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Paper Citation Record · LEDGER

A Survey on Large Language Models for Mathematical Reasoning

As of 14 August 2026, this Paper Citation Record lists 100 of 119 outbound references and 8 inbound Pith citation observations for arXiv:2506.08446.

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

pith.paper-citation-record.v1
2506.08446 v1

Coverage vector

measured 100 of 119 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:47.638235Z

measured 108 of 108 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:33:18.492951Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:15:44.913239Z

Reference resolution

100 of 119 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved97
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 92858079-d068-4c85-b5aa-fde45d646d5c · outbound

This paper cites MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms.

A Survey on Large Language Models for Mathematical Reasoning MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 2

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Observation 850786d7-7666-4924-99e1-566927d8c5ec · outbound

This paper cites A General Language Assistant as a Laboratory for Alignment.

A Survey on Large Language Models for Mathematical Reasoning A General Language Assistant as a Laboratory for Alignment

Reference 7

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source=pdf_text observed=2026-08-07T05:14:44.481427Z digest=sha256:7f48c827b7a14e5a55565845412e1cc560f0e03efc858102f5b5f1d3d3b23724

Observation 423ddb73-26b3-45a4-9256-21baaab7ffb0 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

A Survey on Large Language Models for Mathematical Reasoning Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 9

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source=pdf_text observed=2026-08-07T05:14:44.639711Z digest=sha256:2fa7e0bc7d7f86e881daedd0218b1684bdd80b2fed317e7eb2512dc933879db9

Observation d7f4873e-9094-4262-84c6-2291596e9148 · outbound

This paper cites Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning.

A Survey on Large Language Models for Mathematical Reasoning Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning

Reference 11

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source=pdf_text observed=2026-08-07T05:14:44.763862Z digest=sha256:ba04a317abc91dd1e6e5de64f341373ceb69d41f1271a6278a4238034138d6f9

Observation cf7e1040-bede-4c42-b203-413bc87f8b2f · outbound

This paper cites Reasoning Models Don't Always Say What They Think.

A Survey on Large Language Models for Mathematical Reasoning Reasoning Models Don't Always Say What They Think

Reference 15

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source=pdf_text observed=2026-08-07T05:14:45.045847Z digest=sha256:92d3ba55063b6f4db9b63342ae3e579924631d51d5df55bc3491389e127c605d

Observation 9c986e83-df6e-4663-9a83-cdbc962c7a3f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

A Survey on Large Language Models for Mathematical Reasoning Training Verifiers to Solve Math Word Problems

Reference 16

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source=pdf_text observed=2026-08-07T05:14:45.138451Z digest=sha256:f7299c18ff096dfb6b52d8671d5b0918492472457a76998c83fe1cd40565a1c4

Observation 6a227e99-86ec-48fe-85ab-3e4f0d033d91 · outbound

This paper cites MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning.

A Survey on Large Language Models for Mathematical Reasoning MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning

Reference 18

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source=pdf_text observed=2026-08-07T05:14:45.261962Z digest=sha256:5022274f58d94d58da062459815b01e8c23add44097f9a1f766b88046ab7739a

Observation af8f3ab8-7235-4853-a28a-a49f70889915 · outbound

This paper cites Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving.

A Survey on Large Language Models for Mathematical Reasoning Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving

Reference 19

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source=pdf_text observed=2026-08-07T05:14:45.303181Z digest=sha256:8868d3f8cf3c7535c8d35875e37a43fd865bb490586071964edf6ae0c5f2eabc

Observation e57622d0-4ce0-4135-96c3-45e919579e0b · outbound

This paper cites Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch.

A Survey on Large Language Models for Mathematical Reasoning Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch

Reference 20

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source=pdf_text observed=2026-08-07T05:14:45.384225Z digest=sha256:b75ea688745842eb29a5a77516db6114522d4d52203ed21efb476b0de6244d19

Observation dd362b00-3279-4185-b41b-39c9e8c67466 · outbound

This paper cites SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation.

A Survey on Large Language Models for Mathematical Reasoning SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation

Reference 21

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source=pdf_text observed=2026-08-07T05:14:45.466712Z digest=sha256:64089b15eb467bdc4572229b783c240f40239b7ed4979db4880d48222e61f8d0

Observation e162aed7-c317-45d9-a1f4-cfce0fc533d8 · outbound

This paper cites The Llama 3 Herd of Models.

