{"as_of":"2026-08-13T09:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:15f6c987d07f2d7cdb12a6271588c0254980419f869b5074b2efdf63b0fe51fa","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T04:36:13.585628Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-20T06:52:01.602161Z","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":"2026-05-20T06:53:05.772050Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"cited_work":{"arxiv_id":"2508.03252","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.03252","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust single-stage fully sparse 3d object detection via detachable latent diffusion","venue":null,"work_id":"89011082-76ae-4452-9e4e-334c15cd7b0b","year":2025},"citing_paper":{"arxiv_id":"2604.03306","last_updated":"2026-03-31T02:54:21Z","snapshot_observed_at":"2026-08-12T20:50:16.488175Z","submitted_at":"2026-03-31T02:54:21Z","title":"Deep Image Clustering Based on Curriculum Learning and Density Information","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-14T00:03:05.442554Z"},"links":{"cited_paper":"/paper/2508.03252","citing_paper":"/paper/2604.03306"},"observation_digest":"sha256:290ffa0c844ca84123fa79280a9e92b7bdaa0670162378a80bafc54f52cde82e","observation_id":"2ec9cb73-42d5-47d7-86af-6b9b3b68829d","resolution":{"observed_at":"2026-05-14T00:03:28.574602Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"cited_work":{"arxiv_id":"2508.03252","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.03252","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust single-stage fully sparse 3d object detection via detachable latent diffusion","venue":null,"work_id":"89011082-76ae-4452-9e4e-334c15cd7b0b","year":2025},"citing_paper":{"arxiv_id":"2605.07326","last_updated":"2026-05-08T06:32:12Z","snapshot_observed_at":"2026-07-30T05:23:33.496468Z","submitted_at":"2026-05-08T06:32:12Z","title":"GEM: Generating LiDAR World Model via Deformable Mamba","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-11T01:31:09.604703Z"},"links":{"cited_paper":"/paper/2508.03252","citing_paper":"/paper/2605.07326"},"observation_digest":"sha256:16fd1f2e1c7d68bd997f134df51811266d9e48a5e1a9077e7c3b150c53303ddc","observation_id":"0f3e6d7a-1e6a-426f-b69f-83480d34a3ec","resolution":{"observed_at":"2026-05-11T01:45:51.308753Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"cited_work":{"arxiv_id":"2508.03252","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.03252","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust single-stage fully sparse 3d object detection via detachable latent diffusion","venue":null,"work_id":"89011082-76ae-4452-9e4e-334c15cd7b0b","year":2025},"citing_paper":{"arxiv_id":"2605.19620","last_updated":"2026-05-19T09:56:52Z","snapshot_observed_at":"2026-07-06T23:30:21.283826Z","submitted_at":"2026-05-19T09:56:52Z","title":"B\\'ezier Degradation Modeling for LiDAR-based Human Motion Capture","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-20T06:52:01.602161Z"},"links":{"cited_paper":"/paper/2508.03252","citing_paper":"/paper/2605.19620"},"observation_digest":"sha256:b76e531bd10d212d99bbdd67c259e05ec336374ce9541104dafb830d30538fc1","observation_id":"1aa676fa-ca5f-47bd-a970-5e4f58673313","resolution":{"observed_at":"2026-05-20T06:53:05.774293Z","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/2508.03252/citation-record","integrity":"/paper/2508.03252/integrity","json":"/paper/2508.03252/citation-record.json","paper":"/paper/2508.03252"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T04:36:14.386343Z","title":"A general theoretical paradigm to un- derstand learning from human preferences","venue":null,"work_id":"8d2c8237-fbb4-4ef3-8435-4420fc475b60","year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.149570Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:97a7fba61ea30441c4f41b8ebd845c1364fae8a88990c633d88de770ac265d42","observation_id":"632c0c43-8bbd-47ac-ae0e-ca850f88fcb3","resolution":{"observed_at":"2026-08-06T04:36:14.391465Z","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":"2026-08-06T04:36:13.227412Z","title":"Frozen in time: A joint video and image encoder for end-to-end