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Paper Citation Record · LEDGER

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning

As of 16 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 2 inbound Pith citation observations for arXiv:2506.12860.

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

pith.paper-citation-record.v1
2506.12860 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:13:19.702029Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:54:17.309691Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T01:32:02.805236Z

Reference resolution

49 of 49 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved41
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6478d1d1-643d-427c-be89-2cbe697394ca · outbound

This paper cites OpenAI o1 System Card.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning OpenAI o1 System Card

Reference 1

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source=pdf_text observed=2026-08-15T20:13:19.444466Z digest=sha256:69d745f3b3a553a9a6bb386c7c4b5ff8b7e7a184dc3def142a81e26ad2e6d104

Observation fe82fa2d-2daf-4275-b790-4d4b263eb510 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

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source=pdf_text observed=2026-08-15T20:13:19.451305Z digest=sha256:dd25d25b5bba27a2f18c93fedc3f0d436437bde31677572dccb5ddf3af2e1b52

Observation 6c24ee52-db13-46e0-b614-52d37ccab6c6 · outbound

This paper cites Reasoning with large language models, a survey.arXiv preprint arXiv:2407.11511, 2024.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Reasoning with large language models, a survey.arXiv preprint arXiv:2407.11511, 2024

Reference 3

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source=pdf_text observed=2026-08-15T20:13:19.457178Z digest=sha256:70921ce1f695cff23b3b446c97796e2eec0ce1883367d72c920dd4036573a394

Observation d6e111b5-1ee3-42bb-a74e-f579c0bb7bc2 · outbound

This paper cites Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs

Reference 4

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source=pdf_text observed=2026-08-15T20:13:19.462076Z digest=sha256:e26f1d8ea0c2e77db7a554db1513c8e4493b2696c3dcfff4ec01f7295a53e64c

Observation 9022a234-6e97-4e6c-96b6-c67fd3edada0 · outbound

This paper cites Competitive Programming with Large Reasoning Models.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Competitive Programming with Large Reasoning Models

Reference 5

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source=pdf_text observed=2026-08-15T20:13:19.467637Z digest=sha256:ce52ca0754b4de22e93d63b35f78c4ca0ddb21d72907ff298e382179d9b9090a

Observation 731500e5-d9e6-4222-b1f7-6c1765cda600 · outbound

This paper cites LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!

Reference 6

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source=pdf_text observed=2026-08-15T20:13:19.473338Z digest=sha256:f0da4acf916296137c67744d2f35a682f437a7a71be24de9eecfe6ac26f27ef4

Observation d69bcdc8-f6d1-4524-973d-d000efd419b3 · outbound

This paper cites Enhancing customer contact effi- ciency with graph neural networks in credit card fraud detection workflow.arXiv preprint arXiv:2504.02275, 2025.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Enhancing customer contact effi- ciency with graph neural networks in credit card fraud detection workflow.arXiv preprint arXiv:2504.02275, 2025

Reference 7

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source=pdf_text observed=2026-08-15T20:13:19.479355Z digest=sha256:af9226b53dedeb574d46aa8c357a02f9623ec659a2fcda01b68c14c287066c06

Observation cab37711-97dc-4fd5-92ba-d444732352c4 · outbound

This paper cites Sky-t1: Fully open-source reasoning model with o1-preview performance in 450 budget.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Sky-t1: Fully open-source reasoning model with o1-preview performance in 450 budget

Reference 8

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raw_fallback, observed 2026-08-15T20:13:20.988249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.484914Z digest=sha256:b721c3403c8b94dc97dfc3c4f57e33c0f6bb7f7ba15334bad143dad5389e438b

Observation 284c809b-92f2-4ed3-98d1-2af787fcd0c7 · outbound

This paper cites s1: Simple test-time scaling.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning s1: Simple test-time scaling

Reference 9

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source=pdf_text observed=2026-08-15T20:13:19.489730Z digest=sha256:ac1cdaf522066dd3464d7b8854a023cda13fb020ca62f82cd9bf5273b9b43d64

