{"as_of":"2026-08-10T20:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:974d64fe37b41dad45877f5a64bc6329eabc38471357087058dfa0c801aa8a99","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:20:57.635131Z","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":"2026-07-04T15:09:55.046405Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-08-07T13:20:57.635131Z","title":"Anomalyr1: A grpo-based end-to-end mllm for industrial anomaly detection.arXiv preprint arXiv:2504.11914, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.22039","last_updated":"2026-07-29T09:31:57Z","snapshot_observed_at":"2026-08-07T21:55:46.500544Z","submitted_at":"2025-05-28T07:02:15Z","title":"OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:20:57.635131Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2505.22039"},"observation_digest":"sha256:763154b4a155328ccb270fbd57eb80616152814dc58a9234639e74c8ff02e75b","observation_id":"3355f90a-119c-437b-b7bb-838314bc3c59","resolution":{"observed_at":"2026-08-07T13:20:57.635131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-08-06T12:40:08.901123Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.21619","last_updated":"2025-07-29T09:18:22Z","snapshot_observed_at":"2026-08-07T23:36:20.034251Z","submitted_at":"2025-07-29T09:18:22Z","title":"EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T12:40:08.901123Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2507.21619"},"observation_digest":"sha256:a1795fa3477c3d539f13f4eba9e8b40417dd754f0dc6ff33aef8e7d1ab279c9b","observation_id":"a9db3fcc-f3cb-4f5c-a685-2d35d9a36ffb","resolution":{"observed_at":"2026-08-06T12:40:08.901123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-08-06T00:53:23.177487Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.04175","last_updated":"2025-08-06T08:00:27Z","snapshot_observed_at":"2026-08-07T13:51:30.195940Z","submitted_at":"2025-08-06T08:00:27Z","title":"AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T00:53:23.177487Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2508.04175"},"observation_digest":"sha256:851bdf97b098467b078f1943e3473b16dd4f2723ffc767e6313a6d255f9f2930","observation_id":"d457a7fd-5db9-4127-adfc-e19a8219c197","resolution":{"observed_at":"2026-08-06T00:53:23.177487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2512.13671","last_updated":"2026-04-14T17:48:11Z","snapshot_observed_at":"2026-07-06T22:39:04.164003Z","submitted_at":"2025-12-15T18:57:04Z","title":"AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-16T21:58:58.999285Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2512.13671"},"observation_digest":"sha256:b7e22a027ae8d3205675c4814acecadb930487a7c4898d62ec7a7927a59059cb","observation_id":"326df01e-f706-4a4e-93c2-d7492378f89c","resolution":{"observed_at":"2026-05-16T22:01:17.992892Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2602.09850","last_updated":"2026-05-08T01:43:02Z","snapshot_observed_at":"2026-08-02T13:38:36.392657Z","submitted_at":"2026-02-10T14:54:17Z","title":"Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-16T03:08:58.617137Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2602.09850"},"observation_digest":"sha256:76f919a53323be2667cb405f6d8a17ac1b3d8e0604adeb1cddccac0c9520d23f","observation_id":"db978b2a-e1e2-4eea-b2ef-c9ce2758cf0d","resolution":{"observed_at":"2026-05-16T03:10:32.047937Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2602.17419","last_updated":"2026-05-07T12:09:13Z","snapshot_observed_at":"2026-07-06T22:46:26.082843Z","submitted_at":"2026-02-19T14:50:58Z","title":"EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-15T21:05:11.117495Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2602.17419"},"observation_digest":"sha256:b2b6fff79bfa98fcd4345036d34cd88fad3f9aece50d2c9a28ca5b090578f863","observation_id":"b4e16b81-f00d-443d-834b-d4bdea4e2d0e","resolution":{"observed_at":"2026-05-15T21:06:38.120879Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2603.13054","last_updated":"2026-05-12T23:46:22Z","snapshot_observed_at":"2026-08-01T06:50:59.992967Z","submitted_at":"2026-03-13T15:05:04Z","title":"Topo-R1: Detecting Topological Anomalies via