{"as_of":"2026-08-08T08:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f21cd7579e7682017bdee6425277ec94684e25bb91122e140804fe47b98c8984","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:30:24.218058Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.23815/citation-record","integrity":"/paper/2505.23815/integrity","json":"/paper/2505.23815/citation-record.json","paper":"/paper/2505.23815"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:21.034846Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.034846Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:5e002d0ee35a55b1856fbfba89eb40be6f2dceb30845eb3e9e23d3b83c0deec4","observation_id":"013d5bf4-c53b-4a2f-bade-6c469d129d7d","resolution":{"observed_at":"2026-08-07T13:30:21.034846Z","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":"2026-08-07T13:30:21.154748Z","title":"PROST : P hysical reasoning about objects through space and time","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.154748Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:1c7fc65215ebe22dd2688a599699e7a4e8a04375cfac918e3f6804254ef880e3","observation_id":"d151dc49-3c8a-4e2c-a883-db23cae26983","resolution":{"observed_at":"2026-08-07T13:30:21.154748Z","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":"2026-08-07T13:30:21.244785Z","title":null,"venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.244785Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:0c2d8af9b6f52425f9557afe0c0c5e89a71857bb901ae14af62b29ef6e9f05a5","observation_id":"68975d56-02c7-4bd0-aea9-89810a207976","resolution":{"observed_at":"2026-08-07T13:30:21.244785Z","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":"2026-08-07T13:30:28.930305Z","title":"Art or artifice? large language models and the false promise of creativity","venue":null,"work_id":"548444e9-c9ec-4fbb-9bd9-53177a67995f","year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.344764Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:7b9e50e30abd621fbc13d56a8f6d520fdd034d2317027147ab28dc2c1f1e40e5","observation_id":"abd8ac26-0c19-42aa-8887-82c36425652b","resolution":{"observed_at":"2026-08-07T13:30:29.030572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.15269","last_updated":"2024-11-23T16:19:03Z","snapshot_observed_at":"2026-08-07T17:02:12.842447Z","submitted_at":"2024-04-23T17:57:47Z","title":"Aligning LLM Agents by Learning Latent Preference from User Edits","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.15269","snapshot_observed_at":"2026-08-07T13:30:21.434743Z","title":"Aligning llm agents by learning latent preference from user edits","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.434743Z"},"links":{"cited_paper":"/paper/2404.15269","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:14ee78c7c17c6ae81a88bb55cc9fa3b800aeac8bf0f719ba9803ec60f8344557","observation_id":"4533792d-5434-4edd-9ed2-7dc4c277ae66","resolution":{"observed_at":"2026-08-07T13:30:21.434743Z","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":"2026-08-07T13:30:28.826332Z","title":null,"venue":null,"work_id":"e7f0f8a4-d81d-42f7-bd89-a5a2e5e0596d","year":1966},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.514743Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:4af9e011fb5fb7241c1adaf31674ff9c27401d3a144a108351ac869f853fe843","observation_id":"9d244e8c-8260-46b4-95ae-3b7164cb2962","resolution":{"observed_at":"2026-08-07T13:30:28.892016Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:28.725057Z","title":"Inference-time intervention: Eliciting truthful answers from a language model","venue":null,"work_id":"984a309b-7269-4676-8c7f-e1eb59f87124","year":2023},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.614849Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:261778bf32f9f11bf841c3af6920a01e4381f8b5fd959bb58883f4ab5ff5a262","observation_id":"35335f18-4f8a-4071-8230-cadff30d80c1","resolution":{"observed_at":"2026-08-07T13:30:28.782727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17346","last_updated":"2024-05-27T16:49:29Z","snapshot_observed_at":"2026-07-06T18:20:44.070255Z","submitted_at":"2024-05-27T16:49:29Z","title":"Prompt Optimization with Human Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17346","snapshot_observed_at":"2026-08-07T13:30:21.687814Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.687814Z"},"links":{"cited_paper":"/paper/2405.17346","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:59f166fe7bda7d025cd86bcb4b0ddd959a66f904634a0a8f8d4cce6c3f9efee8","observation_id":"c10bf89f-d525-48b5-8f5b-0b0875d8261d","resolution":{"observed_at":"2026-08-07T13:30:21.687814Z","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":"2026-08-07T13:30:28.592004Z","title":null,"venue":null,"work_id":"61dbd3c1-7626-41c5-8082-9b24834167b9","year":2025},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.797225Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:a6ab50c6bbfa2ab24eb7159c43df54d3d234beb7d614c0abbeb0c53539690e2e","observation_id":"fdc6afa4-afa7-48c5-9115-fe6e1fb0f131","resolution":{"observed_at":"2026-08-07T13:30:28.679715Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5637.36629","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:25.764735Z","title":"Llm-powered