retrieval","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.227412Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:174083ea844a92f56dc4615ba6b4040a8086b7da6a627ac1694e0c1dd4a8d9d0","observation_id":"38f0cbc9-f83c-41bf-b62c-24a819528964","resolution":{"observed_at":"2026-08-06T04:36:13.227412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15127","last_updated":"2023-11-25T22:28:38Z","snapshot_observed_at":"2026-08-07T21:47:08.589400Z","submitted_at":"2023-11-25T22:28:38Z","title":"Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15127","snapshot_observed_at":"2026-08-06T04:36:13.256733Z","title":"Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.256733Z"},"links":{"cited_paper":"/paper/2311.15127","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:730aed50d19cd857237b9f9a80b0a3fae3b09ff694d5332d60346e6b459314eb","observation_id":"70ad7def-4fe4-4484-8640-940cf3e093e4","resolution":{"observed_at":"2026-08-06T04:36:13.256733Z","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":"2026-08-06T04:36:13.359408Z","title":"Align your latents: High-resolution video synthesis with la- tent diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.359408Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:e82850cb6873e516e2f035b5f2fb9eb01c3e038e3e9f18691e19d2ff6aab6e33","observation_id":"17a1bcee-8b99-4b2b-8ed9-a75ca048eef6","resolution":{"observed_at":"2026-08-06T04:36:13.359408Z","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":"2026-08-06T04:36:14.350466Z","title":"The perception-distortion tradeoff","venue":null,"work_id":"ac14687a-8e67-4499-8a27-b2b52746ac1a","year":2018},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.363942Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:46cf0ef30f88e64eea8906c2187ac0d95e86085368b91b4cef0128bc6642687a","observation_id":"7adc31fe-2168-4f9c-9f5b-b5975981d426","resolution":{"observed_at":"2026-08-06T04:36:14.355147Z","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":"2404.11925","last_updated":"2024-04-18T06:02:54Z","snapshot_observed_at":"2026-08-13T00:27:40.134533Z","submitted_at":"2024-04-18T06:02:54Z","title":"EdgeFusion: On-Device Text-to-Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.11925","snapshot_observed_at":"2026-08-06T04:36:13.368190Z","title":"Edgefusion: on-device text-to-image generation.arXiv preprint arXiv:2404.11925,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.368190Z"},"links":{"cited_paper":"/paper/2404.11925","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:489e6abb4e2045400c97fb513f4689c82cdfd1bd14964f309f3cc825f7d12f15","observation_id":"0f5084f2-dba9-43d3-879e-b5599182f536","resolution":{"observed_at":"2026-08-06T04:36:13.368190Z","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":"2026-08-06T04:36:14.335052Z","title":"Videocrafter2: Overcoming data limitations for high-quality video diffu- sion models","venue":null,"work_id":"26cd9111-dfa5-4e50-8709-e50668b9bcc2","year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.373496Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:1bca2c0afebbf2b26743a17e70d60929b322f0dde2c52da375965a065b9fb5b5","observation_id":"41de01d6-d1bb-4260-b928-b1b55056fc06","resolution":{"observed_at":"2026-08-06T04:36:14.340218Z","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":"2401.01335","last_updated":"2024-06-14T21:17:17Z","snapshot_observed_at":"2026-08-10T05:25:44.328250Z","submitted_at":"2024-01-02T18:53:13Z","title":"Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.01335","snapshot_observed_at":"2026-08-06T04:36:13.377813Z","title":"Self-play fine-tuning converts weak lan- guage models to strong language models.arXiv preprint arXiv:2401.01335, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.377813Z"},"links":{"cited_paper":"/paper/2401.01335","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:a9edd6764e26793baabcf8d89e4656f80f792a0810383c08027cc19f62811cbc","observation_id":"237d4d74-4d65-4644-a8c1-843922c7c4ce","resolution":{"observed_at":"2026-08-06T04:36:13.377813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.20491","last_updated":"2025-08-30T10:39:39Z","snapshot_observed_at":"2026-08-11T15:38:47.656211Z","submitted_at":"2025-03-26T12:28:20Z","title":"VPO: Aligning Text-to-Video Generation Models with Prompt Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.20491","snapshot_observed_at":"2026-08-06T04:36:13.382692Z","title":"Vpo: Aligning text-to-video generation models with prompt optimization.arXiv preprint