Vision-Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-15T11:41:27.021776Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2603.13054"},"observation_digest":"sha256:391d5d7a8c9c9bd7f1c87fb5a043bac047c4a5de2864fc52511f05fd8a3322d5","observation_id":"ce5c55d0-bbaf-4405-816d-7eab6fcf94d4","resolution":{"observed_at":"2026-05-15T11:45:32.867019Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2603.13779","last_updated":"2026-04-21T06:55:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-03-14T06:14:44Z","title":"AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T11:54:18.587529Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2603.13779"},"observation_digest":"sha256:6a219035d6582865e549f699b50ba7341254d1e3c3326e2b2d4464c27cc7339e","observation_id":"b03a3b66-eb1a-4d44-bd3f-da79f8cc604f","resolution":{"observed_at":"2026-05-15T11:55:33.344038Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2605.10833","last_updated":"2026-05-11T16:49:38Z","snapshot_observed_at":"2026-07-06T23:22:47.781940Z","submitted_at":"2026-05-11T16:49:38Z","title":"MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-12T05:07:29.463188Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2605.10833"},"observation_digest":"sha256:0899f5c0de5bef9aac017e302a9207184970a85786ee0d902a0e36fdd7136bcb","observation_id":"24d9801a-6a0b-4eb9-bc46-6c35e92ff2c0","resolution":{"observed_at":"2026-05-12T05:36:26.485832Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2605.20682","last_updated":"2026-05-20T03:52:21Z","snapshot_observed_at":"2026-08-02T13:01:00.493889Z","submitted_at":"2026-05-20T03:52:21Z","title":"IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-21T05:33:30.670201Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2605.20682"},"observation_digest":"sha256:c858a0ac596e8f55c2148da376d7f10aa3a32be772e7dfd089748828a9bdc5e5","observation_id":"13b48f17-1f37-42e4-9598-b2361b0c68c0","resolution":{"observed_at":"2026-05-21T05:33:58.466776Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2605.30140","last_updated":"2026-05-28T16:05:42Z","snapshot_observed_at":"2026-08-08T12:49:54.431687Z","submitted_at":"2026-05-28T16:05:42Z","title":"AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T08:10:15.306343Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2605.30140"},"observation_digest":"sha256:78ca56b09adace537236c89379e05a11766320ce5f72e98839af87201ad7d00b","observation_id":"29efe57e-9b06-4554-8462-b1ef55882ff3","resolution":{"observed_at":"2026-06-29T08:13:14.934769Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":"2504.11914","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-04T15:09:55.046405Z","title":"Anoma- lyr1: A grpo-based end-to-end mllm for industrial anomaly detection","venue":null,"work_id":"d618677d-de45-4bcb-8076-494ed990c44a","year":2025},"citing_paper":{"arxiv_id":"2606.26196","last_updated":"2026-06-24T15:20:32Z","snapshot_observed_at":"2026-07-07T00:00:31.981360Z","submitted_at":"2026-06-24T15:20:32Z","title":"From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models","version":1},"reference_index":188,"source":"pdf_text","source_observed_at":"2026-06-26T01:50:54.242508Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2606.26196"},"observation_digest":"sha256:3573a89006cfe5a49e39332712e873007a9b837e2e2a848cb3094ee524d06c80","observation_id":"95463d59-62e4-43a4-948d-f7756b2f49bc","resolution":{"observed_at":"2026-07-04T15:09:55.048657Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-12T06:01:58.407747Z","title":"arXiv preprint arXiv:2504.11914 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.02930","last_updated":"2026-07-03T03:53:58Z","snapshot_observed_at":"2026-08-09T12:29:38.725231Z","submitted_at":"2026-07-03T03:53:58Z","title":"CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-12T06:01:58.407747Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2607.02930"},"observation_digest":"sha256:818f1dac74ae8d254caa016f609edb20584b20bf6ff9ab77b8f78da62b352a32","observation_id":"a16fb88a-b39b-4ada-bf8e-992670bfa17f","resolution":{"observed_at":"2026-07-12T06:01:58.407747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11914","snapshot_observed_at":"2026-07-11T23:45:43.436443Z","title":"arXiv preprint arXiv:2504.11914 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03817","last_updated":"2026-07-04T11:05:57Z","snapshot_observed_at":"2026-08-08T07:15:26.115981Z","submitted_at":"2026-07-04T11:05:57Z","title":"Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-11T23:45:43.436443Z"},"links":{"cited_paper":"/paper/2504.11914","citing_paper":"/paper/2607.03817"},"observation_digest":"sha256:af9a1b37d0ab786171e92cd385e88026e02de55d4b5de77c085080e17982e2a5","observation_id":"e65fb628-7059-4772-a0e7-37e12e3e0ed3","resolution":{"observed_at":"2026-07-11T23:45:43.436443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.11914/citation-record","integrity":"/paper/2504.11914/integrity","json":"/paper/2504.11914/citation-record.json","paper":"/paper/2504.11914"},"outbound":[],"paper":{"arxiv_id":"2504.11914","last_updated":"2025-04-16T09:48:41Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T16:01:52.576967Z","submitted_at":"2025-04-16T09:48:41Z","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2504.11914."}