution,","venue":null,"work_id":"56e6fd1b-3745-4bfd-8352-9c3bba81d51b","year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.958657Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:9cbdea1f17b5b7cbedfc22de4c356b488c810220661e9c5c3229390234d31156","observation_id":"d67bc1d3-e129-4b18-9121-5d24465117c8","resolution":{"observed_at":"2026-08-10T22:32:08.955050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03681","last_updated":"2024-06-14T21:10:32Z","snapshot_observed_at":"2026-08-16T14:21:09.886387Z","submitted_at":"2024-02-06T04:06:06Z","title":"RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03681","snapshot_observed_at":"2026-08-10T22:32:07.962630Z","title":"Rl-vlm-f: Reinforcement learning from vision language foundation model feedback,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.962630Z"},"links":{"cited_paper":"/paper/2402.03681","citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:3872f0b784c7b2a751a91f16bd32e9de962f0cc86315fa44d3f7430cf46f7520","observation_id":"2e8e269f-ae0d-4245-a649-6aad57ad9ed3","resolution":{"observed_at":"2026-08-10T22:32:07.962630Z","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":"2026-08-10T22:32:08.936250Z","title":"State representations in robotics: Identifying relevant factors of variation using weak supervision,","venue":null,"work_id":"e2a6b4b5-d0c6-4d16-b357-0e18251658c8","year":2020},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.966880Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:7360f10614ff9e9d0f7d300b32b7ed07d97db252aa3fa1ad40c967f6808a2079","observation_id":"331e4394-8e99-4d91-8b9b-57c26dfd1e5e","resolution":{"observed_at":"2026-08-10T22:32:08.940924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.923021Z","title":"Weakly super- vised causal representation learning,","venue":null,"work_id":"1098a1cd-8e89-4b29-916a-554f0c3df163","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.970893Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:ea12abdddd96cff6e18f7f536f68e00f4b4690830ca81a340e943eb1ea1c6fd8","observation_id":"b8bb3548-5ffa-4787-ac68-9b54dcf17c4d","resolution":{"observed_at":"2026-08-10T22:32:08.927506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.910067Z","title":"Human-driven feature selection for a robotic agent learning classification tasks from demon- stration,","venue":null,"work_id":"d2094aec-1c5b-4c1f-befa-4f47ba9476ee","year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.974768Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:120e345164d0e5ccb1f51920c345d4236b782a72a80d8d1da0e88e2a25e6395c","observation_id":"e5a5798c-a4ec-46a4-a300-b259ebfca960","resolution":{"observed_at":"2026-08-10T22:32:08.914298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.896399Z","title":"Designing robot learners that ask good questions,","venue":null,"work_id":"4c798d68-2c88-47fb-8acb-54b7a90492be","year":2012},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.978650Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:d0cf87502187d134f58c6448534a4f5fe072f95da9ba20dd7d7a8b1ae06de82a","observation_id":"2dc4dfdc-bcdf-4ce0-8348-9a3616653e59","resolution":{"observed_at":"2026-08-10T22:32:08.901325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.883457Z","title":"Feature expan- sive reward learning: Rethinking human input,","venue":null,"work_id":"780ddef3-d61f-4591-9979-b7742da4ab92","year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.982585Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:b42a617a0bf9389c19207d4e182cc01c6b0052355ba6336800c6bf86c39d64e6","observation_id":"adb25aee-421e-458f-8ff4-876291bc1a30","resolution":{"observed_at":"2026-08-10T22:32:08.887663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.870069Z","title":"Fine-grained driving behavior prediction via context-aware multi-task inverse reinforcement learning,","venue":null,"work_id":"93938d2c-62dc-40f1-88a2-f4f4174543ab","year":2020},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.986698Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:b10e17133dedb778b839ed5c8afa0151d5688eb9f513798370b634adc842236e","observation_id":"38916d55-ba37-45ca-80e2-22050706d98b","resolution":{"observed_at":"2026-08-10T22:32:08.874720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.857455Z","title":"Task-induced rep- resentation learning,","venue":null,"work_id":"5ccd94cc-53bf-4e48-8ecd-1d300ffe8328","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.990648Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:c02b2de720ef5e45bd74e07f340bc64343110a0327fcbbb5ed759d753c4ca677","observation_id":"91027491-5fd7-4908-a657-592ea6f80e19","resolution":{"observed_at":"2026-08-10T22:32:08.861732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.843695Z","title":"Mind meld: Personalized meta-learning for robot-centric imitation