{"as_of":"2026-08-18T17:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:148499f34e0cca7416b91763220e897e6fec646648cbf3c859aa7a21a865e510","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T06:09:46.933171Z","state":"measured"},{"denominator":64,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":64,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T14:24:27.679295Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.11228","snapshot_observed_at":"2026-07-30T14:24:27.679295Z","title":"Deepbias: Adaptive in-depth probing of social biases in lvlms.arXiv preprint arXiv:2607.11228, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23740","last_updated":"2026-07-26T16:25:45Z","snapshot_observed_at":"2026-08-17T02:46:05.127964Z","submitted_at":"2026-07-26T16:25:45Z","title":"Zing: Social Mind for LLMs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-30T14:24:27.679295Z"},"links":{"cited_paper":"/paper/2607.11228","citing_paper":"/paper/2607.23740"},"observation_digest":"sha256:2e2c114341dbd22a3e71c04b0cf6081733b1a368981237f77e8dd22eaf821d51","observation_id":"995dc5cf-08b9-4576-95a7-1737e4d235e2","resolution":{"observed_at":"2026-07-30T14:24:27.679295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2607.11228/citation-record","integrity":"/paper/2607.11228/integrity","json":"/paper/2607.11228/citation-record.json","paper":"/paper/2607.11228"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Vision-language models for vision tasks: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:9cc7091b0bbc96d2f47ad68b6b8f7f96e32e365f6d233f953575395f6ffbaf98","observation_id":"aaa896d5-4085-4add-b585-b9a16e9161e5","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-08-15T14:02:47.366139Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Gpt-4o system card,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f509d7c24597f00417b45d0cab9b0845b04604faef29ebed707740b1db5a0340","observation_id":"0370a6d4-795e-429d-96ea-f5118689a13e","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"ShowUI: One vision-language-action model for GUI visual agent,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:34f1ebcc75f9da3e6ed9b3d27f9292e1edb58f41cd436faf2e5ef94d88afbdda","observation_id":"e3d30110-7da1-4a43-b858-a7f8164e8afa","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Prox- imal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:1f1926d839daec5beaefdb3eb301deae35e6bdc16b6a77417d4846480cf09893","observation_id":"2a8ebd2d-94b9-4503-bd1c-b0ccedc989b7","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Defying distractions in multimodal tasks: A novel benchmark for large vision-language models,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:69fb635cee134d21b5e33377aa1fa2680f91030efd6364e4a5f00be8d667ea96","observation_id":"d67bf5aa-0bf1-42fe-8acc-bd9c74de2707","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Lvlm-ehub: A comprehensive evaluation bench- mark for large vision-language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:519007145462cc9d14ab35d2fa81eccabc052fc42a2b08b1935a04db3160858e","observation_id":"f7108e7c-3528-4c95-8cba-82e57b7dbd5f","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Visogender: A dataset for benchmarking gender bias in image-text pronoun resolution,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:c851350516763392b663f4aef5ab5472b4572417bad0d0242a791f6b43f47129","observation_id":"8467f89b-4499-4d2a-b373-6986280ab09a","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Counterfactually measuring and eliminating social bias in vision-language pre-training models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:94251234d67001cefc9a0f214964d9b2e96cc87e1c0a8cc43b04b18b62607c95","observation_id":"cffea4ea-91cd-4646-aed7-6eb36524c4e1","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12022","last_updated":"2023-11-20T18:57:34Z","snapshot_observed_at":"2026-08-18T14:12:03.598270Z","submitted_at":"2023-11-20T18:57:34Z","title":"GPQA: A Graduate-Level Google-Proof Q&A Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12022","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"GPQA: A graduate-level google-proof q&a benchmark,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2311.12022","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:e481f566384a0450ad135110180184ad5442215f57334d5c75b18baee05ef9c3","observation_id":"9d26c861-cc0d-476f-9f01-f64329f7e78f","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Gemini 3 pro model card,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:761101cbc33b5e693eb67273f2ce50bc637813bbcce9ed383f56be2289733e65","observation_id":"95b93374-9034-4f90-be31-206b80169870","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Direct preference optimization: Your language model is secretly a reward model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:2ad7b50d0bc6a7176046d4db0505c8f4fbc9c33c7d35eeeb52a7d80356f8b50f","observation_id":"51720b04-021c-4f33-8548-3e9d56b25cf7","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Man