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Paper Citation Record · LEDGER

Chaos persists in large-scale multi-agent learning despite adaptive learning rates

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.01032.

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

pith.paper-citation-record.v1
2306.01032 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T03:56:08.162823Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:15:48.120003Z

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 ed19134b-2550-4bf5-8dc0-783272a0c9fa · inbound

Belief Engine: Configurable and Inspectable Stance Dynamics in Multi-Agent LLM Deliberation cites this paper.

Belief Engine: Configurable and Inspectable Stance Dynamics in Multi-Agent LLM Deliberation Chaos persists in large-scale multi-agent learning despite adaptive learning rates

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-19T16:02:38.966303Z

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=arxiv_source observed=2026-05-19T15:58:24.144427Z digest=sha256:b67bf2fb36b9722eba6ff93f27ff3c7d4fed802941fc1896934fa3d6dbfe71cd

Observation a01015b7-f74e-4c31-9d3a-c1441a2fe556 · inbound

Risk-Sensitive Learning in Population Games under Extreme Events: Bifurcations and Chaotic Dynamics cites this paper.

Risk-Sensitive Learning in Population Games under Extreme Events: Bifurcations and Chaotic Dynamics Chaos persists in large-scale multi-agent learning despite adaptive learning rates

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-07-01T15:15:48.121873Z

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=2026-06-30T03:56:08.162823Z digest=sha256:3f79084c50da8dba6f2e03c38bf29b277024ac44bd81794ff4b98de472cb4e2b