{"as_of":"2026-08-13T14:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:21d25f733f549b91a31f3cc35bcce9e27ad4d72127582ea9d63ae9376d0f7deb","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T20:11:46.653735Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:54:34.550318Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T06:55:59.276055Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-11T19:54:34.550318Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06249","last_updated":"2024-12-09T06:47:42Z","snapshot_observed_at":"2026-08-11T19:50:03.366364Z","submitted_at":"2024-12-09T06:47:42Z","title":"Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T19:54:34.550318Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2412.06249"},"observation_digest":"sha256:dcc3ab087e776a2417d0e005bab6c66893241281c68a1fd669a2b6ea935c20c6","observation_id":"efa39af7-6918-407a-9677-f2ba5abc1ecb","resolution":{"observed_at":"2026-08-11T19:54:34.550318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-11T18:05:02.191357Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.08255","last_updated":"2024-12-11T10:06:57Z","snapshot_observed_at":"2026-08-11T17:58:59.745245Z","submitted_at":"2024-12-11T10:06:57Z","title":"Accurate Medical Named Entity Recognition Through Specialized NLP Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T18:05:02.191357Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2412.08255"},"observation_digest":"sha256:855da0d8561bb0adf866e7e2aa48272d6950da82810dfb85904ff03b96353b0d","observation_id":"32162529-e39c-471d-a668-781dcbfe6688","resolution":{"observed_at":"2026-08-11T18:05:02.191357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-11T04:57:17.523439Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.18222","last_updated":"2024-12-24T07:07:14Z","snapshot_observed_at":"2026-08-11T04:52:58.272024Z","submitted_at":"2024-12-24T07:07:14Z","title":"Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T04:57:17.523439Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2412.18222"},"observation_digest":"sha256:6e91351ada07bef54d9d4107d8b4bff1b94d81fe629a7e69a6ca460d6671d014","observation_id":"a103e032-4766-43a0-87a5-2ba1ae22aa4b","resolution":{"observed_at":"2026-08-11T04:57:17.523439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-11T04:50:46.858072Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.18321","last_updated":"2024-12-24T10:13:20Z","snapshot_observed_at":"2026-08-11T09:56:16.571247Z","submitted_at":"2024-12-24T10:13:20Z","title":"Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T04:50:46.858072Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2412.18321"},"observation_digest":"sha256:6534264f432f718a9d8e32fdd1fd935bf898b4d843e7895e4ad301deba83cf6d","observation_id":"2219e53b-7518-414f-b7b2-88c9983e039c","resolution":{"observed_at":"2026-08-11T04:50:46.858072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-11T04:33:58.810330Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.18729","last_updated":"2024-12-25T01:10:25Z","snapshot_observed_at":"2026-08-11T04:29:47.428422Z","submitted_at":"2024-12-25T01:10:25Z","title":"Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T04:33:58.810330Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2412.18729"},"observation_digest":"sha256:58c24435fa1be2595845ede98d59e98f284e86cc919c3119793eeffe360b46b4","observation_id":"c6bb979f-27c9-43f1-9e99-8ae7170ba928","resolution":{"observed_at":"2026-08-11T04:33:58.810330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-11T00:37:46.421790Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19449","last_updated":"2024-12-27T04:37:06Z","snapshot_observed_at":"2026-08-11T00:33:06.192072Z","submitted_at":"2024-12-27T04:37:06Z","title":"Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T00:37:46.421790Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2412.19449"},"observation_digest":"sha256:3129ee15c83bb55a858a6b30836aadb2735bb48ae16783049deec30f58cb54b9","observation_id":"551fe730-d7d2-4466-97dc-504b48c619d4","resolution":{"observed_at":"2026-08-11T00:37:46.421790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-10T23:26:39.832740Z","title":"LoRA -LiteE: A Computationally Efficient Framework