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Paper Citation Record · LEDGER

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving

As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.00031.

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

pith.paper-citation-record.v1
2505.00031 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:53:51.837228Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

31 of 31 outbound references displayed

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  • verified fuzzy2
  • unresolved29
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0ab0af00-0203-4b81-b785-11a0652a0886 · outbound

This paper cites GPT-4 Technical Report.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-16T05:53:51.715403Z digest=sha256:a3a838829abf61e7f839f614558b78f0901043afde69cd2ad5fdbd18edbdb4fb

Observation 39a24eeb-1631-4de6-b590-2bcd7168e6d0 · outbound

This paper cites The Llama 3 Herd of Models.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving The Llama 3 Herd of Models

Reference 6

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source=pdf_text observed=2026-08-16T05:53:51.738521Z digest=sha256:d1acf86bb9ec6a12b6b9ccc695cce9242a49253d7bee21e2c24f49a6239e7dc1

Observation 6358ce5b-8da5-44df-a18b-00271ea8c8af · outbound

This paper cites an unresolved cited work.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Unresolved cited work

Reference 7

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T05:53:51.837228Z digest=sha256:8d2d4bbad872403bf30acf06abe4f0d75e09a5dac1130e486d889e74c3e44466

Observation 3f3a6948-d32a-4551-8dac-14bb9c7cd380 · outbound

This paper cites Think before you speak: Training Language Models With Pause Tokens.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Think before you speak: Training Language Models With Pause Tokens

Reference 8

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source=pdf_text observed=2026-08-16T05:53:51.747135Z digest=sha256:ecca501e28d1a0202d93a83a01d30d416022b923efc4b908f2106f856808b58e

Observation 3c770e85-b40e-4e74-9a33-fb49f8825862 · outbound

This paper cites Reinforced Self-Training (ReST) for Language Modeling.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Reinforced Self-Training (ReST) for Language Modeling

Reference 9

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source=pdf_text observed=2026-08-16T05:53:51.750876Z digest=sha256:c3098a1d7e8ad259436c6281219ed9b39c6efb7ac31b598a5448cdab7bb68988

Observation d5a7d881-c75e-4b4e-b94f-138901121c4e · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Measuring Massive Multitask Language Understanding

Reference 10

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source=pdf_text observed=2026-08-16T05:53:51.755225Z digest=sha256:bba68ba73f2358bc5bd4d8c675918c22fc33bdc287d69d8c66e9f264ff8e1720

Observation 36cebb4a-fe61-4f14-a0b7-78133d42363f · outbound

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

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Measuring Mathematical Problem Solving With the MATH Dataset

Reference 11

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source=pdf_text observed=2026-08-16T05:53:51.759395Z digest=sha256:01c5efe954a403b6ad740bbbe51deed2511af4476de6b5ef00d326108f7d1001

Observation c1461a95-2c4a-43ef-9505-210ab9cc8b96 · outbound

This paper cites Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs

Reference 12

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source=pdf_text observed=2026-08-16T05:53:51.762891Z digest=sha256:bd3f2e881300bdbb6aa47beb8314c22a305af99547b4e0e25b7249bfb489d275

Observation 974ae1e5-e596-4248-851e-4af61fd8b113 · outbound

This paper cites Large Language Models Can Self-Improve.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Large Language Models Can Self-Improve

Reference 13

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source=pdf_text observed=2026-08-16T05:53:51.767042Z digest=sha256:94412b271ff1acdcc98fccaafb029de36b90b7aa49211ad03bcf552b7509eef1

Observation 13a0a7a4-b23b-4a0e-a922-1a6ed87dade6 · outbound

This paper cites Self-Reflection in LLM Agents: Effects on Problem-Solving Performance.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Self-Reflection in LLM Agents: Effects on Problem-Solving Performance

Reference 14

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source=pdf_text observed=2026-08-16T05:53:51.770949Z digest=sha256:4091e4784ae048b78fc5e74dce78541229a02f85d6b97feea3488245f5375408

Observation c0da9749-2e12-439c-b471-e5f0f21f102a · outbound

This paper cites Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Reference 16

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source=pdf_text observed=2026-08-16T05:53:51.778603Z digest=sha256:c585d6184ddcf33f705b3dafb00d5cdf8c5db324527c6b9cbab6eff0c09b9471

Observation 8ef6ab95-f4de-4e23-95e2-88f0341a69ad · outbound

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

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 17

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source=pdf_text observed=2026-08-16T05:53:51.782163Z digest=sha256:a86d964d3ed8832d36b9bd40813d0b703160eccb42cb3d492a401a834acd4beb

Observation 93ac5fe7-0ce6-41ce-beb7-997ced2abae7 · outbound

This paper cites Learning to Learn: Meta-Critic Networks for Sample Efficient Learning.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Learning to Learn: Meta-Critic Networks for Sample Efficient Learning

Reference 18

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source=pdf_text observed=2026-08-16T05:53:51.786072Z digest=sha256:03bebe4534ff429d85afbb3ffa8850e6989961726bbb6725461d9cdf6fda7e4c

Observation 1b0949c7-f089-451e-8559-dabebec686a6 · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 19

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source=pdf_text observed=2026-08-16T05:53:51.790373Z digest=sha256:2b6dbf5bb85adc78245661e8681abb63a6de42fb840de3edcfc63e8777e0079b

Observation f82e66aa-83ca-47e3-a024-27fc3785c3ab · outbound

This paper cites Qwen2 Technical Report.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Qwen2 Technical Report

Reference 21

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source=pdf_text observed=2026-08-16T05:53:51.797997Z digest=sha256:f9b2cf5171f0e6384eba330f62751bdb3c7c84d6177f9a040d0147a86696a6b4

