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Paper Citation Record · LEDGER

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement

As of 16 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2608.13129.

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

pith.paper-citation-record.v1
2608.13129 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:17:47.859203Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

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External citation measurements

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Outbound references

Observation 7b06d7fb-b5f8-4bdc-8053-5107dd05a0e1 · outbound

This paper cites Large Language Models for Mathematical Reasoning: Progresses and Challenges.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Large Language Models for Mathematical Reasoning: Progresses and Challenges

Reference 1

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Observation 10660d69-121f-4372-93e8-4f3bf446ab57 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Training Verifiers to Solve Math Word Problems

Reference 4

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Observation 653e56fd-e6b2-464a-97b0-c6e13a14f11a · outbound

This paper cites Language Models Represent Space and Time.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Language Models Represent Space and Time

Reference 8

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Observation ca6c5c39-b24d-4ce7-94d6-d431765c593b · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 10

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Observation be439f0b-5a90-4fb6-b8a0-38df3a2fbd67 · outbound

This paper cites Let's Verify Step by Step.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Let's Verify Step by Step

Reference 11

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Observation 32b4bf20-268b-4858-8c61-0e73ce9e1d8d · outbound

This paper cites FinGPT: Democratizing Internet-scale Data for Financial Large Language Models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement FinGPT: Democratizing Internet-scale Data for Financial Large Language Models

Reference 12

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Observation 9edbd3f0-7f23-44f6-9c88-02bf4a3250be · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 14

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Observation c2972dbe-339a-4972-b7a5-f4677ee152aa · outbound

This paper cites Investigating the Limitations of Transformers with Simple Arithmetic Tasks.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Investigating the Limitations of Transformers with Simple Arithmetic Tasks

Reference 15

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Observation 4a4c436f-f42b-4fe5-af57-c670a212bcfc · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 16

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Observation 2d9833d8-87f4-46cd-b425-2c7ccb9f1b07 · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement YaRN: Efficient Context Window Extension of Large Language Models

Reference 17

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Observation 44add5cc-345b-4452-a27b-5c0a2b5dad3f · outbound

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

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 18

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Observation c9f5ede2-2d16-493f-8e4d-5088cc97eab6 · outbound

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

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement MathScale: Scaling Instruction Tuning for Mathematical Reasoning

Reference 19

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Observation 1ffe5a15-6abf-401c-b171-0b77ec014f43 · outbound

This paper cites Galactica: A Large Language Model for Science.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Galactica: A Large Language Model for Science

Reference 20

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Observation 209ea593-85d5-4ac5-b81c-daf6192d45ef · outbound

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

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Solving math word problems with process- and outcome-based feedback

Reference 21

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Observation 0225a707-0559-4b98-8a24-61ccf1d970c6 · outbound

This paper cites Number Cookbook: Number Understanding of Language Models and How to Improve It.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Number Cookbook: Number Understanding of Language Models and How to Improve It

Reference 22

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Observation 2f40f32f-55b3-4513-ac66-2457a082f870 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement BloombergGPT: A Large Language Model for Finance

Reference 23

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Observation 70939265-7a32-4bc5-bce6-d759deca8545 · outbound

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

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Measuring Mathematical Problem Solving With the MATH Dataset

Reference 1990

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Observation ad028776-5aac-4b20-8f64-281fd83c127f · outbound

This paper cites The Llama 3 Herd of Models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement The Llama 3 Herd of Models

Reference 2011

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Observation 83ca18ed-267d-4d0c-ba39-c6da0220b4e5 · outbound

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

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2021

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Observation fabd9102-37f0-4681-84e0-ca9ee6280816 · outbound

This paper cites Emergent autonomous scientific research capabilities of large language models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Emergent autonomous scientific research capabilities of large language models

Reference 2022

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Observation 9363f353-05d8-45e4-8d6c-6b8e688ad147 · outbound

This paper cites xVal: A continuous number encoding for large language models.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement xVal: A continuous number encoding for large language models

Reference 2023

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Observation 1ac7a6e7-5c3e-4039-970a-fd576ab5c714 · outbound

This paper cites Transformers Can Do Arithmetic with the Right Embeddings.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Transformers Can Do Arithmetic with the Right Embeddings

Reference 2024

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Observation 9287a38c-ce4a-456e-aa35-3325e8ccd5d4 · outbound

This paper cites Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next.

Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 2025

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Pith citing papers

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