Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Aligning Large Language Models via Fine-grained Supervision
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.02756.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:53.884160Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T07:26:46.053194Z
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 fc99c1ea-9aee-470b-8d7a-de9b68633f0a · inbound
PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models cites this paper.
PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models Aligning Large Language Models via Fine-grained Supervision
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:14:53.884160Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fcdc1f0f-0310-4bd3-85ac-c878661eb97d · inbound
SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models cites this paper.
SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models Aligning Large Language Models via Fine-grained Supervision
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T05:25:54.634495Z
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.
Observation 372cb3f1-8cb4-4715-9678-93889ea37163 · inbound
Selective-Advantage Entropy-Adaptive Horizon GRPO: Asymmetric Token-Level Discounting for Efficient Reinforcement Learning of Language Models cites this paper.
Selective-Advantage Entropy-Adaptive Horizon GRPO: Asymmetric Token-Level Discounting for Efficient Reinforcement Learning of Language Models Aligning Large Language Models via Fine-grained Supervision
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T07:26:46.054856Z
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.