Paper Citation Record · LEDGER

Reasoning with Reinforced Functional Token Tuning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.13389.

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

pith.paper-citation-record.v1
2502.13389 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:31:12.507642Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T19:53:11.907495Z

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 62aaee24-bce0-4060-a351-644d098596a3 · inbound

From System 1 to System 2: A Survey of Reasoning Large Language Models cites this paper.

From System 1 to System 2: A Survey of Reasoning Large Language Models Reasoning with Reinforced Functional Token Tuning

Reference 267

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:36:24.474466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f3959ffb-67f5-4aad-aaeb-fefa8729e1a6 · inbound

Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience cites this paper.

Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience Reasoning with Reinforced Functional Token Tuning

Reference 154

Resolution: unresolved; no resolver link, observed 2026-08-15T23:31:12.507642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 871a9734-5061-49f7-98a0-123e3038f3d5 · inbound

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning cites this paper.

Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning Reasoning with Reinforced Functional Token Tuning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T05:09:45.933741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f293672-f4a6-49dd-8aef-aef353bae496 · inbound

GhostShell: Streaming LLM Function Calls for Concurrent Embodied Programming cites this paper.

GhostShell: Streaming LLM Function Calls for Concurrent Embodied Programming Reasoning with Reinforced Functional Token Tuning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T23:30:47.977582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5918ad04-bf65-4aa1-9194-15a3af9e7734 · inbound

BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence cites this paper.

BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence Reasoning with Reinforced Functional Token Tuning

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-13T19:53:11.909144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 96fe064e-451c-4170-86ca-9ed8d62228f6 · inbound

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models cites this paper.

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models Reasoning with Reinforced Functional Token Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T07:57:26.761537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.