arXiv:2503.20491,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.382692Z"},"links":{"cited_paper":"/paper/2503.20491","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:c5b3f103df95e4ffc11affda3f644d435e67a61e97b450c245c914429ef1919c","observation_id":"05bb116d-a9eb-4eb0-afdc-c876299a0c18","resolution":{"observed_at":"2026-08-06T04:36:13.382692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07863","last_updated":"2024-11-12T11:18:43Z","snapshot_observed_at":"2026-08-12T08:12:44.293962Z","submitted_at":"2024-05-13T15:50:39Z","title":"RLHF Workflow: From Reward Modeling to Online RLHF","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07863","snapshot_observed_at":"2026-08-06T04:36:13.387559Z","title":"Rlhf workflow: From reward mod- eling to online rlhf.arXiv preprint arXiv:2405.07863, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.387559Z"},"links":{"cited_paper":"/paper/2405.07863","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:310f1a781d3f479152dd380937bde8ff6a1aaae8b39a94e2e4c4e175f9e35815","observation_id":"f61c79af-4b4b-4795-ac06-b51bd4c812d1","resolution":{"observed_at":"2026-08-06T04:36:13.387559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01306","last_updated":"2024-11-19T18:12:45Z","snapshot_observed_at":"2026-08-12T21:18:02.964833Z","submitted_at":"2024-02-02T10:53:36Z","title":"KTO: Model Alignment as Prospect Theoretic Optimization","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01306","snapshot_observed_at":"2026-08-06T04:36:13.392364Z","title":"Kto: Model alignment as prospect theoretic optimization.arXiv preprint arXiv:2402.01306,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.392364Z"},"links":{"cited_paper":"/paper/2402.01306","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:93e8a5a603147bf87c4eb99d99ef5f56b602867dae94d9014561c77497a7b555","observation_id":"cd2aaa50-123f-44c5-8747-4f42629cfed2","resolution":{"observed_at":"2026-08-06T04:36:13.392364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-12T23:54:55.785946Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-06T04:36:13.397543Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.397543Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:e235d44ac88bfd2488f028ce2b0d9602f9f50ec98f930016602c2a431a61c9fb","observation_id":"803e3cde-bdbf-4e6e-849f-6e2179ecd459","resolution":{"observed_at":"2026-08-06T04:36:13.397543Z","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":"2026-08-06T04:36:14.318617Z","title":"A theory of the distortion-perception tradeoff in wasserstein space","venue":null,"work_id":"02b35cd4-477a-4b99-90e8-2d5440f29410","year":2021},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.402231Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:2555a4e748a9319466c3922b7caf879c39eb4e46d6913c35b6734a7fa2b2591d","observation_id":"235cfc1f-c472-4abe-82d0-59f16081d7fa","resolution":{"observed_at":"2026-08-06T04:36:14.323661Z","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":"2026-08-06T04:36:14.300312Z","title":"The devil is in the prompts: Retrieval-augmented prompt optimization for text-to-video generation","venue":null,"work_id":"d89eeea8-7014-43a1-a335-0649615ae3e6","year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.406657Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:00030529233101c67d842d61859b6bc0b2128a475a821abc48f27caae6fbb14e","observation_id":"ec32f00b-56b3-44f2-9827-7b55ef1cea66","resolution":{"observed_at":"2026-08-06T04:36:14.306058Z","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":"2026-08-06T04:36:13.412662Z","title":"Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.412662Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:c948747758b8f6c0d8b4e963648154bec180d79f411b03363cfec83821f66ee0","observation_id":"4df43556-7ae8-4e49-82ab-d3e6024f1daa","resolution":{"observed_at":"2026-08-06T04:36:13.412662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04792","last_updated":"2024-02-29T20:59:17Z","snapshot_observed_at":"2026-08-13T04:24:21.093265Z","submitted_at":"2024-02-07T12:31:13Z","title":"Direct Language Model Alignment from Online AI Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04792","snapshot_observed_at":"2026-08-06T04:36:13.417768Z","title":"Direct language model alignment from online ai feedback.arXiv preprint