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Paper Citation Record · LEDGER

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 7 inbound Pith citation observations for arXiv:2506.07240.

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

pith.paper-citation-record.v1
2506.07240 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:45:05.990693Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:53:46.288418Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:38:56.089895Z

Reference resolution

41 of 41 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved26
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0231d295-288a-4551-99ba-21bfe90bf50a · outbound

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

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 1

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source=pdf_text observed=2026-08-07T05:45:05.861996Z digest=sha256:eb44d917d7febdf21c770a8bcfb02bc9640f15de6784f617d2298669e737c1d2

Observation a044d3f3-0a02-4ffb-829c-6c0c6cd1f343 · outbound

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

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 2

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source=pdf_text observed=2026-08-07T05:45:05.866244Z digest=sha256:ef5e006628f6e666611a60fad8d13a71ab22df3a499f6aa5c0c8d8c4507b8d4d

Observation 02abfd8c-8379-4ff1-8f42-2eccf951d33a · outbound

This paper cites A toy model of universality: Reverse engineering how networks learn group operations.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs A toy model of universality: Reverse engineering how networks learn group operations

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.869751Z digest=sha256:406d4a2400ff0d480369432b147b31358de126f715cb2691f3d5d4beeb93846f

Observation 8e931ea9-7b43-4af8-94c2-962011b57ad5 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Training Verifiers to Solve Math Word Problems

Reference 4

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source=pdf_text observed=2026-08-07T05:45:05.873229Z digest=sha256:f75beb1af4895d844a2fbfc5f873471b1efdb73f4e49237f7d0cd4e8f25b6911

Observation 7a20efb5-52fb-44bd-aa45-aeb4e3886dee · outbound

This paper cites A mathematical framework for transformer circuits.Transformer Circuits Thread,.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs A mathematical framework for transformer circuits.Transformer Circuits Thread,

Reference 5

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source=pdf_text observed=2026-08-07T05:45:05.877205Z digest=sha256:55496c6cbc00c0aff80824cd5251467053f5949276811abb3f758190734d82f8

Observation ecc3c45f-832d-48c7-a044-cdfa6b375808 · outbound

This paper cites Metacognition and cognitive monitoring: A new area of cognitive– developmental inquiry.American psychologist, 34(10):906, 1979.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Metacognition and cognitive monitoring: A new area of cognitive– developmental inquiry.American psychologist, 34(10):906, 1979

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T05:45:06.364447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.884722Z digest=sha256:d53a59ede4b372bc79e83825bc5e8a24798a335b7e4a17edddd09193bad8c020

Observation 5bb33186-6281-4c95-9e2f-c6962849dd67 · outbound

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

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

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source=pdf_text observed=2026-08-07T05:45:05.887744Z digest=sha256:5c19dd019a6f0912d278b1ca5359dd25809cd2427e61fb25a62c8095997aae9e

Observation 0a46b507-439a-4189-905e-e33ca0285aef · outbound

This paper cites In-context learning creates task vectors.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs In-context learning creates task vectors

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.891464Z digest=sha256:b8b66e2af0d1cd5da48a7a3696e587809facb0d5801e899648fd32b629048f4f

Observation 65da3b63-4c31-4c70-908a-f40e5fa2548c · outbound

This paper cites OpenAI o1 System Card.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs OpenAI o1 System Card

Reference 9

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source=pdf_text observed=2026-08-07T05:45:05.895305Z digest=sha256:8fb9fb3ababfbabab520e945b742d6a7bf44fad610fc873f9b28f952a0f9be62

Observation 89218d28-ee92-441f-a0dc-0a12d8a1ffdd · outbound

This paper cites The impact of reasoning step length on large language models.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs The impact of reasoning step length on large language models

Reference 10

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source=pdf_text observed=2026-08-07T05:45:05.898359Z digest=sha256:e2be03c50571af50c2d5019ee5990c743280c41c74a0aa06c5f96fd8b5e4752b

Observation f08d32ef-1eaf-4d0c-8735-f98ab106f7d1 · outbound

This paper cites The Impact of Reasoning Step Length on Large Language Models.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs The Impact of Reasoning Step Length on Large Language Models

Reference 11

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source=pdf_text observed=2026-08-07T05:45:05.901186Z digest=sha256:3dc1e3edb22e8b0366e40037dff203aa2c0c42032f2b78319c668134608a675d

Observation bc0b08cb-1694-4431-aca9-5949287d66ea · outbound

This paper cites Language models use trigonometry to do addition.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Language models use trigonometry to do addition