is to computer programmer as woman is to homemaker? debiasing word embeddings,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f19d59211e89af6cf44be1d7967a0b40332067800fd8c2ca84ef80a5d9d7d718","observation_id":"121546f6-0cc6-43f3-aa0c-66a6abf067ef","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Semantics derived au- tomatically from language corpora contain human-like biases,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:772cd8eec5c5ffcbdb838735c0bd2cf8727b0d6a6d37ba7e5d93dfe9563c58dc","observation_id":"7a99bcc2-dd7c-452f-98df-e96d7e4be52e","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Crows-pairs: A challenge dataset for measuring social biases in masked language models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:1f9c3e100ff7aa56705760794ffa0397ca5d3b8fc7ac0562bab846b7ca2b631b","observation_id":"f5d8d80c-3b73-4fff-9d34-c6d909c334a0","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Stereoset: Measuring stereo- typical bias in pretrained language models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f6f31d88d5e59c7f40fae4b0b45bc734adb6c8467f3f0b4c832cad09dc12ba15","observation_id":"8c8c41c4-a0fe-462c-ba56-65e1a1842656","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Bbq: A hand-built bias benchmark for question answering,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:71ed31bc4d8f36c99ad976f74e1186c5caa75bb4e5a3ce483aa6503227a7afa0","observation_id":"eac7f7ab-ee3f-4544-97ac-9f3079d2a1c8","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Genderbias-vl: Benchmarking gender bias in vision language JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 11 models via counterfactual probing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:81c8cd4a45940df90a5584f20da8f9e3040011c5d6f11192162e1e2951358e07","observation_id":"09135223-0d94-4976-b809-de67734222f4","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Vignette: Socially grounded bias evaluation for vision-language models,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:22d658509bcaaf29c7014f408f0e0768eb560b85cee81d2fd10c09ba90609261","observation_id":"81e0af0a-e9c6-49e7-9394-5f94eb9162ed","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Vlbiasbench: A comprehensive benchmark for evaluating bias in large vision-language model,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:4165cac8509481d67365c229822cfec2a32dbc5dfe1bf01de26bef8967d483c6","observation_id":"d0443e52-1c5e-419d-ad75-4cbcc114c672","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"SB- Bench: Stereotype bias benchmark for large multimodal models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:1f51c2485e25fef22a556983e8c681f699296033ce5c17c38c5993650b0b7976","observation_id":"945b361c-88a6-4f63-8f17-280f0e105b66","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Nlp evaluation in trouble: On the need to measure llm data contamination for each benchmark,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:9be29ae6e6a68112ac6ab64113fc4bccffbc5a600e104e0611795e9cd045bf09","observation_id":"34e0034a-5738-4394-969c-92a907590a1a","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Dynabench: Rethinking bench- marking in nlp,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:379c662cc6396474f32c5ded050b1eaabaf79385d7f2f99063ab9cfd23332294","observation_id":"a7d107fb-580f-4b67-95fa-71dca6abe369","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Robust visual question answering: Datasets, methods, and future challenges,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:a40ed68730fde97be30f2ff19528ddf7b4006eae60bb692ca690168219d0a48e","observation_id":"f7973aaf-c614-40e5-8bbf-ffddebeecbab","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Red teaming language models with language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:18c21ce71159ec1e4e08135e57e1a91edfeed7204b34c87f0ead8cbb87e4980b","observation_id":"0d0d07d9-acff-4ecf-8db7-b950bd8e2857","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.07858","last_updated":"2022-11-22T19:12:57Z","snapshot_observed_at":"2026-08-17T08:35:42.452149Z","submitted_at":"2022-08-23T23:37:14Z","title":"Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.07858","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2209.07858","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:7086d188c6c98fef3deccd45ace8157dab0110167a154a4bf5b198e575f6f412","observation_id":"1f623121-ba1c-420f-bc3a-1808e5a3110b","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15043","last_updated":"2023-12-20T20:48:57Z","snapshot_observed_at":"2026-08-12T09:06:50.363435Z","submitted_at":"2023-07-27T17:49:12Z","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15043","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Universal and transferable adversarial attacks on aligned