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Paper Citation Record · LEDGER

Self-Evolved Reward Learning for LLMs

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.00418.

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

pith.paper-citation-record.v1
2411.00418 v3

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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:24:30.794478Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T09:52:14.066563Z

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 ad91e8e8-f36a-4406-a439-0057b6e6c276 · inbound

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy cites this paper.

Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy Self-Evolved Reward Learning for LLMs

Reference 106

Resolution
unresolved
no resolver link, observed 2026-08-07T13:24:30.794478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:24:30.794478Z digest=sha256:6ac27cc37e65edcd9a3170dce7da328849ed32931bd45ce7865725670c6017c1

Observation 27fc0b52-f5ff-4bdb-a168-adbcd38d5fa2 · inbound

Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks cites this paper.

Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks Self-Evolved Reward Learning for LLMs

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-19T09:52:14.068048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-19T09:48:56.990745Z digest=sha256:6bb99f07d02681f04aa53f1c567a9f9c4026834613011ccfdefbda037233b5ab