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Paper Citation Record · LEDGER

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.05006.

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

pith.paper-citation-record.v1
2403.05006 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 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:50:33.252571Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:13:13.622953Z

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 08bfe1a9-d6fb-4fab-90ad-64f0350a547c · inbound

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers cites this paper.

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers Provable Multi-Party Reinforcement Learning with Diverse Human Feedback

Reference 163

Resolution
unresolved
no resolver link, observed 2026-08-07T22:50:33.252571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T22:50:33.252571Z digest=sha256:619bd95dff10364410f64149c5b901b4699cbb04f1957045c34368af32a5a4ff

Observation 86890663-deca-4538-9ae6-381b1a419079 · inbound

Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences? cites this paper.

Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences? Provable Multi-Party Reinforcement Learning with Diverse Human Feedback

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T12:49:12.458231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:49:12.458231Z digest=sha256:b556a4447e993fc1c37c1274e95e18f12a9bb237fc8ee7e9e965e9f98493a1a3

Observation 3fcb66fe-0fc2-42c9-a6cf-fd8b5072d6a7 · inbound

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory cites this paper.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Provable Multi-Party Reinforcement Learning with Diverse Human Feedback

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T01:04:32.828481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:04:32.828481Z digest=sha256:df936f1a2ea319d781155fcb9ece59810251d8353a9df100d5e1e1d02814fa3c

Observation e24cd083-7e1b-48b5-a078-67ba63e0862f · inbound

Reinforcement Learning from Human Feedback: A Statistical Perspective cites this paper.

Reinforcement Learning from Human Feedback: A Statistical Perspective Provable Multi-Party Reinforcement Learning with Diverse Human Feedback

Reference 93

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:13:13.626028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-13T20:10:43.578904Z digest=sha256:23d8d69f2289486895fca94b195cc0f38f7d9b7d42f73463b189bdce621a97c5