A Survey on Large Language Models for Mathematical Reasoning The Llama 3 Herd of Models

Reference 22

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source=pdf_text observed=2026-08-07T05:14:45.523595Z digest=sha256:5fc76f847828a7334405eb4e50126c60a16d06a71f9f5a69cca9884fd50e5498

Observation 73df34a5-358e-4038-b81e-b0c55803c34f · outbound

This paper cites Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo.

A Survey on Large Language Models for Mathematical Reasoning Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo

Reference 24

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source=pdf_text observed=2026-08-07T05:14:45.672892Z digest=sha256:a0bfb13c262ee808af957c60621026ee1b81332ea66da1981bcd5eaf782624be

Observation aebf84d4-8319-4a43-92aa-06539e9b5cb5 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

A Survey on Large Language Models for Mathematical Reasoning Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 27

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source=pdf_text observed=2026-08-07T05:14:45.862320Z digest=sha256:cb20e54ea29a105992d09981d9e9b9d149b7cd830a69ea66dc14db2b85d20ce9

Observation 7dec2777-5ca8-414d-819e-7e8cc6486355 · outbound

This paper cites Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs.

A Survey on Large Language Models for Mathematical Reasoning Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs

Reference 28

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Observation dd7418e1-cbc1-491f-965f-cacd444a1bd3 · outbound

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

A Survey on Large Language Models for Mathematical Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 30

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source=pdf_text observed=2026-08-07T05:14:46.022492Z digest=sha256:d96a17a75e4e92ee211758236db00a6aa34c7cc5dc2ebc183cc5738f58c9ad81

Observation 43ba7354-210c-4d19-9a5a-b8be636546a3 · outbound

This paper cites Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs.

A Survey on Large Language Models for Mathematical Reasoning Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs

Reference 31

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source=pdf_text observed=2026-08-07T05:14:46.082664Z digest=sha256:ec06aeea2cf0a6721f54c5b7857a2e27de966fbe1b590529f93a684c99e67522

Observation 6bbbcc34-bb4b-4f3a-abe9-304fe9f00ac8 · outbound

This paper cites an unresolved cited work.

A Survey on Large Language Models for Mathematical Reasoning Unresolved cited work

Reference 32

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source=pdf_text observed=2026-08-07T05:14:46.160046Z digest=sha256:0120a4e5ac42f20843768db413fb3dade369654a921ad564a6db12c9d9e69dd3

Observation 989216b6-51eb-4a3c-9f83-c3722f59e5a3 · outbound

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

A Survey on Large Language Models for Mathematical Reasoning OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 33

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Observation a5cba8e5-883a-4f8f-bdf1-5dabade57e75 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

A Survey on Large Language Models for Mathematical Reasoning Measuring Massive Multitask Language Understanding

Reference 34

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source=pdf_text observed=2026-08-07T05:14:46.247204Z digest=sha256:a2ed05a6831c2bdfe633bf662562532345c80fee8abbe9a986641a9f57694585

Observation 03bc3294-0ae2-4888-86e6-6f37b4863c03 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

A Survey on Large Language Models for Mathematical Reasoning Distilling the Knowledge in a Neural Network

Reference 35

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Observation 88f00f23-29fb-4bf8-94ff-85ecf7829686 · outbound

This paper cites V-STaR: Training Verifiers for Self-Taught Reasoners.

A Survey on Large Language Models for Mathematical Reasoning V-STaR: Training Verifiers for Self-Taught Reasoners

Reference 36

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source=pdf_text observed=2026-08-07T05:14:46.382496Z digest=sha256:5feb6734a327484e9926618b80c57b9d2c34ab3f419a4ad84f43a97bc29d70df

Observation ba5265b3-05fc-496a-89d6-1fc3cfa8eafd · outbound

This paper cites REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization.

A Survey on Large Language Models for Mathematical Reasoning REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization

Reference 37

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source=pdf_text observed=2026-08-07T05:14:46.417579Z digest=sha256:cb43369b623fa3005d34cb7d940e66e376de4b09fa36cba04136b5fa240d4378

Observation 91187198-02ae-4605-8a1c-41e7c323ee0b · outbound

This paper cites Huang, S.

A Survey on Large Language Models for Mathematical Reasoning Huang, S

Reference 38

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Observation 4a3e3d71-fb7f-4721-95c4-f2d4567803fb · outbound

This paper cites Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning.