learning,","venue":null,"work_id":"790bb5c6-1930-4e8a-921d-a1f7e988139e","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.994623Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:492778331ede6142efd1166354590691b0719ebe8d6bd8e8692ab159d0d8bd07","observation_id":"67eec675-a1b0-45de-9dae-dc9609d014a3","resolution":{"observed_at":"2026-08-10T22:32:08.848955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.830455Z","title":"Reciprocal mind meld: Improving learning from demonstration via personalized, recip- rocal teaching,","venue":null,"work_id":"45d076e8-d712-4532-ac10-e72c8d75ac94","year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:07.998590Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:0fd2c133070862de7cd488ae8ced2efa2637bf875937cd204aedaa8d85f4ad6b","observation_id":"ec1fbaa4-c6f8-49ce-b619-e21706bb1983","resolution":{"observed_at":"2026-08-10T22:32:08.834995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.817213Z","title":"Extending the technology acceptance model with moti- vation and social factors,","venue":null,"work_id":"b104c03c-97cf-4f04-bb2e-634a2612229d","year":1998},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.002446Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:0c1e322ab8cee0f3ccdc6ae9097f7a77b53193186788f4b36c7c3fa366543655","observation_id":"1cad446c-9319-4296-a8c1-951bc2fc56e5","resolution":{"observed_at":"2026-08-10T22:32:08.821449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.803974Z","title":"Relating motivation to information and communication technology acceptance: Self-determination theory perspective,","venue":null,"work_id":"b75f592b-f8c6-464b-be4a-a82b7a702cf2","year":2015},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.006802Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:698843e053a76f8c5ec3efc4eb36eb014b089deb5d44c8fc1454c762f1846ee4","observation_id":"db111f63-4f11-4935-89e3-c8f193648f41","resolution":{"observed_at":"2026-08-10T22:32:08.808264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.791132Z","title":"Exploratory search: from finding to understanding,","venue":null,"work_id":"b9c538eb-729b-4f79-954a-4488bd7a4bc5","year":2006},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.010807Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:b0495464b41ab705c6d16142d6c181472dd29f0bd5e8a2cb53d569de7b5ba8fd","observation_id":"37cd230e-d2a5-4b51-b02f-4fd00ffe3128","resolution":{"observed_at":"2026-08-10T22:32:08.795243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.777266Z","title":"The history of information retrieval research,","venue":null,"work_id":"0fdcf20e-e67e-4e51-97d7-59c6c81e4f2b","year":2012},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.014903Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:910aa172d248942f8458bc6ccbaf6ab5d3196651313d4d5eb2c5bf3b5adfe42f","observation_id":"d8efcfd9-b5c0-4e08-8bc1-b33917e32ce9","resolution":{"observed_at":"2026-08-10T22:32:08.782466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.764339Z","title":"Modern information retrieval: A brief overview,","venue":null,"work_id":"424c385b-58d6-460d-a301-51dbeab205d8","year":2001},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.019114Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:bf30ec4c9e31ca0b188868e01f5c95f6ad25c3163a7a6b52e939771723ae06db","observation_id":"3c630b42-0bb2-4c43-ab5f-002ed9f8bf8a","resolution":{"observed_at":"2026-08-10T22:32:08.768583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.750199Z","title":"Engage!: co-designing search engine result pages to foster interactions,","venue":null,"work_id":"a089ae06-fb2e-44bb-a2a3-0e24105144a1","year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.022968Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:7f556105cace25af7e68779c40983e9603bd8dabd1fddca02252a8e380129b37","observation_id":"5ee52051-0568-4679-b0b7-b7393edfb461","resolution":{"observed_at":"2026-08-10T22:32:08.755543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.736722Z","title":"Searchlens: Composing and capturing complex user interests for exploratory search,","venue":null,"work_id":"0f0a605a-e6a9-4fd0-9160-cc0540bad6db","year":2019},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.026879Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:a607a598dd21a51bf4815638e29de91b06fb776db6cb9afb5c726404502aaea7","observation_id":"f277ece0-30fd-4c74-9a64-7fdcaaf02bb5","resolution":{"observed_at":"2026-08-10T22:32:08.741421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.724033Z","title":"Grapevine: A profile-based exploratory search and recommendation system for finding research advisors,","venue":null,"work_id":"09d83caa-8130-4882-9866-3b19ffb39a86","year":2020},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.030815Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:be7048638d0b0b062bbcdc3b66711af5631d70642794eb2c3c3db1c07a0b2704","observation_id":"ae67fc08-1d25-4f01-a890-9a90f51f2e12","resolution":{"observed_at":"2026-08-10T22:32:08.728271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.710865Z","title":"Beyond