{"as_of":"2026-08-09T00:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ae7414e8f82bbbbd847dd1d5d91096514e08019ac61490e7622c413f62e3d550","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:58:17.245291Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2507.05295/citation-record","integrity":"/paper/2507.05295/integrity","json":"/paper/2507.05295/citation-record.json","paper":"/paper/2507.05295"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:58:22.308299Z","title":"Ai in education: A review of personalized learning and educational technology,","venue":null,"work_id":"033735e3-b378-4816-92e6-12c21415a427","year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.434894Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:50476a6048c02e44aecc855bddaa852e857ced69bf30ce6978036fc3a3fe34f7","observation_id":"2c0098a3-56b7-4a22-b98f-c1a459dce4fe","resolution":{"observed_at":"2026-08-06T19:58:22.416746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:22.104166Z","title":"A comprehensive analysis of personalized learning components,","venue":null,"work_id":"8e244cf7-efe6-4e5d-b9e9-306cfe3d6aaa","year":2021},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.538365Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:6e0d005ffaee076ee255fa8516405c920f02a6ee7fa94e92fffeba89518cf53e","observation_id":"61a319ca-8289-4b46-a978-902451f1a998","resolution":{"observed_at":"2026-08-06T19:58:22.227157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:21.858694Z","title":"Components and strategies for personalized learning in higher education: A systematic,","venue":null,"work_id":"417e734f-dd08-41f6-882a-c2a57e619c1c","year":2022},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.624465Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:e4fbdee5ac07602bdf83938070c997c3d485e4c77b4ac70e51139b540bea978c","observation_id":"f28eb20c-829c-4203-a421-4c6875a4eb90","resolution":{"observed_at":"2026-08-06T19:58:21.964486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:21.708711Z","title":"Personalized learning systems: essential components and their impact on learning outcomes,","venue":null,"work_id":"875d7092-b08b-43ae-b280-c8210c341b39","year":2025},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.690079Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:603c6a89c8332461e63ba37497c13dcc81104d22fb4fb38aecb257888709da28","observation_id":"39f527dc-7caf-484a-b575-138d9106f572","resolution":{"observed_at":"2026-08-06T19:58:21.780954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:21.503781Z","title":"Ai-supported health coaching model for patients with chronic diseases,","venue":null,"work_id":"5a342133-87f7-44eb-b5fd-0d6bd24c6ecd","year":2019},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.746215Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:da5fd58e15c952ebfd9435f16ade0b3df9aa1fb2342e7b2f87f6b9543fa856f5","observation_id":"233aac6a-7dc8-45fa-995d-9facde21015d","resolution":{"observed_at":"2026-08-06T19:58:21.629589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:21.271183Z","title":"Efficacy of an adaptive game- based math learning app to support personalized learning and improve early elementary school students’ learning,","venue":null,"work_id":"ae1db9ce-dd50-43c2-b0f6-719b86714679","year":2023},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.805066Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:8185df1562135a1eefebd0ab3d3f17e56b533795956adaa0150c4ee9e8637f85","observation_id":"c092c987-91c8-4f70-9193-ee1905c24bd0","resolution":{"observed_at":"2026-08-06T19:58:21.398699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:21.082395Z","title":"Fitness guide: A holistic approach for personalized health and wellness recom- mendation system,","venue":null,"work_id":"f6025a37-724d-4a50-b031-fee690e8e5a3","year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.861988Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:7457114c1d7c1ecf0a89e45e9bb5b8f08aa2ff14061b42ebdbfa2904252de582","observation_id":"b4967b21-c589-44d5-b281-cf0530123c16","resolution":{"observed_at":"2026-08-06T19:58:21.170076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:20.926391Z","title":"Opti- mized deep learning framework for personalized nutritional recommen- dations across the menstrual