Paper Citation Record · LEDGER

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2506.19492.

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

pith.paper-citation-record.v1
2506.19492 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:10:27.460894Z

measured 44 of 44 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:54:17.954851Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:54:19.431591Z

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy15
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 548782f0-c7a1-4232-8917-7d7783246534 · outbound

This paper cites Concrete Problems in AI Safety.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Concrete Problems in AI Safety

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:24.360508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d265062-f11e-46eb-b92c-36406210a084 · outbound

This paper cites Chain-of-thought reasoning in the wild is not always faithful.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Chain-of-thought reasoning in the wild is not always faithful

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:24.429267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4047ab3-1937-40f7-8b9c-f670e5675eb9 · outbound

This paper cites Chain-of-Thought Reasoning In The Wild Is Not Always Faithful.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Chain-of-Thought Reasoning In The Wild Is Not Always Faithful

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:24.498510Z

Source-reported events for the cited work

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Observation 6bc124e3-dd59-4762-b3e3-a751462704b5 · outbound

This paper cites Training language models to reason efficiently.arXiv preprint arXiv:2502.04463, 2025.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Training language models to reason efficiently.arXiv preprint arXiv:2502.04463, 2025

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:24.581791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 639c2dd4-23c0-422a-b5cf-7eb820033e8d · outbound

This paper cites Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:24.679284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c43f894-d01d-4e97-b5bc-acd855f5aac8 · outbound

This paper cites Monitoring reasoning models for misbehavior and the risks of promoting obfuscation.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Monitoring reasoning models for misbehavior and the risks of promoting obfuscation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:31.101313Z

Source-reported events for the cited work

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

Observation 0183fd02-3508-4fa5-8ae8-1d3ce12980d9 · outbound

This paper cites Weak-to-strong generalization: Eliciting strong capabilities with weak supervision, 2023.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Weak-to-strong generalization: Eliciting strong capabilities with weak supervision, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:31.088807Z

Source-reported events for the cited work

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

Observation e96086b5-7dee-4f0d-96e2-315d89c06a51 · outbound

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

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:24.922177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b1c90b-26cc-4740-b5d6-3888a514e631 · outbound

This paper cites Reasoning models don’t always say what they think.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Reasoning models don’t always say what they think

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:31.074962Z

Source-reported events for the cited work

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

Observation 07ca73a6-efda-4613-bfbc-57ee614e6c13 · outbound

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

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:25.135228Z

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Unavailable: canonical work link unavailable.

Observation 70f9d6c7-0bb0-43a5-8290-02d958728470 · outbound

This paper cites Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:25.217290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 697d417d-401a-4482-a561-b06c548eb481 · outbound

This paper cites A wolf in sheep’s clothing: Generalized nested jailbreak prompts can fool large language models easily.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs A wolf in sheep’s clothing: Generalized nested jailbreak prompts can fool large language models easily

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:31.062985Z

Source-reported events for the cited work

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

Observation a0f602a2-9bc3-4d3f-8c74-1fa6d18c06b4 · outbound

This paper cites Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective.Synthese, 198(Suppl 27): 6435–6467, 2021.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective.Synthese, 198(Suppl 27): 6435–6467, 2021

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:31.049640Z

Source-reported events for the cited work

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

Observation 851d42d2-bced-48be-af82-31191296ee6e · outbound

This paper cites Syceval: Evaluating llm sycophancy.arXiv preprint arXiv:2502.08177, 2025.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Syceval: Evaluating llm sycophancy.arXiv preprint arXiv:2502.08177, 2025

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:25.467968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60f88d12-c1a4-4506-98c6-e08dc9d13f76 · outbound

This paper cites Who’s asking? user personas and the mechanics of latent misalignment.Advances in Neural Information Processing Systems, 37:125967–126003, 2024.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Who’s asking? user personas and the mechanics of latent misalignment.Advances in Neural Information Processing Systems, 37:125967–126003, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:31.005881Z

Source-reported events for the cited work

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

Observation 8a313f76-fa7e-49ca-9b82-9c58476ae472 · outbound

This paper cites Alignment faking in large language models.CoRR, 2024.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Alignment faking in large language models.CoRR, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:30.816339Z

Source-reported events for the cited work

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

Observation 5c466036-a97c-4154-87d2-9e9a58e59176 · outbound

This paper cites Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:25.729917Z

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Unavailable: canonical work link unavailable.

Observation 36329b67-aed0-429e-95b3-0c8af85eec41 · outbound

This paper cites C3ot: Generating shorter chain-of-thought without compromising effectiveness.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs C3ot: Generating shorter chain-of-thought without compromising effectiveness

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:25.812905Z

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Unavailable: canonical work link unavailable.

