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Paper Citation Record · LEDGER

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

As of 7 August 2026, this Paper Citation Record lists 100 of 256 outbound references and 4 inbound Pith citation observations for arXiv:2507.09662.

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

pith.paper-citation-record.v1
2507.09662 v1

Coverage vector

measured 100 of 256 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:54:17.171387Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:04:38.779777Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:46:46.304224Z

Reference resolution

100 of 256 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved100
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a0b7c23-477f-4df1-bd7f-313352e641ef · outbound

This paper cites online" 'onlinestring :=.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey online" 'onlinestring :=

Reference 1

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no resolver link, observed 2026-08-06T17:53:40.698499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:40.698499Z digest=sha256:cec566fdd3946eaa62e50427079a02993cf589d4bdb439327f91659792353f1c

Observation 5d200419-d45a-49c0-ac52-8f8ed37e6adf · outbound

This paper cites write newline.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey write newline

Reference 2

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no resolver link, observed 2026-08-06T17:53:40.761959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:40.761959Z digest=sha256:377a528213df41cbe68b8408318373417b8ea84b03972bbd2b57c7fd59eccbe3

Observation fe208371-4307-450f-ae9a-f534c222f62b · outbound

This paper cites First Finish Search: Efficient Test-Time Scaling in Large Language Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey First Finish Search: Efficient Test-Time Scaling in Large Language Models

Reference 3

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no resolver link, observed 2026-08-06T17:53:40.869843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:40.869843Z digest=sha256:b10d47384bd7d522ec22617d40d8a6b8ff60261a1f09a64d3e8e288ab877b9d8

Observation 20314879-9b38-4e51-a610-17faaae1b794 · outbound

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

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 4

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no resolver link, observed 2026-08-06T17:53:40.975747Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T17:53:40.975747Z digest=sha256:060eec163d41b25990f0520c482b592fda705e068e541f00f2e54c6dc3483e6a

Observation d872931b-b64c-47a2-a533-49c735061549 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 5

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no resolver link, observed 2026-08-06T17:53:41.076295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:41.076295Z digest=sha256:5576610e40343c6b42c3a098052d98043651337427e63578dca962e67f7214e7

Observation 6100c809-6b85-4fd1-9f0d-4929fc255032 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 6

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no resolver link, observed 2026-08-06T17:53:41.254192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:41.254192Z digest=sha256:d009c89e2c4d233a54dd83d6cb77663d1a15c0c0ebd85b8fbcf591a504504549

Observation 71c0072f-ab49-48f3-8486-0ac8865522ee · outbound

This paper cites Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs

Reference 7

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no resolver link, observed 2026-08-06T17:53:41.436053Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:41.436053Z digest=sha256:b87f352fc17a484b829bc95d83704f1555329d768a3f49322ebffc238c8c8a2e

Observation 2bb1d5f9-c662-4cad-8d2e-fcebadf537ef · outbound

This paper cites MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 8

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no resolver link, observed 2026-08-06T17:53:41.567380Z

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source=arxiv_source observed=2026-08-06T17:53:41.567380Z digest=sha256:0e4ba6f83d593d3f0d1d608809318ed036b451241aab63b342c3b240b8d1a5b6

Observation 87fc04e6-96b4-4920-a3c8-d58bfa498013 · outbound

This paper cites Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models

Reference 9

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source=arxiv_source observed=2026-08-06T17:53:41.668412Z digest=sha256:82174a0b0e20dd3b1df1f99d6a56c3253ef84cf70885ab4e6409d9048847c13b

Observation 63bca234-92ea-4d95-a162-1f1c413eccf6 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 10

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no resolver link, observed 2026-08-06T17:53:41.758287Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:41.758287Z digest=sha256:f3d9cbb0c4f13bbc62405e0f8bcf8e740e48f7e82e138717f9cc49be684b89ad

Observation ad4fd6ec-3220-459d-a46e-24f94acae7ad · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 11

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no resolver link, observed 2026-08-06T17:53:41.840260Z

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source=arxiv_source observed=2026-08-06T17:53:41.840260Z digest=sha256:a6b51ab25848db072d1da352546e119b5c2fd999ab1652c9f2fe12ee95f977ba

Observation 96594b81-a157-4ee2-a786-af55bbae08e4 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 12

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no resolver link, observed 2026-08-06T17:53:41.929511Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:41.929511Z digest=sha256:360db322ec55de2214c2df523704ca33444a9c4706f84ec5705e81f0c24c59a9

Observation 32c443ce-0f3a-4399-93eb-8a80342e74f5 · outbound

This paper cites Program Synthesis with Large Language Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Program Synthesis with Large Language Models

