Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.18702.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T22:18:32.215062Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T12:10:31.641498Z
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 575fe8ae-216c-4123-9d87-f73b891ba8be · inbound
From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents cites this paper.
From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship
Reference 185
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:18:32.215062Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30755c48-b0d0-47d8-8ca1-7c42cda5a57c · inbound
Political-LLM: Large Language Models in Political Science cites this paper.
Political-LLM: Large Language Models in Political Science Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:52:04.012015Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 648b0171-958d-449e-9959-6730a5c25643 · inbound
A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science cites this paper.
A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-11T12:10:31.649432Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 8d6e4084-99e8-4b0e-a3c9-da1228ec3728 · inbound
Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics cites this paper.
Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T10:12:21.653120Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bccc81dc-193a-4976-88a4-883980344237 · inbound
ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech cites this paper.
ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:55:19.236444Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.