actions: Exploring the discovery of tactics from user logs,","venue":null,"work_id":"72e4fe06-53c9-458f-9bc3-ee87610d3e9c","year":2016},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.034703Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:bc1bb9644322556c1fb10b405d85033d4c9a81a9a18380362747dc6c88de2347","observation_id":"58a2dd87-55f5-47f6-a57b-5d5653f5445c","resolution":{"observed_at":"2026-08-10T22:32:08.715064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.697159Z","title":"Click-through rate prediction in online advertising: A literature review,","venue":null,"work_id":"349769a5-30f8-40f9-880d-7bb4139d5bdd","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.038576Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:2b36a00cd365ecda28b0240c33c66ccd8f7ad7cc4a0e3c9b5301a11e18e3c864","observation_id":"ee615f00-7040-4038-a52c-71e68a0f86ba","resolution":{"observed_at":"2026-08-10T22:32:08.701410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.684267Z","title":"Learning perceptual ker- nels for visualization design,","venue":null,"work_id":"304d33ff-0979-42b7-813a-b1c11f2325e5","year":1933},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.042365Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:e41da978e88877cf496c71738dc46731507911c79db38e25f49ead4fa08967e3","observation_id":"55656c18-c155-492c-b56c-a1433240e7c9","resolution":{"observed_at":"2026-08-10T22:32:08.688680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.671143Z","title":"Dimensionality reduction by learning an invariant mapping,","venue":null,"work_id":"d93d37e1-d94f-4945-a76d-ac888498e7ea","year":2006},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.046334Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:66bfb699ecdc067c8bc0abf03e0db39ff1209fad9abdcdaf9478ed62dfcf423f","observation_id":"aa9666be-b822-478c-a372-3df3fabdc713","resolution":{"observed_at":"2026-08-10T22:32:08.675449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.656864Z","title":"Deep metric learning using triplet network,","venue":null,"work_id":"219f0c53-a562-495e-899d-eb9fe9aa2ebd","year":2015},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.050568Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:8ce50e40ad56844df54ce7d08a8b13a9a81f65423a41a9eddb8c528b342b4cb3","observation_id":"29d2784e-6fb1-4dea-811c-99a34b9f142b","resolution":{"observed_at":"2026-08-10T22:32:08.661556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.642846Z","title":null,"venue":null,"work_id":"3d1253dd-b74c-4851-b589-18ef9d8dc29a","year":2015},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.054460Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:415979af5e6d93c5fb6a818823a2a625fdb23617ddda777671972af40c3d6907","observation_id":"36fd83ee-2b02-49f7-8017-1343fd835f7c","resolution":{"observed_at":"2026-08-10T22:32:08.647097Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.628964Z","title":"Query chains: learning to rank from implicit feedback,","venue":null,"work_id":"6dcdf7f5-e763-4e15-b238-423bbd84091b","year":2005},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.058308Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:4a06abfd38505223a9389cd25e17b42b1c2bebd3aff539cb0c980815274d6fc2","observation_id":"adb9699f-958b-4465-8f18-92c779ca2d57","resolution":{"observed_at":"2026-08-10T22:32:08.634040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.615316Z","title":"Transfer learning of human preferences for proactive robot assistance in assembly tasks,","venue":null,"work_id":"8e220828-90e3-4b0f-a11b-90943ed1fdef","year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.062130Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:c896c9a162e26c6bdf37e622ae2964b0ec3bdf6015f9c4c09d756b0102602967","observation_id":"f60aff33-b721-4b05-acfb-5b43d4dd2a34","resolution":{"observed_at":"2026-08-10T22:32:08.620008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.601988Z","title":"Approx- imating gradients for differentiable quality diversity in reinforcement learning,","venue":null,"work_id":"43628257-5f72-4951-aab1-59f8d62ee231","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.066007Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:c3c75a6dfa87c12c3523a7efadbdf73edc3a8d9f7e7c67515db0bc361716c850","observation_id":"cbf5aee0-7183-449a-b0ae-d81b408b6ce3","resolution":{"observed_at":"2026-08-10T22:32:08.606491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.587837Z","title":"Active model learning and diverse action sampling for task and motion