Reference 13

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no resolver link, observed 2026-08-06T17:53:42.030840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.030840Z digest=sha256:7e1604e05ff48e0f0fe48598406ef9523e91a6e36604fedd7faa2e6c76c67a16

Observation 2d0619af-4dfc-46c7-9c97-fdf5ee4c5e51 · outbound

This paper cites Aytes, Jinheon Baek, and Sung Ju Hwang.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Aytes, Jinheon Baek, and Sung Ju Hwang

Reference 14

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no resolver link, observed 2026-08-06T17:53:42.123994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.123994Z digest=sha256:e93cd8bb1c5dbd5b777a3abbc5eca25ebef92f9d6ab4f3f531de25daba372489

Observation c77ad4c0-4c76-47b7-8cf7-155dceb2d64e · outbound

This paper cites ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics

Reference 15

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no resolver link, observed 2026-08-06T17:53:42.240964Z

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source=arxiv_source observed=2026-08-06T17:53:42.240964Z digest=sha256:7550a520ea723bee2f9689d7d524d9247c0e72714d77d60cff131f7ea5e175e1

Observation 51aee12e-6396-45f0-b72c-0e7ca297b72f · outbound

This paper cites MathArena: Evaluating LLMs on Uncontaminated Math Competitions.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey MathArena: Evaluating LLMs on Uncontaminated Math Competitions

Reference 16

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no resolver link, observed 2026-08-06T17:53:42.316942Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.316942Z digest=sha256:832e3f4404f69dd906ccc101c6154cc6c1256ec4f2cf745ec3251a63a1e03ee5

Observation 2db77673-bc70-4d40-ba64-9068e8e70c14 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 17

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no resolver link, observed 2026-08-06T17:53:42.414144Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.414144Z digest=sha256:d24419393784a79408d7af31785ba73d337b0f668f3748b3a3bf26c2ae8a6326

Observation 886a8086-8a37-47e7-b6b7-6e48c1be4dfe · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 18

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no resolver link, observed 2026-08-06T17:53:42.508903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.508903Z digest=sha256:973d32137d7e1066ca95de66236282911f394e737b2ff968b5f48cde1eb6230b

Observation 09dbf26a-3bd2-49b6-ab64-a4838098c80e · outbound

This paper cites Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations

Reference 19

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no resolver link, observed 2026-08-06T17:53:42.595083Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.595083Z digest=sha256:9e332295c1397dcaeea185006b0ef2068c40ad4e6739da4ce96f628ba2475d22

Observation 9f4e6ebd-ede7-4f4c-95a2-7cc441f33626 · outbound

This paper cites Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition

Reference 20

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no resolver link, observed 2026-08-06T17:53:42.697574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.697574Z digest=sha256:20a27f053f9f12454f58daf493de2b894b733c9711f52b7ce5029d052d139e4e

Observation 47e22f27-d737-4be9-b2e1-805d958ae1b2 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Evaluating Large Language Models Trained on Code

Reference 21

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no resolver link, observed 2026-08-06T17:53:42.796824Z

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source=arxiv_source observed=2026-08-06T17:53:42.796824Z digest=sha256:e6361bde1e1d19e6e1ce9ecbdb357d443fc782f11f28bd79d07e38980ce02ecc

Observation c4a21e1f-0b74-4d62-8754-b9d3b243d2d7 · outbound

This paper cites The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training

Reference 22

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no resolver link, observed 2026-08-06T17:53:42.914250Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:42.914250Z digest=sha256:63eb7cf9828327188f3779290482a6985e0cb959b871bc42989c09742dd40d5f

Observation 56bad3b4-13dd-468b-9fd2-31b8b13b0f57 · outbound

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

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 23

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no resolver link, observed 2026-08-06T17:53:43.042949Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:43.042949Z digest=sha256:9e59dec60b836dd28799c03fa1bf5e4e82e862afe5991e2dd76ed7c927224bc9

Observation 244b5332-625a-4826-9ff6-fdc1dbceba8e · outbound

This paper cites VeriThinker: Learning to Verify Makes Reasoning Model Efficient.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey VeriThinker: Learning to Verify Makes Reasoning Model Efficient

Reference 24

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no resolver link, observed 2026-08-06T17:53:43.171581Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:43.171581Z digest=sha256:f84711fe922d5bacee22338cd816178bd9bb52315b0a87e60abe882c588111ee

Observation 28d1183b-85ba-4d71-bba6-7e7d7ecf7b84 · outbound

This paper cites Compressed Chain of Thought: Efficient Reasoning Through Dense Representations.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Compressed Chain of Thought: Efficient Reasoning Through Dense Representations

