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Paper Citation Record · LEDGER

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 6 inbound Pith citation observations for arXiv:2505.16315.

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

pith.paper-citation-record.v1
2505.16315 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:06:58.875636Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:59:21.349196Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T18:02:27.343921Z

Reference resolution

37 of 37 outbound references displayed

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  • verified fuzzy2
  • unresolved35
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 40e6d876-0ad2-4beb-b3a2-8df8a1fb2637 · outbound

This paper cites A Survey of Large Language Models.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning A Survey of Large Language Models

Reference 1

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source=pdf_text observed=2026-08-07T15:06:58.757616Z digest=sha256:d4bf3af982d2779dc3c37786f892cc07767ee5d5a6b8bb019522398028c42346

Observation 631cc139-b023-4c6a-a28f-3a51a4978009 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2

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source=pdf_text observed=2026-08-07T15:06:58.761100Z digest=sha256:d2de1562c140396d5d186559f328f29a8994229bbf20d65f7aab5a5f28aca4a5

Observation 34178338-ac09-4965-a898-58b989749e86 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 3

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source=pdf_text observed=2026-08-07T15:06:58.764354Z digest=sha256:bd0344b6f69b70246c8f6291d4ce5482f87a92770d1e9216e87a181b0b321093

Observation a1ca278f-b4a3-4800-9fc7-c9e82256239e · outbound

This paper cites OpenAI o1 System Card.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning OpenAI o1 System Card

Reference 4

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source=pdf_text observed=2026-08-07T15:06:58.767625Z digest=sha256:48e5f90245cc4e54c14b77832126741d12fc6a50e3b0b63a1d0ddd1af79af4ac

Observation 2e7eda70-d865-4960-aff9-3c5b91c33ffd · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Qwq-32b: Embracing the power of reinforcement learning, March 2025

Reference 5

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source=pdf_text observed=2026-08-07T15:06:58.770887Z digest=sha256:567cf1606420a59da2fa4378386121c59cc281bf045bddd7d900a891ef5f7496

Observation 37006777-0af7-474e-86ff-e21f40abc57e · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 6

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source=pdf_text observed=2026-08-07T15:06:58.774554Z digest=sha256:9a560fd7e352c4627e62a0d24059b24805377d75536c54f64d04188cd91068df

Observation 86671df9-d6f7-4882-8d9b-8655307df6fc · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning The impact of reasoning step length on large language models

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T15:06:58.777975Z digest=sha256:a8471e37acf894b5c04d5e4c5c69973b38855e3bf81e7538c19a005c15db3ac4

Observation d68ddd08-8b94-4d77-9574-c3e037422155 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Chain-of-thought prompting elicits reasoning in large language models

Reference 8

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source=pdf_text observed=2026-08-07T15:06:58.780961Z digest=sha256:9947db3721d8605fa62019cf8a5610770859a3dac91ad7314ec31852ba67d599

Observation 7863a5f1-ea24-47b8-98a4-8484427ddce8 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 9

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source=pdf_text observed=2026-08-07T15:06:58.783881Z digest=sha256:bbfb51c03abc3dd4f3c78da08495d666229c9184b614837b36784a3acc25b239

Observation 559b1c77-b436-4690-a958-aeae34291c61 · outbound

This paper cites Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

Reference 10

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source=pdf_text observed=2026-08-07T15:06:58.787156Z digest=sha256:3c3bd5d51ae10ac9394285628a96e0dc1333d97a23fe8405ac01b93d999f8b8f

Observation d6fae705-f5e0-4880-98c2-a047458aa56d · outbound

This paper cites A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond.CoRR, abs/2503.21614, 2025.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond.CoRR, abs/2503.21614, 2025

Reference 11

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source=pdf_text observed=2026-08-07T15:06:58.790732Z digest=sha256:34ec9c8b573be78af988d91d34b4bd8b1d94de6a540faba2ced98528f64930d2

Observation 596edc00-34c3-4586-b19f-a24e477eb60b · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 12

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source=pdf_text observed=2026-08-07T15:06:58.793817Z digest=sha256:92c665948c6d201f9a79d36bb31353dc0e605e59e274a5802cc50bea636361bf

Observation 3d44e152-b91d-4e7a-8552-d4efc50cb707 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067, 2025

Reference 13

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source=pdf_text observed=2026-08-07T15:06:58.797874Z digest=sha256:2e13ff7bd5f77b7feb0bf0bfd0a4fe4efcf80201e0307ad8eb301c374681b6c9

