Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.12214.
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 2026-08-07T12:38:58.257097Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T06:55:29.375772Z
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 313c5f69-0ace-448c-9ea0-494ca7f5c280 · inbound
SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought cites this paper.
SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:38:58.257097Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d13e51fa-9963-4ea5-811a-d2e14021821f · inbound
Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers cites this paper.
Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T06:55:29.378327Z
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.