Paper Citation Record · LEDGER

Towards best practices in AGI safety and governance: A survey of expert opinion

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.07153.

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

pith.paper-citation-record.v1
2305.07153 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:15.293676Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

15
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eeae9bea-45aa-43b8-a792-b2226bf151a0 · inbound

An Overview of Catastrophic AI Risks cites this paper.

An Overview of Catastrophic AI Risks Towards best practices in AGI safety and governance: A survey of expert opinion

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:03:46.756688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 77db4e86-0d51-49ef-b89a-be27da8b1815 · inbound

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities cites this paper.

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities Towards best practices in AGI safety and governance: A survey of expert opinion

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:15.293676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d4d6207-90ef-4fa3-9608-4101bc207a93 · inbound

Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact cites this paper.

Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact Towards best practices in AGI safety and governance: A survey of expert opinion

Reference 261

Resolution: unresolved; no resolver link, observed 2026-08-06T21:07:11.553330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f7fd53a-103a-4d93-88f9-f6cf2e21eb5f · inbound

From Turing to Tomorrow: The UK's Approach to AI Regulation cites this paper.

From Turing to Tomorrow: The UK's Approach to AI Regulation Towards best practices in AGI safety and governance: A survey of expert opinion

Reference 154

Resolution: unresolved; no resolver link, observed 2026-08-06T20:32:41.853884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05a491c2-c828-469c-875a-8e7cb4eccdbb · inbound

Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts cites this paper.

Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts Towards best practices in AGI safety and governance: A survey of expert opinion

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-06-28T04:31:38.932036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f75a8da-a0dd-4176-bd27-34d0bbc149c0 · inbound

From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems cites this paper.

From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems Towards best practices in AGI safety and governance: A survey of expert opinion

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-01T11:15:42.861685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.