A Survey on Large Language Models for Mathematical Reasoning Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning

Reference 39

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Observation 7dad16ad-24f0-45dc-bdfe-1de6f2882d3c · outbound

This paper cites BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation.

A Survey on Large Language Models for Mathematical Reasoning BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation

Reference 40

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Observation 98dc69be-baa6-438e-8347-93f9b4bf7439 · outbound

This paper cites Controlling Large Language Model with Latent Actions.

A Survey on Large Language Models for Mathematical Reasoning Controlling Large Language Model with Latent Actions

Reference 41

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Observation 92276698-cada-47d6-9c23-96e35c64541b · outbound

This paper cites Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning.

A Survey on Large Language Models for Mathematical Reasoning Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning

Reference 42

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Observation 028dd44c-b983-4b28-8faf-c48757077907 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

A Survey on Large Language Models for Mathematical Reasoning Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 43

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Observation 19f48c8d-9eca-4a63-b045-e5846f9847a8 · outbound

This paper cites FastText.zip: Compressing text classification models.

A Survey on Large Language Models for Mathematical Reasoning FastText.zip: Compressing text classification models

Reference 44

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Observation b5831ea4-39e8-4076-8209-7b0759f037f6 · outbound

This paper cites Koncel-Kedziorski, S.

A Survey on Large Language Models for Mathematical Reasoning Koncel-Kedziorski, S

Reference 46

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Observation e5b75ff6-312d-4c46-bca0-bf7366a1a13e · outbound

This paper cites Kwiatkowski, E.

A Survey on Large Language Models for Mathematical Reasoning Kwiatkowski, E

Reference 47

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Observation 57ddbc18-1394-495a-918c-997dabc16c9a · outbound

This paper cites Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping.

A Survey on Large Language Models for Mathematical Reasoning Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Reference 49

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Observation 6b9f175b-09bf-4290-a1c0-5860167f218d · outbound

This paper cites Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference.

A Survey on Large Language Models for Mathematical Reasoning Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference

Reference 50

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Observation d380343b-e0fa-4e43-85c3-f8f5eb1ed691 · outbound

This paper cites Solving Quantitative Reasoning Problems with Language Models.

A Survey on Large Language Models for Mathematical Reasoning Solving Quantitative Reasoning Problems with Language Models

Reference 51

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Observation 6dee0255-b8b4-4be3-b2bc-7589364559df · outbound

This paper cites Common 7B Language Models Already Possess Strong Math Capabilities.

A Survey on Large Language Models for Mathematical Reasoning Common 7B Language Models Already Possess Strong Math Capabilities

Reference 52

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Observation aca1611a-9a7e-42cc-aac2-3e1b73269c53 · outbound

This paper cites ToRL: Scaling Tool-Integrated RL.

A Survey on Large Language Models for Mathematical Reasoning ToRL: Scaling Tool-Integrated RL

Reference 53

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source=pdf_text observed=2026-08-07T05:14:47.327873Z digest=sha256:5a6d2b9fb25102ce25d0acde84bb77305073a3ec8870ad76e8e5d9dbbf288816

Observation a63cf0ac-1907-4a3d-8d7d-f087791850cb · outbound

This paper cites From System 1 to System 2: A Survey of Reasoning Large Language Models.

A Survey on Large Language Models for Mathematical Reasoning From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 54

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Observation 09e79395-936d-46a7-98ee-7cec07e863ac · outbound

This paper cites Liang, W.

A Survey on Large Language Models for Mathematical Reasoning Liang, W

Reference 55

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source=pdf_text observed=2026-08-07T05:14:47.337519Z digest=sha256:f15bb4d71ba2e8aba7901810dd1ef014ed01cbdaa5d72925c1d968b6c1b293da

Observation 1df2c8fb-993f-448a-9222-7358b3fea192 · outbound

This paper cites Let's Verify Step by Step.

A Survey on Large Language Models for Mathematical Reasoning Let's Verify Step by Step

Reference 56

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Observation 90df85d0-0cb4-48ec-a842-12ca096028a7 · outbound

This paper cites On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization.

A Survey on Large Language Models for Mathematical Reasoning On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization

Reference 57

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Observation 403d8a32-ff29-4590-a22e-5aee1a14e224 · outbound

This paper cites Rho-1: Not All Tokens Are What You Need.

