Paper Citation Record · LEDGER

CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2409.13903.

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

pith.paper-citation-record.v1
2409.13903 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:05:08.742309Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-07T18:04:00.474791Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0ffca8af-c042-406e-947c-3ef74962dd72 · inbound

Position: Contextual Integrity is Inadequately Applied to Language Models cites this paper.

Position: Contextual Integrity is Inadequately Applied to Language Models CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T21:05:08.742309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 122808a1-8f34-4730-988d-0a707af0c190 · inbound

Can Large Language Models Really Recognize Your Name? cites this paper.

Can Large Language Models Really Recognize Your Name? CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:01:38.563082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f69d212-7e44-4e3a-b3e3-18fa67d7dd94 · inbound

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning cites this paper.

Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:37:25.713365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c38d06ad-4f11-4b63-b2d9-112ee129d2b8 · inbound

A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications cites this paper.

A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T00:48:13.037687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30cc4f22-486d-449e-9b3f-c77957b3395c · inbound

ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance cites this paper.

ContextLens: Modeling Imperfect Privacy and Safety Context for Legal Compliance CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:41:06.488475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 80ca46cb-5242-4da2-b729-1900d5847410 · inbound

CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents cites this paper.

CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:21:05.273412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e207d4f4-ce29-4a45-89e0-bf49821bfb21 · inbound

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems cites this paper.

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 130

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:51:39.497557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 74be99ed-9274-4dd0-b1ba-5b32de5857b6 · inbound

PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems cites this paper.

PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-20T16:18:37.482686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1df25b65-ad67-45e4-90df-7cf232614560 · inbound

Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents cites this paper.

Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T10:53:13.407325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bd350178-12da-4804-b50c-23d1c044e536 · inbound

It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs cites this paper.

It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:09:51.870107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e25920cb-6317-411b-87c3-76d6dd0cccdf · inbound

Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security cites this paper.

Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 133

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:45:01.656020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 95f40bc4-3821-48e7-a960-5328e128020a · inbound

Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation cites this paper.

Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:46:33.067704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da7efa1f-aa7b-469a-b4cc-5f315c12f21f · inbound

MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations cites this paper.

MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:49:45.806939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1bb4dee-efe0-4c46-b275-2456f55ce743 · inbound

Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents cites this paper.

Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:09:53.243414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3dc817ec-d845-4631-b4de-18b3b6106fed · inbound

PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems cites this paper.

PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-07-07T18:04:00.476894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.