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Paper Citation Record · LEDGER

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models

As of 20 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 4 inbound Pith citation observations for arXiv:2511.16940.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2511.16940 v3

Coverage vector

measured 5 of 5 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:09:14.032276Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:16:36.082783Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T20:06:18.457464Z

Reference resolution

5 of 5 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved3
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 80767aa9-1d6d-4631-bd25-9ab085d10655 · outbound

This paper cites "The image contains indirect identifiers such as barcodes, postal codes, and reference numbers like \.

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models "The image contains indirect identifiers such as barcodes, postal codes, and reference numbers like \

Reference 1

Resolution
malformed identifier
no resolver link, observed 2026-08-03T21:09:14.032276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:09:14.032276Z digest=sha256:892e49fb89647ae2205ffbc0ad08ad9df502e9a50926f6ce73da6bb5bc0c76f9

Observation 80b51d6f-65dc-46ec-af64-d148eda45e3a · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-03T21:09:13.708820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:09:13.708820Z digest=sha256:06efbf7efc80777d246acd7a22ca206b48958657196190ebd09c256061e67fd4

Observation 0de2b4eb-c81f-4ddb-ac02-53d3012c919b · outbound

This paper cites Beyond Memorization: Violating Privacy Via Inference with Large Language Models.

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models Beyond Memorization: Violating Privacy Via Inference with Large Language Models

Reference 2017

Resolution
malformed identifier
no resolver link, observed 2026-08-03T21:09:13.933921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:09:13.933921Z digest=sha256:f3021c69238fac2bf1e3ad9d67272d5ccbb88cd72d8a875bc0145161c9ec7f6e

Observation b227eecb-fc76-48ce-8a4c-1e98d468b424 · outbound

This paper cites GPT-4o System Card.

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models GPT-4o System Card

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-03T21:09:13.798052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:09:13.798052Z digest=sha256:9b670ea5deae9700766ee863ad50e3f7ee60c9d435caee7eecb58fd6fdb7f128

Observation 4c20dbad-0a8a-46b7-bf7b-cb81c16b9fea · outbound

This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.

MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-03T21:09:13.251555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:09:13.251555Z digest=sha256:5c18a729e3dd7eab7987e75f5867337ecd66e2f2c5add5903550419e593e9cb2

Pith citing papers

Observation b041df1c-e7c1-4604-9c4c-f382c93d74a5 · inbound

How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study cites this paper.

How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-06-01T02:02:21.957785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-08T16:29:44.865799Z digest=sha256:8f9f641c6776edb870297ccb0c781f660e530b50c8aadb229f386dcaa2fa2a05

Observation d0e1a3a1-d58b-409b-94b0-61f926d20a8c · inbound

How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study cites this paper.

How Far Are VLMs from Privacy Awareness in the Physical World? An Empirical Study MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-06-01T02:02:21.957785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-11T01:16:36.082783Z digest=sha256:34dedb29de39f2cab248c0c53946fc02739e12c85a8987ffa50d09b5c1b9f981

Observation b027d3a9-39d6-410f-8dc2-4234d6b1ac23 · inbound

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence cites this paper.

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-06-01T02:02:21.957785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-08T10:12:58.421050Z digest=sha256:d2ac0e47d3c6e50599208ea68bf7f2aa87bc4a87a9360a5a0540e2eace0cd703

Observation f0754315-2403-4f9a-8b12-241c6f12bbc2 · inbound

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence cites this paper.

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-06-01T02:02:21.957785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-11T00:56:48.838028Z digest=sha256:2321b3e200f9b0145368909ca3d1a7aa8f4a7fdce3693339b3f46c008d3e44d7