A Survey on Large Language Models for Mathematical Reasoning Rho-1: Not All Tokens Are What You Need

Reference 58

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Observation cd0b74e3-e7f8-4123-afa6-7e9b08e39471 · outbound

This paper cites TinyGSM: achieving >80% on GSM8k with small language models.

A Survey on Large Language Models for Mathematical Reasoning TinyGSM: achieving >80% on GSM8k with small language models

Reference 59

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Observation 4cbe8432-6f43-47d5-974b-ba53821b4941 · outbound

This paper cites Augmenting Math Word Problems via Iterative Question Composing.

A Survey on Large Language Models for Mathematical Reasoning Augmenting Math Word Problems via Iterative Question Composing

Reference 60

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Observation 34a0950d-c0c3-4a42-be75-21193a08aebf · outbound

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

A Survey on Large Language Models for Mathematical Reasoning Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Reference 61

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Observation 51d62f74-a62d-42eb-aa32-a95cc4babe5a · outbound

This paper cites A Survey in Mathematical Language Processing.

A Survey on Large Language Models for Mathematical Reasoning A Survey in Mathematical Language Processing

Reference 62

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source=pdf_text observed=2026-08-07T05:14:47.368782Z digest=sha256:a29ae4c776ce47dfafbad49f233fc1751b22d6ad5769cb64d3715a390e59fa66

Observation 17e0d335-3124-4d7f-94a1-32fe35b845e5 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

A Survey on Large Language Models for Mathematical Reasoning SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 63

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Observation eaca2e78-73e7-4e72-941b-d16778da5f75 · outbound

This paper cites Teaching language models to support answers with verified quotes.

A Survey on Large Language Models for Mathematical Reasoning Teaching language models to support answers with verified quotes

Reference 64

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source=pdf_text observed=2026-08-07T05:14:47.377791Z digest=sha256:7594018d715757aacf0905eeb174bc2415d3ca6ce364e21c2865a304aa358b0c

Observation 74f17dfe-d8b2-4955-9c65-d04da26f80e8 · outbound

This paper cites A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers.

A Survey on Large Language Models for Mathematical Reasoning A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers

Reference 65

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source=pdf_text observed=2026-08-07T05:14:47.382134Z digest=sha256:97fb923ed1d999e90ca842b2846a7ca664d6d6b4f7dacba692c5752ee4e6a7e3

Observation 5e987609-d3ad-4f91-abe1-e7e989bd362e · outbound

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

A Survey on Large Language Models for Mathematical Reasoning GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 66

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source=pdf_text observed=2026-08-07T05:14:47.386262Z digest=sha256:29050f1e27c19319494564c92c60b7acc7c33fa92518019f0febb18d7250a392

Observation 020d6ff9-4b00-424b-b5e8-73c30d22a992 · outbound

This paper cites Mishra, M.

A Survey on Large Language Models for Mathematical Reasoning Mishra, M

Reference 67

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source=pdf_text observed=2026-08-07T05:14:47.390452Z digest=sha256:7b7c0b2aabf81febb1cc7fd6ee9fd4ee7723a7eb9d6a320693252217fbf95342

Observation 0ae512b6-e427-4fd1-a33d-869871d40704 · outbound

This paper cites s1: Simple test-time scaling.

A Survey on Large Language Models for Mathematical Reasoning s1: Simple test-time scaling

Reference 68

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source=pdf_text observed=2026-08-07T05:14:47.394917Z digest=sha256:f1d17f3e5f97ea178be95c6ef46d413882f4f5e9f2c7a3b2df0bc6294462a273

Observation 0879db80-99b2-4043-a290-81b8f3154c19 · outbound

This paper cites Are NLP Models really able to Solve Simple Math Word Problems?.

A Survey on Large Language Models for Mathematical Reasoning Are NLP Models really able to Solve Simple Math Word Problems?

Reference 69

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source=pdf_text observed=2026-08-07T05:14:47.399427Z digest=sha256:3bcd87567d6802a22a4ff76e4f5c8279f8c958b22cdcaa5680deba8b80ca0329

Observation 37a248a9-d6a0-4500-a2e7-2bb4d3e7da51 · outbound

This paper cites ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement.

A Survey on Large Language Models for Mathematical Reasoning ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement

Reference 70

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Observation abc2e1c9-5ec0-4dc3-8259-8f4ce574e109 · outbound

This paper cites an unresolved cited work.