arXiv:2402.04792, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.417768Z"},"links":{"cited_paper":"/paper/2402.04792","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:f5af02b9286d79e509c2d48d42d201bfb16e922d4bdb60abf8fd9de8161d9f99","observation_id":"a9e3b838-9524-4038-8924-5fd067ad16f7","resolution":{"observed_at":"2026-08-06T04:36:13.417768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.04725","last_updated":"2024-02-08T18:08:57Z","snapshot_observed_at":"2026-08-12T14:50:50.360929Z","submitted_at":"2023-07-10T17:34:16Z","title":"AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.04725","snapshot_observed_at":"2026-08-06T04:36:13.423235Z","title":"Animatediff: Animate your personalized text- to-image diffusion models without specific tuning.arXiv preprint arXiv:2307.04725, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.423235Z"},"links":{"cited_paper":"/paper/2307.04725","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:3098c84ebcda9cb0ee7cf56a7911b3eb7f4ee86dc5f7de8349ef1a5937705597","observation_id":"ea10c170-18c9-427e-8326-40712468e14a","resolution":{"observed_at":"2026-08-06T04:36:13.423235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.15252","last_updated":"2024-10-14T04:08:53Z","snapshot_observed_at":"2026-08-12T23:37:24.872455Z","submitted_at":"2024-06-21T15:43:46Z","title":"VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.15252","snapshot_observed_at":"2026-08-06T04:36:13.428878Z","title":"Videoscore: Building automatic metrics to simulate fine-grained human feedback for video genera- tion.arXiv preprint arXiv:2406.15252, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.428878Z"},"links":{"cited_paper":"/paper/2406.15252","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:98b8030fcb16bce178b2e9e01b038543ee967d7a8d110a4c5c7c231b50e09e1f","observation_id":"22539301-87c3-4ef8-91cb-5d08fdaf0ed7","resolution":{"observed_at":"2026-08-06T04:36:13.428878Z","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":"2026-08-06T04:36:14.273764Z","title":"Channel pruning for accelerating very deep neural networks","venue":null,"work_id":"d511aff8-4028-40a5-91bf-3ed8980e7a73","year":2017},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.433909Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:ef2e1ff97b0bb24a5e2d18c9a3da617bc3ac33ef7d0724a6ed2fa400642f3ede","observation_id":"7a9e5f69-3ce7-4d62-92a9-10192490ad59","resolution":{"observed_at":"2026-08-06T04:36:14.278528Z","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":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-06T04:36:13.438561Z","title":"Distill- ing the knowledge in a neural network.arXiv preprint arXiv:1503.02531, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.438561Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:03dbf12e4489b471f589680c373f80680d11e2b47b66eba7514dab9f819c9f9c","observation_id":"06b3874f-7676-4ad9-b2d1-55a327f60c23","resolution":{"observed_at":"2026-08-06T04:36:13.438561Z","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":"2026-08-06T04:36:13.443125Z","title":"Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.443125Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:a8aeb2c3bf8402967edbd68ee554b3f07dceea1d1147e0588ede82f142a4882a","observation_id":"6fd42625-c92c-4098-a991-50cb0c6c58b4","resolution":{"observed_at":"2026-08-06T04:36:13.443125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07691","last_updated":"2024-03-14T07:47:08Z","snapshot_observed_at":"2026-08-13T04:59:53.332269Z","submitted_at":"2024-03-12T14:34:08Z","title":"ORPO: Monolithic Preference Optimization without Reference Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07691","snapshot_observed_at":"2026-08-06T04:36:13.447632Z","title":"Orpo: Mono- lithic preference optimization without reference model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.447632Z"},"links":{"cited_paper":"/paper/2403.07691","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:7610b08e9e1a4b6f77dc1cf046f3204043a429d468116a5bc14e080791580abf","observation_id":"ef552e67-54f9-4b6f-9c72-79299b5c0a6d","resolution":{"observed_at":"2026-08-06T04:36:13.447632Z","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":"2026-08-06T04:36:14.247976Z","title":"Vbench: Comprehensive bench- mark suite for video generative models","venue":null,"work_id":"81648350-5e16-4edc-b943-285ec0116172","year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.452642Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:e038d997803dfb18ce0925471b12285a6c444d1622c40cd4e70c77c7a1406810","observation_id":"39059006-f283-4f00-bf04-bb2d78a67c49","resolution":{"observed_at":"2026-08-06T04:36:14.252936Z","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":"2026-08-06T04:36:14.232059Z","title":"Unpacking