Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.08707.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:07:11.328206Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T08:01:00.756088Z
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 abc28a49-2110-4f3d-b9d7-99987f0121f3 · inbound
APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training cites this paper.
APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:07:11.328206Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b6e48b6-382a-4eae-9a4c-57a50d1bd63c · inbound
Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs cites this paper.
Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
Reference 121
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T07:45:59.680242Z
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.
Observation 61368f32-2fc3-4c2f-9618-ed9c5301d302 · inbound
Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning cites this paper.
Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes
Reference 106
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:01:00.758534Z
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.