A Survey on Large Language Models for Mathematical Reasoning Unresolved cited work

Reference 71

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source=pdf_text observed=2026-08-07T05:14:47.408849Z digest=sha256:0f071a55aa53c00a24264ea8fe7c901d2a4e0968bd8eae40b38f50beb443b41a

Observation c41213d8-5b64-47db-a922-48ff14c5eb63 · outbound

This paper cites Proximal Policy Optimization Algorithms.

A Survey on Large Language Models for Mathematical Reasoning Proximal Policy Optimization Algorithms

Reference 72

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source=pdf_text observed=2026-08-07T05:14:47.412936Z digest=sha256:ad0244208cafa39c647047dc1da2cc30cb750be102710dd81e0185af6e922d89

Observation 2d292879-49e9-42b3-944b-7c8604848b27 · outbound

This paper cites Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

A Survey on Large Language Models for Mathematical Reasoning Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Reference 73

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source=pdf_text observed=2026-08-07T05:14:47.417243Z digest=sha256:bf54ed6dd6ff16e0315f7a805c18d0bea3217ac8a2193edfe90f686130c7afdd

Observation 362fc352-03c7-401b-9fc7-b3ea6258bbc9 · outbound

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

A Survey on Large Language Models for Mathematical Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 74

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source=pdf_text observed=2026-08-07T05:14:47.421531Z digest=sha256:427a821216c596c952342dcf33718909338867ab59581c4aeaec6ce4b17b2f55

Observation 9fce8929-7c3b-4dd7-9c7f-21a23129442c · outbound

This paper cites Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search.

A Survey on Large Language Models for Mathematical Reasoning Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search

Reference 75

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source=pdf_text observed=2026-08-07T05:14:47.425928Z digest=sha256:d2374ef8435c3f5de630ccfe93b1a1abe989560ad6e311bce415b90bb60df602

Observation b90f843b-3951-4437-95a1-e045abff2fbc · outbound

This paper cites LLM With Tools: A Survey.

A Survey on Large Language Models for Mathematical Reasoning LLM With Tools: A Survey

Reference 76

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source=pdf_text observed=2026-08-07T05:14:47.430521Z digest=sha256:c8d706585d764b77bb8ad5583f549f666c20e846bc4ab21ebb0e1afcb6caceeb

Observation 979d1d98-7b39-45f1-8d6f-5f93bc93edda · outbound

This paper cites Language Models are Multilingual Chain-of-Thought Reasoners.

A Survey on Large Language Models for Mathematical Reasoning Language Models are Multilingual Chain-of-Thought Reasoners

Reference 77

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Observation 59e55a45-8989-4177-bb30-51b3d0214426 · outbound

This paper cites To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning.

A Survey on Large Language Models for Mathematical Reasoning To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Reference 79

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source=pdf_text observed=2026-08-07T05:14:47.445151Z digest=sha256:78f96e7f90db412cf9a5d50686c07b6ea89faa4aa548d740cae2a48614e45f67

Observation d8ca55c6-c610-4492-a39b-43abdc6ec8b6 · outbound

This paper cites Learning to summarize from human feedback.

A Survey on Large Language Models for Mathematical Reasoning Learning to summarize from human feedback

Reference 80

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source=pdf_text observed=2026-08-07T05:14:47.449825Z digest=sha256:6411000fa4ccd0c70ff823f321d23cdaa72d6763de951e1e4e2ebda2a3607c56

Observation fcbb9b44-03ee-4613-afc5-70ca7e2c66c1 · outbound

This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.

A Survey on Large Language Models for Mathematical Reasoning ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Reference 82

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source=pdf_text observed=2026-08-07T05:14:47.460598Z digest=sha256:afff163790300e98b79d4209c5fd6cb5266fc91f19b1c393d23248bd1732f4cc

Observation 6112e44e-c844-4656-900a-780d7406dd0b · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

A Survey on Large Language Models for Mathematical Reasoning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 83

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source=pdf_text observed=2026-08-07T05:14:47.465567Z digest=sha256:d70844f4f8421d396accf6b7a1f7e62788a0908bf78214b0c35ca899e8f7e7d5

Observation 39fed019-8d97-404a-8f67-9f4e88353b29 · outbound

This paper cites OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data.