Reference 12

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source=pdf_text observed=2026-08-07T05:45:05.904768Z digest=sha256:5b28fddf35ee0852123edcc1c65da0d631d5a7bf2bd2e45bc559c71e7718ac08

Observation 6dd0b6f6-9bf1-4cbd-8220-0f842847dfa2 · outbound

This paper cites Large language models are zero-shot reasoners.Advances in neural information processing systems, 35:22199–22213, 2022.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Large language models are zero-shot reasoners.Advances in neural information processing systems, 35:22199–22213, 2022

Reference 13

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source=pdf_text observed=2026-08-07T05:45:05.907966Z digest=sha256:40636b0f5d1db0e9f9b4cf3d2b04520c65c6a68236447a1ae3d05ab649ba5d7c

Observation cb98dd89-e174-4ea3-80aa-c8f23e93f11b · outbound

This paper cites Abstractive document summa- rization with summary-length prediction.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Abstractive document summa- rization with summary-length prediction

Reference 14

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source=pdf_text observed=2026-08-07T05:45:05.910704Z digest=sha256:efd7990425a25678976f5d8d38a272c8ceb30225bae9be4f29611c1f34ba4fdd

Observation 15b9d150-8463-45f1-b570-8644811dc9d1 · outbound

This paper cites Let's Verify Step by Step.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Let's Verify Step by Step

Reference 15

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source=pdf_text observed=2026-08-07T05:45:05.913374Z digest=sha256:615ad9e12f3238f100b0c3ed0f2bfef9889f788d0bc913fc423243def0456cac

Observation 50817e3a-26f3-49b5-985b-f5185461d87c · outbound

This paper cites s1: Simple test-time scaling.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs s1: Simple test-time scaling

Reference 16

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source=pdf_text observed=2026-08-07T05:45:05.916237Z digest=sha256:c6aa231cf9483c74c3e7d223464befa32a1619c9742b09b59a4b0bcfffd3cd34

Observation bfee3169-31af-4d50-9ba8-b6fc95d6fa88 · outbound

This paper cites Progress mea- sures for grokking via mechanistic interpretability.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Progress mea- sures for grokking via mechanistic interpretability

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.919557Z digest=sha256:0030e31666c94deaeff119e3674885009342892153f236189117b178656b2d5b

Observation 8350d7b8-3b48-4e8f-8393-f580596c2a67 · outbound

This paper cites Metamemory: A theoretical framework and new findings.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Metamemory: A theoretical framework and new findings

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.923004Z digest=sha256:47bd440571ef2642ee624c93531276a38933cc42341f6e13ef0017c106aa7e44

Observation ec5c8fd3-450d-49c7-8158-c0242b10be0d · outbound

This paper cites Zoom in: An introduction to circuits.Distill, 2020.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Zoom in: An introduction to circuits.Distill, 2020

Reference 19

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source=pdf_text observed=2026-08-07T05:45:05.926206Z digest=sha256:e5883a65e4a31bbed32b0d2fd7b51289cd5944c20ddb4d23317b3b233834383c

Observation 8a160824-a9cb-48de-adb7-4425207eee18 · outbound

This paper cites In-context Learning and Induction Heads.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs In-context Learning and Induction Heads

Reference 20

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source=pdf_text observed=2026-08-07T05:45:05.929188Z digest=sha256:322ddecc06617c24ed05d825ff9ebf59714fd943132345b61de0ba3b80b73c54

Observation ded680a8-00e2-4475-a59f-b38f5db91c4b · outbound

This paper cites Chatgpt: Optimizing language models for dialogue.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Chatgpt: Optimizing language models for dialogue

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.932583Z digest=sha256:18a0ea6a1b8017db896be6bd55b9f65d2c7e097334ea0a6fa1add1ecd44d9e5b

Observation 175042fa-9c8d-42b1-b5b2-dd7db6996995 · outbound

This paper cites Zero-Shot Strategies for Length-Controllable Summarization.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Zero-Shot Strategies for Length-Controllable Summarization

Reference 22

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source=pdf_text observed=2026-08-07T05:45:05.935596Z digest=sha256:21a16be8c7afac2a46c6b3b2c9908b4066e48f5b4c5588ce48ba00faaa0f2ef8

Observation 05494799-79e0-49f7-b31b-90228978d7b0 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 23

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source=pdf_text observed=2026-08-07T05:45:05.938671Z digest=sha256:05c1ae6cabf9db07cc432ec0e144e477c0c2274645f23f64286d4b2358fe4230