for Chatbot Preference -Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.20345","last_updated":"2024-12-29T04:07:58Z","snapshot_observed_at":"2026-08-13T00:24:45.044747Z","submitted_at":"2024-12-29T04:07:58Z","title":"Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T23:26:39.832740Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2412.20345"},"observation_digest":"sha256:ae42e6673a2b6fd482224a54fb35f495f5a7b03850b442e30889eeffdf3e7ac3","observation_id":"5f138ca8-dc92-4867-8be0-b356225de79b","resolution":{"observed_at":"2026-08-10T23:26:39.832740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-08-10T14:55:04.100403Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.14859","last_updated":"2025-01-24T18:54:14Z","snapshot_observed_at":"2026-08-13T08:03:12.485213Z","submitted_at":"2025-01-24T18:54:14Z","title":"Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T14:55:04.100403Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2501.14859"},"observation_digest":"sha256:427ce278cb128567afbbdf0a5f78eee9fb95fb3dcc499908830a0c86b8e90fe5","observation_id":"2a6ce578-0846-4955-b71b-d2eb69a82691","resolution":{"observed_at":"2026-08-10T14:55:04.100403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"cited_work":{"arxiv_id":"2411.09947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.09947","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a66691db-9bfa-4031-943a-85339410cef9","year":2025},"citing_paper":{"arxiv_id":"2604.07173","last_updated":"2026-04-08T15:01:04Z","snapshot_observed_at":"2026-08-11T00:10:11.734155Z","submitted_at":"2026-04-08T15:01:04Z","title":"InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T17:23:40.872418Z"},"links":{"cited_paper":"/paper/2411.09947","citing_paper":"/paper/2604.07173"},"observation_digest":"sha256:cf380b1a2339c4d59d4ce6d89d1102ed4936fae2b4e50320c48e8745827e74f7","observation_id":"9e895f02-26b7-4bfc-aad3-c8bff5b8b1fa","resolution":{"observed_at":"2026-05-11T06:55:59.280114Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.09947/citation-record","integrity":"/paper/2411.09947/integrity","json":"/paper/2411.09947/citation-record.json","paper":"/paper/2411.09947"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:11:47.766014Z","title":"An overview of chatbot technology","venue":null,"work_id":"4871e533-3d35-49a0-ad07-127a68ab3c3f","year":2020},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.411828Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:b4f1ad95bf8e6efd94b6a28a887c9ea914601b89907c177e6e751f001f947f33","observation_id":"c583d6e0-864f-4be2-8f11-fa130d1af553","resolution":{"observed_at":"2026-08-12T20:11:47.772152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.748103Z","title":"Conversational agents in healthcare: a systematic review","venue":null,"work_id":"2c62c938-100b-458e-8333-6ecf67803e17","year":2018},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.417776Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:08ebe9b5d6b8329e12c2fdabce295aab98ca2bfcb5b3445bd002d031dcce725b","observation_id":"4813ddfd-10c2-46b8-ae37-48250f65a378","resolution":{"observed_at":"2026-08-12T20:11:47.753751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.728913Z","title":"Unleashing the potential of chatbots in education: A state-of-the-art analysis","venue":null,"work_id":"3df91948-42f5-46f2-9730-d82c7ba14c36","year":2018},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.423296Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:e24a7b4daf42a8c11678a1772a6b1b770760606261f6ff3782d3ec900c14f20a","observation_id":"82bbcf05-02cb-46de-800e-6ff36afcec79","resolution":{"observed_at":"2026-08-12T20:11:47.734833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.710890Z","title":"Neural approaches to conversational ai","venue":null,"work_id":"4ebb0e14-5e80-4eed-b708-1b32cd1a3fb8","year":2018},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.428462Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:3c072f4c86239c417f0d7a8c8f91fc3531d1edd3ea548b900276e54adc1b13a6","observation_id":"377d8f89-7f31-4417-963d-0a6d1389ef18","resolution":{"observed_at":"2026-08-12T20:11:47.717100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.05415","last_updated":"2019-06-13T06:01:04Z","snapshot_observed_at":"2026-08-09T16:02:35.397999Z","submitted_at":"2019-01-16T18:02:44Z","title":"Learning