dpo and ppo: Dis- entangling best practices for learning from preference feed- back.Advances in neural information processing systems, 37:36602–36633, 2025","venue":null,"work_id":"02a86a35-320b-4ee2-8e12-30a3cf1f9a00","year":2025},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.457785Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:1e8700773758db212bc3424eaf4e562401028fff9ba444d0c6ef6d222888f486","observation_id":"71b96985-1aff-41ab-abec-cff0d3a55db4","resolution":{"observed_at":"2026-08-06T04:36:14.237193Z","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":"2502.01690","last_updated":"2025-02-02T16:55:42Z","snapshot_observed_at":"2026-08-10T05:20:12.605634Z","submitted_at":"2025-02-02T16:55:42Z","title":"HuViDPO:Enhancing Video Generation through Direct Preference Optimization for Human-Centric Alignment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01690","snapshot_observed_at":"2026-08-06T04:36:13.462247Z","title":"Huvidpo: Enhancing video generation through direct preference optimization for human-centric alignment.arXiv preprint arXiv:2502.01690, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.462247Z"},"links":{"cited_paper":"/paper/2502.01690","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:28e097f6ba774fd557ac179f9248ce4c8d2ce70e4d1cab263769270f39867d29","observation_id":"17694a07-6653-4fc7-abf0-14399d5e251b","resolution":{"observed_at":"2026-08-06T04:36:13.462247Z","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":"2026-08-06T04:36:14.215129Z","title":"Bk-sdm: A lightweight, fast, and cheap ver- sion of stable diffusion","venue":null,"work_id":"fc04ff7e-67e9-4204-8825-c694ecfd0537","year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.467053Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:ac9bfbddd85a8015759e39a644f28f0dd46c6d8d2798d17b59a38c1775968cca","observation_id":"b24fdadd-0f1d-4263-b7c9-7771ef88ead4","resolution":{"observed_at":"2026-08-06T04:36:14.220077Z","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":"2026-08-06T04:36:13.471300Z","title":"Auto-encoding vari- ational bayes, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.471300Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:4e3e09d3a6ecac70db5021e816e5072d2c0900155bbe01ef74b042ec228d0f4e","observation_id":"6abae9e3-e867-4128-8050-b003a875de91","resolution":{"observed_at":"2026-08-06T04:36:13.471300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.04838","last_updated":"2021-09-10T12:46:32Z","snapshot_observed_at":"2026-08-10T23:35:07.182089Z","submitted_at":"2021-09-10T12:46:32Z","title":"Block Pruning For Faster Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.04838","snapshot_observed_at":"2026-08-06T04:36:13.475418Z","title":"Block pruning for faster transformers.arXiv preprint arXiv:2109.04838, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.475418Z"},"links":{"cited_paper":"/paper/2109.04838","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:b39d7dedc4c76363c2b30ee77a39368debcf52c421f5b5c9b2bdce7c3fc61a10","observation_id":"91df55b7-289f-441b-a200-cf31e57d590c","resolution":{"observed_at":"2026-08-06T04:36:13.475418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18750","last_updated":"2024-10-11T07:50:49Z","snapshot_observed_at":"2026-08-12T23:55:32.837720Z","submitted_at":"2024-05-29T04:26:17Z","title":"T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18750","snapshot_observed_at":"2026-08-06T04:36:13.480170Z","title":"T2v- turbo: Breaking the quality bottleneck of video consis- tency model with mixed reward feedback.arXiv preprint arXiv:2405.18750, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.480170Z"},"links":{"cited_paper":"/paper/2405.18750","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:c7b2f078bc77e9bdc9b27f3a53ede5854fd35975da80908c468f430fb8a24836","observation_id":"53f460a6-c475-454f-8a14-175e3d47424d","resolution":{"observed_at":"2026-08-06T04:36:13.480170Z","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":"2026-08-06T04:36:13.484534Z","title":"Snap- fusion: Text-to-image diffusion model on mobile devices within two seconds.Advances in Neural Information Pro- cessing Systems, 