language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2307.15043","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f512d9ba3e05f10e66124bfb39ac964a7b9e9fce15f635ee999c70750d76d4f9","observation_id":"b6374088-dd37-4a10-9b91-b9ab648d076e","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Automatically auditing large language models via discrete optimization,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:a244c338eeba2220a844161a99d45d16d03bf22cbf05841f65067418b5abcb3b","observation_id":"4feffe79-de21-4d36-9afd-7325d6067bd2","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Figstep: Jailbreaking large vision-language models via typographic visual prompts,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:d0273b2a91af0475944dc3eae6ba31fb0cad64cf6d357123c63611156d3cc1f2","observation_id":"32433c8f-ee1a-4fbb-808a-d0f14c486bce","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02309","last_updated":"2024-02-04T01:29:24Z","snapshot_observed_at":"2026-08-18T07:20:18.686556Z","submitted_at":"2024-02-04T01:29:24Z","title":"Jailbreaking Attack against Multimodal Large Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02309","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Jailbreaking attack against multimodal large language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2402.02309","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:aba2e82437154784f923c4765aea1d79e3284750c64e87ac59ca9b55add35df8","observation_id":"28df8a8c-e9c4-47d3-a7fe-de824eca5015","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:a25575d21065b77430c528e9ffe61ea92894fa1b68e4dae2917014fc053b640b","observation_id":"e74046af-49bf-4f5b-852d-09ea2fdde198","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Red-teaming the multimodal reasoning: Jailbreaking vision-language models via cross- modal entanglement attacks,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:71d15f261b5c3cd9c7d3d72919aeeded2d880e6ac270decc55113d099f1bcfb1","observation_id":"6ba95883-0266-452c-9632-5b7bc9b46113","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Semantic-aligned adversarial evolution triangle for high- transferability vision-language attack,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:651af732d7403bdb573001c544c160a676945a4ea25919219ec6ac1a6c498381","observation_id":"c773bf16-e5bd-494c-bee1-c5457721c8da","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Jailbreaking black box large language models in twenty queries,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:e5830b0cf5bc5e822d74e17a252b0b5827f72a898faa7f4d994e23f26aa41cb9","observation_id":"9f503c05-bcc8-43fc-a1e6-a9db2dfe0384","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Tree of attacks: Jailbreaking black-box llms automatically,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:338306b6db7cbc67c88be2fffcd34f6fd6ccfed976d1a2c8f250fcd29c6919db","observation_id":"130085cf-791e-4f99-8200-36866b695cd9","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Autodan: Generating stealthy jailbreak prompts on aligned large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:6e10ef0004787ea043fff93765406adebc771f5b6eceba6512cf05616fcc02c2","observation_id":"72ff5e74-1d9f-453f-867c-0f57abdfed02","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Treeteaming: Autonomous red-teaming of vision-language models via hierarchical strategy exploration,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:7729feed729687623549ae69270d11f0704f4021a7785072b6bfd79ce130e0e9","observation_id":"d77e0ab6-acbf-47bc-849e-584d45404073","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"ARMs: Adaptive red-teaming agent against multimodal models with plug-and-play attacks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:b42e80fffd6743e652f5bbe5182d6bebfc507ccf551284ba4704705848b08aca","observation_id":"5539d0fe-8e18-42b0-9c33-20733c8e2da2","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.12966","last_updated":"2026-06-09T04:02:52Z","snapshot_observed_at":"2026-08-18T16:51:21.181937Z","submitted_at":"2026-02-13T14:33:13Z","title":"ProbeLLM: Automating Principled Diagnosis of LLM Failures","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.12966","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"ProbeLLM: Automating principled diagnosis of LLM failures,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2602.12966","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:b1dfc0080427037cabdfe0de79ab2d41d02d8964fd6942d0dabe4fff3b1c7cf4","observation_id":"d69426c1-14cb-4eb9-bfc8-27e268cffb13","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"RedHit: Adaptive red-teaming of large language models via search, reasoning, and pref- erence optimization,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:2777fc0a6ff6a471d36cc7bafc6364a3c4bc011b3469ef4337d8e414b6013929","observation_id":"196edb01-756f-4415-92f3-f948d350cb30","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Training language models to follow instructions with human