from Dialogue after Deployment: Feed Yourself, Chatbot!","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.05415","snapshot_observed_at":"2026-08-12T20:11:46.433498Z","title":"Learning from dialogue after deployment: Feed yourself, chatbot! arXiv preprint arXiv:1901.05415 , 2019","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.433498Z"},"links":{"cited_paper":"/paper/1901.05415","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:3ab45fd7090c30ab0dc3f187a2dbb32b07cebb97b4a1ca8d86bcc798a1f79840","observation_id":"4d8c66ac-a00d-4991-a1a4-316c8c3af131","resolution":{"observed_at":"2026-08-12T20:11:46.433498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.08593","last_updated":"2020-01-08T23:02:36Z","snapshot_observed_at":"2026-08-11T04:34:07.318549Z","submitted_at":"2019-09-18T17:33:39Z","title":"Fine-Tuning Language Models from Human Preferences","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.08593","snapshot_observed_at":"2026-08-12T20:11:46.439735Z","title":"Fine- tuning language models from human preferences","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.439735Z"},"links":{"cited_paper":"/paper/1909.08593","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:83f53cbcc97d1bfe226961c5aac6d5f14667ae387d65a75ab4106329f0bf7487","observation_id":"b7e9b645-79b3-403f-a8a9-3a4f032d0c98","resolution":{"observed_at":"2026-08-12T20:11:46.439735Z","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":"2026-08-12T20:11:47.692277Z","title":"A reduction of imitation learning and structured prediction to no-regret online learning","venue":null,"work_id":"6c5c38ba-5a66-42fc-a80d-d93ae5779054","year":2011},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.446524Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:692984f59fe969e57d723384592f97e7b5581fef95b43d71d95266088986bf16","observation_id":"4666a5fe-7dc3-4649-a747-c868bc84282b","resolution":{"observed_at":"2026-08-12T20:11:47.698294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.674612Z","title":"Dialog-based language learning","venue":null,"work_id":"00eacc63-395f-4e34-b100-738a1dbaf171","year":2016},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.451924Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:adea634ef19b869adc17b59d6b824c47ec99abca7659c5c4dd82ac442834c420","observation_id":"644c630d-c090-41c8-86ae-1c5f27ab76e3","resolution":{"observed_at":"2026-08-12T20:11:47.679924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.656520Z","title":"Deep reinforcement learning from human prefer- ences","venue":null,"work_id":"74954226-36d5-4493-8559-261a935e2c0d","year":2017},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.456992Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:7fc3c4b864f0f654a10ebe45fcfe9963ca8f4a084d7352184ff025ee703eb990","observation_id":"0ca59fe9-7148-4d99-9aa8-c45c5ee4656e","resolution":{"observed_at":"2026-08-12T20:11:47.663128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:11:46.461609Z","title":"Training language models to follow instructions with human feedback","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.461609Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:e7f4600786df6909a192c6cf1aff9cb43e6a3ae20ac33c7e1b01149ba3b3f97d","observation_id":"cd182ba2-6d41-4ecc-8530-58df5f1584ea","resolution":{"observed_at":"2026-08-12T20:11:46.461609Z","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":"2026-08-12T20:11:47.626750Z","title":"A contrastive deep learning approach to cryptocurrency portfolio with us