plan- ning,","venue":null,"work_id":"0c886d55-915b-47d4-97b0-a4cdae956bfa","year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.069994Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:e509cabdccc956377a7441928328f2ba012d5c4cfe29f84895fc4567d031c70b","observation_id":"ea8fe1ad-bafc-486d-963e-36c10af9010f","resolution":{"observed_at":"2026-08-10T22:32:08.592745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.074050Z","title":"Robotic vision for human-robot interaction and collaboration: A survey and systematic review,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.074050Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:38061c9b257738db49109b755917680b4ba3084b5f785a1438cfaf766072aa40","observation_id":"e01cbaee-e988-4eac-8291-c6d631ce45d4","resolution":{"observed_at":"2026-08-10T22:32:08.074050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:32:08.077969Z","title":"Deep reinforcement learning: A brief survey,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.077969Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:11dd3cb1654378977c08300bc4bb100bb45e699a63da75603f078f8292feb2ac","observation_id":"9764f914-2f53-4268-a020-4c0d56f41014","resolution":{"observed_at":"2026-08-10T22:32:08.077969Z","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":"2026-08-10T22:32:08.556647Z","title":"Dataset search: a survey,","venue":null,"work_id":"362f3472-abc8-40ad-8ebf-3c57070b2085","year":2020},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.082355Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:524fd8fcd01d7893afaf7dd0154254582c3fb53959f344a2cedabdeeed1b514f","observation_id":"ddcd4502-e6a9-41ed-ac53-4f0a7dfa682a","resolution":{"observed_at":"2026-08-10T22:32:08.561865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.543109Z","title":"Advances in collaborative filtering,","venue":null,"work_id":"82dad1c6-0e5d-42a9-b8ce-2e14973ff3c3","year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.086419Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:071e129b4d9934e6d4142f4f55d76cead01056b07b3ff6d6a88a569810892d3d","observation_id":"f1640ee3-8be7-48f6-ba7d-2db923b8caeb","resolution":{"observed_at":"2026-08-10T22:32:08.547377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.529342Z","title":"Faceted metadata for image search and browsing,","venue":null,"work_id":"606c02b8-23a6-4585-bc07-35cbdc3f2f19","year":2003},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.090302Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:f571810f09ce571f88d4cf0cab1dcee803bc458b0d488c060cb838cd636493a5","observation_id":"bfecb549-2e29-4392-a699-729fd7ab0708","resolution":{"observed_at":"2026-08-10T22:32:08.534423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.094284Z","title":"Rank analysis of incomplete block designs: I. the method of paired comparisons,","venue":null,"work_id":null,"year":1952},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.094284Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:9b0689341083082b3eff7c7d1aee98886a7b596538a915ff539a4daad22f3005","observation_id":"0415c5a6-3e07-4f2a-b77c-4d247bf8356c","resolution":{"observed_at":"2026-08-10T22:32:08.094284Z","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":"2026-08-10T22:32:08.506768Z","title":"Active preference-based gaussian process regression for reward learning and optimization,","venue":null,"work_id":"9a216ef6-ffef-41fa-a788-5ae7161f7e4b","year":2024},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.098293Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:e03cd431b3745bcd8f0ea2ee68e10634d830c8726bfda1e4d2e333dea56efaa5","observation_id":"21722ba3-00b6-4c9c-8007-b45393c5187f","resolution":{"observed_at":"2026-08-10T22:32:08.511090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.493022Z","title":"Team, “Kuri,” Aug 2018","venue":null,"work_id":"3b4f44ee-8d4f-404b-8527-bb9a29ce1476","year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.102341Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:22ffb5a5bdbf6462b1455c1bf60237cc8b803805a5c18ede96a2fafa006ff178","observation_id":"e78d154e-f155-4439-ac7a-b4b11a52d2d9","resolution":{"observed_at":"2026-08-10T22:32:08.497268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09382","last_updated":"2024-01-17T17:54:38Z","snapshot_observed_at":"2026-08-20T21:27:18.016904Z","submitted_at":"2024-01-17T17:54:38Z","title":"POE: Acoustic Soft Robotic Proprioception for Omnidirectional End-effectors","version":1},"cited_work":{"arxiv_id":"2401.09382","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.09382","snapshot_observed_at":"2026-08-10T22:32:08.246200Z","title":"POE: Acoustic Soft Robotic Proprioception for Omnidirectional End-effectors","venue":"cs.RO","work_id":"ed15c124-b433-4068-bbb9-92ede85a8d27","year":2024},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.106339Z"},"links":{"cited_paper":"/paper/2401.09382","citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:8404f94696fc8b1a02f89a6b008b21b60e1dbeec5020e4a26887bbae94d9db98","observation_id":"5bb6c394-0bee-43e6-817c-bbb0bfcf2956","resolution":{"observed_at":"2026-08-10T22:32:08.252481Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.479255Z","title":"Design