A Survey on Large Language Models for Mathematical Reasoning OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data

Reference 84

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source=pdf_text observed=2026-08-07T05:14:47.470616Z digest=sha256:d19f4bdff19f76603461fa901b2bb495275d1c9df82585f78846c5eeb8185377

Observation ecab038f-f639-4b9f-8f6b-0ff5f54fa06d · outbound

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

A Survey on Large Language Models for Mathematical Reasoning Planning In Natural Language Improves LLM Search For Code Generation

Reference 86

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source=pdf_text observed=2026-08-07T05:14:47.479998Z digest=sha256:5392979f31e3d318b1c8003af892822dc265d8105430701641a4ec972086eb23

Observation 54fb2e41-39d7-4389-8afe-9e00bba53076 · outbound

This paper cites Chain-of-Thought Reasoning Without Prompting.

A Survey on Large Language Models for Mathematical Reasoning Chain-of-Thought Reasoning Without Prompting

Reference 87

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source=pdf_text observed=2026-08-07T05:14:47.484973Z digest=sha256:ef0d532402a6c3022e0fdc63155ec91efe39c7fa75ffa7d271b8970c11c5e890

Observation 436646e0-5c66-404e-8563-54bd7f7feea6 · outbound

This paper cites an unresolved cited work.

A Survey on Large Language Models for Mathematical Reasoning Unresolved cited work

Reference 88

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source=pdf_text observed=2026-08-07T05:14:47.489293Z digest=sha256:614cf8a04138f1a4007c346192875e4e890bcf3b20228b2fdf214714500be07f

Observation 5071db1e-a347-4308-b909-6cc07d7a0e06 · outbound

This paper cites Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs.

A Survey on Large Language Models for Mathematical Reasoning Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs

Reference 89

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source=pdf_text observed=2026-08-07T05:14:47.493623Z digest=sha256:5465fea19f390fb72bbf76981b14aa12fef0a1d8d53cab645cd30c3e99b0c436

Observation fc9bcd51-9b6a-45ef-9940-0bba359c9a5f · outbound

This paper cites Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions.

A Survey on Large Language Models for Mathematical Reasoning Analyzing Chain-of-Thought Prompting in Large Language Models via Gradient-based Feature Attributions

Reference 91

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source=pdf_text observed=2026-08-07T05:14:47.502844Z digest=sha256:7abd0fa9740cda58fa2df636599c0add2ebb74013443cae4dc641baeb9ca78d5

Observation a9939626-aa35-4f40-89cd-667aa4f3b015 · outbound

This paper cites Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.

A Survey on Large Language Models for Mathematical Reasoning Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 92

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source=pdf_text observed=2026-08-07T05:14:47.507268Z digest=sha256:fde4de1fac40e962ce6b6fa8ff0b8fafe5cc70f37a4dea876ad4d17d604d612a

Observation 96b8d507-a2f3-417b-807c-1d4d8df6c155 · outbound

This paper cites A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges.

A Survey on Large Language Models for Mathematical Reasoning A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges

Reference 93

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source=pdf_text observed=2026-08-07T05:14:47.511366Z digest=sha256:d8fbdbcbcd43c39157a8f3f8bb7c592e2659cd4b662159a22b69f362ad46cf35

Observation 0cbdd02f-672e-4ace-8d8c-e02727bc721c · outbound

This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.

A Survey on Large Language Models for Mathematical Reasoning Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Reference 94

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source=pdf_text observed=2026-08-07T05:14:47.515590Z digest=sha256:82dfebf5ec5646884cca051425fed67068c29ee2d0d11abc454059f2d8cfef2d

Observation ac5546ec-9df7-4b56-ab61-5bdf12648fba · outbound

This paper cites Looped Transformers are Better at Learning Learning Algorithms.

A Survey on Large Language Models for Mathematical Reasoning Looped Transformers are Better at Learning Learning Algorithms

Reference 95

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source=pdf_text observed=2026-08-07T05:14:47.520785Z digest=sha256:f8f7c2e079802a9a253203b1aa5dd76989cfae32507bf7f8747dd7301ee52c3e

Observation 94edd8fa-aa7d-4681-978a-f0cf0230288f · outbound

This paper cites LemmaHead: RAG Assisted Proof Generation Using Large Language Models.

A Survey on Large Language Models for Mathematical Reasoning LemmaHead: RAG Assisted Proof Generation Using Large Language Models

Reference 96

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source=pdf_text observed=2026-08-07T05:14:47.525722Z digest=sha256:19ac05d65c0cca3a4ff8543d3d13a3a9c4f69ff03af010f4bf99f43ba5b78759

Observation 3124071d-b431-4a8c-b33f-7df13da07041 · outbound

This paper cites Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems.