Observation b353b1f4-4872-485d-aac9-0b57be157502 · outbound

This paper cites Self-Training Elicits Concise Reasoning in Large Language Models.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Self-Training Elicits Concise Reasoning in Large Language Models

Reference 14

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source=pdf_text observed=2026-08-07T15:06:58.800947Z digest=sha256:2fd2adfafb39294a876ddcfa42fe7d961bd721434aa80d0a3684cb80039c8ded

Observation 1e3d892d-cbc6-4b61-838f-cca9455213e3 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 15

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source=pdf_text observed=2026-08-07T15:06:58.804399Z digest=sha256:ca9164cb62a1dd16cfbb22fe7820475cd2e7bec45640c8e3909c27dd725f8aa0

Observation eab8a010-3810-459a-8f34-765aef0d4436 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Reference 16

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source=pdf_text observed=2026-08-07T15:06:58.807563Z digest=sha256:39114f561006665b6ae278d4950b6216db209f1f4524b2ba1901f702e5759edd

Observation 9ce75ed7-72fb-49b6-a397-64450dfdd3f4 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 17

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source=pdf_text observed=2026-08-07T15:06:58.810890Z digest=sha256:3c08b1940688f3e2996e7dcc7f5b1db7b3285548694655ef7ef3e45e92021ce1

Observation e781f5d7-331d-4568-8405-279cc34d151f · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Dast: Difficulty-adaptive slow-thinking for large reasoning models

Reference 18

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source=pdf_text observed=2026-08-07T15:06:58.814572Z digest=sha256:e20e3778f88afde7afc1088c0bc2cf823cad375c6012c1d360c8f263ab79548b

Observation e8fb5eb0-a2f1-43ce-b432-574500221f0e · outbound

This paper cites Thinking, fast and slow.Farrar, Straus and Giroux, 2011.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Thinking, fast and slow.Farrar, Straus and Giroux, 2011

Reference 19

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source=pdf_text observed=2026-08-07T15:06:58.818080Z digest=sha256:dc1527a98157aa93a090bea892036ed9254b4a0d416502ce39fe9166f3bdb931

Observation 97a01589-3817-4871-98c8-61efd9896d47 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 20

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source=pdf_text observed=2026-08-07T15:06:58.821434Z digest=sha256:d9bf6c3dbbef9e08780a0a8c84c6bbe7d6eb69d4e15106a09810635ac441e05d

Observation 3d7de5d4-2103-433c-97d1-18a516d19133 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Proximal Policy Optimization Algorithms

Reference 21

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source=pdf_text observed=2026-08-07T15:06:58.824688Z digest=sha256:ed61be6d82a17b8fda6b770404d3d977b7812c3c58a84556d273b4c5dcc907e7

Observation 93029c25-a3a0-4fa0-89d8-bb662725937a · outbound

This paper cites LIMO: Less is More for Reasoning.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning LIMO: Less is More for Reasoning

Reference 22

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source=pdf_text observed=2026-08-07T15:06:58.827932Z digest=sha256:46259ccdc20bc0107b9516c354e2eaf901aae868f424534133cee785fd423966

Observation 19124775-f016-4cf3-a790-a55026db63b2 · outbound

This paper cites Towards thinking-optimal scaling of test-time compute for llm reasoning.arXiv preprint arXiv:2502.18080, 2025.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Towards thinking-optimal scaling of test-time compute for llm reasoning.arXiv preprint arXiv:2502.18080, 2025

Reference 23

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source=pdf_text observed=2026-08-07T15:06:58.831772Z digest=sha256:64e33bfe84c12d88ac341d7fe38faf345a237bc2fd434d34d3bcc40f67db161b

Observation 0be56c47-d2ea-4c84-9c2d-d7e46ed6075d · outbound

This paper cites GPT-4 Technical Report.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning GPT-4 Technical Report

Reference 24

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source=pdf_text observed=2026-08-07T15:06:58.835438Z digest=sha256:4028faedf4c2ae5a13dd4d2a2505a14aa4963a6cea2c941ef29b3cd4e7e5f4a2

Observation 7c0c6fcb-e9e2-4468-9472-d6f18b9adbbc · outbound

This paper cites Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Tang, Manan Roongta, Colin Cai, Jeffrey Luo, Li Erran Li, Raluca Ada Popa, and Ion Stoica