cycle,","venue":null,"work_id":"a3320e32-e475-421b-bc9c-ad11fba543b8","year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.917755Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:5e5c4bb07c9bdfffa5e9afec4da441b3f0bcc739f9271c73c2cb29e66bfa43ef","observation_id":"5c34dc6f-7671-4490-b2a1-a96ac96be91b","resolution":{"observed_at":"2026-08-06T19:58:21.003919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:20.741321Z","title":"Full personalized learning path recommendation: A literature review,","venue":null,"work_id":"b26561b2-76d2-4e86-ad52-2c647f1ad5be","year":2023},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:15.985538Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:4c8c5f7a656153247675186115e0d9147b7a96d7723266b6cf8843d8cc10e72c","observation_id":"c35d9ebf-cd18-4069-913d-746c051df212","resolution":{"observed_at":"2026-08-06T19:58:20.810535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:20.488048Z","title":"A survey on learning path recommenda- tion,","venue":null,"work_id":"353ee57b-15a9-4165-9447-40b96dc9abe2","year":2021},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.034438Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:fe0189b2a6f888e26bc7bebb1dec9eb73310054ffbc33f569216c1eaa7b938b5","observation_id":"417f2b91-190a-456d-8980-205e2e5abf02","resolution":{"observed_at":"2026-08-06T19:58:20.609248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:20.273452Z","title":"A literature review of im- plemented recommendation techniques used in massive open online courses,","venue":null,"work_id":"ec257d5d-f5a4-4e09-9e8e-dfc19dc821fa","year":2022},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.085004Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:c0d9e02cbab39da05f2bca319ce59ede6825dd0cf5f92f3aea747265c58bc671","observation_id":"88cf7f68-9cb0-4a65-95e8-a3a11196f558","resolution":{"observed_at":"2026-08-06T19:58:20.389322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:20.050619Z","title":"Ld–lp generation of per- sonalized learning path based on learning diagnosis,","venue":null,"work_id":"18c9ba17-26f3-4695-8646-2df17ae1f1cf","year":2021},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.137864Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:4c66ab5a24d8f80b8eae1c40249cac1f56fb2e9f0987269d92b3a3efc05d5ae1","observation_id":"95749cea-e862-45a3-901d-06fe4a885271","resolution":{"observed_at":"2026-08-06T19:58:20.158196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.07274","last_updated":"2018-11-26T04:56:31Z","snapshot_observed_at":"2026-07-06T05:27:30.168672Z","submitted_at":"2017-01-25T11:52:11Z","title":"Deep Reinforcement Learning: An Overview","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.07274","snapshot_observed_at":"2026-08-06T19:58:16.191261Z","title":"Deep reinforcement learning: An overview,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.191261Z"},"links":{"cited_paper":"/paper/1701.07274","citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:6ab8eb2484e4f925d9d09b8bd0e368dc89311ab7e3edfbb9eda151b34e0437ac","observation_id":"932e718b-1d02-4afe-92c4-0b547a2c506c","resolution":{"observed_at":"2026-08-06T19:58:16.191261Z","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":"2026-08-06T19:58:19.800292Z","title":"Set-to-sequence ranking-based concept- aware learning path recommendation,","venue":null,"work_id":"da33be07-07ab-4b4f-b7d0-cfd1080ccb6e","year":2023},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.273637Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:0a5164831d6274aefa55dbdc181784e00568a05ae8a0ebb7898e525d672a4063","observation_id":"31c835b1-6934-4d46-ac96-916db48a400e","resolution":{"observed_at":"2026-08-06T19:58:19.907892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:19.498915Z","title":"Item- difficulty-aware learning path recommendation: From a real walking perspective,","venue":null,"work_id":"3dddbf2b-036e-40c2-80ec-383662187824","year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.323182Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:68103efcefb4fe298f7e5051f2cc5cb27261b504cde8e96bfd968c04d09a45bd","observation_id":"125d9675-8885-4743-a342-4474686285e0","resolution":{"observed_at":"2026-08-06T19:58:19.643943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:19.239756Z","title":"Privileged knowledge state distillation for reinforcement learning-based educational path