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Paper Citation Record · LEDGER

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning

As of 22 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2508.15507.

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

pith.paper-citation-record.v1
2508.15507 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:52:53.445928Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-10T05:07:40.106099Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T05:16:48.074824Z

Reference resolution

29 of 29 outbound references displayed

  • verified exact0
  • verified fuzzy5
  • unresolved24
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0b627236-f27f-45ad-a1fe-0d2226fa2b0d · outbound

This paper cites GPT-4 Technical Report.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning GPT-4 Technical Report

Reference 1

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source=arxiv_source observed=2026-08-05T17:52:50.669316Z digest=sha256:ca9eb8dd56fc125245c417de0f0dabe2de198e6b99c91b178968e4e99b5008f6

Observation c7f84408-7fcf-4d83-8055-9c344349d084 · outbound

This paper cites Introducing claude, 2023.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Introducing claude, 2023

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T17:52:50.790048Z digest=sha256:8c1ce08278719d8c51e782be39ae4ca2a451c764a18f94ad21870e5347424765

Observation 8e0980ac-8ac2-40da-9a79-fdca930d724b · outbound

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

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 3

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source=arxiv_source observed=2026-08-05T17:52:50.905967Z digest=sha256:d605cfb57ef04b2aa23a3c29a26360c6b7c7a721c9e20f159baf43e5b2f5b691

Observation 1e3c8cae-e25e-4cf6-b18e-f9a292a5dfc9 · outbound

This paper cites Learning to Route LLMs with Confidence Tokens.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Learning to Route LLMs with Confidence Tokens

Reference 4

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source=arxiv_source observed=2026-08-05T17:52:51.004589Z digest=sha256:964448c6391b3f1c729ebc8b5444da923fc75b21457d5b8f65de32026b16f19e

Observation b9490e6c-3a40-47f8-b36b-55aaedaecd12 · outbound

This paper cites Thinkless: LLM Learns When to Think.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Thinkless: LLM Learns When to Think

Reference 5

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source=arxiv_source observed=2026-08-05T17:52:51.135256Z digest=sha256:c9066952c770ab2b2e1e4ab77dd39c1f68e4e0ed4cc9761b1b26b216cdf827ad

Observation b8ac97b5-6c8d-463f-89c4-09829b5bbdf0 · outbound

This paper cites Efficient reasoning models: A survey.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Efficient reasoning models: A survey

Reference 6

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source=arxiv_source observed=2026-08-05T17:52:51.349711Z digest=sha256:3633154b99bcd819f13f83fd2e07df73af4b910e973e420760495f5a11bde920

Observation 8bdf5890-8269-4120-8256-d003a7aae2d0 · outbound

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

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

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source=arxiv_source observed=2026-08-05T17:52:51.492438Z digest=sha256:0bfdf98e138e50767f5624e3bebd025c499227e34ab9dc3018de2aa111b23ae3

Observation 575b0549-953d-40ae-be1c-6af61e157957 · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Token-Budget-Aware LLM Reasoning

Reference 8

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source=arxiv_source observed=2026-08-05T17:52:51.696489Z digest=sha256:26c4516243b97e4329edd37e85b702f7b11e21df6505e85a4cbe1009c71b393c

Observation 8cb5254d-fb9d-4664-8d51-aba720dc1ec8 · outbound

This paper cites DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 9

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source=arxiv_source observed=2026-08-05T17:52:51.895375Z digest=sha256:d6016f9c9f470a5e96294756de57c034d19dc74fb51f25fe254504e9fbe47432

Observation a84ec10c-6605-44ca-b9e3-7e244331ac21 · outbound

This paper cites Editing Models with Task Arithmetic.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Editing Models with Task Arithmetic

Reference 10

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source=arxiv_source observed=2026-08-05T17:52:52.092535Z digest=sha256:3a7cbb146e3e197e520d86bc3c4710c32216781e450c29bdc95722cc66d99e83

Observation 6ab767aa-de33-436c-8a15-05882c6696e6 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Understanding R1-Zero-Like Training: A Critical Perspective

Reference 11

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source=arxiv_source observed=2026-08-05T17:52:52.283518Z digest=sha256:154bb06301daafad41e57ba2b4aac243c682a283e855cd45883472f32e0aac20

Observation 1ac68ec2-bfcf-465c-994f-1888247bd7c3 · outbound

This paper cites AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning

Reference 12

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source=arxiv_source observed=2026-08-05T17:52:52.523700Z digest=sha256:4d97bf56d15d8f4c79fe15561885396d94386b9008b085eed404e478f55c72ef

Observation 14d52365-1887-41a2-a201-78b2a02cf6e0 · outbound

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

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning

Reference 13

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source=arxiv_source observed=2026-08-05T17:52:52.681207Z digest=sha256:d4b69470732d1371834038b005917d152c69c972be7e8610af3859f731d797bd

Observation 8af2296a-259e-4128-9a50-1261907bcb46 · outbound

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

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 14

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source=arxiv_source observed=2026-08-05T17:52:52.886563Z digest=sha256:06a7d500f8689aefa77b4a52faa7409c79eb7a1ef398bf7a7b729e1e1e384c5b

Observation 6dd1c6ef-849e-4b47-90f8-835eeec0ffae · outbound

This paper cites RouteLLM: Learning to Route LLMs with Preference Data.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning RouteLLM: Learning to Route LLMs with Preference Data

Reference 15

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source=arxiv_source observed=2026-08-05T17:52:53.058030Z digest=sha256:daa8ef6515742976fe1176080dfa112adcde6620829f452571d197e108f22240

