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Paper Citation Record · LEDGER

A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2501.09431.

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

pith.paper-citation-record.v1
2501.09431 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:08:46.276876Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:43:59.990308Z

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 6bb64adf-bd3a-4824-816d-194fc1d4a16f · inbound

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study cites this paper.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-08T15:08:46.276876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T15:08:46.276876Z digest=sha256:d1c89a125b582daa0e9d1798a7ac95f127f24012b2dba5299efeb7d31a8e42b0

Observation 608def36-6fb3-4a36-bd2f-175dc63aa673 · inbound

OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs cites this paper.

OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T14:22:07.668901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:22:07.668901Z digest=sha256:bd6cf2c0fc429029e17b6ad906d2f18e336316c7b69bbee396abc3085d0fd15a

Observation 5c910af9-fedd-4fce-b51a-78926666481f · inbound

Localizing Persona Representations in LLMs cites this paper.

Localizing Persona Representations in LLMs A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T12:25:33.195929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:25:33.195929Z digest=sha256:7be4aefd11eed61a845001316a6026bd3cda8dab2183e969b35d56c6d2584fcd

Observation b6c7961d-5001-47c1-bb1e-881ca83cc6b0 · inbound

A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy cites this paper.

A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-07T04:52:00.973600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:52:00.973600Z digest=sha256:84a66260d14eb7948d925edc0b9c0b7dbb0718006876024fc410814e5df89db8

Observation bd9339db-ee10-409f-8975-1322c879dfdf · inbound

AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated Text cites this paper.

AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated Text A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:44:00.077851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T22:43:58.894896Z digest=sha256:21dda13a98577f4c988c566b7e75f02fd5c0375a39670a2a6834c3d192d8e4f6