treasuries","venue":null,"work_id":"f13e9ae7-2819-494a-88f2-aa897e0d4d55","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.466775Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:158a58cd53a73dacdf48895e34b34522ffd275761bafb80b8630789c251e2bf2","observation_id":"d31dff68-3f67-488a-aac3-71eb787cffc6","resolution":{"observed_at":"2026-08-12T20:11:47.631998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-12T20:11:46.471431Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.471431Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:4c92bac879905ccc20fee15a3eab95c8963c8eb3716c7b5e1bc236749756fb9b","observation_id":"0e025321-6bf2-4e87-9b4f-5357a3fa3cdd","resolution":{"observed_at":"2026-08-12T20:11:46.471431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-12T20:11:46.476977Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.476977Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:1e1be2bed92d87dddd20a6da77d2bea13da76bcc5670413066b86081833ab068","observation_id":"05d6fbda-7141-4cdd-9416-f7ba90c68086","resolution":{"observed_at":"2026-08-12T20:11:46.476977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04132","last_updated":"2024-03-07T01:22:38Z","snapshot_observed_at":"2026-08-02T17:55:33.750637Z","submitted_at":"2024-03-07T01:22:38Z","title":"Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04132","snapshot_observed_at":"2026-08-12T20:11:46.481978Z","title":"Chatbot arena: An open platform for evaluating llms by human preference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.481978Z"},"links":{"cited_paper":"/paper/2403.04132","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:7088e4427f2204c43a3c8c44be4c71992385e93540af9aee4fed7ab6ef27e985","observation_id":"48095b9d-3a9f-412f-ad92-2fdc250272d3","resolution":{"observed_at":"2026-08-12T20:11:46.481978Z","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":"2026-08-12T20:11:47.609330Z","title":"Incorporating economic indicators and market sentiment effect into us treasury bond yield prediction with machine learning","venue":null,"work_id":"fd92e01e-b4ef-45b9-90c3-02d5be179ed8","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.487159Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:68780a8c0a1c3fe78e698d128e0bfc6deb4036a51ee9e205dbb2e2dec106fe97","observation_id":"1fb6b168-4fa2-40be-882e-48bd3e5575a8","resolution":{"observed_at":"2026-08-12T20:11:47.614601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-12T20:11:46.492842Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.492842Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:275b599073be38ef4e05eb720ad162244a63dfeeb04a5b5d50d9d744844b5b23","observation_id":"90c8cea8-bd2c-47f0-9de3-39196dc82a82","resolution":{"observed_at":"2026-08-12T20:11:46.492842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00118","last_updated":"2024-10-02T15:22:49Z","snapshot_observed_at":"2026-08-02T16:20:09.773989Z","submitted_at":"2024-07-31T19:13:07Z","title":"Gemma 2: Improving Open Language Models at a Practical Size","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00118","snapshot_observed_at":"2026-08-12T20:11:46.497792Z","title":"Gemma 2: Im- proving open language models at a practical size","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.497792Z"},"links":{"cited_paper":"/paper/2408.00118","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:f54dcd2d8f21c0f08bd128bbee8d4a7ed3914790a1423eb4dc32c6e700c53afd","observation_id":"0472a396-e900-4835-b5b1-74cff64fd7e4","resolution":{"observed_at":"2026-08-12T20:11:46.497792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02746","last_updated":"2024-12-23T05:43:01Z","snapshot_observed_at":"2026-08-12T23:50:09.570842Z","submitted_at":"2024-06-04T20:02:52Z","title":"RATT: A Thought Structure for Coherent and Correct LLM Reasoning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02746","snapshot_observed_at":"2026-08-12T20:11:46.502602Z","title":"Ratt: Athought structure for coherent and correct llmreasoning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.502602Z"},"links":{"cited_paper":"/paper/2406.02746","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:9e2dce6131d0b4e8f9bab7a9b8220f9e0a2040e2f62c7e1b643715f406d83f87","observation_id":"25c91759-2dcf-4eaf-b53b-a53af4df5e4c","resolution":{"observed_at":"2026-08-12T20:11:46.502602Z","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":"2026-08-12T20:11:46.508404Z","title":"Thought space explorer: Navigating and expanding thought space for large language model reasoning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.508404Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:4cf4dc24c861aac1f0341f83dea883fae0d4470f12b2b6c577184b1b37dcac17","observation_id":"eedd649d-02aa-4695-a456-0a44e741cec2","resolution":{"observed_at":"2026-08-12T20:11:46.508404Z","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":"2026-08-12T20:11:46.512955Z","title":"Direct