Observation cbd4d4ce-d319-434b-bf78-9174ec402acb · outbound

This paper cites Learning to reason with llms.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Learning to reason with llms

Reference 16

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T17:52:53.153987Z digest=sha256:e141956ad43597b730c347b100b4b68e798613b79917fb17c2125b920ff6e67e

Observation 5d8d35a7-9a6a-46ad-bc7d-376d1775c972 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 17

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source=arxiv_source observed=2026-08-05T17:52:53.251199Z digest=sha256:23826f2063ac74687e591c0d310e6eb7b38c74fa9dce52747de4eb131871205d

Observation ab821603-dbbd-463c-94d0-04f2cc30329d · outbound

This paper cites Dast: Difficulty-adaptive slow-thinking for large reasoning models.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Dast: Difficulty-adaptive slow-thinking for large reasoning models

Reference 18

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source=arxiv_source observed=2026-08-05T17:52:53.353473Z digest=sha256:2f38c8904acc591ecf7b57ace96e471bc75f2ca3c48f1bbff48e5902c0f424e7

Observation a6129052-e383-40c4-a498-dd1cd2ce2e11 · outbound

This paper cites Hybridflow: A flexible and efficient rlhf framework.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Hybridflow: A flexible and efficient rlhf framework

Reference 19

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T17:52:53.407554Z digest=sha256:1eb958b0f368c7eff6e03bf4c8b1ac60ef5d8e5cca61e159b1cb4d89bba161ac

Observation b536c32a-61ac-40f2-9ad0-ea5d8f5f5780 · outbound

This paper cites The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

Reference 20

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source=arxiv_source observed=2026-08-05T17:52:53.410664Z digest=sha256:16eca3d9c71320ecc6bad80def6b2ed0248191758d869672b075d819d6fc5e08

Observation 0118140d-16f0-4279-abdc-633e72ac25bb · outbound

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

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 21

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source=arxiv_source observed=2026-08-05T17:52:53.421410Z digest=sha256:d46823b53a25b7b981abee77c20d9b2c3f723a8f2b219fbfa073311b392ad29c

Observation c0a0d4a0-0758-4d97-b33f-0715776dc601 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning LLaMA: Open and Efficient Foundation Language Models

Reference 22

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source=arxiv_source observed=2026-08-05T17:52:53.424239Z digest=sha256:6e27cf6249167508a08c80efb32f2dcd49faf071d4b796e18ac466f2e8c13a98

Observation 57de77eb-0c46-42f3-95c5-c32efd5598ae · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Chain-of-thought prompting elicits reasoning in large language models

Reference 23

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source=arxiv_source observed=2026-08-05T17:52:53.427287Z digest=sha256:849c1e01d5bfdaf8aaa08e4aee305a235b4d060e497c7224eea2d675cb378846

Observation 480cd40d-c380-4944-bdfc-c4479e40a417 · outbound

This paper cites Ties-merging: Resolving interference when merging models.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Ties-merging: Resolving interference when merging models

Reference 24

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source=arxiv_source observed=2026-08-05T17:52:53.430503Z digest=sha256:1a00a16c6c3c32907345b952817c76a33f015c033f6f151c000019e07d1d6e35

Observation 4bba23b6-c111-4d47-9dfe-9b8fa9472e2c · outbound

This paper cites Qwen3 Technical Report.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Qwen3 Technical Report

Reference 25

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source=arxiv_source observed=2026-08-05T17:52:53.433211Z digest=sha256:22747d258c01d153d34cd36e76f07f25f54bfbf5f70299a160edb0b4ee2f6381

Observation 4ea709e9-de68-4d28-87f8-9807bdaaa9fe · outbound

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

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 26

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source=arxiv_source observed=2026-08-05T17:52:53.436084Z digest=sha256:ffc309a4fce8f49de15521c820f371efa7b6cb859b0fa1e24108a5106455d081

Observation 6ad07181-b80d-42e4-9bd8-6a0515ee3dee · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 27

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source=arxiv_source observed=2026-08-05T17:52:53.439600Z digest=sha256:ffd25d0f93e57abbb64afded0865852e3b3caeb90f28ecf7730ffc44f8aa3ea1

Observation f29d768e-c978-44b7-93fe-2e6c01e1c2d5 · outbound

This paper cites AdaptThink: Reasoning Models Can Learn When to Think.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning AdaptThink: Reasoning Models Can Learn When to Think

Reference 28

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source=arxiv_source observed=2026-08-05T17:52:53.442926Z digest=sha256:8bfb8a737c4d816405cdad84a52de5f26b57f723bdee5f53c6d88eb0ca4cda2c

Observation 4ee43093-275e-4889-a7ee-cdc94ccd2022 · outbound

This paper cites Swift: a scalable lightweight infrastructure for fine-tuning.

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning Swift: a scalable lightweight infrastructure for fine-tuning

Reference 29

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raw_fallback, observed 2026-08-05T17:52:53.844030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T17:52:53.445928Z digest=sha256:488b3551a39788ff888ee0144ccde011925d5e6beb340240b14110989570a7f7

Pith citing papers

Observation dc21e25c-2802-4e4a-977c-f18ea9d45bc3 · inbound

Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning cites this paper.

Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning

Reference 24

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local_arxiv, observed 2026-07-10T05:16:48.076132Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-07-10T05:07:40.106099Z digest=sha256:5bb035d9fd9f0097e02fa410b9f8826281f2804550ea1a046d46ef2dd9d02941