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Paper Citation Record · LEDGER

Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2406.05673.

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

pith.paper-citation-record.v1
2406.05673 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:45.875515Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T15:13:24.868638Z

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 36232d4c-d9a0-4274-a9b5-0a5b94a9f82c · inbound

Training Large Language Models to Reason in a Continuous Latent Space cites this paper.

Training Large Language Models to Reason in a Continuous Latent Space Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:29:05.870587Z

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.

source=pdf_text observed=2026-05-11T10:29:05.384381Z digest=sha256:1a36db3cb39a485a96d0a75185ee3bbffd974e71d96f4e134db8fe1b78d5aa6c

Observation 455dccc2-f458-4c90-88c2-d46d74c4b78a · inbound

BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks cites this paper.

BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:45.875515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:45.875515Z digest=sha256:560ed65b3bad596b3460094f13ef4d3cdf9d9810249aadb8e6c9a2cece7f207e

Observation 54827aa5-0c1a-4514-8a52-7272b63e2eab · inbound

Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space cites this paper.

Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T15:15:43.528312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:15:43.528312Z digest=sha256:73249eb143e0c5134389d1494cec50b771d5a6ff92c83b4f879e69a6e491cbac

Observation 14fb4903-b29e-4fd2-8f07-b72e63265d87 · inbound

Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training cites this paper.

Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-07T14:06:57.990138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:06:57.990138Z digest=sha256:fb81192b75ec261f3d5ae0f8cb6bfc283a5fe2a3adbaca727eed99857cff79e6

Observation 1ca5d673-53dc-49c1-84be-7ffa980db0f6 · 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 Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T12:38:59.455169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:38:59.455169Z digest=sha256:adedced7dad9046e55abc64b8a158124a6d17eb1f9c583b999d457ddd27dcd62

Observation 4db3e952-dcfe-46f6-9041-2372de42ad58 · inbound

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks cites this paper.

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T23:49:19.556576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:49:19.556576Z digest=sha256:69e8b6360df51a44d7284d939458094e31c1db7851de1f29837874ca9654ad3f

Observation 835c932b-298c-4e15-866d-633984780d41 · inbound

Vision-aligned Latent Reasoning for Multi-modal Large Language Model cites this paper.

Vision-aligned Latent Reasoning for Multi-modal Large Language Model Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-16T07:50:44.261909Z

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.

source=pdf_text observed=2026-05-16T07:48:23.272002Z digest=sha256:bfa44a7cb2344e0571cb1c1ce490d628ee492b9140c1f1bd0e4b9f7473ac2e3a

Observation 444079a8-6acb-4995-955c-b7b8a73abb5c · inbound

SeLaR: Selective Latent Reasoning in Large Language Models cites this paper.

SeLaR: Selective Latent Reasoning in Large Language Models Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:35:49.680147Z

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.

source=arxiv_source observed=2026-05-10T18:27:36.132030Z digest=sha256:10e3c8ff6f51678f4b564c55dce5e7633a6383e4f02ae0abddc825bc70ba154a

Observation 008c89e0-65e7-411b-a82d-289b4dcddd76 · inbound

DISA: Offline Importance Sampling for Distribution-Matching LLM-RL cites this paper.

DISA: Offline Importance Sampling for Distribution-Matching LLM-RL Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-20T15:13:24.870383Z

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.

source=arxiv_source observed=2026-05-20T15:11:43.235574Z digest=sha256:7665ea8c114371f08736cfd405b1cd780ae7265279f87d10dab91413738a0127

Observation 172a9ddb-8948-4072-8be2-e07234ac5abf · inbound

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning cites this paper.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 39

Resolution
unresolved
no resolver link, observed 2026-07-13T05:38:27.357469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T05:38:27.357469Z digest=sha256:38e288dad21eb3ea91a23f78aa99ed6d881880f6a4bfb65a4364b43402fc32e8

Observation 10ff0623-4e5b-4213-8f30-0c0d18132869 · inbound

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning cites this paper.

TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-02T07:51:14.686446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T07:51:14.686446Z digest=sha256:7e2764fa11c705550c0c835e078a26033fa1a80c64168058d5ac2824c59049b2

Observation c6c32b39-5805-404b-9603-d65a33bb2deb · inbound

Weak-to-Strong On-Policy Distillation cites this paper.

Weak-to-Strong On-Policy Distillation Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Reference 121

Resolution
unresolved
no resolver link, observed 2026-08-01T00:26:29.730528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T00:26:29.730528Z digest=sha256:3806da1f5d575f4a9b2e60b8042462947bcf5c0ab24636be45c418baaad78f64