preference optimization: Your language model is secretly a reward model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.512955Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:621e76172f03acbfc5707ba7c0cfc6578c780d7909299f35a6398230fd80c51b","observation_id":"d1897f40-4759-4719-ab6d-d78fcaf433f5","resolution":{"observed_at":"2026-08-12T20:11:46.512955Z","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":"2026-08-12T20:11:46.518438Z","title":"In-context time series predictor","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.518438Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:965d4fcb613e036955a4477805f68210ebcc135c42e21eaa87c40670f8c1fc18","observation_id":"a82889d2-93c7-4e35-886a-517e84ae2761","resolution":{"observed_at":"2026-08-12T20:11:46.518438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18043","last_updated":"2025-08-12T02:33:15Z","snapshot_observed_at":"2026-08-13T00:20:13.147289Z","submitted_at":"2024-04-28T01:38:38Z","title":"Utilizing Large Language Models for Information Extraction from Real Estate Transactions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18043","snapshot_observed_at":"2026-08-12T20:11:46.524211Z","title":"Utilizing large language models for information extraction from real estate transactions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.524211Z"},"links":{"cited_paper":"/paper/2404.18043","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:11432c996334052fe9d2a1238fa05e87bcf1901ff76d5310f4f44dc9ac6c7996","observation_id":"f6cd7e1a-9343-497b-9acd-a8454147d297","resolution":{"observed_at":"2026-08-12T20:11:46.524211Z","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":"2026-08-12T20:11:47.583289Z","title":"Using large language models in real estate transactions: A few-shot learning approach","venue":null,"work_id":"10b85f36-51c6-426e-ad68-13677fae88d0","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.529470Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:aba80972efc2f379cbb1f49b4697e95b0647e86a8cc2d419558275ad3de6b8d7","observation_id":"4d4b316b-afd7-451a-a4fb-5500ca73e3e1","resolution":{"observed_at":"2026-08-12T20:11:47.588418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17882","last_updated":"2024-06-10T20:27:28Z","snapshot_observed_at":"2026-08-13T08:21:24.045252Z","submitted_at":"2024-02-27T20:48:24Z","title":"BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17882","snapshot_observed_at":"2026-08-12T20:11:46.534357Z","title":"Blendsql: A scalable dialect for unifying hybrid question answering in relational algebra","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.534357Z"},"links":{"cited_paper":"/paper/2402.17882","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:9ccda3847555e7cd8048f811d7bb17fb8161fd1aa6272023c02431302e37b89b","observation_id":"ffafc69c-8af1-4a3d-8b6d-66d571f1a958","resolution":{"observed_at":"2026-08-12T20:11:46.534357Z","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":"2026-08-12T20:11:47.568131Z","title":"Accurate training of web-based question answering systems with feedback from ranked users","venue":null,"work_id":"ddf95d87-2c58-45a8-bc76-483e5ff60a9b","year":2023},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.539529Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:99577f0c091a0df3a2849d1751574b08b5586d46a339b5edfa43a827675d39b0","observation_id":"bb198edd-afb2-423c-a67b-0e749262e6ed","resolution":{"observed_at":"2026-08-12T20:11:47.572923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.16150","last_updated":"2024-07-23T03:26:07Z","snapshot_observed_at":"2026-08-12T23:16:47.843136Z","submitted_at":"2024-07-23T03:26:07Z","title":"Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis","version":1},"cited_work":{"arxiv_id":"2407.16150","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.16150","snapshot_observed_at":"2026-08-12T20:11:47.018313Z","title":"Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis","venue":"cs.LG","work_id":"68b065c5-1d8f-450a-a2c6-a32864310600","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.544429Z"},"links":{"cited_paper":"/paper/2407.16150","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:fbac2d40b84df674389a34ae5897087971374e2c236cb543c66ae6995ab4e635","observation_id":"1b028d99-7be8-4f40-b275-48fdc9cff3e8","resolution":{"observed_at":"2026-08-12T20:11:47.024832Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18962","last_updated":"2024-07-18T05:18:59Z","snapshot_observed_at":"2026-08-13T04:07:50.988053Z","submitted_at":"2024-07-18T05:18:59Z","title":"Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.18962","snapshot_observed_at":"2026-08-12T20:11:46.550133Z","title":"Autonomous