Reference 25

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no resolver link, observed 2026-08-06T17:53:43.359274Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:43.359274Z digest=sha256:8ef98e67dfb0d30d150a66c18f2a2c3de4ffefa1abca86193ded18464be2f908

Observation 43790c91-5fa8-4bce-bc02-1aa8b6fb833a · outbound

This paper cites Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

Reference 26

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no resolver link, observed 2026-08-06T17:53:43.511503Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:43.511503Z digest=sha256:4bfa75f0006fb6645f0280d156c2286d94f3135f2d2dff32115fba19bc046cd0

Observation c7517ef2-8821-4e65-bceb-1e3c99d4c465 · outbound

This paper cites Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking

Reference 27

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no resolver link, observed 2026-08-06T17:53:43.699099Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:43.699099Z digest=sha256:2b81a914f70959573470fd7fd96e73586fa887bc654bc6fa1ccf3d1831425784

Observation bc6ab941-4ace-481f-bf7d-10f787ff3438 · outbound

This paper cites Optimizing Length Compression in Large Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Optimizing Length Compression in Large Reasoning Models

Reference 28

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no resolver link, observed 2026-08-06T17:53:43.839273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:43.839273Z digest=sha256:4bba923d6b38d3e1232de17c60557181c26f0b5e2b9eba75dc1e93ae9b8fc4d2

Observation 49be9cc1-e36f-4143-8382-2c618303a228 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 29

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no resolver link, observed 2026-08-06T17:53:44.005640Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:44.005640Z digest=sha256:a1fe53edfa1c6e3e818d05162ed83efb693d86fccaf8155e28933040caff6ae5

Observation 4439fa04-21c9-49f5-9195-690462cb077b · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 30

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no resolver link, observed 2026-08-06T17:53:44.198240Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:44.198240Z digest=sha256:f829b71e514409aa47c923ea98e7e0676f7716eb6135f5ee5176ffcae524a814

Observation 051d2c97-a989-4540-a149-7f15d6312e62 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Training Verifiers to Solve Math Word Problems

Reference 31

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no resolver link, observed 2026-08-06T17:53:44.341581Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:44.341581Z digest=sha256:85142701a2b3e2d7d155d87504e3ad92faa96dc164cb57a00037760d6c1f7a23

Observation 74290e82-c7e2-4db7-9343-e30894c3a557 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 32

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no resolver link, observed 2026-08-06T17:53:44.475128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:44.475128Z digest=sha256:d23db851159f5c2b1a38102ec26612601df710c965db89acec8b30eca2b4fec0

Observation d24371c2-a8a9-46c0-91fc-e679b4fce704 · outbound

This paper cites The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Reference 33

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no resolver link, observed 2026-08-06T17:53:44.615059Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:44.615059Z digest=sha256:0b830d01ac1c5e006c7807e829a10fdad5188f5cecb4d3f0dbc2cc750b7c03ca

Observation de734c04-13db-49cd-9a68-5edadd38d82b · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 34

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no resolver link, observed 2026-08-06T17:53:44.817200Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:44.817200Z digest=sha256:94f441c7d1beafe7ba8e54b54761a7bd4207a51e91eda0226b8a4d979e6a69a6

Observation 2c084992-182d-4f3e-88c8-5ccd9b783e3a · outbound

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

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 35

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no resolver link, observed 2026-08-06T17:53:44.986290Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:44.986290Z digest=sha256:da4b430a88c97d422ddb26082cbad60674aca33f78d1649247693319ae50b4ca

Observation 95cb9dae-e09f-4b21-b4f3-6d5f198114b3 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 36

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no resolver link, observed 2026-08-06T17:53:45.122698Z

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source=arxiv_source observed=2026-08-06T17:53:45.122698Z digest=sha256:bfd099af1db94c5679c38a859a1cf2c89f09e48a0635bac57a9164a87731b537

Observation eb8ed258-ce88-41fb-9ef7-1c55b86e6cbc · outbound

This paper cites From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step

Reference 37

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no resolver link, observed 2026-08-06T17:53:45.269187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:45.269187Z digest=sha256:632ba565156e42643f84f464a5c54a3720970201429f3650dab93a288bf1db3f

Observation 7f79b699-5894-46b6-a425-456a09a5a222 · outbound

This paper cites Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model

Reference 38

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source=arxiv_source observed=2026-08-06T17:53:45.466886Z digest=sha256:dfe0e0c2001ff004cb563992e3fee5c967ac02b21da7bea07a1ce481df256950

