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Paper Citation Record · LEDGER

MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.21582.

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

pith.paper-citation-record.v1
2504.21582 v3

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 2026-08-07T04:08:13.825732Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:52:49.431667Z

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 58edab1e-78e6-453d-9344-69075a9b443b · inbound

EconGym: A Scalable AI Testbed with Diverse Economic Tasks cites this paper.

EconGym: A Scalable AI Testbed with Diverse Economic Tasks MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T04:08:13.825732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:08:13.825732Z digest=sha256:5d1288249fcb6097f704e813359a7820a890bf6bd69ccd47bdb09d91b268d7fe

Observation 8cf53596-189b-4ac0-85bc-a6d7f57ade69 · inbound

IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics cites this paper.

IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:36:01.139018Z

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=2026-05-10T17:34:56.485882Z digest=sha256:61b9d93256d21f363d480ae77d5444860bd7b2c32da04a117070a1074d05e9c7

Observation 48aec1e0-5a87-438a-a6f9-f184cea9b915 · inbound

Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits cites this paper.

Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-20T13:43:19.615340Z

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=2026-05-20T13:40:04.275438Z digest=sha256:b8334472f30a16d10af2d212e0ec3c276707ae213259f54a2c8d6869512fa5cb

Observation 43b56e76-8da0-4045-83f3-e1424ae2adfc · inbound

EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations cites this paper.

EASE Configuration Facilitates A Reproducible Science of LLM Social Simulations MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework

Reference 41

Resolution
malformed identifier
arxiv_id, observed 2026-06-28T23:52:49.433033Z

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=2026-06-28T23:50:43.045559Z digest=sha256:65228e9c5240d214ee3f94d3e405146a6f304a40339ab14c8ea50809289a0918