navigation of unmanned vehicle through deep reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.550133Z"},"links":{"cited_paper":"/paper/2407.18962","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:6c6442a66d0d31d79218cc5ea254043247e1d179f617ea43920f77c66bb5e02e","observation_id":"5cec7fbf-9c82-4b82-9de4-5c917fe424f2","resolution":{"observed_at":"2026-08-12T20:11:46.550133Z","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":"2026-08-12T20:11:47.551906Z","title":"Can speculative sampling accelerate react without compromising reasoning quality? In The Second Tiny Papers Track at ICLR 2024","venue":null,"work_id":"d0677f47-5d5e-4dd5-9399-0596abe7ac4d","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.556862Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:c12f59f8080a1156df5eb5254feaf26459f099485758e58ecfff10ebaf5bb201","observation_id":"e2b7aada-30b1-4280-882a-2798f01646aa","resolution":{"observed_at":"2026-08-12T20:11:47.557442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.16670","last_updated":"2024-10-22T03:59:53Z","snapshot_observed_at":"2026-08-12T22:17:56.081260Z","submitted_at":"2024-10-22T03:59:53Z","title":"CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.16670","snapshot_observed_at":"2026-08-12T20:11:46.562196Z","title":"Cops: Empowering llm agents with provable cross-task experience sharing","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.562196Z"},"links":{"cited_paper":"/paper/2410.16670","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:fb08f43f08b05d7a1b1973f627839d1997cfc52b03ba2a4dcf5d80f830975359","observation_id":"c6557637-c5ff-4491-9f99-ee93eee4422a","resolution":{"observed_at":"2026-08-12T20:11:46.562196Z","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":"2026-08-12T20:11:47.534274Z","title":"Integrated optimization of large language models: Synergizing data utilization and compression techniques","venue":null,"work_id":"f63e37d9-b651-4bca-a364-99b3109e086d","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.567907Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:ebb28df648b8ec18cad4464a8cf384c25115bd25eb1b3dc8c9c975b53a8e3385","observation_id":"2676466d-399b-4a03-a098-f2774b330387","resolution":{"observed_at":"2026-08-12T20:11:47.539822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.11703","last_updated":"2024-09-18T04:56:52Z","snapshot_observed_at":"2026-08-12T22:42:45.423740Z","submitted_at":"2024-09-18T04:56:52Z","title":"Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.11703","snapshot_observed_at":"2026-08-12T20:11:46.572876Z","title":"Harnessing llms for api interactions: A framework for classification and synthetic data generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.572876Z"},"links":{"cited_paper":"/paper/2409.11703","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:592d60808169344987e60b27c679e468341e06883e82561445d9a5d6d34644a6","observation_id":"deacd70e-1dad-44c9-8b98-7040baef49ac","resolution":{"observed_at":"2026-08-12T20:11:46.572876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.04585","last_updated":"2024-09-14T03:19:10Z","snapshot_observed_at":"2026-08-12T23:06:25.868556Z","submitted_at":"2024-08-08T16:54:40Z","title":"Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04585","snapshot_observed_at":"2026-08-12T20:11:46.579271Z","title":"Towards resilient and efficient llms: A comparative study of efficiency, performance, and adversarial