recom- mendation,","venue":null,"work_id":"79c049c9-0c79-4c86-a41c-c578bdcee7fa","year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.387993Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:916361e761efbda70ae45040055577038e864671d40933f39bc9817488a60db3","observation_id":"2e738bf6-0287-4601-bf7e-2d2045230a4f","resolution":{"observed_at":"2026-08-06T19:58:19.370250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05098","last_updated":"2017-06-15T21:38:12Z","snapshot_observed_at":"2026-08-04T14:30:44.904839Z","submitted_at":"2017-06-15T21:38:12Z","title":"An Overview of Multi-Task Learning in Deep Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05098","snapshot_observed_at":"2026-08-06T19:58:16.444678Z","title":"An overview of multi-task learning in deep neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.444678Z"},"links":{"cited_paper":"/paper/1706.05098","citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:61ac422e8dd834be14527e9de0be5f6d5d9edb344da3eb802d7a3edae5991609","observation_id":"1caa037d-ecbe-4538-a062-813cce146467","resolution":{"observed_at":"2026-08-06T19:58:16.444678Z","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":"2026-08-06T19:58:19.026284Z","title":"Deep knowledge tracing,","venue":null,"work_id":"d1a94dd8-ea21-4019-9567-3bf1ef3543b9","year":2015},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.506405Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:8c1bbad6b2bc5dae813f894eb03546c39ea9d1df3788417c241f71c917430d52","observation_id":"5e5e7192-cb22-4b0d-b99d-2a8171c84781","resolution":{"observed_at":"2026-08-06T19:58:19.135590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01078","last_updated":"2018-02-22T19:28:28Z","snapshot_observed_at":"2026-08-06T20:27:07.216060Z","submitted_at":"2017-12-29T00:57:22Z","title":"Recent Advances in Recurrent Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01078","snapshot_observed_at":"2026-08-06T19:58:16.566396Z","title":"Recent advances in recurrent neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.566396Z"},"links":{"cited_paper":"/paper/1801.01078","citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:b766f6c2f6c091f3634031bd20c3c25d3a9047cb1c4a3dab117e85ba98ed6038","observation_id":"354e3315-3d7a-4afd-9e40-0b7ce4c3a327","resolution":{"observed_at":"2026-08-06T19:58:16.566396Z","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":"2026-08-06T19:58:18.808148Z","title":"Attention, please! a survey of neural attention models in deep learning,","venue":null,"work_id":"878cfa6b-cc74-4366-b70e-2118854c9cdb","year":2022},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.625218Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:2151849147d4ad7023184b94c575387289511db6f72615664e4c8745052ab53e","observation_id":"0f3eaa1e-5515-4cba-9dc0-a255cf3d3e8f","resolution":{"observed_at":"2026-08-06T19:58:18.924587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:18.516854Z","title":"A survey on multi-task learning,","venue":null,"work_id":"56880194-de32-490b-9f56-8012ed28aa56","year":2021},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.705479Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:60a235ecf15a6875dc94b922d65e84cf1fc166bbe5609fa77be9615861ca1011","observation_id":"156cb230-9490-4243-86e6-9dac7adf4a38","resolution":{"observed_at":"2026-08-06T19:58:18.644608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:18.316662Z","title":"New types of deep neural network learning for speech recognition and related applications: An overview,","venue":null,"work_id":"d880b927-cc63-49a0-8869-3821b10cbab4","year":2013},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.773203Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:50dc63bab460e38c2464c30e0360fa69de7ef2e34ca428a8bd8a586d8fd8f77b","observation_id":"17e9e258-9ebb-4dba-af2a-f61e5dedabbf","resolution":{"observed_at":"2026-08-06T19:58:18.413840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1502.02072","last_updated":"2015-02-06T23:04:01Z","snapshot_observed_at":"2026-08-01T18:48:32.102204Z","submitted_at":"2015-02-06T23:04:01Z","title":"Massively Multitask Networks for Drug Discovery","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.02072","snapshot_observed_at":"2026-08-06T19:58:16.854337Z","title":"Massively multitask networks for drug discovery,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.854337Z"},"links":{"cited_paper":"/paper/1502.02072","citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:9a955fbba2f7a47ef9a433a5a252836c5c0b40e38ee7451111cef42570338410","observation_id":"60e42e44-3073-4d49-8bb5-69eaae085675","resolution":{"observed_at":"2026-08-06T19:58:16.854337Z","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":"2026-08-06T19:58:16.921187Z","title":"Fast