A Survey on Large Language Models for Mathematical Reasoning Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems

Reference 97

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source=pdf_text observed=2026-08-07T05:14:47.530143Z digest=sha256:c8b005f48f4e3e375be5f3cc969fb58d7e80681421866a3723df47f1c625c45a

Observation d4a2b4e3-7922-49d6-93be-5d8aa849c85d · outbound

This paper cites LIMO: Less is More for Reasoning.

A Survey on Large Language Models for Mathematical Reasoning LIMO: Less is More for Reasoning

Reference 98

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source=pdf_text observed=2026-08-07T05:14:47.534507Z digest=sha256:f2b926c9415d820ccf334af33b97aaf6856dba7cde34146bb3925b3fcdba43cb

Observation a9a9a4f4-9812-4eb0-9fc6-58f53eb32267 · outbound

This paper cites Lean Workbook: A large-scale Lean problem set formalized from natural language math problems.

A Survey on Large Language Models for Mathematical Reasoning Lean Workbook: A large-scale Lean problem set formalized from natural language math problems

Reference 99

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source=pdf_text observed=2026-08-07T05:14:47.539027Z digest=sha256:3bd13d2d3dd85b42386e1a6ba48faed712866b10e16a2a49da50956ca0245853

Observation d56dc75e-cda6-408c-84b6-5867ff745e4d · outbound

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

A Survey on Large Language Models for Mathematical Reasoning DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 100

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source=pdf_text observed=2026-08-07T05:14:47.543926Z digest=sha256:413b83195293edf24bea6116c4798d60bb02a66e9d88496e924fd7d13fdd9057

Observation bef27764-4685-468c-b21e-1d1a41041de7 · outbound

This paper cites Free Process Rewards without Process Labels.

A Survey on Large Language Models for Mathematical Reasoning Free Process Rewards without Process Labels

Reference 101

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source=pdf_text observed=2026-08-07T05:14:47.548062Z digest=sha256:5533c34bc7520509e14cc35d6ac07037b50cf6670cddb01e4221b0d5c923f74b

Observation 2eca3266-6d6a-4d49-8891-57e20a7381df · outbound

This paper cites MAmmoTH2: Scaling Instructions from the Web.

A Survey on Large Language Models for Mathematical Reasoning MAmmoTH2: Scaling Instructions from the Web

Reference 102

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source=pdf_text observed=2026-08-07T05:14:47.552410Z digest=sha256:6410be4f000dcc443318171107bbda45ca7d91856bbd42eecef8a7e907ff1977

Observation 5b4ca524-062d-47ab-b6d6-e368299a1c91 · outbound

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

A Survey on Large Language Models for Mathematical Reasoning Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Reference 103

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source=pdf_text observed=2026-08-07T05:14:47.557953Z digest=sha256:596e764a4656d27703674d82de4fc1742062f7297df09747488adaabb4f7996a

Observation 23992e21-febb-44fc-9fa7-f6724f8031a0 · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

A Survey on Large Language Models for Mathematical Reasoning SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 104

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source=pdf_text observed=2026-08-07T05:14:47.563365Z digest=sha256:34aee690e83a6d639890b0a467e8833e8a50b2fc486335261f71d9f30a1e4747

Observation f71f70ce-d610-405f-826f-a07976bbf522 · outbound

This paper cites The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers.

A Survey on Large Language Models for Mathematical Reasoning The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers

Reference 105

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T05:14:47.569688Z digest=sha256:50a3f0d73592e2589a5ec7dea436bb375ff018d3d1b65b77f6ccf7a6d9c1147f

Observation 898d3920-df61-443f-a8bb-61b92375818a · outbound

This paper cites LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning.

A Survey on Large Language Models for Mathematical Reasoning LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning

Reference 106

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source=pdf_text observed=2026-08-07T05:14:47.574551Z digest=sha256:fec8315107f835d4aae0ce404d19aacb688a403a7f6ad315a3e41448edac97b6

Observation ec4e74dc-0903-4efc-9260-4e17c0535f2c · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

A Survey on Large Language Models for Mathematical Reasoning Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 107

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source=pdf_text observed=2026-08-07T05:14:47.579805Z digest=sha256:4d78aa35a78f19f1dbff2053d3494dc01ef085df31adbd461c31f8747851f436

Observation 88fa932b-ec92-4839-a37e-5fcf057a7eeb · outbound

This paper cites Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining.