Observation cf501460-914e-4ed4-90cb-128d29a74534 · outbound

This paper cites Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing

Reference 39

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source=arxiv_source observed=2026-08-06T17:53:45.603078Z digest=sha256:12f7304006db4422949f0beb6f55b170883f46cea65c6419a44812c2f18ca094

Observation 35e7fd26-2a7f-4381-8ebf-94305e2da024 · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 40

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no resolver link, observed 2026-08-06T17:53:45.792486Z

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source=arxiv_source observed=2026-08-06T17:53:45.792486Z digest=sha256:baa53d1afee4733cfbea76f0ff90d4ec18383b29f5ead2888a6943a1b9622629

Observation a04357ef-830c-4fc4-b3fa-494e9dd57aab · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 41

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no resolver link, observed 2026-08-06T17:53:45.957328Z

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source=arxiv_source observed=2026-08-06T17:53:45.957328Z digest=sha256:3a1bc6444939a46aabcde159bc803345030fecfe6ee78dd4f5d7b58510040fd0

Observation 47f83da4-5200-47fe-8120-9b2d8a75cc12 · outbound

This paper cites ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models

Reference 42

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source=arxiv_source observed=2026-08-06T17:53:46.148845Z digest=sha256:591556b47418387b83888a93a2de7204028a59443fa250611b8443dddb8f11b9

Observation acb242c7-f4d7-45b2-b81a-5b4966a3560b · outbound

This paper cites Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs

Reference 43

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

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source=arxiv_source observed=2026-08-06T17:53:46.288418Z digest=sha256:88e14987c663cc7e1325492e0a46c2ce51afa12df877ba1fe24ab9de80a95a92

Observation 7b1e292f-e86d-4cb2-80ed-e1ce78c596fc · outbound

This paper cites Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?

Reference 44

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no resolver link, observed 2026-08-06T17:53:46.429746Z

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source=arxiv_source observed=2026-08-06T17:53:46.429746Z digest=sha256:9589c7e6ff4a388a4fef1a6dc2c34543edbab101cf581cb59d4ad00f9fb3f352

Observation 72f9dbe2-f9a8-4967-b09e-57f50ac6cd58 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 45

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source=arxiv_source observed=2026-08-06T17:53:46.648328Z digest=sha256:d5475ebb8ee0f7a99da834b7639625aad9119bc9e48720eba6f6f9b08032c546

Observation 8cc5bc8c-2fbe-4908-8efc-8ad752250969 · outbound

This paper cites Thinkless: LLM Learns When to Think.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Thinkless: LLM Learns When to Think

Reference 46

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source=arxiv_source observed=2026-08-06T17:53:46.856179Z digest=sha256:9f9623f1071c48a604fc73b3edc2a7ad8e534aec69853d0210ae093013417d78

Observation bf8b5af7-bd2d-43c6-b9d2-6ab02c652ebc · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 47

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source=arxiv_source observed=2026-08-06T17:53:47.067440Z digest=sha256:0222dca30fb14b172fd2d34907bba86c622be55dd9ad1e098c10861841db8ecb

Observation 0a333de5-9428-468c-bac6-675c8d34cbb3 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 48

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source=arxiv_source observed=2026-08-06T17:53:47.267699Z digest=sha256:369bf8f145371816103988e43867f7ab50e1711b581927b23be03584a503731a

Observation 0ad79643-2639-4ba8-aefc-9be4eb14e850 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 49

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no resolver link, observed 2026-08-06T17:53:47.387677Z

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source=arxiv_source observed=2026-08-06T17:53:47.387677Z digest=sha256:1fc298bdc084338d03dc5e2a54842658872f78738c05f4cc452b01acc089149e

Observation 87a3f983-2e7c-44fb-b190-219dd66e7440 · outbound

This paper cites Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models

Reference 50

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no resolver link, observed 2026-08-06T17:53:47.566505Z

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source=arxiv_source observed=2026-08-06T17:53:47.566505Z digest=sha256:9567ab45e3b77b68f6f47fbd7ad08ae9091444166d80265d5bdb08eaaafa0cfd

Observation 16b6c32b-4501-4794-b2a6-2006fb004230 · outbound

This paper cites Efficiently Scaling LLM Reasoning with Certaindex.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Efficiently Scaling LLM Reasoning with Certaindex

Reference 51

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no resolver link, observed 2026-08-06T17:53:47.696525Z

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source=arxiv_source observed=2026-08-06T17:53:47.696525Z digest=sha256:fa8c2579bdf13f70de19306a4bac14c0de31e1163b46cc0c5b27721018148263

Observation 32029885-2627-44ca-a02e-fa8a0510044d · outbound

This paper cites Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 52

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source=arxiv_source observed=2026-08-06T17:53:47.832078Z digest=sha256:5942d257e53492401637d299402ff0e1e712de573f5ca5da57537568b086673f

Observation c1c4647e-b274-4e3b-8244-f619c76f0c3b · outbound

This paper cites How Far Are We from Optimal Reasoning Efficiency?.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey How Far Are We from Optimal Reasoning Efficiency?