hierarchical language agent for real-time human-ai coordination","venue":null,"work_id":"a75f1d3b-12ab-467a-a2d8-9e2b82053078","year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:21.937190Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:ff7a2045b39d045e4078e5482cb337c8492f4daac4dfed0b7413eeec27a140ae","observation_id":"6cf85125-6bc7-47ca-962d-b07785e76b5a","resolution":{"observed_at":"2026-08-07T13:30:25.904762Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"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":"2026-08-07T13:30:22.045668Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.045668Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:dc251c98abef81eeb33d2f3dd8fa9e02fd4ab18c6c5f99e31bf2f2a2d5872951","observation_id":"39dc3f3c-40ec-498e-9178-81be1f084ad6","resolution":{"observed_at":"2026-08-07T13:30:22.045668Z","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":"2026-08-07T13:30:22.158182Z","title":"Training language models to follow instructions with human feedback","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.158182Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:3ad9b79e19d7c93f3041cac7c68fc1dcd74eade104756db83802e35955ac1b10","observation_id":"9d952153-77d8-4ce8-95f2-217def04825b","resolution":{"observed_at":"2026-08-07T13:30:22.158182Z","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":"2026-08-07T13:30:28.487587Z","title":"Z., Sumers, T","venue":null,"work_id":"3f4a8704-d96a-4b0d-bf01-542485226af5","year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.239944Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:6e6ffa07d0572c4adf57d7a92ca0b48e67f7c612d62581e11ea2483ea86ad071","observation_id":"c0396970-80b8-4bcd-93cf-444f3c36a46c","resolution":{"observed_at":"2026-08-07T13:30:28.534367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:22.287941Z","title":"and Hruschka, E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.287941Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:7eb0c6c5bafd1ceab3aa16f996163cfe7b0ae22680f29f4b0d07f4e8e4a6b49b","observation_id":"7af9e6cc-2b41-4b39-bb56-5b36027e040a","resolution":{"observed_at":"2026-08-07T13:30:22.287941Z","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":"2026-08-07T13:30:22.376574Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.376574Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:084895e243e69a6da9d383a12fa657ec83783dbe2bb20dfc267a97dce8d3aa54","observation_id":"10a1c055-94c4-474f-8fbf-51a2843b67a3","resolution":{"observed_at":"2026-08-07T13:30:22.376574Z","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":"2026-08-07T13:30:22.503063Z","title":"D., Ermon, S., and Finn, C","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.503063Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:dd3ecc46ab6a4d3aa64e41b9c0cf9e894b9f90ce2cca7adf936a22e9d15e41df","observation_id":"301b8453-97ce-4af9-b7cc-e05b67074eea","resolution":{"observed_at":"2026-08-07T13:30:22.503063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11406","last_updated":"2024-06-05T03:29:31Z","snapshot_observed_at":"2026-07-06T15:18:45.940190Z","submitted_at":"2023-04-22T13:42:04Z","title":"LaMP: When Large Language Models Meet Personalization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11406","snapshot_observed_at":"2026-08-07T13:30:22.645052Z","title":"Lamp: When large language models meet personalization, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.645052Z"},"links":{"cited_paper":"/paper/2304.11406","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:b7dad18d8c677499be514651270a869bc6132a7213696d43fd03c5de012ced8e","observation_id":"431d7f5a-84a0-4a64-8de0-75affe2d92e4","resolution":{"observed_at":"2026-08-07T13:30:22.645052Z","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":"2026-08-07T13:30:28.014857Z","title":"Whose opinions do language models reflect? In International Conference on Machine Learning, pp.\\ 29971--30004","venue":null,"work_id":"d208ae01-9bea-49c4-a83f-64c7c886931a","year":2023},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.736290Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:48d6cc206d5873dfa2b4828f2a11a3805850685c65266e946caf1ede52cf4bfb","observation_id":"06b77df8-33f8-4cd7-9f65-7892a8689fe3","resolution":{"observed_at":"2026-08-07T13:30:28.125665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00888","last_updated":"2025-04-18T19:45:34Z","snapshot_observed_at":"2026-08-03T23:08:24.202894Z","submitted_at":"2024-06-02T23:13:56Z","title":"Aligning Language Models with Demonstrated Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00888","snapshot_observed_at":"2026-08-07T13:30:22.805705Z","title":"Show, don't tell: Aligning language models with demonstrated