36:20662–20678, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.484534Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:b6f183d55dbf7adbd9d14519a434a41b04cd3039cdaddae20cb39e0b42bd20f4","observation_id":"7112bad8-e6c6-40e5-8f09-8c2e5bcd269c","resolution":{"observed_at":"2026-08-06T04:36:13.484534Z","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":"2026-08-06T04:36:14.176567Z","title":"Evaluation of text-to-video generation models: A dy- namics perspective.Advances in Neural Information Pro- cessing Systems, 37:109790–109816, 2024","venue":null,"work_id":"fdc0bd01-8aba-4d58-84e5-bf2d3eab6509","year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.489315Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:34713bb3a0de86ad450f5fc38701e64297f98487fa4e5faf033164feecf37a14","observation_id":"428af24a-063b-4889-90a0-928a7dcb2dc2","resolution":{"observed_at":"2026-08-06T04:36:14.181765Z","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":"2026-08-06T04:36:14.160881Z","title":"Evaluation of text-to-video generation models: A dy- namics perspective.Advances in Neural Information Pro- cessing Systems, 37:109790–109816, 2025","venue":null,"work_id":"1fb903e9-57ea-48ad-9922-f5abccf434fc","year":2025},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.493665Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:db5098e009a0c0c6a3ae4d9d3e44d26be81b6b3720c966bad714f4e357113827","observation_id":"0143b742-586e-4c9a-87d5-dde8964edb7b","resolution":{"observed_at":"2026-08-06T04:36:14.165953Z","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":"2403.12706","last_updated":"2024-03-19T13:08:54Z","snapshot_observed_at":"2026-08-13T00:50:22.589007Z","submitted_at":"2024-03-19T13:08:54Z","title":"AnimateDiff-Lightning: Cross-Model Diffusion Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12706","snapshot_observed_at":"2026-08-06T04:36:13.498711Z","title":"Animatediff-lightning: Cross-model diffusion distillation.arXiv preprint arXiv:2403.12706, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.498711Z"},"links":{"cited_paper":"/paper/2403.12706","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:0bde583627fb786fe3e57e5f55fbee192c95e8e0622c3cc9f6a7511d1454aa7f","observation_id":"09ae27cd-f644-4a37-9fd7-28533ce04417","resolution":{"observed_at":"2026-08-06T04:36:13.498711Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13929","last_updated":"2024-03-02T09:09:32Z","snapshot_observed_at":"2026-08-12T13:08:43.602176Z","submitted_at":"2024-02-21T16:51:05Z","title":"SDXL-Lightning: Progressive Adversarial Diffusion Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13929","snapshot_observed_at":"2026-08-06T04:36:13.508246Z","title":"Sdxl- lightning: Progressive adversarial diffusion distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.508246Z"},"links":{"cited_paper":"/paper/2402.13929","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:035d472118f37287d3a5285e8b499071d77d0972be40ec70dc45720827282276","observation_id":"1a4f4e14-d71e-4f91-852e-95177a16d5bc","resolution":{"observed_at":"2026-08-06T04:36:13.508246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13918","last_updated":"2025-10-27T08:22:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-23T18:55:41Z","title":"Improving Video Generation with Human Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13918","snapshot_observed_at":"2026-08-06T04:36:13.512837Z","title":"Improving video generation with human feedback.arXiv preprint arXiv:2501.13918, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.512837Z"},"links":{"cited_paper":"/paper/2501.13918","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:8488ddf3c24c7b986d85e2d20d326d9091525993945b08702cac7d11b65b64b8","observation_id":"15e82785-5e46-4177-9137-2add6e89ec27","resolution":{"observed_at":"2026-08-06T04:36:13.512837Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.14167","last_updated":"2024-12-18T18:59:49Z","snapshot_observed_at":"2026-08-11T12:22:39.635800Z","submitted_at":"2024-12-18T18:59:49Z","title":"VideoDPO: Omni-Preference Alignment for Video Diffusion Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.14167","snapshot_observed_at":"2026-08-06T04:36:13.518047Z","title":"Videodpo: Omni- preference alignment for video diffusion generation.arXiv preprint