{"as_of":"2026-08-12T12:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bc52baf52ba40a629a4499e958b9260b22b90c6d53dbe7303dcba4d12c938f9a","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":32,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:21:29.055002Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T22:05:05.682988Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2305.09620","last_updated":"2026-05-19T19:24:46Z","snapshot_observed_at":"2026-07-06T15:28:16.998343Z","submitted_at":"2023-05-16T17:13:07Z","title":"AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction","version":4},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-05-24T08:47:23.231930Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2305.09620"},"observation_digest":"sha256:cd4570cc185f4cb625fed654e3f15f12a397632262480b0f354f3d446f3fe5e5","observation_id":"e9d49559-b609-4cc6-b530-9cc16c6a1200","resolution":{"observed_at":"2026-05-24T08:49:13.951880Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2404.13501","last_updated":"2024-04-21T01:49:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-04-21T01:49:46Z","title":"A Survey on the Memory Mechanism of Large Language Model based Agents","version":1},"reference_index":145,"source":"pdf_text","source_observed_at":"2026-05-15T07:21:39.440092Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2404.13501"},"observation_digest":"sha256:b44eda232d7ffc152a5852a981864150e0038d00bac4460cd25403d83ff573f8","observation_id":"8da25e47-5f9b-475a-b65a-343acbfd0343","resolution":{"observed_at":"2026-05-15T07:21:39.587506Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2408.09049","last_updated":"2026-04-19T19:02:30Z","snapshot_observed_at":"2026-08-08T20:15:07.345025Z","submitted_at":"2024-08-16T23:24:10Z","title":"Inertia in Moral and Value Judgments of Large Language Models","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-23T22:05:17.165589Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2408.09049"},"observation_digest":"sha256:abf96052e441caef124880a32a7e3ffad45096cd5caa89cd6b4a7da8d3b7d3e0","observation_id":"5ebc6945-c207-4d0f-9441-63f4e2ed3e4d","resolution":{"observed_at":"2026-05-23T22:05:50.145721Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-11T17:55:52.694183Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.08389","last_updated":"2024-12-11T13:56:04Z","snapshot_observed_at":"2026-08-11T17:49:37.313361Z","submitted_at":"2024-12-11T13:56:04Z","title":"SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent","version":1},"reference_index":585,"source":"pdf_text","source_observed_at":"2026-08-11T17:55:52.694183Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2412.08389"},"observation_digest":"sha256:1d055184bc630ec73a11e161289630c77685e3d6db743588969b7f72920de805","observation_id":"af9a959f-4e5d-40f9-88a1-ca180a8b35ca","resolution":{"observed_at":"2026-08-11T17:55:52.694183Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-11T13:31:41.410146Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13103","last_updated":"2024-12-17T17:17:03Z","snapshot_observed_at":"2026-08-11T13:23:21.275373Z","submitted_at":"2024-12-17T17:17:03Z","title":"AI PERSONA: Towards Life-long Personalization of LLMs","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T13:31:41.410146Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2412.13103"},"observation_digest":"sha256:71be51e6a0464c0d3895ff995e1f9268907983146701a793920c268be66a193c","observation_id":"294b1b21-43b3-4814-8b5c-02167c713594","resolution":{"observed_at":"2026-08-11T13:31:41.410146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-11T11:33:31.517785Z","title":"M.; Peng, Z.; Que, H.; Liu, J.; Zhou, W.; Wu, Y.; Guo, H.; Gan, R.; Ni, Z.; Yang, J.; Zhang, M.; Zhang, Z.; Ouyang, W.; Xu, K.; Huang, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15349","last_updated":"2024-12-19T19:30:42Z","snapshot_observed_at":"2026-08-11T16:31:36.057217Z","submitted_at":"2024-12-19T19:30:42Z","title":"Adaptive Urban Planning: A Hybrid Framework for Balanced City Development","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T11:33:31.517785Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2412.15349"},"observation_digest":"sha256:58a1a0dba9b30f3d6e9e597c7260f3deeeaed049baaa3a9cf9e767ba40e07b2b","observation_id":"fde1a923-0a51-40e1-acaf-3154b566f1eb","resolution":{"observed_at":"2026-08-11T11:33:31.517785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-10T22:54:57.994831Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.00430","last_updated":"2025-01-03T02:50:59Z","snapshot_observed_at":"2026-08-11T15:57:48.408831Z","submitted_at":"2024-12-31T13:11:20Z","title":"Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T22:54:57.994831Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2501.00430"},"observation_digest":"sha256:fec0589479034b3195118a45fc10d4f27b5deac6967e681235f0c1d4ba9b091d","observation_id":"c4c9a7de-9eed-4665-8330-a38cc2edcb65","resolution":{"observed_at":"2026-08-10T22:54:57.994831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-11T20:21:29.055002Z","title":"arXiv