Observation 4f1ecc85-f918-474b-a21a-1bd0f5588e05 · outbound

This paper cites Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs

Reference 24

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source=pdf_text observed=2026-08-07T05:45:05.941541Z digest=sha256:0476c50eecfcc363a950c196093b9e107175845560be92faad3b9bdf61855d9b

Observation 46ca0faa-844b-42f2-b163-65a19201d998 · outbound

This paper cites Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 25

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source=pdf_text observed=2026-08-07T05:45:05.944751Z digest=sha256:97539f6871451750f9d30cf4020dcd3d671b552cc204bcead2a9d05ee19b17cf

Observation 9ab04672-346d-4d5f-9acc-3ac96ce57346 · outbound

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

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Reference 26

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source=pdf_text observed=2026-08-07T05:45:05.947519Z digest=sha256:3c4c6f7d641c5ed8668fdd4edf00ac3f7b6c87630f8f5d7c48c93d99bdfd420a

Observation b6bf9361-0c4d-4e7e-adf3-0fe1e16161bf · outbound

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

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Chain-of-thought prompting elicits reasoning in large language models

Reference 27

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source=pdf_text observed=2026-08-07T05:45:05.950388Z digest=sha256:dac668a52844299cd749bf2bd2898d8bca797219495db662f364784a7d3a9a42

Observation c062ef60-2ff6-4845-bad7-00bc850adc90 · outbound

This paper cites From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

Reference 28

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source=pdf_text observed=2026-08-07T05:45:05.953393Z digest=sha256:1cbc9d1cd3f6b2cbe21df81d43477eaff012e26f6d6c8fcdcea3693b8a7719be

Observation b7532860-234e-492c-8416-3b43e5989a59 · outbound

This paper cites Effectively Controlling Reasoning Models through Thinking Intervention.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Effectively Controlling Reasoning Models through Thinking Intervention

Reference 29

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source=pdf_text observed=2026-08-07T05:45:05.956792Z digest=sha256:38c8c1896a9312b3f9bdf9044616048c2492874d8f7b4140ad3035a091ad38fa

Observation e4b35af5-fd32-4008-bf3c-be45b3bcfb23 · outbound

This paper cites When More is Less: Understanding Chain-of-Thought Length in LLMs.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs When More is Less: Understanding Chain-of-Thought Length in LLMs

Reference 30

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source=pdf_text observed=2026-08-07T05:45:05.959682Z digest=sha256:6226695f124bc421b12cdb25a688aa38673ac96b8fee6929c7cb49d7e15bf2f8

Observation 9c4f7e78-738b-45de-80a8-2c66ad7b109a · outbound

This paper cites The clock and the pizza: Two stories in mechanistic explanation of neural networks.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs The clock and the pizza: Two stories in mechanistic explanation of neural networks

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T05:45:06.296296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.962915Z digest=sha256:d23b03ac58c7d0e0d1ab38a7511a030b99c94b895c85729ba66a534f111a26a2

Observation f88d29d3-6c9a-49a7-a41c-2b5186956cfb · outbound

This paper cites hmm,” “wait,.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs hmm,” “wait,

Reference 32

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raw_fallback, observed 2026-08-07T05:45:06.286550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.966706Z digest=sha256:f29d7c0dc3ba241c322ee4d1625c2f3794c46ebb204f5f45db79638a2771f260

Observation 7f265bf2-a3d0-4477-8627-3e27b7d2de35 · outbound

This paper cites an unresolved cited work.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Unresolved cited work

Reference 34

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source=pdf_text observed=2026-08-07T05:45:05.970293Z digest=sha256:9a4883680f8847b39500b5b238c8aaa77185ca7ee493c7cb03a86feb5a41dcce

Observation c0ebbfc1-149d-4cc4-b87a-2f93d673e057 · outbound

This paper cites an unresolved cited work.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Unresolved cited work

Reference 35

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.973049Z digest=sha256:24acad77d2f41dff792539a64c3815696bc96285f1e55e72d178dff722c88758

Observation ecfe036e-3a9a-4b08-bace-a6672e6af137 · outbound

This paper cites Suzanne doesn’t walk on the 28th day, so 36 miles is the minimum.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Suzanne doesn’t walk on the 28th day, so 36 miles is the minimum

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-07T05:45:06.258342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.975922Z digest=sha256:5021bab5c22187f5da60e84ce7c94af6c2295a42e1fdb61baac60771d5870539

Observation d39f6285-40e4-4d17-8272-b34cc744468d · outbound

This paper cites Each time, I’m adding the two previous numbers to get the next one.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Each time, I’m adding the two previous numbers to get the next one

