Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.08733.
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 2026-08-06T20:21:22.819294Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:36:17.496619Z
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 24af02b0-806f-4345-a562-006b35e871f8 · inbound
SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models cites this paper.
SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:21:22.819294Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 502261bd-df6c-4ba8-a39b-4ca760a6842f · inbound
See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection cites this paper.
See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:36:17.503350Z
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.