preprint arXiv:2310.00746 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.14731","last_updated":"2024-12-08T09:02:04Z","snapshot_observed_at":"2026-08-12T04:48:32.771014Z","submitted_at":"2024-12-08T09:02:04Z","title":"From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:29.055002Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2501.14731"},"observation_digest":"sha256:db92c3506deb6a9d2e529a632d385690b3313038c73bafb4063d2c1fc36e8b75","observation_id":"ada6bf37-7ea5-4c15-8a56-798e140a5f97","resolution":{"observed_at":"2026-08-11T20:21:29.055002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-10T14:21:24.254616Z","title":"Rolellm: Benchmarking, eliciting, and enhancing role-playing abilities of large language models,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.15427","last_updated":"2025-02-18T04:04:05Z","snapshot_observed_at":"2026-08-10T14:16:13.576974Z","submitted_at":"2025-01-26T07:07:01Z","title":"OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T14:21:24.254616Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2501.15427"},"observation_digest":"sha256:dc8302127d3d30e2d031af5433244b66f1138d98b1b45f7c0d0923bb5195b647","observation_id":"94da1345-9c82-41cb-9b39-740e362bd02a","resolution":{"observed_at":"2026-08-10T14:21:24.254616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-08T11:43:10.770324Z","title":"RoleLLM: benchmarking, eliciting, and enhancing role-playing abilities of large language models,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.07763","last_updated":"2025-02-11T18:46:01Z","snapshot_observed_at":"2026-08-08T11:37:30.423945Z","submitted_at":"2025-02-11T18:46:01Z","title":"Great Power Brings Great Responsibility: Personalizing Conversational AI for Diverse Problem-Solvers","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T11:43:10.770324Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2502.07763"},"observation_digest":"sha256:2200fc4401f81ca8af1dd1cf5cf0043e6489f8c9e83cb2e197ff25cad87d40d8","observation_id":"09600050-530f-44ee-996c-e84908eeaf12","resolution":{"observed_at":"2026-08-08T11:43:10.770324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-10T15:05:32.735270Z","title":", author Peng, Z","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2503.03752","last_updated":"2025-01-24T15:35:03Z","snapshot_observed_at":"2026-08-10T14:58:41.933648Z","submitted_at":"2025-01-24T15:35:03Z","title":"Multimodal Generative AI and Foundation Models for Behavioural Health in Online Gambling","version":1},"reference_index":149,"source":"arxiv_source","source_observed_at":"2026-08-10T15:05:32.735270Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2503.03752"},"observation_digest":"sha256:530fb2e918378dcdc5aed71ea6004caf1a86bf4be09967917a282d0982cacf84","observation_id":"9744dde9-3704-4e10-9fe7-8a43e811b5c8","resolution":{"observed_at":"2026-08-10T15:05:32.735270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-07T15:31:31.658059Z","title":"Rolellm: Benchmarking, eliciting, and enhancing role-playing abilities of large language models.arXiv preprint arXiv:2310.00746, 2023","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14905","last_updated":"2025-05-20T20:59:59Z","snapshot_observed_at":"2026-08-10T06:58:39.647477Z","submitted_at":"2025-05-20T20:59:59Z","title":"Concept Incongruence: An Exploration of Time and Death in Role Playing","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T15:31:31.658059Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2505.14905"},"observation_digest":"sha256:6237bb397e952d4a86efc26490c76cf5535e63426458952689a271e0f6af11ab","observation_id":"c3445007-aab9-468f-93f3-c3b8d8ae0335","resolution":{"observed_at":"2026-08-07T15:31:31.658059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-07T14:01:32.967829Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.20277","last_updated":"2025-06-05T14:53:28Z","snapshot_observed_at":"2026-08-12T04:53:34.495530Z","submitted_at":"2025-05-26T17:55:06Z","title":"OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T14:01:32.967829Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2505.20277"},"observation_digest":"sha256:ee7a0a9ec33caac31a78370d273c0d04380e0bb45afca2e3d70830f45ade3e5c","observation_id":"0c5ba767-8aad-40c3-b612-8f2dce9df986","resolution":{"observed_at":"2026-08-07T14:01:32.967829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2506.04565","last_updated":"2026-05-08T15:50:43Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-05T02:34:43Z","title":"From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems","version":2},"reference_index":191,"source":"pdf_text","source_observed_at":"2026-05-19T11:49:36.574471Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2506.04565"},"observation_digest":"sha256:78d6c1d82e0bbdc4d7c0c5c92a867a116ad327bb99ca8350cd13beab2261704c","observation_id":"d77d69a2-d7ea-4813-8ba0-7f4c36681d48","resolution":{"observed_at":"2026-05-19T11:52:16.113045Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-07T10:19:40.728914Z","title":"Rolellm: Benchmarking, eliciting, and enhancing role-playing abilities of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05614","last_updated":"2025-06-05T21:58:44Z","snapshot_observed_at":"2026-08-11T15:19:04.501072Z","submitted_at":"2025-06-05T21:58:44Z","title":"Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:40.728914Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2506.05614"},"observation_digest":"sha256:0c8daed7df97605f126b1a6585f23b6ab74aa6c35eaf3819f53db1ab3610b17b","observation_id":"9d4b3d7f-30fa-41ce-91af-568d219633d1","resolution":{"observed_at":"2026-08-07T10:19:40.728914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-07T05:28:11.168723Z","title":"M., Peng, Z., Que, H., Liu, J., Zhou, W., Wu, Y ., Guo, H., Gan, R., Ni, Z., Yang, J., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07947","last_updated":"2025-06-09T17:11:07Z","snapshot_observed_at":"2026-08-09T13:00:24.106493Z","submitted_at":"2025-06-09T17:11:07Z","title":"Statistical Hypothesis Testing for Auditing Robustness in Language Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:11.168723Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2506.07947"},"observation_digest":"sha256:a178f950e895a39342e1d8236c2c798b031a6271e55beaf59a9cbaa4df18db07","observation_id":"428fe31f-9dfc-4ecf-9332-74760eb580f7","resolution":{"observed_at":"2026-08-07T05:28:11.168723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-06T15:02:27.047188Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17147","last_updated":"2025-07-23T02:26:33Z","snapshot_observed_at":"2026-08-09T18:26:18.572534Z","submitted_at":"2025-07-23T02:26:33Z","title":"CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T15:02:27.047188Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2507.17147"},"observation_digest":"sha256:9c8e7e153362efa3d0019dcab5da91be453e23f339fe33208838f7ad023f8823","observation_id":"9c47933e-6571-489f-9442-701dc090511f","resolution":{"observed_at":"2026-08-06T15:02:27.047188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-05T22:46:28.470303Z","title":"M.; Peng, Z.; Que, H.; Liu, J.; Zhou, W.; Wu, Y.; Guo, H.; Gan, R.; Ni, Z.; Yang, J.; Zhang, M.; Zhang, Z.; Ouyang, W.; Xu, K.; Huang, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.06388","last_updated":"2025-08-08T15:17:24Z","snapshot_observed_at":"2026-08-10T11:40:39.416021Z","submitted_at":"2025-08-08T15:17:24Z","title":"LLMs vs. Chinese Anime Enthusiasts: A Comparative Study on Emotionally Supportive Role-Playing","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-05T22:46:28.470303Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2508.06388"},"observation_digest":"sha256:04f28f1c88ef4e142f41654574ff1eb7338c5e511c6020689f8a4a6756ca4795","observation_id":"2368317d-5021-43a4-bff6-150762fe1417","resolution":{"observed_at":"2026-08-05T22:46:28.470303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-05T10:36:06.461904Z","title":"arXiv preprint arXiv:2310.00746","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03940","last_updated":"2025-09-04T07:03:46Z","snapshot_observed_at":"2026-08-08T20:51:35.281735Z","submitted_at":"2025-09-04T07:03:46Z","title":"VoxRole: A Comprehensive Benchmark for Evaluating Speech-Based Role-Playing Agents","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:36:06.461904Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2509.03940"},"observation_digest":"sha256:904266098b5837d1c867293cadcfa7b4703bdfa4369dfab602461c563c48ab5a","observation_id":"db3614dc-7a7f-4451-be46-e0fce240432e","resolution":{"observed_at":"2026-08-05T10:36:06.461904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2511.08565","last_updated":"2026-05-14T13:52:44Z","snapshot_observed_at":"2026-08-11T17:52:09.454849Z","submitted_at":"2025-11-11T18:47:44Z","title":"Moral