arXiv:2412.14167, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.518047Z"},"links":{"cited_paper":"/paper/2412.14167","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:9c2bdc70cb5393b32d0a5926f146d5b35031711a60ce8632e89a93206b2fbd3c","observation_id":"224177d5-4bbd-4e69-92e5-da7163db341e","resolution":{"observed_at":"2026-08-06T04:36:13.518047Z","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":"2026-08-06T04:36:14.144498Z","title":"Learning efficient convolutional networks through network slimming","venue":null,"work_id":"1db9200d-9834-4966-b7f4-9366ff5b18fd","year":2017},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.523545Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:5c6f5b15260cbedc1e8783c4faa96201f43dc7eeeae2e829450f8de7efdd2ee5","observation_id":"8644df19-b8ee-4525-84a2-f6004f9f61ba","resolution":{"observed_at":"2026-08-06T04:36:14.150031Z","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":"2405.16436","last_updated":"2024-12-04T08:15:35Z","snapshot_observed_at":"2026-08-12T23:57:54.839711Z","submitted_at":"2024-05-26T05:38:50Z","title":"Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16436","snapshot_observed_at":"2026-08-06T04:36:13.529125Z","title":"Provably mitigating overoptimization in rlhf: Your sft loss is implicitly an adversarial regularizer.arXiv preprint arXiv:2405.16436, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.529125Z"},"links":{"cited_paper":"/paper/2405.16436","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:e28f71cf7591c327918c6c14340114ca468b2e047b2ee6fee5d2198cee823843","observation_id":"4b856fd7-88d8-4400-897b-592296f9adef","resolution":{"observed_at":"2026-08-06T04:36:13.529125Z","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":"2026-08-06T04:36:14.126895Z","title":"Simpo: Sim- ple preference optimization with a reference-free reward","venue":null,"work_id":"cee642b5-be8b-42b7-8571-af5d9e9f64e8","year":2025},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.533925Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:4a7ad42b841461c9caaac369dfed122fab46fb8e9c7205e3f32dd8af71fbac20","observation_id":"047881ae-dfb9-4755-8ba6-63a507ef2769","resolution":{"observed_at":"2026-08-06T04:36:14.132285Z","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":"2410.00418","last_updated":"2025-02-04T07:00:12Z","snapshot_observed_at":"2026-08-12T22:33:43.605285Z","submitted_at":"2024-10-01T05:54:07Z","title":"Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.00418","snapshot_observed_at":"2026-08-06T04:36:13.539090Z","title":"Posterior- mean rectified flow: Towards minimum mse photo-realistic image restoration.arXiv preprint arXiv:2410.00418, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.539090Z"},"links":{"cited_paper":"/paper/2410.00418","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:abe4243ae7f495bf3dc04f7d6e0b7035e31b18b1306caa1dc15691ecdfcb486b","observation_id":"b8568a7a-7c7a-4415-a747-250c11e0062a","resolution":{"observed_at":"2026-08-06T04:36:13.539090Z","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":"2026-08-06T04:36:13.544259Z","title":"Training language models to follow instructions with human feedback.Ad- vances in neural information processing systems, 35:27730– 27744, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.544259Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:32713d62c53b7650eb69d43662796af669541729898b1705091d4975884c2e1f","observation_id":"21d0d155-60e5-48ab-a39d-f0c9fa3e647b","resolution":{"observed_at":"2026-08-06T04:36:13.544259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13228","last_updated":"2024-07-03T13:46:33Z","snapshot_observed_at":"2026-08-08T16:03:10.053914Z","submitted_at":"2024-02-20T18:42:34Z","title":"Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13228","snapshot_observed_at":"2026-08-06T04:36:13.548796Z","title":"Smaug: Fixing failure modes of preference optimisation with dpo-positive.arXiv preprint