robustness","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.579271Z"},"links":{"cited_paper":"/paper/2408.04585","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:7b85de6afc7d6253df3271178ed197c11cb8ac05b9a3cc83bdc4fd63b861400b","observation_id":"65b51124-8f91-4896-8d7f-7fb4fab057eb","resolution":{"observed_at":"2026-08-12T20:11:46.579271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01046","last_updated":"2019-03-22T20:25:57Z","snapshot_observed_at":"2026-08-09T11:18:21.803795Z","submitted_at":"2019-02-04T06:27:41Z","title":"Towards Federated Learning at Scale: System Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01046","snapshot_observed_at":"2026-08-12T20:11:46.584260Z","title":"Towards federated learning at scale: Syste m design","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.584260Z"},"links":{"cited_paper":"/paper/1902.01046","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:1d0df413da7870b93bb99930323e5aee9d9d4729fd0e8096926a8f5394465211","observation_id":"2c6310b7-d4c6-4cee-ae6b-934473f91c39","resolution":{"observed_at":"2026-08-12T20:11:46.584260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08815","last_updated":"2022-05-06T10:05:52Z","snapshot_observed_at":"2026-07-06T11:01:05.577957Z","submitted_at":"2021-04-18T11:04:49Z","title":"FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08815","snapshot_observed_at":"2026-08-12T20:11:46.590224Z","title":"Fednlp: A research platform for federated learning in natural language processing","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.590224Z"},"links":{"cited_paper":"/paper/2104.08815","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:8440d43bbaaefc4ac734f92eee4bacf2887fde8460865f0892048401cbf174ab","observation_id":"80950e19-eb26-4812-b179-9d8e2e24a5a0","resolution":{"observed_at":"2026-08-12T20:11:46.590224Z","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":"2026-08-12T20:11:47.514588Z","title":"Pmfl: Partial meta-federated learning for heterogeneous tasks and its applications on real-world medical records","venue":null,"work_id":"eb73b847-1631-4885-b9f0-eeca80d50804","year":2022},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.597059Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:1af27ea82f6afac3c7cad03a62b27198b4494728237f97ec00fcf3b5bb8bb916","observation_id":"9cfac17b-ba69-40cd-a179-466bb25bf17e","resolution":{"observed_at":"2026-08-12T20:11:47.520464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18888","last_updated":"2025-06-05T04:46:31Z","snapshot_observed_at":"2026-08-13T04:07:38.987086Z","submitted_at":"2024-02-29T06:13:10Z","title":"Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos","version":4},"cited_work":{"arxiv_id":"2402.18888","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.18888","snapshot_observed_at":"2026-08-12T20:11:46.860624Z","title":"Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos","venue":"cs.LG","work_id":"f4902619-a185-4c9a-a6c1-be85afb9d1e7","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.602646Z"},"links":{"cited_paper":"/paper/2402.18888","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:b4993978cee7a130aa4441d8aa0919706d8604c5db334b00c399c1f67fbdb02f","observation_id":"579effa3-93e8-4779-a43f-e711da416900","resolution":{"observed_at":"2026-08-12T20:11:46.867894Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-12T20:11:46.608612Z","title":"Roberta: A robustly optimized bert pretraining approach","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.608612Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:f974e21ec6db067b00cc89300b61aec2b2467b977ae8a65d8a98a58bb935a8fa","observation_id":"9c1f1fa6-30a1-4b1c-b18e-a89090ec9d10","resolution":{"observed_at":"2026-08-12T20:11:46.608612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10555","last_updated":"2020-03-23T21:17:42Z","snapshot_observed_at":"2026-08-11T12:55:51.308870Z","submitted_at":"2020-03-23T21:17:42Z","title":"ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10555","snapshot_observed_at":"2026-08-12T20:11:46.617294Z","title":"Electra: Pre-training text encoders as discriminators rather than generators","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.617294Z"},"links":{"cited_paper":"/paper/2003.10555","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:ab961089f1577506b6949ffa76a43efd022d152a2c77752a62e877fd99893a40","observation_id":"807be690-9e25-453f-9b04-5f8fe6acebbf","resolution":{"observed_at":"2026-08-12T20:11:46.617294Z","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":"2026-08-12T20:11:47.496053Z","title":"Ensemble