Paper Citation Record · LEDGER

Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2308.04445.

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

pith.paper-citation-record.v1
2308.04445 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:29:05.153703Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:59:52.249685Z

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 08971d88-65e1-4bdd-b7b5-a0fb12380736 · inbound

Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning cites this paper.

Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-24T09:09:15.473863Z

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.

Observation acdb6949-71ce-4d71-981f-c35c2c6de934 · inbound

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment cites this paper.

Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T10:29:05.153703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5893eb26-2701-486a-8cb2-437418d56bb3 · inbound

Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models cites this paper.

Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T18:45:45.131773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ce01817-2683-48d1-83c8-7a038a7fd4fc · inbound

Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI) cites this paper.

Adaptive Utility driven Resource Orchestration for Resilient AI (AURORA-AI) Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:59:52.251730Z

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.