r-cnn,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.921187Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:2226a9f225244eefc9a3c2b27b436f6eda40463fd99d3bd2f4e766c4bb33ec47","observation_id":"93809f1f-9674-4313-b8bf-1563c6ae66fe","resolution":{"observed_at":"2026-08-06T19:58:16.921187Z","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":"2026-08-06T19:58:18.083926Z","title":"A unified architecture for natural language processing: Deep neural networks with multitask learning,","venue":null,"work_id":"a7d656f6-81ec-423c-8f6d-8a509a1531f3","year":2008},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.958769Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:6c6b9a73230847c76056c3845a08309b9474153d1b624c0e381152a5d18e2114","observation_id":"b90d9789-9a96-4b79-a813-569126b2b639","resolution":{"observed_at":"2026-08-06T19:58:18.207804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:17.804673Z","title":"Addressing the assessment challenge with an online system that tutors as it assesses,","venue":null,"work_id":"ddbb4267-2cc8-4428-aab7-99656c4ae5e4","year":2009},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:16.961385Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:dded09467910d1847634e42b462ae28ffe4c4b64b13885b1a14c843f4805ebe7","observation_id":"67ade119-4021-41f9-9b18-ba095297407f","resolution":{"observed_at":"2026-08-06T19:58:17.930848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:17.599381Z","title":"Doubly constrained offline reinforcement learning for learning path recommendation,","venue":null,"work_id":"cb3c2ecc-6a4e-45f3-ada8-2fa316f416e1","year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:17.049388Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:1ecab76019ea88fd23ebad38bf57bb16f94e31d0e669929788eaa1cb71a26585","observation_id":"67f2732e-930e-4e71-b49e-4e68b03d2dcd","resolution":{"observed_at":"2026-08-06T19:58:17.710786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:58:17.402046Z","title":"On learning path planning algorithm based on collaborative analysis of learning behavior,","venue":null,"work_id":"07e81655-f5da-4eba-b332-fcaa651560b4","year":2020},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:17.119169Z"},"links":{"citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:7116a188a5547c5f5d25468cd11a2db82ed1c253a744aa67b252c5af7746447f","observation_id":"4c236578-0373-4499-8c65-8e83b6579a20","resolution":{"observed_at":"2026-08-06T19:58:17.486742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11773","last_updated":"2024-07-16T14:32:56Z","snapshot_observed_at":"2026-07-06T18:47:13.029539Z","submitted_at":"2024-07-16T14:32:56Z","title":"Educational Personalized Learning Path Planning with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.11773","snapshot_observed_at":"2026-08-06T19:58:17.177331Z","title":"Educational personalized learning path planning with large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:17.177331Z"},"links":{"cited_paper":"/paper/2407.11773","citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:691e402236b48a480b15fe3d63e99b871d9dc528d615f435dc1b90021277bb6d","observation_id":"aa4b47df-eebd-49b5-ac10-05d2ec818037","resolution":{"observed_at":"2026-08-06T19:58:17.177331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12442","last_updated":"2024-05-21T01:35:36Z","snapshot_observed_at":"2026-07-06T18:17:04.010991Z","submitted_at":"2024-05-21T01:35:36Z","title":"Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12442","snapshot_observed_at":"2026-08-06T19:58:17.245291Z","title":"Learn- ing structure and knowledge aware representation with large language models for concept recommendation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:58:17.245291Z"},"links":{"cited_paper":"/paper/2405.12442","citing_paper":"/paper/2507.05295"},"observation_digest":"sha256:9da52f517465a18cddbd4448adbbb6694de4afffd0ab338dce083eaacdf527b6","observation_id":"e5ae7830-03c0-4d35-bb65-6be6b58f933b","resolution":{"observed_at":"2026-08-06T19:58:17.245291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.05295","last_updated":"2025-07-05T21:16:02Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-08T04:05:58.471929Z","submitted_at":"2025-07-05T21:16:02Z","title":"Enhancing Learning Path Recommendation via Multi-task Learning"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":23},"total_outbound_references":30},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.05295."}