metaphors for understanding user expectations of socially interactive robot embodiments,","venue":null,"work_id":"acb05681-1832-4904-aab8-ef596ee15b02","year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.110694Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:0e52448ebd9f2fccb97180f743306b5685fe1ef43c0adcd0129c6f018c9b256b","observation_id":"643fd210-fb8e-457a-8567-86efe5b1089f","resolution":{"observed_at":"2026-08-10T22:32:08.483791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.464879Z","title":"Concerning trends in likert scale usage in human-robot interaction: Towards improving best practices,","venue":null,"work_id":"8b914aad-5419-4198-a88f-2385f1e53471","year":2023},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.114955Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:61cdcd7f00e85ef71ae75ea3bc534e63580552d917fed619e5bec03013c7d57b","observation_id":"fe74190e-887b-4692-b98b-7d14e6a7ad11","resolution":{"observed_at":"2026-08-10T22:32:08.469473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.450948Z","title":"Using exploratory search to learn representations for human preferences,","venue":null,"work_id":"f1d9f132-55e4-4540-ae26-28ad90619eee","year":2024},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.118947Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:72499525017c42b4f187412bd268b5849995de9e3dff1449f2181308f94f6f99","observation_id":"5a41e70f-8bcb-4453-a4b5-1c13887a431f","resolution":{"observed_at":"2026-08-10T22:32:08.455527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.122885Z","title":"X-clip: End- to-end multi-grained contrastive learning for video-text retrieval,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.122885Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:8fed66ba2881ca4da34fac75599ee667b2988ef6c5f593153e3a49b2f974674f","observation_id":"a9e65a1d-c067-47be-a4d4-1ec42d9dc0ea","resolution":{"observed_at":"2026-08-10T22:32:08.122885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.01778","last_updated":"2021-07-08T20:16:28Z","snapshot_observed_at":"2026-08-17T23:50:11.173365Z","submitted_at":"2021-04-05T05:26:29Z","title":"AST: Audio Spectrogram Transformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.01778","snapshot_observed_at":"2026-08-10T22:32:08.126937Z","title":"Ast: Audio spectrogram trans- former,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.126937Z"},"links":{"cited_paper":"/paper/2104.01778","citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:97fdc8ac37c47349c356dfe4f92c15fe81c4b540a1678fb176ead68f06c1070e","observation_id":"f0847e25-1c21-4ff7-b343-246d6907fb77","resolution":{"observed_at":"2026-08-10T22:32:08.126937Z","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":"2026-08-10T22:32:08.427759Z","title":"Learning elastic embeddings for customizing on-device recommenders,","venue":null,"work_id":"7a10f5c0-59d1-4abf-be79-a51bfb4ecfff","year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.131243Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:eee25117b4df16200c609c94d15203aa0f03632a48f084b72cdd9f0c36a1ba26","observation_id":"cde5743f-305f-409d-b60c-7fa73b3adc4a","resolution":{"observed_at":"2026-08-10T22:32:08.432770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.413437Z","title":"Personalizing user engagement dynamics in a non-verbal communication game for cerebral palsy,","venue":null,"work_id":"e75eb446-3b36-4b36-bac3-3da4b8d2d32d","year":2021},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.135201Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:76676c63185b99b2c7c972e26ef60980a94e108a177aa14dc82623b65688327f","observation_id":"ec4a76d7-dd11-4bec-a5d9-8218596ee434","resolution":{"observed_at":"2026-08-10T22:32:08.417962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.399526Z","title":"Self-supervised pretraining improves self-supervised pretraining,","venue":null,"work_id":"44ad2842-48df-4e30-98cc-556bf05afb61","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.139662Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:5e1d900d10518840dd53538039d2dc58cc8c3b25d098df2109f24a9bdd5a8791","observation_id":"ee0de3b5-4610-49bb-8498-7fbd6762053f","resolution":{"observed_at":"2026-08-10T22:32:08.404391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.385764Z","title":"Case-based explanation of non-case-based learning