Paper Citation Record · LEDGER

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules

As of 12 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.13536.

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

pith.paper-citation-record.v1
2412.13536 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:06:11.673219Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

verified exact1
verified fuzzy5
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e9054731-9e5f-4288-a5ff-91cc0e45901d · outbound

This paper cites Benchmarking large language models’ performances for myopia care: a comparative analysis of chatgpt-3.5, chatgpt-4.0, and google bard,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Benchmarking large language models’ performances for myopia care: a comparative analysis of chatgpt-3.5, chatgpt-4.0, and google bard,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:12.234259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ad69c068-a590-4e77-be3d-242d1ad4a26d · outbound

This paper cites TL;DR: Mining Reddit to learn automatic summarization,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules TL;DR: Mining Reddit to learn automatic summarization,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:12.220066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 336325ae-52c9-42bf-a9c8-bdad54941b19 · outbound

This paper cites Teaching machines to read and comprehend,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Teaching machines to read and comprehend,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:12.201445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 58f7186e-2211-4375-a8e8-cdc633042d99 · outbound

This paper cites Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.509395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dace9266-a623-4ad5-8832-68839850a217 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.516828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 027f8d59-7b0d-4f61-b121-e9ea7343e21a · outbound

This paper cites Measuring massive multitask language understanding,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Measuring massive multitask language understanding,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.525585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91cdad9e-b5fe-4172-b82e-c96fff911022 · outbound

This paper cites MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.538653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57cd2896-daf0-49df-a92b-1b17d2f6eee4 · outbound

This paper cites WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.543772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1908fab8-15e1-48b7-a5d0-bd9bbe27e1a6 · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.548684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32612a7f-383e-452d-8a52-53b6691d3b51 · outbound

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

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.555016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9834307-3fa4-454f-b971-16208b637f7b · outbound

This paper cites Attention is all you need,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Attention is all you need,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.560067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e43b6c4-5164-4c2f-90a9-b515bdac7230 · outbound

This paper cites Language mod- els are few-shot learners,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Language mod- els are few-shot learners,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.570309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71aee01e-10a6-492c-8cdf-4f7e293e0250 · outbound

This paper cites Least-to- most prompting enables complex reasoning in large language models,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Least-to- most prompting enables complex reasoning in large language models,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.575169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 751ea197-41f7-401c-8815-30c6dcfdb05c · outbound

This paper cites Complexity-Based Prompting for Multi-Step Reasoning.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Complexity-Based Prompting for Multi-Step Reasoning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.586333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fae613d-5876-43c8-8bf3-4c1c60d91366 · outbound

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

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.590713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8140e833-fbc2-40ba-8bfd-e742d90268bb · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Finetuned Language Models Are Zero-Shot Learners

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.595336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4b3bd28-3085-4ed4-b0e3-53ba9f41b709 · outbound

This paper cites Meta-Learning: A Survey.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Meta-Learning: A Survey

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.600521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43cd946e-c2d9-456f-ad11-0221fa9eff97 · outbound

This paper cites Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.606081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f25d004-1fa5-4d42-bb5d-841010fa49b8 · outbound

This paper cites Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets ,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets ,

Reference 20

Resolution: verified exact; doi, observed 2026-08-11T13:06:11.720167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dbe42ef8-b8aa-4b56-93f5-ce775e90bd8d · outbound

This paper cites ERNIE: Enhanced Language Representation with Informative Entities.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules ERNIE: Enhanced Language Representation with Informative Entities

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.615690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c8500ab-805e-4bfa-a3c0-e729301cb80f · outbound

This paper cites Human-like systematic generalization through a meta-learning neural network,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Human-like systematic generalization through a meta-learning neural network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:12.144146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 217f661f-ee97-417e-99e7-411d4448e1b9 · outbound

This paper cites OpenAI’s ChatGPT: A Revolution in Language AI,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules OpenAI’s ChatGPT: A Revolution in Language AI,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:12.124565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f0fd0ba2-4c9e-4053-be64-11b59d90a4d9 · outbound

This paper cites Qwen Technical Report.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Qwen Technical Report

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.635447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97ed3084-0826-4ab5-a17b-c91fc5135236 · outbound

This paper cites PaLM 2 Technical Report.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules PaLM 2 Technical Report

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.641154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beb9798c-d319-4607-887b-6aa1d0b87f1c · outbound

This paper cites Palm: Scal- ing language modeling with pathways,.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Palm: Scal- ing language modeling with pathways,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.647444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 930e2593-97f6-487f-bae1-a2e92b24ae5b · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.654029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2805f43f-3944-48e4-bc84-54e91b33d2b8 · outbound

This paper cites Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.662121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5cba4c-958a-4700-8a87-f3a8be1d048a · outbound

This paper cites Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Recursion of Thought: A Divide-and-Conquer Approach to Multi-Context Reasoning with Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.667656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2252685f-8636-4ed1-ad06-4a373b6eae4a · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Training Verifiers to Solve Math Word Problems

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.673219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66a8b758-419f-411b-a1bf-c5b88d61bb29 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Measuring Massive Multitask Language Understanding

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.531607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74934d38-5d71-4db9-8e1d-d612cfbeb940 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

MetaRuleGPT: Recursive Numerical Reasoning of Language Models Trained with Simple Rules Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:11.581895Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.