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Paper Citation Record · LEDGER

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.14419.

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

pith.paper-citation-record.v1
2505.14419 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:39:20.362861Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

39 of 39 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved38
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8bdbdd77-ab79-4fef-ad92-f0230ac9b9d9 · outbound

This paper cites an unresolved cited work.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Unresolved cited work

Reference 1

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verified exact
doi, observed 2026-08-07T15:39:20.620092Z

Source-reported events for the cited work

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Observation 58157a7a-196f-4952-a0fe-cc8c9c97bf12 · outbound

This paper cites DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale

Reference 2

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Observation 23661ad3-865f-46a4-8d5f-1d471ef134ca · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Training Verifiers to Solve Math Word Problems

Reference 3

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Observation 4c21f57e-250e-4acb-a566-c31e18c93254 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Process Reinforcement through Implicit Rewards

Reference 4

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Observation 608e0ec1-e507-49b9-85d3-12ccf408443e · outbound

This paper cites The Llama 3 Herd of Models.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation The Llama 3 Herd of Models

Reference 5

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Observation f67a7076-9d68-40a0-920b-580c6667ec10 · outbound

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

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 6

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Observation e9e03965-52e5-419e-8245-f17a1224cb1e · outbound

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

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 7

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Observation c23b895a-696b-44da-9548-52d402e97743 · outbound

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

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Measuring Mathematical Problem Solving With the MATH Dataset

Reference 8

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Observation 6b2d8094-f823-453c-91e9-61d5d6b707bb · outbound

This paper cites Qwen2.5-Coder Technical Report.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Qwen2.5-Coder Technical Report

Reference 9

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Observation 1e55cd05-7eaa-4f38-9936-6fab2eaf0b14 · outbound

This paper cites OpenAI o1 System Card.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation OpenAI o1 System Card

Reference 10

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Observation 558a436b-7883-4f23-8d1c-99f72a9d1a48 · outbound

This paper cites Solving Quantitative Reasoning Problems with Language Models.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Solving Quantitative Reasoning Problems with Language Models

Reference 11

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Observation ddc4663a-397a-40cf-ad81-a110dc7c8caf · outbound

This paper cites MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline

Reference 12

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Observation 2bfd0813-8ebc-4cb2-bcd7-c25918c24ab6 · outbound

This paper cites Let's Verify Step by Step.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Let's Verify Step by Step

Reference 13

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Observation 68c0a8dd-4635-4c3e-aac6-b25f520116af · outbound

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation DeepSeek-V3 Technical Report

Reference 14

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Observation 7200b91b-dfc9-4992-9364-aadc5822300c · outbound

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Decoupled Weight Decay Regularization

Reference 15

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Observation 05b214a7-d29e-4547-8fde-1b6edb5ec25c · outbound

This paper cites AutoPSV: Automated Process-Supervised Verifier.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation AutoPSV: Automated Process-Supervised Verifier

Reference 16

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Observation c6bff7ee-01e8-448f-aad4-043c09284bec · outbound

This paper cites Improve Mathematical Reasoning in Language Models by Automated Process Supervision.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Reference 17

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Observation bff25260-97a9-4a8b-994d-1b6105d91c7c · outbound

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Unresolved cited work

Reference 18

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Observation 7491b98c-359c-42be-a52e-1da6cee32f58 · outbound

This paper cites an unresolved cited work.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Unresolved cited work

Reference 19

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Observation a9d12f6f-81ac-49d0-8533-dac6752028eb · outbound

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 20

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Observation b797e3cb-84b5-476e-ad5d-0f64dda8771d · outbound

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 21

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Observation dd91ebac-6a9a-4b6d-9eee-107ab3d4a443 · outbound

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

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 22

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Observation ab3cd7c2-c07f-497e-9079-8349a5f866e9 · outbound

This paper cites MathScale: Scaling Instruction Tuning for Mathematical Reasoning.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation MathScale: Scaling Instruction Tuning for Mathematical Reasoning

Reference 23

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Observation 3b10e4fc-30af-4132-9a8e-1a14d43fe9e0 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Solving math word problems with process- and outcome-based feedback

Reference 24

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This paper cites Do NLP Models Know Numbers? Probing Numeracy in Embeddings.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Do NLP Models Know Numbers? Probing Numeracy in Embeddings

Reference 25

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 26

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Observation 60d052a9-dcb5-45c1-9584-344c8260366a · outbound

This paper cites Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewards.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewards

Reference 27

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Reference 28

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Observation adde7db5-ab95-4602-9232-8aedca8da5d9 · outbound

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Reference 29

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Observation 3070daa4-dcf3-4556-955e-33259d8daf33 · outbound

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Reference 30

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Observation 1f5e1919-66bc-4e72-8a73-67257a4f9690 · outbound

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge

Reference 31

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Reference 32

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 33

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning

Reference 34

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation Free Process Rewards without Process Labels

Reference 35

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Unavailable: canonical work link unavailable.

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation ProcessBench: Identifying Process Errors in Mathematical Reasoning

Reference 36

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SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation SGLang: Efficient Execution of Structured Language Model Programs

Reference 37

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Reference 38

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unresolved
no resolver link, observed 2026-08-07T15:39:20.251704Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T15:39:20.251704Z digest=sha256:e3ea4d9874a5cb7f34d0678daf05213a7a1dd0e2156d30b6f2c1561387b34b0c

Observation ac12a92f-7163-47a3-9ad4-8847b2affdde · outbound

This paper cites write newline.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation write newline

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T15:39:20.362861Z

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source=arxiv_source observed=2026-08-07T15:39:20.362861Z digest=sha256:b2f69c9a50afd572dac154329438dbccd3416f0cc7b1f51b88b7fd3d139b5317

Pith citing papers

No inbound Pith citation observations are available.