Reference 53

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no resolver link, observed 2026-08-06T17:53:48.051608Z

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source=arxiv_source observed=2026-08-06T17:53:48.051608Z digest=sha256:ed8ef8e7e6efc6eb86f031aaa5b341cb27fd38c3833adb14333c22c238669ae0

Observation c18fb6b8-1113-41c4-a018-ba9bd253fd2d · outbound

This paper cites Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

Reference 54

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no resolver link, observed 2026-08-06T17:53:48.158659Z

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source=arxiv_source observed=2026-08-06T17:53:48.158659Z digest=sha256:182f1246b26072290a5c720b9e4ee6ddec87233c561fe47fc58dde191ab8ebf2

Observation 586a64b1-870b-4cfe-9015-0303166944e0 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 55

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no resolver link, observed 2026-08-06T17:53:48.332285Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:48.332285Z digest=sha256:85310685bdcbb8e4fb364bc9f5c0d4e00877c38bfd8d6031d79b1eb18909d210

Observation c18b781b-5781-400b-b533-4b55f27af9bc · outbound

This paper cites Efficient Reasoning via Chain of Unconscious Thought.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Efficient Reasoning via Chain of Unconscious Thought

Reference 56

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source=arxiv_source observed=2026-08-06T17:53:48.470130Z digest=sha256:3fc965e79f47493738cfecc0c167a184458ce07d2bc7527d4a4e64e76d5879e2

Observation 67b2451b-8f48-45a6-8da9-256d3bd3beaf · outbound

This paper cites The Llama 3 Herd of Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey The Llama 3 Herd of Models

Reference 57

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no resolver link, observed 2026-08-06T17:53:48.676873Z

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source=arxiv_source observed=2026-08-06T17:53:48.676873Z digest=sha256:fd352b3707150acb9df8869449e7729e39f0b017f916ba9e7f482888d67edc93

Observation a3bbd973-0637-4ebe-965d-185f338bbaca · outbound

This paper cites OpenThoughts: Data Recipes for Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey OpenThoughts: Data Recipes for Reasoning Models

Reference 58

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no resolver link, observed 2026-08-06T17:53:48.823607Z

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source=arxiv_source observed=2026-08-06T17:53:48.823607Z digest=sha256:e050d58924711572710190895f628de74e25b9f9b1d169ba493415bb8550ba33

Observation 80b42856-a8dc-48f0-b4e3-3c11df7ae6c5 · outbound

This paper cites Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think

Reference 59

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no resolver link, observed 2026-08-06T17:53:49.044481Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:49.044481Z digest=sha256:69df77ca1cfb129bd44c7bef10cb6a246eb99222e54da26fdab17436610be644

Observation c475e668-4e9f-4785-ae17-e9edcca43f50 · outbound

This paper cites Token-Budget-Aware LLM Reasoning.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Token-Budget-Aware LLM Reasoning

Reference 60

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source=arxiv_source observed=2026-08-06T17:53:49.241722Z digest=sha256:901a5f8a86ff0d6d00d6a7fca8902a6a658a6ef124da8b0ffc41b9d6283eb5b6

Observation 518bb2ea-afd1-4603-9574-2a4106cc60ee · outbound

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

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Training Large Language Models to Reason in a Continuous Latent Space

Reference 61

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no resolver link, observed 2026-08-06T17:53:49.430955Z

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source=arxiv_source observed=2026-08-06T17:53:49.430955Z digest=sha256:595429e5e5a239828be4a159fe3228b1d0449d34b95338454d09f7109d4eabd1

Observation 23dcc8d3-0386-4689-951c-59336783ed0c · outbound

This paper cites DNR Bench: Benchmarking Over-Reasoning in Reasoning LLMs.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey DNR Bench: Benchmarking Over-Reasoning in Reasoning LLMs

Reference 62

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source=arxiv_source observed=2026-08-06T17:53:49.562911Z digest=sha256:cda9bc2a4abecc6f7faeed5e6d07456d503db8aebeec6f7ad37445dea7962ae9

Observation 731ed8e6-433b-4028-8106-726d0d596df9 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 63

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no resolver link, observed 2026-08-06T17:53:49.747267Z