Reference 25

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source=pdf_text observed=2026-08-07T15:06:58.838844Z digest=sha256:14267945f368988b106c120ce5bf7b277f4c3df555ee962cea615dacbf2750c4

Observation ae6bea58-246b-4a2f-afdb-9148b5a5af40 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 26

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source=pdf_text observed=2026-08-07T15:06:58.842174Z digest=sha256:1e89e0b9aac3b06cd9ec1227aaf667fa3f853be6e1a109ec50c87e762d600193

Observation 16e0138a-47f6-4a22-a5af-f5ae87c620b8 · outbound

This paper cites Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems

Reference 27

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source=pdf_text observed=2026-08-07T15:06:58.845789Z digest=sha256:29eb92113b38b0caf96c228b971fb0ea659f6cab989f8436878a0cb762a89fbc

Observation 92ec3b08-f1f1-473b-ad08-f51d53c6d44d · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Training Verifiers to Solve Math Word Problems

Reference 28

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source=pdf_text observed=2026-08-07T15:06:58.848805Z digest=sha256:24509da21bc6606344167e89cb5ba504e3379dffcdd5ddab48dff19de427d0fc

Observation 8e392a9d-02ff-4e98-a703-af81e18f3af8 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 29

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source=pdf_text observed=2026-08-07T15:06:58.851992Z digest=sha256:4131bc21d2eae5d009ba0ef9553b5743d86436cee02db54431c8da344996e466

Observation b269056b-7064-4c75-b208-6816f62a1007 · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 30

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source=pdf_text observed=2026-08-07T15:06:58.854861Z digest=sha256:86448945a54c549803bfd91210a6b98f817fa72810c6f7b67bb7ae13777c24d8

Observation 044889fb-6f67-40bc-ace4-a159533a2574 · outbound

This paper cites Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024

Reference 31

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source=pdf_text observed=2026-08-07T15:06:58.857808Z digest=sha256:2a415bc79022ff5b45feddac0facb3e548128a7e313c409c7ceb5c729c1309bb

Observation c4f6bcfe-4f23-4d51-b594-9c2e2b636817 · outbound

This paper cites verl: V olcano engine reinforcement learning for llms.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning verl: V olcano engine reinforcement learning for llms

Reference 32

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T15:06:58.860545Z digest=sha256:3d46714344a58d3042f1b06c4476add5841758610c29d54fbe4781bacd41d058

Observation a1b76cd7-a7ca-4d6f-a460-fff577dd0fa1 · outbound

This paper cites System-1.x: Learning to Balance Fast and Slow Planning with Language Models.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning System-1.x: Learning to Balance Fast and Slow Planning with Language Models

Reference 33

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source=pdf_text observed=2026-08-07T15:06:58.863587Z digest=sha256:c7337cf6cfea6c17d7203be419012c7fb378d1230f5b0d5ee33864bba973f1a4

Observation 7e0fbd0d-2620-4af3-b02d-46896bac3c29 · outbound

This paper cites Visual Agents as Fast and Slow Thinkers.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Visual Agents as Fast and Slow Thinkers

Reference 34

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source=pdf_text observed=2026-08-07T15:06:58.866572Z digest=sha256:34c74816550ae71d34394bc2e3a6b74442ebd28a790bd4ccea1b084e90f16185

Observation bf34db47-73be-4c31-9063-cbaad67c715d · outbound

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

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking

Reference 35

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source=pdf_text observed=2026-08-07T15:06:58.869704Z digest=sha256:9d26d176b8241998aee0abd1eeb3d2dcf01c37eb71aac4652fffe001cc045c79

Observation 757f813f-95bb-4880-9028-c6a936323c1c · outbound

This paper cites DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models

Reference 36

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Observation 5cb68726-9f4e-46b3-82b2-9906673826fe · outbound

This paper cites Qwen2.5 Technical Report.

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning Qwen2.5 Technical Report

Reference 37

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Pith citing papers

Observation 3c4525fe-af41-4fa9-babb-cba72c3fbd0c · inbound

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

Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

Reference 21

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Strategic Reflectivism In Intelligent Systems cites this paper.

Strategic Reflectivism In Intelligent Systems Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

Reference 25

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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 Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

Reference 26

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ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure cites this paper.

ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

Reference 2024

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Observation 4abc1421-51de-4d05-8a8f-4dcea382e645 · inbound

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost cites this paper.

Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

Reference 246

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Observation 6c760e19-6127-4138-9599-611d73c04bdd · inbound

ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks cites this paper.

ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning

Reference 50

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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