feedback,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:6b7a97c8a59343c3226bab470122b1d63b218043900627fbf08743a23d780118","observation_id":"4aab50af-ddfa-422c-b05f-7df38e001a54","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Self-instruct: Aligning language models with self- generated instructions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:56729f880b262261e478688dfd683bc8660c069a2d5e68deae09877afdc748e2","observation_id":"dff08923-36e5-486a-ac28-077b6ec39833","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"WizardLM: Empowering large pre-trained language models to follow complex instructions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:e87b9ae0a38bf9af56f143366ef46b93613f967515e53c09f2ef9ea80ca0c124","observation_id":"038e91f2-bd03-4234-b90e-3a9cdeac3a4d","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10020","last_updated":"2025-03-28T00:06:51Z","snapshot_observed_at":"2026-08-15T01:20:08.134494Z","submitted_at":"2024-01-18T14:43:47Z","title":"Self-Rewarding Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10020","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Self- rewarding language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2401.10020","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:e656b53c2250528e69df3e9b248a47ab877a462ca276adbab8f12b354d73f79e","observation_id":"be6e5875-72f5-48ee-b4e1-3f8dd21fcfb8","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:a94e362e5ca6ca54db2c2abefeb0159a35967ab37fd2b40e4f3bd206fa1cbec2","observation_id":"f4b951c9-af13-43cf-8d42-2964cbd7f89a","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"LoRA: Low-rank adaptation of large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f04857e09283cdeb28432db22738b12d9c28136bc27d74d9a73dfbe7d3ece26a","observation_id":"e244d014-ad92-4be1-96e5-2bcc5a8831b1","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.10479","last_updated":"2025-04-19T03:47:21Z","snapshot_observed_at":"2026-08-17T09:56:52.502317Z","submitted_at":"2025-04-14T17:59:25Z","title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.10479","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2504.10479","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:2680836b41f011e01649063e02d32c8ba760e703caa7e86d111b2f99228b05e6","observation_id":"c3ce597e-9e54-464d-a440-ceb101235150","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-08-14T04:17:22.593941Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Qwen2.5-vl technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f7db8f3deb61c7d7fd45f01c0e0dfd5218901555ff93587b0614d4d824639acc","observation_id":"75a6b37d-7414-4479-aa9f-fb6fe076dab8","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.19786","last_updated":"2025-03-25T15:52:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-03-25T15:52:34Z","title":"Gemma 3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.19786","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Gemma 3 technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2503.19786","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:70c6d31d1b6fcd8d6b7a4621f6aafe64cf1f2d1ce608d985641f3a76ad04afce","observation_id":"4ef0e3fa-e9c9-4a3c-ab52-41a42aa9b8f7","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Qwen3 technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:b06f3d352be4a25bd1bfb0a2bc7c593c9693fa193d5d8138b2ebbe4348b040e5","observation_id":"39834ee2-a0cc-4c6e-b846-fdc425541b4b","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.18265","last_updated":"2025-08-27T14:39:45Z","snapshot_observed_at":"2026-08-17T12:32:16.575866Z","submitted_at":"2025-08-25T17:58:17Z","title":"InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.18265","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Internvl3.5: Advancing open-source multi- modal models in versatility, reasoning, and efficiency,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2508.18265","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:166fdf3869c78c0be144ee766b65a3d2c5bd9f78e5ba18389f80c7f8243bb9bd","observation_id":"2b692c2c-8718-46d7-917c-65e237f4f5ce","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.21631","last_updated":"2025-11-27T12:16:54Z","snapshot_observed_at":"2026-08-17T13:26:10.378579Z","submitted_at":"2025-11-26T17:59:08Z","title":"Qwen3-VL Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.21631","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Qwen3-vl technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2511.21631","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f46bfeb03f2fd87f81a16de72611957fc8b60a238eb2552f77fb9721b75a2a44","observation_id":"e003e222-dc61-4dec-b028-c93c9211a2fd","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.10302","last_updated":"2024-12-13T17:37:48Z","snapshot_observed_at":"2026-08-07T03:01:29.031129Z","submitted_at":"2024-12-13T17:37:48Z","title":"DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.10302","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Deepseek-vl2: Mixture-of-experts vision-language models for advanced multimodal understanding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2412.10302","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:f914e04ae1155d041ac080c4b7a3157162a6a5685898652eed2d0cd9bcf0c74a","observation_id":"3484d2a6-5ed6-4356-94fd-da4f09170131","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03326","last_updated":"2024-10-26T16:35:13Z","snapshot_observed_at":"2026-08-18T11:56:50.710310Z","submitted_at":"2024-08-06T17:59:44Z","title":"LLaVA-OneVision: Easy Visual Task Transfer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03326","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"LLaV A-OneVision: Easy visual task transfer,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2408.03326","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:2e6a58e091e8d0ea818c0fc4409e740662f23ea9cdc2002131208aa36a5d2bdd","observation_id":"c4733237-168d-4bf2-a3e3-91b96eb5cce5","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Are we done with mmlu?","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:06b784e40979102966e5f1f9436c3a7a61e64fe6d4bd5a888f01f242980572c2","observation_id":"b55dadca-97ce-4d11-a517-ebf2f6ce4494","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Introducing GPT-5.5,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:cf22238ae55fbe14d3b65f1689e27ebd3e78e8a6a1e473d32bd38517e5daf399","observation_id":"41678e18-76bb-4c09-9f6b-af3a51d4ac27","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"The Claude family of models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:6b9a35cb017ae75d79a9d859e690e3cece3338e9d36d3b279586e441f18d0b79","observation_id":"049c9861-d015-456c-8337-caafd0c691b3","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Gemini 3 flash model card,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:0fbf3edddb4d4761d6b11033741e489ba014c8384da4eae288686da82a135a22","observation_id":"fe913dfa-0475-4bde-a556-da7f840fdc6b","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Gemini 2.5 Flash Model Card,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:90c7c78a75ce1352b0603c0b6bd9ad1686a5d99c560435e4616ce8f8cd198821","observation_id":"ec5c27d7-8650-4001-a64a-50232c1c6f1f","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Glm-4.1 v-thinking: Towards versatile multimodal reasoning with scalable reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:dcdcaea7b1037e0a46d094de44f2a2732ad9a602c78356e90e6cfc10a59e76a3","observation_id":"cdc7909b-c561-429c-9651-2db06b3aa0da","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.01800","last_updated":"2024-08-03T15:02:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-03T15:02:21Z","title":"MiniCPM-V: A GPT-4V Level MLLM on Your Phone","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.01800","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Minicpm-v: A gpt-4v level mllm on your phone,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2408.01800","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:001d94fc8bd53a4ecaedaaa46bdba5cc73fee60e0c9c5e8f032b4c6d38235458","observation_id":"9c9c2ca0-adf2-4382-8f93-c86e9a39a3e7","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.07073","last_updated":"2024-10-10T17:59:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-09T17:16:22Z","title":"Pixtral 12B","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.07073","snapshot_observed_at":"2026-07-14T06:09:46.933171Z","title":"Pixtral 12b,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"cited_paper":"/paper/2410.07073","citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:fc3e1b54a010005c6541d5b117d330f5b55f3955acd06d1711f74aa5a00f8474","observation_id":"8350dde7-f38d-4620-8b21-a395c90cb9d6","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Visual instruction tuning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:24ef65d2afb32ec0f6dad3166aee38c23dc3a5d911cc276c4abffb30a5e40a4c","observation_id":"f4ef928c-ddb4-4850-b9f9-29a4f86c57f0","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","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":"2026-07-14T06:09:46.933171Z","title":"Visionllama: A unified llama backbone for vision tasks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-07-14T06:09:46.933171Z"},"links":{"citing_paper":"/paper/2607.11228"},"observation_digest":"sha256:13c261b8d54b03156b07033848c9c63bf389297bdea3e3b93dd4af89ddfb4cac","observation_id":"b60fe5b0-542c-4b3c-84ff-061458e9326b","resolution":{"observed_at":"2026-07-14T06:09:46.933171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.11228","last_updated":"2026-07-13T08:19:09Z","latest_version":1,"primary_category":"cs.CY","snapshot_observed_at":"2026-08-15T01:20:39.570977Z","submitted_at":"2026-07-13T08:19:09Z","title":"DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":63,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":63},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2607.11228."}