Paper Citation Record · LEDGER

ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.02108.

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

pith.paper-citation-record.v1
2410.02108 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:54:29.226988Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:07:40.444936Z

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 e5b3017f-fd9b-4e7d-bfe2-61582cf493dc · inbound

Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges cites this paper.

Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-09T14:54:29.226988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c37a47f-d018-470c-ba5e-70ffe8817970 · inbound

Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models cites this paper.

Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:03.104482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37a248a9-d6a0-4500-a2e7-2bb4d3e7da51 · inbound

A Survey on Large Language Models for Mathematical Reasoning cites this paper.

A Survey on Large Language Models for Mathematical Reasoning ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:14:47.403953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ef8e6cc-f621-4be5-80ca-6230b650ad25 · inbound

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search cites this paper.

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement

Reference 213

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:07:40.451653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.