Observation e7ca230a-1160-4478-9e47-f279a4a20fef · outbound

This paper cites LIMO: Less is More for Reasoning.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning LIMO: Less is More for Reasoning

Reference 10

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source=pdf_text observed=2026-08-15T20:13:19.494991Z digest=sha256:62cddc80afba50deed1f43862f0db76208a25b967c3bde0f670b3349fbc28474

Observation 4a6725a9-3105-479f-ae7a-59515fe3a52f · outbound

This paper cites Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 11

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source=pdf_text observed=2026-08-15T20:13:19.500677Z digest=sha256:d5967ba0e4fe6001b33008ece345400f0384c3e71660db5f5e2135e463daafd5

Observation 410d4bae-04aa-4806-92f5-e0260bd428de · outbound

This paper cites Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Reference 12

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source=pdf_text observed=2026-08-15T20:13:19.506091Z digest=sha256:f5356df029b485c8e9083e64f612cd79585c81597bcd1d775c4231cc78d5014e

Observation 0b9f4a97-9abd-4318-b919-3514f1e14a99 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 13

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source=pdf_text observed=2026-08-15T20:13:19.510923Z digest=sha256:b3792f2c10c53d0b06659d0c9685678e5b3e3fbe5c2323da00f5c99c9fa89e8a

Observation d681014e-746e-4422-96d1-24fa5647f349 · outbound

This paper cites O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning

Reference 14

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source=pdf_text observed=2026-08-15T20:13:19.515683Z digest=sha256:a84becca1123b28390a51e6cfb938a9d125ac3617c6a3c022fa173f0b0e0594d

Observation d8f85ba2-8d9b-4677-9e25-45d9fbc472b2 · outbound

This paper cites L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 15

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source=pdf_text observed=2026-08-15T20:13:19.521206Z digest=sha256:f0fc20dfdf40e42a7386e48a837279b82b06de1ff0932d76521ae808bdc1424f

Observation af617170-69e0-441a-bbd2-ea102ac5095e · outbound

This paper cites CoT-Valve: Length-Compressible Chain-of-Thought Tuning.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 16

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source=pdf_text observed=2026-08-15T20:13:19.526332Z digest=sha256:446995709bd1804ad58e915a46b42550d280556694035d0723a15552ead78af4

Observation 4b8a67c8-74a6-42b2-a81e-9ea4a99fe009 · outbound

This paper cites Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067, 2025.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067, 2025

Reference 17

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source=pdf_text observed=2026-08-15T20:13:19.531298Z digest=sha256:a25ae753efc448706d152ace7bf909b786d424339af2fc83bee6ac5d047708cd

Observation eb9712c2-67a8-4cf7-8572-5d2be9f59159 · outbound

This paper cites Understanding catastrophic forgetting in language models via implicit inference.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Understanding catastrophic forgetting in language models via implicit inference

Reference 18

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source=pdf_text observed=2026-08-15T20:13:19.535919Z digest=sha256:7d20974fd9b0cd7e17ee390517794398c33e25bc436374d9b0f21a11a0f9b6b1

Observation 11e8e042-dd9f-4993-b8a2-6c2c0e7b3944 · outbound

This paper cites Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Reference 19

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source=pdf_text observed=2026-08-15T20:13:19.542361Z digest=sha256:51369bc34a4adec63de277cb2af605f0a45c7bc2a44af2c635146b1d195c8ea4

Observation a4d4df8c-4ebf-47cf-a123-bddba362a9e8 · outbound

This paper cites Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models

Reference 20

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source=pdf_text observed=2026-08-15T20:13:19.547205Z digest=sha256:ac0e2bf5e1eb55b914af936324d536b42c35df6aaee1b1c7d8a7410da80d15ec

Observation 9fda95af-33fa-4545-9992-f2bd59798996 · outbound

This paper cites Note on cohen’s kappa.Psychological reports, 65(1):223–226, 1989.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Note on cohen’s kappa.Psychological reports, 65(1):223–226, 1989

