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Paper Citation Record · LEDGER

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

As of 22 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 6 inbound Pith citation observations for arXiv:2504.14047.

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

pith.paper-citation-record.v1
2504.14047 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:01:25.062339Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:23:26.596654Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T13:51:05.463740Z

Reference resolution

22 of 22 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved17
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ebdfc761-c22a-4558-bedd-3d09b92ce365 · outbound

This paper cites Qwen Technical Report.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Qwen Technical Report

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fd325984-30b5-4395-8d65-772d3d87d627 · outbound

This paper cites The Llama 3 Herd of Models.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals The Llama 3 Herd of Models

Reference 4

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source=pdf_text observed=2026-08-16T12:01:24.944835Z digest=sha256:537c85eae14da9a2b0c6e6cd5401aed08fcfe8fe53d565c4716a58bae49911c5

Observation 2d98a4bc-3f98-4bb2-9846-b892e5cff3e7 · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 6

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source=pdf_text observed=2026-08-16T12:01:24.959287Z digest=sha256:cc66cf0ee95d9c38830cade550a38babf3d00b244b0c9fd733ec73293648f4b0

Observation 7332027a-d2ca-45a3-b4f1-255403222ff5 · outbound

This paper cites Mct-self-refine: Integrating monte carlo tree search with self- evaluation in language models.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Mct-self-refine: Integrating monte carlo tree search with self- evaluation in language models

Reference 7

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verified exact
raw_fallback, observed 2026-08-16T12:01:25.655408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 601b7525-c5d8-4d5e-b30b-554df353e8dd · outbound

This paper cites On character expansion and Gaussian regularization of Itzykson-Zuber measure.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals On character expansion and Gaussian regularization of Itzykson-Zuber measure

Reference 8

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local_arxiv, observed 2026-08-16T12:01:25.572632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T12:01:24.972892Z digest=sha256:138f7b21109e22316b6cd94fcdccf86716bfadd4ec3d8ba354aeb0a7aea2e115

Observation 75491ede-3eb2-43fb-a5ac-1803552c43bc · outbound

This paper cites Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling

Reference 9

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source=pdf_text observed=2026-08-16T12:01:24.978443Z digest=sha256:fbb52dbcb92a35a0b4f915f5ebcaabea078178ea538243a06fa27d29a4c492a1

Observation e607e097-df24-43b4-882c-73a066ad288a · outbound

This paper cites OpenAI o1 System Card.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals OpenAI o1 System Card

Reference 10

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source=pdf_text observed=2026-08-16T12:01:24.984442Z digest=sha256:2d63e95cc2584b88252b0f5b9b658b6d8fe7e633432f65dd122f6ca261d16871

Observation 56da1d9a-30ac-41ab-8e1e-8072a0e79679 · outbound

This paper cites Memory-augmented language models with mcts planning for text-based game agents.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Memory-augmented language models with mcts planning for text-based game agents

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T12:01:24.994171Z digest=sha256:a8c832b9f1a7481779fad9dfa33dfbd339893d80b250822fb0f961c91fe6c278

Observation 0de42831-6df6-4dca-ac6c-d52c933be485 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 12

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source=pdf_text observed=2026-08-16T12:01:25.000873Z digest=sha256:55027e7a7324ed9baed8ca4e2fd670aa20abd61ce0cec74e55c93c888ad10dc7

Observation c9977d9a-bc81-4d29-b774-e03e52bcf551 · outbound

This paper cites Branch-Solve-Merge Improves Large Language Model Evaluation and Generation.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

Reference 13

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source=pdf_text observed=2026-08-16T12:01:25.006653Z digest=sha256:0705b513312895ec3bb497e7411e66b6609d1788fd13404adbddc27a397de101

Observation cf5c4029-de56-4b66-8f5f-d5b4e99df62f · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Gemini: A Family of Highly Capable Multimodal Models

Reference 14

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source=pdf_text observed=2026-08-16T12:01:25.012330Z digest=sha256:9a79d00c3621afd94c30e248599783c41d3d17e26e050e952378d58215b9df6e

Observation a4efc565-347f-4827-bf8a-bb2bdc326f71 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals LLaMA: Open and Efficient Foundation Language Models

Reference 15

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source=pdf_text observed=2026-08-16T12:01:25.017615Z digest=sha256:c95120df12154eab13fac8f22c2581f57567d114e06fd17cb3dbd9c339c141cc

Observation 2c3e956d-027f-4d1d-a112-27c56404a9cb · outbound

This paper cites Planning In Natural Language Improves LLM Search For Code Generation.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Planning In Natural Language Improves LLM Search For Code Generation

Reference 16

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source=pdf_text observed=2026-08-16T12:01:25.023309Z digest=sha256:ca2abaf7d311d3295b79cacc92c90e901e2623ee89f71e5b6798e36c86bb071c

Observation 489fbc41-4540-4fb4-9384-7cc836b99f99 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 18