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source=arxiv_source observed=2026-08-06T17:53:49.747267Z digest=sha256:8fa25df8e3514743fc7fe1fe038785f149edfc9073db2cc853021894b58511dd

Observation ed9dcf4f-7583-4a8e-8fbb-32d5c90116b7 · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 64

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no resolver link, observed 2026-08-06T17:53:51.145958Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:51.145958Z digest=sha256:486d91184b8bc3f52614d28f097b5aece083d6fdd912ede70c0d6bdaa90e620a

Observation 30e6b349-5ffa-4b03-9fb0-302220c965e0 · outbound

This paper cites Skywork Open Reasoner 1 Technical Report.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Skywork Open Reasoner 1 Technical Report

Reference 65

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no resolver link, observed 2026-08-06T17:53:52.251439Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:52.251439Z digest=sha256:6db28abc5af925bb59e5624bed8d81744277bbbaf682a325a75bff5ed27dcbfe

Observation 7e7a21a8-4621-49e5-9442-57a0a154703f · outbound

This paper cites Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models

Reference 66

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no resolver link, observed 2026-08-06T17:53:52.363919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:52.363919Z digest=sha256:dc7d13a24f71415edd671a13cb75b35ce611f4a9dc013d12ee2653ecec427a13

Observation af85c3e5-6ac5-4ac7-895e-10dc0ed2f373 · outbound

This paper cites Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning

Reference 67

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no resolver link, observed 2026-08-06T17:53:52.502812Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:52.502812Z digest=sha256:69d6ea224aabcfb383575e21f3f091766e422fba1e7201cbdd3a047f20f73279

Observation d75d1d5f-8fca-4e09-ab1b-ccda58c5303b · outbound

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

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 68

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no resolver link, observed 2026-08-06T17:53:52.720237Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:52.720237Z digest=sha256:da07c373203d85b9c5424ecfef57f432b4c10e0d5d0f5613d5366b1c99da7c27

Observation a9213a47-cd53-455d-bf9c-d5e072454976 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Measuring Massive Multitask Language Understanding

Reference 69

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source=arxiv_source observed=2026-08-06T17:53:54.842488Z digest=sha256:7def241c2baa48f8319cc72357939825f30ed8521c1713923309781d528a2dce

Observation 5447ad88-e402-4b96-8672-b226a34df41f · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Measuring Mathematical Problem Solving With the MATH Dataset

Reference 70

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no resolver link, observed 2026-08-06T17:53:54.892679Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:54.892679Z digest=sha256:d6948dbf5217f816c684be142ab1619cd6f0f2033305f71d22ce8fbf3f28f479

Observation 40aa3233-ed35-4e1a-9f4c-ac76430f94b0 · outbound

This paper cites ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 71

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no resolver link, observed 2026-08-06T17:53:54.980836Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:54.980836Z digest=sha256:780a19cabaeb22e3f93106eeeeca229405d735689c467425d59851d5210d262a

Observation 7ce0e35d-1576-4390-ad21-01f36c4dcd33 · outbound

This paper cites RouterBench: A Benchmark for Multi-LLM Routing System.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey RouterBench: A Benchmark for Multi-LLM Routing System

Reference 72

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no resolver link, observed 2026-08-06T17:53:55.111015Z

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source=arxiv_source observed=2026-08-06T17:53:55.111015Z digest=sha256:1ef44b3fb5525953ac779fdbafc9aa543d7e2bfe851cbb720c2e687799eb7ed8

Observation c96aa8d3-02e4-4893-9609-bd1d7c6da9d3 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 73

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:55.163201Z digest=sha256:686c327bba214ccc5409b79c2693e280a2f33af555662d6878714bf7ca5b684e

Observation 1b4b4aca-d6c4-48a0-b33d-231b88897716 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 74

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no resolver link, observed 2026-08-06T17:53:55.200197Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:55.200197Z digest=sha256:64c61764913d114acf2e62ad29a8493d34698bc8eb4906e0881dc8a6123d78b7

Observation 2abac593-2382-49a4-ac21-714f15579db6 · outbound

This paper cites Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable

Reference 75

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source=arxiv_source observed=2026-08-06T17:53:55.257867Z digest=sha256:bfcb33b68129b80450c39c621115f95da2a3c54233cadc260615e37c7c74a6d5

Observation 7b6e5b18-b87d-4ec9-921e-e7a58c2057fb · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 76

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:53:55.312111Z digest=sha256:d21009ac1107778f28d15f458167eb53f680c1dca741301da8f99f84040b0bd0

Observation cb882a60-0d8b-48cd-9080-4ebfe9076283 · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 77

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Observation f1a2441a-650d-4995-b354-2315bee783fd · outbound