methods","venue":null,"work_id":"83718304-6210-4e4e-8cd3-89f259bcac0b","year":1999},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.144820Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:4cf201f0ae8be6d4045e935cbef332d8167fbc424342c2b82addd5a8d065cf1c","observation_id":"eea44efe-cfd3-4d9b-b324-b4cacb066e91","resolution":{"observed_at":"2026-08-10T22:32:08.390602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.04765","last_updated":"2018-03-13T13:02:13Z","snapshot_observed_at":"2026-08-14T19:36:43.134642Z","submitted_at":"2018-03-13T13:02:13Z","title":"Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.04765","snapshot_observed_at":"2026-08-10T22:32:08.149210Z","title":"Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.149210Z"},"links":{"cited_paper":"/paper/1803.04765","citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:d1f1d7152218026605c3909abec6b100411047dab5c3e08a6fa237444166d0ce","observation_id":"ed981691-1b6f-4332-9729-5180d4dbe08c","resolution":{"observed_at":"2026-08-10T22:32:08.149210Z","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":"2026-08-10T22:32:08.372598Z","title":"The bayesian case model: a generative approach for case-based reasoning and prototype classification,","venue":null,"work_id":"83223e57-06a7-4d45-bab7-db7275bb489b","year":2014},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.153655Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:7574a6fb92cfead14a123e6a1c478feffcc8dc2f3a2deedd4c98fe7eb5342d28","observation_id":"67abe660-1502-4782-a078-04e4d3bfc1f7","resolution":{"observed_at":"2026-08-10T22:32:08.376953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.359134Z","title":"Generating visual explanations,","venue":null,"work_id":"80bdbb88-8ba3-421e-89cf-f0c789e2b444","year":2016},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.158279Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:258026623422e9a06310e2b84fa747a21540d02408c2daa2f14c00bebe33e027","observation_id":"83eee66a-cd31-42cc-8aa1-91adf741052c","resolution":{"observed_at":"2026-08-10T22:32:08.363707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.162370Z","title":"Distributed representations of words and phrases and their composi- tionality,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.162370Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:7a1a780c9329c8461d14e4c89100e533b0216e0a9090e35e0e1914fbee6db879","observation_id":"5d2b2e55-bc60-4dbb-a293-aab0a314f838","resolution":{"observed_at":"2026-08-10T22:32:08.162370Z","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":"2026-08-10T22:32:08.337707Z","title":"Learning language-conditioned robot behavior from offline data and crowd- sourced annotation,","venue":null,"work_id":"1a59ed20-5f8e-458b-ad27-174332f38f13","year":2022},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.167041Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:8766999361d1cf1750ac4fbce39ee85acd20998bbd5b0fd4dce188c7ff1de544","observation_id":"d5c83160-b9e1-43c7-a0e5-d9213578a7d8","resolution":{"observed_at":"2026-08-10T22:32:08.342108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.171417Z","title":"Roboclip: One demonstration is enough to learn robot policies,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.171417Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:6577d4c08c2cb251fbddb2f5c54c8a9da26cde92249e28f3931c0f0d510ee5dd","observation_id":"348ab21b-ca1d-4dfc-aff9-4cf34ea1629c","resolution":{"observed_at":"2026-08-10T22:32:08.171417Z","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":"2026-08-10T22:32:08.315614Z","title":"Person search with natural language description,","venue":null,"work_id":"c0a7c1be-04b0-4976-b792-d5a3def5a16e","year":2017},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.176454Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:aef9bb55a858783c7246d91bd0a2ef373aaca9ead4220c713c81faf47765ab7d","observation_id":"9fe326f6-dc66-4b9a-89a8-0e396e866cf9","resolution":{"observed_at":"2026-08-10T22:32:08.320221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-10T22:32:08.302267Z","title":"Fast, cheap, and good: Why animated gifs engage us,","venue":null,"work_id":"299b0cd2-e5f0-4636-9168-58b517d91419","year":2016},"citing_paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-10T22:32:08.180370Z"},"links":{"citing_paper":"/paper/2501.01367"},"observation_digest":"sha256:bedd761bb21f1a40a067fae8cb5e53d2d62ebef4a091eb81d82df55f6f320978","observation_id":"c61d6aa5-918a-4be2-9660-8444073f5233","resolution":{"observed_at":"2026-08-10T22:32:08.306557Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.01367","last_updated":"2025-01-02T17:26:01Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-17T19:30:15.272449Z","submitted_at":"2025-01-02T17:26:01Z","title":"Contrastive Learning from Exploratory Actions: Leveraging Natural Interactions for Preference Elicitation"},"reference_resolution":{"displayed":91,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":33,"verified_exact":1,"verified_fuzzy":56},"total_outbound_references":91},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 91 of 91 outbound references and 0 inbound Pith citation observations for arXiv:2501.01367."}