Observation 91f29c4a-bcc5-4e15-8794-2a82d943533f · outbound

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

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:25.880397Z

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Observation dabfeedd-0961-4400-934d-aab56adf40b8 · outbound

This paper cites Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:25.963095Z

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Observation 23da9ed6-0f8d-41f2-bffa-d936c0ace837 · outbound

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

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.017771Z

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Observation 157ca23f-801b-430f-8ecb-79ca607520ae · outbound

This paper cites Reasoning Models Can Be Effective Without Thinking.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Reasoning Models Can Be Effective Without Thinking

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.064315Z

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Observation 302d724a-88a7-427d-8cea-bfac70a48a75 · outbound

This paper cites Are self-explanations from large language models faithful? InFindings of the Association for Computational Linguistics ACL 2024, pages 295–337, 2024.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Are self-explanations from large language models faithful? InFindings of the Association for Computational Linguistics ACL 2024, pages 295–337, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:30.727087Z

Source-reported events for the cited work

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

Observation 8d016c3c-1c87-4295-98dd-c8eb93774d26 · outbound

This paper cites Frontier Models are Capable of In-context Scheming.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Frontier Models are Capable of In-context Scheming

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.170863Z

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Observation 28c01fdc-bcfb-41dd-9cbc-175f53360a1a · outbound

This paper cites Self-training elicits concise reasoning in large language models.CoRR, 2025.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Self-training elicits concise reasoning in large language models.CoRR, 2025

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:30.583304Z

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

Observation 50a0b1e3-21f2-4cd2-91b4-da88d7ff332f · outbound

This paper cites Show your work: Scratchpads for intermediate computation with language models.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Show your work: Scratchpads for intermediate computation with language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:30.395731Z

Source-reported events for the cited work

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

Observation 50b54a60-db45-4499-b960-bc72af29f821 · outbound

This paper cites Learning to reason with llms.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Learning to reason with llms

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:30.195407Z

Source-reported events for the cited work

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

Observation 41429ca3-8eaf-4e6b-b9b1-ce061caa908c · outbound

This paper cites Discovering language model behaviors with model-written evaluations.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Discovering language model behaviors with model-written evaluations

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.439725Z

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Observation 55b4560e-fccb-42ea-9770-3ae2d9078c4c · outbound

This paper cites Do Anything Now.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Do Anything Now

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:29.918948Z

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

Observation 6293ab38-fd35-4d91-aa75-d87d80608643 · outbound

This paper cites Defining and characterizing reward gaming.Advances in Neural Information Processing Systems, 35:9460–9471, 2022.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Defining and characterizing reward gaming.Advances in Neural Information Processing Systems, 35:9460–9471, 2022

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.574076Z

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Observation a15feb78-3f2c-49ae-835a-4615d4cebdbd · outbound

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

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.642653Z

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Observation b04d6c25-3c44-4b35-8bad-021012d32e03 · outbound

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

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.694880Z

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Observation b33198e8-c487-45a2-a93a-dbb6066d5cb3 · outbound

This paper cites Qwen3, April 2025.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Qwen3, April 2025

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.755752Z

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Observation 838e7af5-614b-4797-ba56-5c4ae7712324 · outbound

This paper cites Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965, 2023.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting.Advances in Neural Information Processing Systems, 36:74952–74965, 2023

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:26.845600Z

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Observation f6c5e37f-e127-415b-9a2c-0fb8a1b965af · outbound

This paper cites an unresolved cited work.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:10:29.642751Z

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

Observation 887d404b-d7e8-4e37-9f48-572700bbd90f · outbound

This paper cites Large language models often say one thing and do another.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Large language models often say one thing and do another

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:29.323877Z

Source-reported events for the cited work

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

Observation 653ad9f5-5790-4dd2-a177-8a3e4883d017 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:27.042013Z

Source-reported events for the cited work

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Observation f57189ff-5941-47fe-9c36-df4163c9c159 · outbound

This paper cites an unresolved cited work.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:10:29.089201Z

Source-reported events for the cited work

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

Observation 471e33ee-960e-48f7-9c1a-1ec2a0a71a2f · outbound

This paper cites IC” if the two responses show an inconsistency (refusal vs. answer, or different final results). - “CO.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs IC” if the two responses show an inconsistency (refusal vs. answer, or different final results). - “CO

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:28.869126Z

Source-reported events for the cited work

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

Observation 9b4843ca-055d-464b-bd7b-e6d392f0c876 · outbound

This paper cites an unresolved cited work.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:10:28.670354Z

Source-reported events for the cited work

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

Observation f0f8efbe-7847-4426-a1dd-a6774df564c8 · outbound

This paper cites an unresolved cited work.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:10:28.465354Z

Source-reported events for the cited work

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

Observation ba0e5744-defa-4b10-aa7e-f23fd86b589b · outbound

This paper cites an unresolved cited work.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:10:28.200050Z

Source-reported events for the cited work

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

Observation fa8d8b32-6988-4364-92fe-122977d98eef · outbound

This paper cites Deploymode.

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs Deploymode

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:10:28.026131Z

Source-reported events for the cited work

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

Pith citing papers

Observation ba371586-8389-4de2-b0ec-df63a0b3ba18 · 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 Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs

Reference 220

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:54:19.436865Z

Source-reported events for the cited work

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