This paper cites Editing Models with Task Arithmetic.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Editing Models with Task Arithmetic

Reference 78

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source=arxiv_source observed=2026-08-06T17:53:55.388924Z digest=sha256:5d362df1a920defa874916d25d6bda58d9b34d8b33be7a965405908854d889e1

Observation ec1ece21-38b9-4b50-9bef-07ef62b29898 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 79

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source=arxiv_source observed=2026-08-06T17:53:55.441693Z digest=sha256:fdc58b8d07f24af2943263cd15e6a8871deff60119a1574b0b3da062f1bdf5f2

Observation 569a9e75-fe45-440a-850e-c3fe30892779 · outbound

This paper cites AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale

Reference 80

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source=arxiv_source observed=2026-08-06T17:53:55.523287Z digest=sha256:9161406625bbac2e06e6c8f0cbee753f82255a8e15dc925b02e3156318c997e9

Observation c2e345ca-a9cf-4acd-ac90-7c630980ee51 · outbound

This paper cites SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities

Reference 81

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source=arxiv_source observed=2026-08-06T17:53:55.578953Z digest=sha256:b612b712abbf3d62752e900ee30e44d51ca38ee1ea687024349eb17812db26d5

Observation 6a275686-2446-4bce-88b6-789900f8e8e4 · outbound

This paper cites What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning

Reference 82

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source=arxiv_source observed=2026-08-06T17:53:55.675806Z digest=sha256:bffd1f730cf184765cd67f6001aca198105fd20469ee60c79172cb0c4bb620e4

Observation c7bb9b01-d0e6-49bd-a1c4-cc63e00270a8 · outbound

This paper cites FlashThink: An Early Exit Method For Efficient Reasoning.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey FlashThink: An Early Exit Method For Efficient Reasoning

Reference 83

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source=arxiv_source observed=2026-08-06T17:53:55.734994Z digest=sha256:202d371f561161a314062e23d57ec6826c85dcd7f30f965b2909ef99aeecddf8

Observation deeda259-d89e-4528-877a-206597c2a1cf · outbound

This paper cites Think Only When You Need with Large Hybrid-Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Think Only When You Need with Large Hybrid-Reasoning Models

Reference 84

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source=arxiv_source observed=2026-08-06T17:53:55.825220Z digest=sha256:f76d53dd3addcd41020fc3bc92842bcb0fbfd3705ed13809bab1477ae506ce50

Observation b85bafcf-12fa-4773-be6f-b943b8595e59 · outbound

This paper cites WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models

Reference 85

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source=arxiv_source observed=2026-08-06T17:53:55.883753Z digest=sha256:9a9d279389ef21c5b9cad3c1c3232b9c766881175cd66b13f288d1e720b6e203

Observation f2c8ab78-de91-4f7a-b67c-1b8bdcb28778 · outbound

This paper cites DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models

Reference 86

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source=arxiv_source observed=2026-08-06T17:53:55.967911Z digest=sha256:4c387baa0e1d4d51437f8b69846cbee930d89bdf3db76efbfb51692990e9bf19

Observation 521e0b49-9a84-4ec1-8268-2d14528096f6 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 87

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source=arxiv_source observed=2026-08-06T17:54:16.665520Z digest=sha256:0c961f34156c5ccdedbba5e894f8d12892c6e5c1f235da5e91b74e1237020983

Observation 73e17fc6-e8b1-41cd-9360-889355df89ce · outbound

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

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey The Impact of Reasoning Step Length on Large Language Models

Reference 88

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source=arxiv_source observed=2026-08-06T17:54:16.725850Z digest=sha256:90dafc42ed024d2936668775400d2a5349d0d7ab34396cdd784b22fa53c261a8

Observation c6533a2e-66b8-495e-a0f5-78258de6cca8 · outbound

This paper cites ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization

Reference 89

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source=arxiv_source observed=2026-08-06T17:54:16.766414Z digest=sha256:35ac86abafcb8700e40f4bdbbbb5643971b551fffb4f2d78988e82904d679c39

Observation 86b70ea3-0d87-484b-ae84-2fe9f10771f6 · outbound

This paper cites C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness

Reference 90

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source=arxiv_source observed=2026-08-06T17:54:16.820007Z digest=sha256:73d677c384dab3d3f14f157fd34df742533700f601d08e19de97d3830b05278b

Observation e68ea4db-dfb5-4766-ac60-6e7494be64eb · outbound

This paper cites an unresolved cited work.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Unresolved cited work

Reference 91

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source=arxiv_source observed=2026-08-06T17:54:16.893404Z digest=sha256:5d37cd4d6c46769f2c79553e08712a3c758ce99c47fd8184420c93a47406e6f0

