Paper Citation Record · LEDGER

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2506.15647.

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

pith.paper-citation-record.v1
2506.15647 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:57:50.767337Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy1
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b1d70a70-6199-4cd1-a34d-c20fa68626f2 · outbound

This paper cites OpenAI o1 System Card.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement OpenAI o1 System Card

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.209603Z

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Observation 108494e2-3ff9-424f-a687-68782e076037 · outbound

This paper cites Language models are few-shot learners.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Language models are few-shot learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.305914Z

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Observation 2d75d2a4-5520-4495-ac39-3bc3a92a640c · outbound

This paper cites Qwen2.5 Technical Report.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Qwen2.5 Technical Report

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.675230Z

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Observation cebba81e-61c3-42fd-a33d-665141521f6d · outbound

This paper cites From System 1 to System 2: A Survey of Reasoning Large Language Models.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.801465Z

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Observation 461d7299-653e-4da8-96c7-ce45944e75b2 · outbound

This paper cites Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.902282Z

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Observation 79f72b9e-21d1-4250-b9be-0de7904e1adb · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.989200Z

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Observation aaa3139f-cedc-442c-930f-2fd1916800bb · outbound

This paper cites The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.074950Z

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Observation aba58508-9c1f-4947-9f53-da72f6d51fe7 · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.215668Z

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Observation 81c44ba2-4a3b-476c-88a7-fc7da04f80e3 · outbound

This paper cites Training language models to reason efficiently.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Training language models to reason efficiently

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.324199Z

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Observation 762ccc96-3c59-4fdb-8fbd-995fcdbcbab9 · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.452402Z

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Observation 278f6c69-2b5f-40b4-9fda-74409491e923 · outbound

This paper cites Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.571611Z

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Observation 1196a5a5-c4a7-4ae5-9eb7-461753e9e388 · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement CoT-Valve: Length-Compressible Chain-of-Thought Tuning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.672171Z

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Observation 77d123a3-49a9-450b-8f0e-accf6670caf2 · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Self-Training Elicits Concise Reasoning in Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.769760Z

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Observation 0c92e803-ab31-41c8-891d-06bb45dd29b2 · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.044995Z

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Observation 8a8d902a-7442-40ba-8aae-626dd0f7e034 · outbound

This paper cites Seal: Steerable reasoning calibration of large language models for free.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Seal: Steerable reasoning calibration of large language models for free

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.149235Z

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Observation 882bd2c2-c2a2-4478-9e93-14dcd4cb9356 · outbound

This paper cites Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.448142Z

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Observation e4091cfb-0c99-4295-8cde-6f87f225c880 · outbound

This paper cites Adasteer: Your aligned llm is inherently an adaptive jailbreak defender.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Adasteer: Your aligned llm is inherently an adaptive jailbreak defender

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.563963Z

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Observation bbf5df97-2701-4356-b711-0030df1f6650 · outbound

This paper cites The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.672078Z

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Observation a1ca104c-647a-4029-bfbb-dc9e94223d60 · outbound

This paper cites Linear Representations of Sentiment in Large Language Models.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Linear Representations of Sentiment in Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.745268Z

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Observation f0659298-9418-4b6b-99af-8b600cf5b2fc · outbound

This paper cites Refusal in Language Models Is Mediated by a Single Direction.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Refusal in Language Models Is Mediated by a Single Direction

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.748153Z

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Observation fd874a25-a014-4544-84e4-432546bebc65 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Training Verifiers to Solve Math Word Problems

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.751182Z

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Observation e6aba00f-7612-44fc-81f0-824ceadad77b · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.753770Z

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Observation df79aa5d-7898-4f1f-8c00-f2dfa68ba15b · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.756857Z

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Observation e446d52c-cb09-4f00-b1fd-e0bce8946ed7 · outbound

This paper cites OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.759396Z

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Observation 9f37d404-d02e-4d75-9a88-7652c76cf170 · outbound

This paper cites Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.761865Z

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Observation b00c361c-29b1-4b9f-bec4-2b0f095f6977 · outbound

This paper cites A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement A survey of efficient reasoning for large reasoning models: Language, multimodality, and beyond

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.764373Z

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Observation baea6d90-6828-48eb-a040-0426ee851e64 · outbound

This paper cites Trade-offs in large reasoning models: An empirical analysis of deliberative and adaptive reasoning over foundational capabilities.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Trade-offs in large reasoning models: An empirical analysis of deliberative and adaptive reasoning over foundational capabilities

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.767337Z

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Observation 71cbb090-e8fd-42c1-9d1e-60af7363ecd0 · outbound

This paper cites Linguistic regularities in continuous space word representations.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Linguistic regularities in continuous space word representations

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:57:51.368934Z

Source-reported events for the cited work

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

Observation ebc5cc7f-de45-4184-9e8a-839344c4bee7 · outbound

This paper cites The Llama 3 Herd of Models.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement The Llama 3 Herd of Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.462167Z

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Observation 2a21f87a-3f81-4075-8720-f0df015b0e62 · outbound

This paper cites Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:49.924159Z

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Observation 334778ba-83ca-4f57-8cec-cb7489f21733 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Representation Engineering: A Top-Down Approach to AI Transparency

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:50.425158Z

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Observation 2f97d2ff-d673-40b6-91c1-cf6e34f528b8 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement Gemma 2: Improving Open Language Models at a Practical Size

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.545734Z

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Observation a192a918-ce99-4235-900f-81b9741e75aa · outbound

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

Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T23:57:48.247521Z

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Pith citing papers

No inbound Pith citation observations are available.