Reference 21

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raw_fallback, observed 2026-08-15T20:13:20.960956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.552305Z digest=sha256:1c12fdf1a480cb151be160f2a709e999c57916501cf3a7d6289f5295f759f6e6

Observation 77cc036f-07ca-4040-a651-826bbccb146a · outbound

This paper cites Let's Verify Step by Step.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Let's Verify Step by Step

Reference 22

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source=pdf_text observed=2026-08-15T20:13:19.557196Z digest=sha256:313b52b97001df3f27ab479e32d52074154ac0ff7100e49602d9b27078a2bcb8

Observation 6ab8ea00-f355-4a11-ad97-8056d7fd460d · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 23

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source=pdf_text observed=2026-08-15T20:13:19.561839Z digest=sha256:24702aed7920af78af33c370003a1fccb7f09b4d4103b0eec91d3774a3480c36

Observation 832a2b08-852e-41c7-8c19-eb99e36a0870 · outbound

This paper cites Data engineering for scaling language models to 128k context, 2024.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Data engineering for scaling language models to 128k context, 2024

Reference 24

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source=pdf_text observed=2026-08-15T20:13:19.566969Z digest=sha256:6c00c0240ae0a4989efa3fbfadbad81cba2fe111bdc9438c19bf578c51b0e693

Observation 0fb71177-5120-4b2f-8915-acf63f0d1539 · outbound

This paper cites Huang, Jie Fu, Xiang Yue, and Wenhu Chen.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Huang, Jie Fu, Xiang Yue, and Wenhu Chen

Reference 25

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source=pdf_text observed=2026-08-15T20:13:19.571639Z digest=sha256:0006b8815f3c62d9ff4c5ffc56bbcb010f2280d2d03248990f3e12d636268f43

Observation fba6b2e5-4321-4c40-aae9-59d80ccb4f49 · outbound

This paper cites A survey of transfer learning.Journal of Big data, 3:1–40, 2016.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning A survey of transfer learning.Journal of Big data, 3:1–40, 2016

Reference 26

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source=pdf_text observed=2026-08-15T20:13:19.577083Z digest=sha256:b0b16a7c67dac356d6097eece47bf78589bdc848e24ae658c1df47f96a37f4de

Observation d821aa8f-7943-4686-87b5-3b53e9a99659 · outbound

This paper cites A comprehensive survey on transfer learning.Proceedings of the IEEE, 109(1):43–76, 2020.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning A comprehensive survey on transfer learning.Proceedings of the IEEE, 109(1):43–76, 2020

Reference 27

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raw_fallback, observed 2026-08-15T20:13:20.915080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.582011Z digest=sha256:18a0d60012e9369b5290874386e60c24c89af5d9dca551c54bfcd81c4b97564c

Observation 4bbe045c-425f-4cd5-ae92-5cee80c3c80f · outbound

This paper cites Qwen2.5 Technical Report.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Qwen2.5 Technical Report

Reference 28

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source=pdf_text observed=2026-08-15T20:13:19.587765Z digest=sha256:388b1ec3f4110b27d9803d4bd84112249978c3ed6f2eb57d347e67a1be908076

Observation cecd0ffc-442a-4c58-83a3-b27310fde19b · outbound

This paper cites Llamafactory: Unified efficient fine-tuning of 100+ language models.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Llamafactory: Unified efficient fine-tuning of 100+ language models

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-15T20:13:20.897878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.593456Z digest=sha256:85160e6e7117d9c5b5903e97b1edde2d2d10bd708176b95f4600685947c8cd37

Observation 757e694b-d357-42e1-ad9d-01909fa8fc9c · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Training Verifiers to Solve Math Word Problems

Reference 30

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source=pdf_text observed=2026-08-15T20:13:19.598384Z digest=sha256:967651a05003c471e1db9729eb477f4c63433d61815d26c3a8dfebfa2f8734df