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source=pdf_text observed=2026-08-16T12:01:25.035125Z digest=sha256:372413bfbc0ff62f0aa5a88717edd6ac1433129cea9eef3d11706133f5503fff

Observation eb1fd9ea-10c4-4898-a842-6a16a47c6602 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 19

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source=pdf_text observed=2026-08-16T12:01:25.041417Z digest=sha256:3eb836e6032ec04b8aabfbee09e5014b19ef2e25637302a08a18e68b162bde68

Observation b8c0b2a2-f116-44bc-a3f1-3d61e04bd327 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 20

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source=pdf_text observed=2026-08-16T12:01:25.050387Z digest=sha256:f96568ca38074bf5b449fd74ea47879d7b08bd709926524eb404e48d81df5f09

Observation 7092e3ad-aac7-44cc-99c1-fffb8f147815 · outbound

This paper cites Confidence-informed self-consistency for efficient infer- ence.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Confidence-informed self-consistency for efficient infer- ence

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T12:01:25.056823Z digest=sha256:0e0ffd2efb006a196e1bb8a35fbb290fe9388a90cf2d3495a008f2a2a405b739

Observation 37f0875a-02ec-4cb4-82de-716dd9a632f1 · outbound

This paper cites ⟨ think⟩.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals ⟨ think⟩

Reference 22

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-16T12:01:25.062339Z digest=sha256:0ca036f59c4b31d8d541ee6886c03a92b88630237565787002f7713bbbf64f4a

Observation 3384a1b4-4f8b-413c-9151-7bdbc7f16277 · outbound

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

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 2021

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source=pdf_text observed=2026-08-16T12:01:24.951948Z digest=sha256:2bb13497480f583732144082d5757ecef6a1ed07861fe3471c73f088fc0cb8e1

Observation ef93732f-2a36-437e-97c7-8684601736db · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2023

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source=pdf_text observed=2026-08-16T12:01:24.931721Z digest=sha256:7c0cd1a7a7d12e92816ae4595f05be6b88dca870aa94ed3c9a2e04c1cebd3673

Observation 25b5de5a-40d8-4f44-a916-c9cef9ca1d62 · outbound

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

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2024

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source=pdf_text observed=2026-08-16T12:01:24.937738Z digest=sha256:673559c67fd20e216a8587bc2be92fe47e060b34f220232584d7640a3036bcc2

Observation aaef42cd-3d1a-4b30-94b2-72b0f01e89e3 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 2025

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source=pdf_text observed=2026-08-16T12:01:25.029375Z digest=sha256:8fcf04e3d76545922153f665bf5e64a0326f716c42b0c88bb7f78296e6976cb9

Pith citing papers

Observation 15c831c6-742f-4d5a-b80f-194e6211e26c · inbound

Iterative Deepening Sampling as Efficient Test-Time Scaling cites this paper.

Iterative Deepening Sampling as Efficient Test-Time Scaling Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 20

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source=pdf_text observed=2026-08-08T19:23:26.596654Z digest=sha256:eb4d37ec1d4bfdbd82701b5d98310d56b0e686e403d930620b5595c5527fc601

Observation c5c45858-6dec-423c-862c-3210acfec7d6 · inbound

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

VeriThinker: Learning to Verify Makes Reasoning Model Efficient Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 55

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source=pdf_text observed=2026-08-07T14:42:14.526344Z digest=sha256:a970abc234a6626d8a9c2c78aa00d37a02b787fafc401373c8db3c8a50bcef84

Observation f6ad73c1-ae5e-45b7-8cd3-1feca1aa0d6f · inbound

How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning cites this paper.

How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 29

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source=pdf_text observed=2026-08-07T12:32:23.957399Z digest=sha256:2ae757b12c472527b7f691743afa5626add3d186f9816bd28c229963d907505e

Observation 3d47c2c8-f0d3-470a-91d3-6b778fa7cdc3 · inbound

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling cites this paper.

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 3

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source=pdf_text observed=2026-08-06T23:48:23.226273Z digest=sha256:be5afa01d19cfc807db71c8ccd856dd3846bc90f39fc9f6c87e1a051e36cd9ac

Observation 3244a49a-43a8-48a4-9b42-85e0e7cd0d7a · inbound

Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations cites this paper.

Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 40

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arxiv_id, observed 2026-08-04T00:30:16.661537Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-09T23:55:50.606359Z digest=sha256:0d92056423e81e3a066adfc859889c2548a6c185abce3d6360f788e56bd76b88

Observation 14620a36-c3a6-48bc-a052-d02270950edc · inbound

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI cites this paper.

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI Rethinking Inference-Time Scaling: Efficiency Limits and Linguistic Signals

Reference 171

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source=pdf_text observed=2026-08-02T11:29:34.612861Z digest=sha256:7fca854277ea4b9041347f54215debe28c2170ca494da2a06699a0741614562d