Susceptibility and Robustness under Persona Role-Play in Large Language Models","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-17T23:16:56.957905Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2511.08565"},"observation_digest":"sha256:4d715296c83c94eb604464c45b659e307e88c74f02e172af37b5b3fc5b2b9e1f","observation_id":"feb2f6ca-856a-4cd8-acf5-d11a1fe0ab96","resolution":{"observed_at":"2026-05-17T23:20:27.787379Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2601.06352","last_updated":"2026-04-26T19:11:11Z","snapshot_observed_at":"2026-08-11T00:41:56.699749Z","submitted_at":"2026-01-09T23:16:39Z","title":"CARD: Cluster-level Adaptation with Reward-guided Decoding for Personalized Text Generation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-16T15:24:38.212981Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2601.06352"},"observation_digest":"sha256:e68b8f0363d3a1c716e72c118d597cbfae80c96948228bbb496e6addeda87dea","observation_id":"5f7033f3-7c6f-44b3-9068-e1e0382ce92b","resolution":{"observed_at":"2026-05-16T15:28:02.408055Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2604.05939","last_updated":"2026-04-07T14:34:20Z","snapshot_observed_at":"2026-08-11T13:36:27.991548Z","submitted_at":"2026-04-07T14:34:20Z","title":"Context-Value-Action Architecture for Value-Driven Large Language Model Agents","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T19:53:10.939745Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2604.05939"},"observation_digest":"sha256:c09c7113c280cc8c274bb59988e91cfe9a8b34ffd8e04a81e48c7c8f242be6de","observation_id":"09d34916-7578-4e27-9e4d-4da4f8b1e016","resolution":{"observed_at":"2026-05-10T22:25:51.348044Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2604.07054","last_updated":"2026-04-09T07:49:38Z","snapshot_observed_at":"2026-08-02T09:08:32.703047Z","submitted_at":"2026-04-08T13:06:37Z","title":"Sell More, Play Less: Benchmarking LLM Realistic Selling Skill","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-10T17:36:10.725278Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2604.07054"},"observation_digest":"sha256:728d9e92c5b9a4ee7684de67c88242199741370a930f151c60268f7c45ae029b","observation_id":"99666af2-91d6-486c-bf73-c61989ee908b","resolution":{"observed_at":"2026-05-11T06:31:02.381381Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2604.09557","last_updated":"2026-05-28T14:40:40Z","snapshot_observed_at":"2026-08-10T22:40:12.860257Z","submitted_at":"2026-02-10T16:19:56Z","title":"SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-16T03:13:37.202362Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2604.09557"},"observation_digest":"sha256:0e345615445057d8119c7330cb19972e4622c95bc681401441245cda1fed1aff","observation_id":"5468e0b7-588f-49f3-bf4a-296a70dda8e4","resolution":{"observed_at":"2026-05-16T03:17:12.565697Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-03T02:40:58.562331Z","title":"M., Peng, Z., Que, H., Liu, J., Zhou, W., Wu, Y., Guo, H., Gan, R., Ni, Z., Zhang, M., Zhang, Z., Ouyang, W., Xu, K., Chen, W., Fu, J., and Peng, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2604.09557","last_updated":"2026-05-28T14:40:40Z","snapshot_observed_at":"2026-08-10T22:40:12.860257Z","submitted_at":"2026-02-10T16:19:56Z","title":"SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-03T02:40:58.562331Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2604.09557"},"observation_digest":"sha256:7d6e7b42452cb17753078345c4c447d3a993139ec444f9d474693515e050d333","observation_id":"09ca9687-c2db-4d74-b1e1-13dfdddf5a45","resolution":{"observed_at":"2026-08-03T02:40:58.562331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2604.13804","last_updated":"2026-04-15T12:39:03Z","snapshot_observed_at":"2026-07-06T23:01:41.643335Z","submitted_at":"2026-04-15T12:39:03Z","title":"Character Beyond Speech: Leveraging Role-Playing Evaluation in Audio Large Language Models via Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T14:22:25.660785Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2604.13804"},"observation_digest":"sha256:46df2e406d3851a45458a011544449878fc8e55d272243738024cfe0cebbd74c","observation_id":"02f4014a-b37a-4fed-880f-b224b044916e","resolution":{"observed_at":"2026-05-10T14:25:30.181017Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2605.23969","last_updated":"2026-05-13T06:36:24Z","snapshot_observed_at":"2026-08-09T04:48:40.503387Z","submitted_at":"2026-05-13T06:36:24Z","title":"SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T22:02:30.217607Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2605.23969"},"observation_digest":"sha256:9ae64e7d5b852bc8233e478efb2161500b76706bb259d7985901c981c12adede","observation_id":"512345cd-19ad-48b9-b230-2f4280775dc2","resolution":{"observed_at":"2026-06-30T22:05:05.684709Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2605.24279","last_updated":"2026-05-22T23:13:21Z","snapshot_observed_at":"2026-07-31T10:30:03.841807Z","submitted_at":"2026-05-22T23:13:21Z","title":"ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-06-30T15:17:37.904831Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2605.24279"},"observation_digest":"sha256:dadfab6ed7db80f11f563e1d7d46eabf118ebb519708e8e7d436e7029dcc27ec","observation_id":"264fea4f-e1e3-452a-8e15-8d0555e4adc6","resolution":{"observed_at":"2026-06-30T15:24:50.198225Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":"2310.00746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-06-30T22:05:05.682988Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":"17db8c47-8e5d-46c4-8d76-2b7ccb6b1bb8","year":2023},"citing_paper":{"arxiv_id":"2605.27914","last_updated":"2026-06-09T02:24:01Z","snapshot_observed_at":"2026-07-06T23:37:36.244456Z","submitted_at":"2026-05-27T03:41:11Z","title":"Does Capability Transfer to Subjective Behavior -- and Would Our Instruments Tell Us? A Self-Evolving, Trust-by-Construction Evaluation Paradigm","version":2},"reference_index":123,"source":"pdf_text","source_observed_at":"2026-06-29T12:54:36.818698Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2605.27914"},"observation_digest":"sha256:6d18c55e3c3286894e612c5754d3620887c6e9dd7bce0141aaa12caedb2a96c9","observation_id":"9e043c74-196d-4eb5-8b8e-caf4335a92a3","resolution":{"observed_at":"2026-06-29T13:03:26.789118Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-07-14T12:06:16.011748Z","title":"Huang, Jie Fu, and Junran Peng","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10390","last_updated":"2026-07-11T16:37:06Z","snapshot_observed_at":"2026-08-09T12:14:32.954651Z","submitted_at":"2026-07-11T16:37:06Z","title":"A Stepwise Questioning Expert-Editor Multi-Agent Framework for Long-Document Summarization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T12:06:16.011748Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2607.10390"},"observation_digest":"sha256:f6fd86a01b189770f372b6bc91569983db78d7a42018e1acb27029ebeccb2ee2","observation_id":"4b8b331f-1e6e-4306-8f3e-1ffe52ce7f19","resolution":{"observed_at":"2026-07-14T12:06:16.011748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-01T07:12:17.699323Z","title":"arXiv preprint arXiv:2310.00746 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21550","last_updated":"2026-07-23T17:35:20Z","snapshot_observed_at":"2026-08-08T08:48:36.880078Z","submitted_at":"2026-07-23T17:35:20Z","title":"X$^3$-OPD: Distilling Reasoning into Large Audio-Language Models via On-Policy Alignment","version":1},"reference_index":150,"source":"arxiv_source","source_observed_at":"2026-08-01T07:12:17.699323Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2607.21550"},"observation_digest":"sha256:03d0e24de3bbdf43d1e394bfa919f844367765be22a668c516b2c7b9c7fe5549","observation_id":"7f5c21eb-0d96-440f-aaad-e334a13303ee","resolution":{"observed_at":"2026-08-01T07:12:17.699323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00746","snapshot_observed_at":"2026-08-01T08:29:28.888679Z","title":"arXiv preprint arXiv:2310.00746 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27379","last_updated":"2026-07-29T18:37:14Z","snapshot_observed_at":"2026-08-12T05:39:53.006615Z","submitted_at":"2026-07-29T18:37:14Z","title":"HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-01T08:29:28.888679Z"},"links":{"cited_paper":"/paper/2310.00746","citing_paper":"/paper/2607.27379"},"observation_digest":"sha256:95985a3be4f7d99476214a92469be03344ba3268a69d034bf889ca2c70c3a05a","observation_id":"d91f4ac1-41c4-40cc-9663-ba1306916aa7","resolution":{"observed_at":"2026-08-01T08:29:28.888679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2310.00746/citation-record","integrity":"/paper/2310.00746/integrity","json":"/paper/2310.00746/citation-record.json","paper":"/paper/2310.00746"},"outbound":[],"paper":{"arxiv_id":"2310.00746","last_updated":"2024-06-18T13:08:24Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-06T18:33:39.368740Z","submitted_at":"2023-10-01T17:52:59Z","title":"RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2310.00746."}