Observation 248f92f3-2cd4-425c-b5fb-57b869915726 · outbound

This paper cites Solving quantitative reasoning problems with language models.Advances in Neural Information Processing Systems, 35:3843–3857, 2022.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Solving quantitative reasoning problems with language models.Advances in Neural Information Processing Systems, 35:3843–3857, 2022

Reference 31

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source=pdf_text observed=2026-08-15T20:13:19.604579Z digest=sha256:cbb8a261fdbfcaff23e7f25a627739e9eff6d629b416d75651af730f771022a3

Observation d1337496-15ee-45b9-819e-6e69766ed375 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Gpqa: A graduate-level google-proof q&a benchmark

Reference 32

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source=pdf_text observed=2026-08-15T20:13:19.609291Z digest=sha256:7c9f6d054b4b20cf3423c62d5631f517e95265b5383da66df0bbab29636c4f6f

Observation 1fc9a099-61b7-4da5-87e7-922a127fcae9 · outbound

This paper cites Mmlu-pro: A more robust and challenging multi-task language understanding benchmark.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Mmlu-pro: A more robust and challenging multi-task language understanding benchmark

Reference 33

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source=pdf_text observed=2026-08-15T20:13:19.614233Z digest=sha256:eb80206d08c4049c042faabe3c4ac172c2c8e61d2fb7923c19de94200b70ea84

Observation 9080ec50-7e1d-40e6-a940-955d5fd8fb60 · outbound

This paper cites Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Reference 34

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source=pdf_text observed=2026-08-15T20:13:19.618743Z digest=sha256:ee1157fea405f24adc67b0a64085ee62cbc5e59d96205b7a9b0ca3cd2b3d6fac

Observation 7f8fd240-236b-45fd-b009-a50246a9ee96 · outbound

This paper cites Minicheck: Efficient fact-checking of llms on grounding documents.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Minicheck: Efficient fact-checking of llms on grounding documents

Reference 35

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source=pdf_text observed=2026-08-15T20:13:19.623791Z digest=sha256:b2b95e199767b1be9402f678f139c3a73bb75d2dc09bb5648a0c20e8075ce80f

Observation d1237f9f-dbee-4c95-9547-6ef319a02bc8 · outbound

This paper cites Why rare diseases are an important medical and social issue.The Lancet, 371(9629):2039–2041, 2008.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Why rare diseases are an important medical and social issue.The Lancet, 371(9629):2039–2041, 2008

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:13:20.839142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.628374Z digest=sha256:0ff9571ec4863b9beb4deb6d03a28b4dfc0e250f4e91558b961017b631114ec7

Observation 85bfae40-0013-4f7c-a721-94746c67ab62 · outbound

This paper cites Significance of the rare event in geology.AAPG Bulletin, 51(11):2197–2206, 1967.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Significance of the rare event in geology.AAPG Bulletin, 51(11):2197–2206, 1967

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:13:20.822276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.632687Z digest=sha256:1d22d7ed767bbbaf9f537775be0c71e830aaa53906b2f6e36dab59162db277d9

Observation 50ae5d0e-288f-42dc-a48f-a27d6bc31549 · outbound

This paper cites Qwq-32b: Embracing the power of reinforcement learning, March 2025.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Qwq-32b: Embracing the power of reinforcement learning, March 2025

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.638393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.638393Z digest=sha256:53c623f02920a00890cf1983d2c2e684ef25da23ee3ade0b3a201920ef45bddc

Observation ccad7bd4-bf18-4b00-88b4-642f8342f311 · outbound

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

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.643131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.643131Z digest=sha256:8f9aa824385f7486bc046505f6eac2d8f2faebd7c9a233f3710a6bde9fb0e9c7

Observation 91d0c8f7-0616-4232-b332-d8e27cec8ae8 · outbound

This paper cites Reasoning on a spectrum: Aligning llms to system 1 and system 2 thinking.arXiv preprint arXiv:2502.12470, 2025.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Reasoning on a spectrum: Aligning llms to system 1 and system 2 thinking.arXiv preprint arXiv:2502.12470, 2025