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:45:06.247994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.979120Z digest=sha256:32fef0ca4e9252c4d9c4a982b5be4052e4d136f7a81680ed059a5dc8410ce0b2

Observation 4bffe1e7-45c8-4805-9074-88ed46ac41ac · outbound

This paper cites First, compute.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs First, compute

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:45:06.238327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.982407Z digest=sha256:8b3b690bbd6396597119adc721d44686d948da8cebf1353fff36314f865d0ab2

Observation 450149e7-d1c9-4119-bb3e-b198cf13176c · outbound

This paper cites So, I’m confident that the 9th Fibonacci number is 34.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs So, I’m confident that the 9th Fibonacci number is 34

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:45:06.228960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.985190Z digest=sha256:bbaf2b15d85384ec64d1572b5a1eb516396ef60325adc706b529f91d6e66b9db

Observation 3b3fcb1d-a7d0-4e0d-a7e6-695716b4235e · outbound

This paper cites an unresolved cited work.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-07T05:45:06.219058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.988043Z digest=sha256:0f3029de5d864e082ccfe97327f65b0ac4d7483396cbb1908207ea4a0bf7497d

Observation d3fc7b84-4689-4a74-916b-cb77f74fc720 · outbound

This paper cites I need to solve for X and Y.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs I need to solve for X and Y

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:45:06.208429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T05:45:05.990693Z digest=sha256:2ae20395d937d2d89655f7826bf87b63eeb44d7d04d56266e59d9ca6562807e3

Observation 62a1e34f-852a-4eb0-a3a2-506d1d49426c · outbound

This paper cites an unresolved cited work.

Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs Unresolved cited work

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T05:45:05.880603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:45:05.880603Z digest=sha256:9d0aae491d08cbc083463e003478a4182b4cf1002d46f1cb8e7a129ab4787a2b

Pith citing papers

Observation acb242c7-f4d7-45b2-b81a-5b4966a3560b · 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 Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T17:53:46.288418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:46.288418Z digest=sha256:84d607051dbbf04cf136c4ac237fa0ab7f9e79de3df6b8a265c6d2de1e770608

Observation 69f620d1-167f-40b0-b1a9-baf1624960f2 · inbound

Spatiotemporal Hidden-State Dynamics as a Signature of Internal Reasoning in Large Language Models cites this paper.

Spatiotemporal Hidden-State Dynamics as a Signature of Internal Reasoning in Large Language Models Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T16:21:08.860349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-09T17:20:19.586214Z digest=sha256:458dbdfc76479f145dcbcaae1d41e112592092b4007e9110bd5864d6ef3ec03c

Observation bba556c0-d8f0-4170-a981-545798c0682d · inbound

Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training cites this paper.

Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-13T02:42:08.363768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-13T02:40:01.079531Z digest=sha256:f85e085610ded80b69b53018d7123c4eea85436dd717579eeb2458f4d6fc738d

Observation d9333a41-1f81-473e-a0e7-cb112e66c692 · inbound

Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training cites this paper.

Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-25T06:45:26.251643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-25T06:41:18.569888Z digest=sha256:159bbbc99d631756df8bbd07da11c666429ef70f51b79eddfd272ff3d578f0a0

Observation f199f689-dbcc-468e-a391-249d3fa2a4b2 · inbound

Uncovering the Representation Geometry of Minimal Cores in Overcomplete Reasoning Traces cites this paper.

Uncovering the Representation Geometry of Minimal Cores in Overcomplete Reasoning Traces Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T21:05:03.810139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-30T21:04:02.263300Z digest=sha256:38be499d6d7d968e3f9030a11deca7b37591077f59e80820d328a316f70ebce3

Observation caf2942c-1f7a-489d-882e-65988c8cf220 · inbound

Prefix-Safe Bayesian Belief Tracking for LLM Reasoning Reliability:Separating Calibration from Ranking cites this paper.

Prefix-Safe Bayesian Belief Tracking for LLM Reasoning Reliability:Separating Calibration from Ranking Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-29T17:03:40.967322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-29T16:59:25.127619Z digest=sha256:ec1f939850d183137b3b07948a3a0c2aa50b8bd5cf3b657b55563c7209295982

Observation 937493f9-7aad-492b-9f55-bfc5fb69d524 · inbound

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning cites this paper.

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 85

Resolution
verified exact
arxiv_id, observed 2026-07-03T20:38:56.091484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T01:13:11.483599Z digest=sha256:b5e654378522e91d4256c10e416f8fefbedfcaab19c89bf40967337375b84cf6