Observation eeb2d815-694a-47a1-afe4-d495821f0d78 · outbound

This paper cites How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach

Reference 92

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source=arxiv_source observed=2026-08-06T17:54:16.926500Z digest=sha256:428e930e1b1614089e030f228daef2ebd100b2ab1c857b33a87d422738fc25d9

Observation 5e71face-cb27-4ca1-baa1-71adfac923c3 · outbound

This paper cites Solving Quantitative Reasoning Problems with Language Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Solving Quantitative Reasoning Problems with Language Models

Reference 93

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source=arxiv_source observed=2026-08-06T17:54:16.964248Z digest=sha256:f8f539c0b6b7629756d487b97f6e9ee6092c5c9ed1e7a04af01fc09c31b8ae00

Observation 4fa7ffbf-94a8-42f9-acc6-ca513a5a4233 · outbound

This paper cites Steering LLM Thinking with Budget Guidance.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Steering LLM Thinking with Budget Guidance

Reference 94

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source=arxiv_source observed=2026-08-06T17:54:16.991909Z digest=sha256:61e1320252db2382d8181ae8fd2bedc2aeab0fb7aef8a9d70ba53060b2ef5aa7

Observation 1f7ae74f-a42d-4847-bd46-4703c28619f4 · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 95

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source=arxiv_source observed=2026-08-06T17:54:17.055985Z digest=sha256:d4c074c107f012074a20dbc8d223527a5e85eb7e10ad4fc745466db90c503cc9

Observation 7c0cc948-52aa-4765-9fc1-eb82e0bb28a1 · outbound

This paper cites DynamicMind: A Tri-Mode Thinking System for Large Language Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey DynamicMind: A Tri-Mode Thinking System for Large Language Models

Reference 96

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source=arxiv_source observed=2026-08-06T17:54:17.116472Z digest=sha256:d63b2405c644d5660faf8a4e88d281d13fc1217bcca1ac1646f744dea85b5575

Observation 850fe3bc-5b08-4a40-863b-9137de35c69b · outbound

This paper cites Output Length Effect on DeepSeek-R1's Safety in Forced Thinking.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey Output Length Effect on DeepSeek-R1's Safety in Forced Thinking

Reference 97

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source=arxiv_source observed=2026-08-06T17:54:17.142549Z digest=sha256:cd2990231be9b87c74479d28c35811a3285a13949a0f31d31cb4b08fee9b41b2

Observation 295f469a-b0ef-4505-8c58-b9efe0e55c88 · outbound

This paper cites SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning

Reference 98

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source=arxiv_source observed=2026-08-06T17:54:17.154316Z digest=sha256:ef0216dd04daf09cf4314d953708ae0a23d734dfc708a6bb0a07f113f5a81ba8

Observation 9f5055de-f5b5-42b8-8bc3-4c8f0321b50f · outbound

This paper cites THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models

Reference 99

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source=arxiv_source observed=2026-08-06T17:54:17.162463Z digest=sha256:984e93c43005ff08d95a2c3a2c4503072c9385bcf1eb03589e43f56311ec9be5

Observation 822f149e-6e33-47c7-9794-c105730b54fa · outbound

This paper cites TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression

Reference 100

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source=arxiv_source observed=2026-08-06T17:54:17.171387Z digest=sha256:3ee5dc0e80e869f94be68f7fda027a09457637c840d9a30e7592501ee9ea0650

Pith citing papers

Observation 039f2c5e-7771-4517-98aa-e509f67f7d66 · inbound

BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens cites this paper.

BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

Reference 48

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source=arxiv_source observed=2026-08-05T17:04:38.779777Z digest=sha256:d0546c2e8985e8f99dd5253d27e3abe64204cc103529c10efede14f0ee7b69b0

Observation 671f6194-10a2-4574-8463-5ec909b91d45 · inbound

ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute cites this paper.

ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

Reference 55

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source=arxiv_source observed=2026-08-05T13:52:07.635896Z digest=sha256:203cd134c679f125839d27c97604141d0a038d9feee53d199cc4ea8a8ee646e2

Observation 7f697a82-72ea-4cae-8cf1-7673abd13878 · inbound

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle cites this paper.

Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

Reference 258

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source=pdf_text observed=2026-08-04T16:07:48.912958Z digest=sha256:2e52a131401ed1e85f1ecc0647765cc7446f653b3e1ff3a8886c957317f31f83

Observation 074414b9-d065-402c-8dad-4edbcb8f6ac4 · inbound

Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation cites this paper.

Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

Reference 13

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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