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.648436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.648436Z digest=sha256:7188dc138f3e53f53d9ef3c21d8dc3013256dbf0d6c6bae484b87744888c7af9

Observation 0e2059b1-2d5c-40fa-9711-59a7e9ad626a · outbound

This paper cites Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.653697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.653697Z digest=sha256:264a7ca22c109e7ed007ee1e3fe882d42cfdbdaccbb12f73784a4e1e901dcc0d

Observation 359673b4-0121-41ec-a1f8-eb6ce2f73693 · outbound

This paper cites Open r1: A fully open reproduction of deepseek-r1, January 2025.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Open r1: A fully open reproduction of deepseek-r1, January 2025

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.659151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.659151Z digest=sha256:5e04e7f1aea5a7d3bd641bf5ea0e44effccdfbc74ce3bf20d85fe421371ce513

Observation 78baf988-73fd-4ad1-b3ec-1dacafdef125 · outbound

This paper cites AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.664070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.664070Z digest=sha256:b8d3c7e0fcc1ebed02a41ce3daf721490132f4abc2df11c4d207b23803840c2f

Observation 7123be22-f343-4591-b8fc-350f7f20fd46 · outbound

This paper cites Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.669504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.669504Z digest=sha256:316014d986d57adc9d6210e542eec1bc40d4a3f2d00954d176ed46f31b50e152

Observation 3055ca9f-cded-48ba-a6a4-2936abe7b363 · outbound

This paper cites Chain of Draft: Thinking Faster by Writing Less.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Chain of Draft: Thinking Faster by Writing Less

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.676107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.676107Z digest=sha256:151382f74142572375ffc484f4517c4ee59227d2b0b3af59a20ddb4ddb0a25de

Observation 2b171910-0d1d-472a-a71f-bb2b975994f3 · outbound

This paper cites Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.682513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.682513Z digest=sha256:a67c15c28dabdbe9176393ae5934b6f64f1f7e0a40bfd1776f25fd81c295b174

Observation 873ceeaa-86de-45a0-b5d3-2a57b058e0c9 · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Token-Budget-Aware LLM Reasoning

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-15T20:13:19.688333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:13:19.688333Z digest=sha256:432fd9b07f4bd1c03b68d9387c57e790d4a0c1aa502aff563c6b948c22190010

Observation 69e1daac-97d1-4c5c-836b-6014dffe76e3 · outbound

This paper cites Longrecipe: Recipe for efficient long context generalization in large language models, 2024.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning Longrecipe: Recipe for efficient long context generalization in large language models, 2024

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:13:20.783670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.696974Z digest=sha256:c3da31a183622a3b453620555281b1f34d17de4c0bf1f1b4dc075c0174f4309c

Observation 5587fdc2-3a62-4e4d-b356-178ac38ba10f · outbound

This paper cites let me double-check.

QFFT, Question-Free Fine-Tuning for Adaptive Reasoning let me double-check

Reference 49

Resolution
malformed identifier
raw_fallback, observed 2026-08-15T20:13:20.765088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-15T20:13:19.702029Z digest=sha256:fa85773bcfd341ea8710be761a34db60473dd1ceee722fbbe02275264f6d6988

Pith citing papers

Observation 57d117e4-8407-4a4e-8afe-e1ef151f08a2 · inbound

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey cites this paper.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey QFFT, Question-Free Fine-Tuning for Adaptive Reasoning

Reference 116

Resolution
unresolved
no resolver link, observed 2026-08-06T17:54:17.309691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:54:17.309691Z digest=sha256:c09976b56caf587e88a289015b5066d807707cc0d2c76b46209b3189e212ed27

Observation aa2970fe-c146-4d00-8f29-0e27550d27cb · inbound

Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning cites this paper.

Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning QFFT, Question-Free Fine-Tuning for Adaptive Reasoning

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:32:02.808216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-13T01:31:21.377700Z digest=sha256:ff1e3431215746fb6c4910f6ea154ca2924072e9ad419545d27097fde9a184ca