feedback","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.805705Z"},"links":{"cited_paper":"/paper/2406.00888","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:5c23ca7fb6a141315f5a8cb319d32bb88e5c313e0494d79f189d07af18b9c5df","observation_id":"6a94c13f-caca-4a28-8740-1e285bcad0b0","resolution":{"observed_at":"2026-08-07T13:30:22.805705Z","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":"2026-08-07T13:30:22.899023Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:22.899023Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:2fd3934709626e9685412ec83a82b6b943e710e9b45f07ae20e13e2545f1ae5f","observation_id":"546ccb6e-cca1-47fc-afdb-a3051f30cc15","resolution":{"observed_at":"2026-08-07T13:30:22.899023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08677","last_updated":"2024-04-07T03:05:57Z","snapshot_observed_at":"2026-08-06T10:09:54.946968Z","submitted_at":"2024-04-07T03:05:57Z","title":"PMG : Personalized Multimodal Generation with Large Language Models","version":1},"cited_work":{"arxiv_id":"2404.08677","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.08677","snapshot_observed_at":"2026-08-07T13:30:24.849860Z","title":"PMG : Personalized Multimodal Generation with Large Language Models","venue":"cs.IR","work_id":"8d68759d-0ad4-4e7f-b883-712724e24dd1","year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.026121Z"},"links":{"cited_paper":"/paper/2404.08677","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:2d0c0726043c7ce5f7c39da6126e0013865f805411a3201410b8a9d31ff01211","observation_id":"03cdc084-2e30-449f-9ab8-c9380064f693","resolution":{"observed_at":"2026-08-07T13:30:24.984811Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:27.687243Z","title":"M., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P","venue":null,"work_id":"f471cd73-170a-4982-83fe-b044c63ebc5d","year":2020},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.121700Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:b8c78399e4645453f05657c81e5cbea6940652ec482f41aeb6c65a4448bf12d7","observation_id":"668575e3-93c7-4c3b-a0be-401117460d2e","resolution":{"observed_at":"2026-08-07T13:30:27.855745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:27.422235Z","title":"Principle-driven self-alignment of language models from scratch with minimal human supervision","venue":null,"work_id":"27fe36b4-2931-44b7-9894-9d6a9f5c9888","year":2023},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.225440Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:fdbe7338ed4db1ecd252927b7de3a8817c173dd848be0d752b1f1865475a2633","observation_id":"cb612c1d-2226-4570-9657-f550653f3952","resolution":{"observed_at":"2026-08-07T13:30:27.474739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:27.081035Z","title":"D., Yang, Y., and Gan, C","venue":null,"work_id":"13128650-8bbd-468d-8f8a-e73a802e06a2","year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.305705Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:f40d3f43c393051d236105aa6d770e8abedc77ecd287ad32f0bb1a357a6e3eb9","observation_id":"20ffa8c8-64be-45d7-9aab-9fde5c193855","resolution":{"observed_at":"2026-08-07T13:30:27.194950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04401","last_updated":"2025-02-08T20:01:56Z","snapshot_observed_at":"2026-08-06T06:47:59.560379Z","submitted_at":"2024-02-06T21:03:52Z","title":"Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04401","snapshot_observed_at":"2026-08-07T13:30:23.380941Z","title":"Democratizing large language models via personalized parameter-efficient fine-tuning, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.380941Z"},"links":{"cited_paper":"/paper/2402.04401","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:4c3e47ad16c724265378ed76ecbaf1a628dd1875c8b9b0394076656ea117bac7","observation_id":"68f80e06-3d45-43a9-b360-e97ce81fa804","resolution":{"observed_at":"2026-08-07T13:30:23.380941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.10248","last_updated":"2024-10-10T13:20:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-08-20T12:21:05Z","title":"Steering Language Models With Activation Engineering","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.10248","snapshot_observed_at":"2026-08-07T13:30:23.460336Z","title":"M., Thiergart, L., Leech, G., Udell, D., Vazquez, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.460336Z"},"links":{"cited_paper":"/paper/2308.10248","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:aff4d4653ee4fd22c25e6346b028c6a2836a11350d84559630f8c14208a1f0cf","observation_id":"b1a8d50d-e3da-46e1-8b9c-aff4224f09ed","resolution":{"observed_at":"2026-08-07T13:30:23.460336Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-07T13:30:23.584750Z","title":"Qwen2.5 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.584750Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:750811953e260c6a245adfd6ffec30f2be67af746184db845036b1e4b6162a00","observation_id":"e25facdc-56ba-481a-af1c-2b9d10df184d","resolution":{"observed_at":"2026-08-07T13:30:23.584750Z","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":"2026-08-07T13:30:26.764743Z","title":"Y., Hartmann, B., and