arXiv:2402.13228, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.548796Z"},"links":{"cited_paper":"/paper/2402.13228","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:692b042e683023b277ea54345b309fefaa327be2b8b0c9d6003d59c6624c8ede","observation_id":"768872e6-31f0-40ef-831b-6e1710052e17","resolution":{"observed_at":"2026-08-06T04:36:13.548796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-06T04:36:13.553512Z","title":"Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.553512Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:bc123d04ff5950a3fdb41aa64f0adf24138f2209d576dcd9e0f600e3e392f87f","observation_id":"9943a228-dfc3-41de-94bb-f853bba5bb9f","resolution":{"observed_at":"2026-08-06T04:36:13.553512Z","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":"2026-08-06T04:36:14.098200Z","title":"Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023","venue":null,"work_id":"4f1dc75c-c39d-4a1c-a51e-8f672d71d9d6","year":2023},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.558004Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:007b0fd756d4353a2630af3d3ade836ce754ce452ce8a5a8a79345b66a24d538","observation_id":"005be49b-0c88-4989-9a92-846998f5448d","resolution":{"observed_at":"2026-08-06T04:36:14.103293Z","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":"2502.01667","last_updated":"2025-02-01T16:08:43Z","snapshot_observed_at":"2026-08-12T13:07:22.151043Z","submitted_at":"2025-02-01T16:08:43Z","title":"Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01667","snapshot_observed_at":"2026-08-06T04:36:13.562345Z","title":"Refining alignment framework for diffusion models with intermediate-step preference ranking.arXiv preprint arXiv:2502.01667, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.562345Z"},"links":{"cited_paper":"/paper/2502.01667","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:9de97fe3067a52b70ecca1f9bb6941e980ab5601bab999cc9b5532d46b97e14b","observation_id":"4db1fb50-de6b-458a-9aae-e8c32b57898e","resolution":{"observed_at":"2026-08-06T04:36:13.562345Z","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":"2026-08-06T04:36:14.081294Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"3fc6dc55-f41b-4132-9444-ace5619050e2","year":2022},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.567403Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:0d081933cc218d17f6cdd24be80c10736e8fd120b21ec11ebb3ee465ff7a4f60","observation_id":"5f578bf9-e756-4c76-bcb6-2acccb1009c5","resolution":{"observed_at":"2026-08-06T04:36:14.086300Z","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":"2026-08-06T04:36:14.064558Z","title":"- Excessive mentions of country names (distracts from motion evaluation)","venue":null,"work_id":"2d354074-eab8-4ca0-8b98-64bd3dd03707","year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.571913Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:6d7e55b555bd18cc2476e7af2fc27fd2ad7b6d6400e8757770f5baa0e7e78699","observation_id":"0d6f56bd-f5fb-4a72-b4f2-b1bb09131135","resolution":{"observed_at":"2026-08-06T04:36:14.069993Z","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":"2026-08-06T04:36:14.047350Z","title":"Visual Quality","venue":null,"work_id":"b56969cb-c050-42b1-bc03-f9252ab5bc6e","year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.576356Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:310fe2e9123b39492029a26fc32b41b159a61b034b3572377cdf8d634800baf3","observation_id":"8a3bfad0-f598-462d-acda-1918389089e9","resolution":{"observed_at":"2026-08-06T04:36:14.052190Z","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":"2026-08-06T04:36:14.031080Z","title":"- Excessive mentions of country names (distracts evaluation)","venue":null,"work_id":"db831230-0dca-4b59-93fe-e58897c73bdd","year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.581153Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:fa091dcd6c83eb379cf8ff16ad6422c978218eaab4e3e6197bce1890c9f94906","observation_id":"8caaa602-5acd-4696-a3d8-8d0bc53a0de2","resolution":{"observed_at":"2026-08-06T04:36:14.036093Z","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":"2026-08-06T04:36:14.011121Z","title":"No mention of visual attributes like lighting, colors, resolution, or atmosphere","venue":null,"work_id":"104da043-8a95-4a1a-accb-a5f2730a3ef8","year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.585628Z"},"links":{"citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:be4667c9482448c44e8c6809fb3962e71235e8e22e83a3a95c26923f8acc4295","observation_id":"fee566db-1d07-4e22-a929-90c94560f041","resolution":{"observed_at":"2026-08-06T04:36:14.018941Z","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"}}],"paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T02:13:16.637160Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":50},"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 50 of 50 outbound references and 3 inbound Pith citation observations for arXiv:2508.03252."}