methods in machine learning","venue":null,"work_id":"15d5d160-43c2-4517-845b-3bba0791f830","year":2000},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.623344Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:44792eb3acd728005c1fc6cd0911a7f74e950bad38db82c0d519160877cf6aa5","observation_id":"3a8b8169-0d53-470a-8f09-0ae6e7cbb7b9","resolution":{"observed_at":"2026-08-12T20:11:47.501976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.479121Z","title":"Restful- llama: Connecting user queries to restful apis","venue":null,"work_id":"54d7eafd-85d7-448d-9423-f7b4d8862d19","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.628680Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:fcc178b2fd46694be0c6ed692908cf54f994565fb63a63d47f7c8d97491a71c7","observation_id":"bec5c858-8421-4b82-a0cc-26fc054141d4","resolution":{"observed_at":"2026-08-12T20:11:47.483987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.458993Z","title":"Dual learning for machine translation","venue":null,"work_id":"5d4d783a-2bf3-4049-917f-7a016f2c9c7d","year":2016},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.633347Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:84e27664cceb421e49417d0524f5b4cbf9241c2d28f0c786f8c65c2cd177ff8f","observation_id":"976fc49e-3871-49b2-b3f0-ae5ae854e0a6","resolution":{"observed_at":"2026-08-12T20:11:47.466747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.439550Z","title":"An ensemble approach to stock price prediction using deep learning and time series models","venue":null,"work_id":"bebeb7e6-cb1c-4d09-8d22-32473d5ce819","year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.639252Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:183c06841da696b61c523ff71eab1e301e19c9e96315107f03e69a8d6d91b21e","observation_id":"60515112-a7dd-4912-bef9-3371790bb408","resolution":{"observed_at":"2026-08-12T20:11:47.445350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T20:11:47.420343Z","title":"Meta learning enabled adversarial defense","venue":null,"work_id":"0b3d181f-9069-4993-a7f4-6b5415287d90","year":2023},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.643878Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:979f6dd79b4baa7d3984485eddc1928d57f6df186dbf5ebb3e1085081b616c57","observation_id":"b217f65e-3e74-4154-9ebe-17c7370ee81f","resolution":{"observed_at":"2026-08-12T20:11:47.426161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:11:46.648732Z","title":"Steerdiff: Steering towards safe text-to-image diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.648732Z"},"links":{"citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:158ee0b13461051258f9f4b4c61ea80193079380c344ed278573d2bea649812d","observation_id":"4700cc71-cdca-4c49-a293-1d1f814ea8d9","resolution":{"observed_at":"2026-08-12T20:11:46.648732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09582","last_updated":"2024-09-24T05:23:31Z","snapshot_observed_at":"2026-08-12T22:44:47.552400Z","submitted_at":"2024-09-15T01:54:17Z","title":"NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.09582","snapshot_observed_at":"2026-08-12T20:11:46.653735Z","title":"Nevlp: Noise- robust framework for efficient vision-language pre-training","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T20:11:46.653735Z"},"links":{"cited_paper":"/paper/2409.09582","citing_paper":"/paper/2411.09947"},"observation_digest":"sha256:cbb76de100f4dbbe3ed7da194574ef8e22ed2b0386d8051be080d40b8069e37d","observation_id":"f8c24c52-e0a5-41db-adcd-5ae0382f15c2","resolution":{"observed_at":"2026-08-12T20:11:46.653735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.09947","last_updated":"2025-01-08T09:01:16Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-12T20:05:37.552073Z","submitted_at":"2024-11-15T04:57:13Z","title":"LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":2,"verified_fuzzy":19},"total_outbound_references":45},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 9 inbound Pith citation observations for arXiv:2411.09947."}