Yang, Q","venue":null,"work_id":"c6384bd1-e75f-4ecb-915a-f66b0ec801ba","year":2023},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.667806Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:cc417d7118907e32dc4fa52f3246659f721ca9ac7eac494312538d7733b26fee","observation_id":"0714e5d9-3102-4d17-a6a2-b02ac8245622","resolution":{"observed_at":"2026-08-07T13:30:26.956302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:30:23.754751Z","title":"Q., and Artzi, Y","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.754751Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:ca99b271d5b9cad22a85e48bae10f33ecff81f869beab0c376a4e1b2eec596c7","observation_id":"28489972-3aec-4062-acb2-540f8b0dcad1","resolution":{"observed_at":"2026-08-07T13:30:23.754751Z","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":"2026-08-07T13:30:23.807838Z","title":"E., and Stoica, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.807838Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:8bfb24e0475c851a01fe8b979b4e13af6bd4dc9db31e371e10e55c5096f27286","observation_id":"b4c15a79-e315-4e43-92ba-61b6833b7de9","resolution":{"observed_at":"2026-08-07T13:30:23.807838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01910","last_updated":"2023-03-10T17:20:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-11-03T15:43:03Z","title":"Large Language Models Are Human-Level Prompt Engineers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.01910","snapshot_observed_at":"2026-08-07T13:30:23.876476Z","title":"I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.876476Z"},"links":{"cited_paper":"/paper/2211.01910","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:5e0152a0f14fab4586119e589e1a5157a763fb1758048baa63e77e7c2773b580","observation_id":"1c936125-0d5d-4c7c-ae55-4784b2b0994b","resolution":{"observed_at":"2026-08-07T13:30:23.876476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02888","last_updated":"2024-10-25T21:01:05Z","snapshot_observed_at":"2026-08-03T15:21:29.653476Z","submitted_at":"2024-06-05T03:08:46Z","title":"HYDRA: Model Factorization Framework for Black-Box LLM Personalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02888","snapshot_observed_at":"2026-08-07T13:30:23.975627Z","title":"Hydra: Model factorization framework for black-box llm personalization, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:23.975627Z"},"links":{"cited_paper":"/paper/2406.02888","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:513520628cc7df627f19e8f8dfb95eaa94b36a6f82ee9e7559e69497ac98e5d5","observation_id":"9a5de857-ba1f-40f6-a0c4-ee416d66231e","resolution":{"observed_at":"2026-08-07T13:30:23.975627Z","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":"2026-08-08T07:44:49.921146Z","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":"2026-08-07T13:30:24.009574Z","title":"M., Stiennon, N., Wu, J., Brown, T","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:24.009574Z"},"links":{"cited_paper":"/paper/1909.08593","citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:7f5bb98eb7fca7457c66d33569538eb681ca9986ed80b62b46f3fe7434834792","observation_id":"e437c8c6-8424-4950-97cc-fd3fc54b0e10","resolution":{"observed_at":"2026-08-07T13:30:24.009574Z","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":"2026-08-07T13:30:24.084746Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:24.084746Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:423c50025916c8752708a974d18b973cddaa7eace1d4343647ca601ac4f08e93","observation_id":"e2b6b2da-0958-4c1d-b2cd-cade2d81ddee","resolution":{"observed_at":"2026-08-07T13:30:24.084746Z","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":"2026-08-07T13:30:24.167968Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:24.167968Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:6782ea3263a0caf898479917ba2c8e9667b722db5aed969abd958f1537aa460d","observation_id":"b86dbe9c-96c9-4c39-8182-b9d23b67b249","resolution":{"observed_at":"2026-08-07T13:30:24.167968Z","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":"2026-08-07T13:30:24.218058Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T13:30:24.218058Z"},"links":{"citing_paper":"/paper/2505.23815"},"observation_digest":"sha256:5b4bcb2cc8cf5a7f130165a23c6376bac5a7d8584a648e10f128ddca39b5d214","observation_id":"3602191d-48be-4126-8e30-4f43296dd175","resolution":{"observed_at":"2026-08-07T13:30:24.218058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.23815","last_updated":"2025-05-27T20:20:20Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T05:30:38.312423Z","submitted_at":"2025-05-27T20:20:20Z","title":"Aligning LLMs by Predicting Preferences from User Writing Samples"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":26,"verified_exact":1,"verified_fuzzy":8},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.23815."}