Observation 546ef39a-7d6b-4294-b15d-ffd82a5cadf7 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving ReAct: Synergizing Reasoning and Acting in Language Models

Reference 22

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source=pdf_text observed=2026-08-16T05:53:51.801618Z digest=sha256:dff54bb5c1ea6744d693f7521ec08978bf4e371400facad8e7d2bdfc5acd5d7f

Observation eae6b90c-700f-43e0-b163-8bfa1e4f4c1c · outbound

This paper cites Scaling Relationship on Learning Mathematical Reasoning with Large Language Models.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Scaling Relationship on Learning Mathematical Reasoning with Large Language Models

Reference 24

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source=pdf_text observed=2026-08-16T05:53:51.809247Z digest=sha256:ce037a63bc78e5876c30af4697de0fe1d59e87b2da63a53c96188d6613ebedcb

Observation 85216d06-567b-449e-aeb1-7fc105fef085 · outbound

This paper cites Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 25

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source=pdf_text observed=2026-08-16T05:53:51.812868Z digest=sha256:9d4ae30b2b1b64a5d11fd447210158e74f73d01c68941d4a13ba07e5e0e52e15

Observation fca9c069-583a-44eb-bb2e-42d79ef81be5 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 26

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source=pdf_text observed=2026-08-16T05:53:51.816831Z digest=sha256:84e0d36d66d3c98dcfc2487eb00b1f6d72e5022e030cda9726f57d96335a05dd

Observation 7287a431-953f-4da7-a8b8-a33671cbae4b · outbound

This paper cites A Survey of Large Language Models.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving A Survey of Large Language Models

Reference 27

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source=pdf_text observed=2026-08-16T05:53:51.821254Z digest=sha256:fcc602a09224b8647fbb6975082a79a9305eedac3ddb38243a6dc53a5f55ca25

Observation 88999ce9-d762-4956-8171-4525a8390e5f · outbound

This paper cites Progressive-Hint Prompting Improves Reasoning in Large Language Models.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Progressive-Hint Prompting Improves Reasoning in Large Language Models

Reference 28

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source=pdf_text observed=2026-08-16T05:53:51.825376Z digest=sha256:b5207e5408f62556054e2e39ab1948b498951b3db15fe0e6a79364a7c78c9440

Observation 8d61f70e-92b2-4a6c-a948-18a5f2a60f40 · outbound

This paper cites Different ways of utilizing inference compute.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Different ways of utilizing inference compute

Reference 29

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T05:53:51.829307Z digest=sha256:bcd51a516652dbe7b2622effbcdfd551447e63129699552d26d0033a53c186c0

Observation 8377f31a-d38a-4593-a7c8-0411e43c56ed · outbound

This paper cites an unresolved cited work.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Unresolved cited work

Reference 30

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source=pdf_text observed=2026-08-16T05:53:51.833270Z digest=sha256:3d8ee1600312ee8655398c087e970d9ede076a272ad4bc8ff6b89841bb0afa55

Observation d3e294ba-301f-4a9b-9aaf-286911abd31f · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 2010

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source=pdf_text observed=2026-08-16T05:53:51.794385Z digest=sha256:3661228acc24c88393a0ea379e82638469b25808adef76f32acf73f143c9e715

Observation c1563b3f-7503-4da5-b4bb-7c45086bfe0a · outbound

This paper cites Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge

Reference 2017

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source=pdf_text observed=2026-08-16T05:53:51.742773Z digest=sha256:0afc8bcc639b6c44183fd3a3dabb2f9452fc9020fbd0cce714f61611ac3aa80d

Observation b37a86b8-8b1b-440c-b67e-31eae90f012f · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Proximal Policy Optimization Algorithms

Reference 2018

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source=pdf_text observed=2026-08-16T05:53:51.774784Z digest=sha256:d7a2105168f0ce58f12d25e3a870e3e203449a41ae1fd11bf879208d616258d4

Observation 79890b80-e686-4967-88d3-b5cbb1aab6d7 · outbound

This paper cites Re-ReST: Reflection-Reinforced Self-Training for Language Agents.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Re-ReST: Reflection-Reinforced Self-Training for Language Agents

Reference 2019

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source=pdf_text observed=2026-08-16T05:53:51.734199Z digest=sha256:71e07e82c15e39d6ed51884c7972c4faebc36da9e64733754ca6856f5e5d80af

Observation 5571f14e-8568-4e4b-b151-01e6454d84b5 · outbound

This paper cites Self-playing Adversarial Language Game Enhances LLM Reasoning.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Self-playing Adversarial Language Game Enhances LLM Reasoning

Reference 2020

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source=pdf_text observed=2026-08-16T05:53:51.724793Z digest=sha256:d4362177cac90038a460c8a9cc3885c214d25d7d29e86a312e19b2884ad8a73d

Observation 28b1e9f6-3319-4957-bff2-6a4f52ded961 · outbound

This paper cites Lumos: Learning agents with unified data, modular design, and open-source llms.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Lumos: Learning agents with unified data, modular design, and open-source llms

Reference 2022

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T05:53:51.805636Z digest=sha256:07e9b0c5213efb955da6386e6526c7da22cb2991e8b0afc7affff686ded46592

Observation e932a855-25f0-4f62-8bb9-5d35fa4b3049 · outbound

This paper cites Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling

Reference 2023

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source=pdf_text observed=2026-08-16T05:53:51.720400Z digest=sha256:142721f21ac55f98a459ae543ae2f9ba597bdfaaca33c2df0254eb75f56339a3

Observation eb59e3fb-77c8-444b-aa9d-1e0c957ecb48 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 2024

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source=pdf_text observed=2026-08-16T05:53:51.729135Z digest=sha256:d99ad466f25fdcc805b279baad73eac39cecd241cc69621a123418977fdcca25

Pith citing papers

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