A Survey on Large Language Models for Mathematical Reasoning Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining

Reference 108

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source=pdf_text observed=2026-08-07T05:14:47.584200Z digest=sha256:74d56d2b69d23b88a5ee7ae3c5a571f3ded5ef01a991158f243d4c495456b405

Observation 1c1541b2-6419-45e2-9d10-d52f09458061 · outbound

This paper cites A Survey of Large Language Models.

A Survey on Large Language Models for Mathematical Reasoning A Survey of Large Language Models

Reference 109

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source=pdf_text observed=2026-08-07T05:14:47.588776Z digest=sha256:6cdd1546d72940ddbd3050415bc6978efb629f010032ee7dbed17b3b9acddec7

Observation 928d2302-cf88-4957-b914-7a68b47edec4 · outbound

This paper cites Automatic Curriculum Expert Iteration for Reliable LLM Reasoning.

A Survey on Large Language Models for Mathematical Reasoning Automatic Curriculum Expert Iteration for Reliable LLM Reasoning

Reference 110

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source=pdf_text observed=2026-08-07T05:14:47.593151Z digest=sha256:f8a25597205810712e5c61a34df693659ff915ee2c0eea79ea67650b0dc17db9

Observation 82526cc1-5e50-4945-93cc-1b0404f6d1a8 · outbound

This paper cites ProcessBench: Identifying Process Errors in Mathematical Reasoning.

A Survey on Large Language Models for Mathematical Reasoning ProcessBench: Identifying Process Errors in Mathematical Reasoning

Reference 111

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source=pdf_text observed=2026-08-07T05:14:47.598513Z digest=sha256:7b0a7109c6c87bfbc2ef59ed7a09aa126b49a839db4481120d837240d751a4fd

Observation 2a3cc23c-209f-4bcb-baf2-ff5bad5522f9 · outbound

This paper cites MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics.

A Survey on Large Language Models for Mathematical Reasoning MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics

Reference 112

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source=pdf_text observed=2026-08-07T05:14:47.603907Z digest=sha256:966cf99dd94fb793a3c2dd654fda9d2b7819c42e4b3e16fc115f7ad9005cc9b8

Observation dd28f020-592d-4f50-a337-ffa99d744497 · outbound

This paper cites Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems.

A Survey on Large Language Models for Mathematical Reasoning Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

Reference 113

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source=pdf_text observed=2026-08-07T05:14:47.608760Z digest=sha256:27f63f19e5bd94812ce7417797f8cdc95bca2d2eaf24b2e9ab452fa7d06705da

Observation ee40d895-7924-4007-a0d4-ec9278bbf768 · outbound

This paper cites Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models.

A Survey on Large Language Models for Mathematical Reasoning Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models

Reference 114

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source=pdf_text observed=2026-08-07T05:14:47.613495Z digest=sha256:ed987b5f85713e01a330750add64faf423f7e28eb5c581913661c76a12375c40

Observation 4afc5bbd-4af8-4bb6-a82f-66db6fce831a · outbound

This paper cites Teaching Algorithmic Reasoning via In-context Learning.

A Survey on Large Language Models for Mathematical Reasoning Teaching Algorithmic Reasoning via In-context Learning

Reference 115

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source=pdf_text observed=2026-08-07T05:14:47.618381Z digest=sha256:129ab1fba29f8bd7dd96fcfbfd29cdc3b9feb9e75ada0130546d3b9ac9947346

Observation 4aa692bd-fc69-43a2-88a0-5f4741231aa3 · outbound

This paper cites JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models.

A Survey on Large Language Models for Mathematical Reasoning JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models

Reference 116

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source=pdf_text observed=2026-08-07T05:14:47.623400Z digest=sha256:f3cbcfded5a5518968363c57047f012a612dd1d31e4e6b189854f01fd38744f3

Observation 7f9d84ea-ffff-426d-ad3c-29e9e9315946 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

A Survey on Large Language Models for Mathematical Reasoning Fine-Tuning Language Models from Human Preferences

Reference 117

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source=pdf_text observed=2026-08-07T05:14:47.627869Z digest=sha256:53ecc22794eb400700d212c000911999c87eb0e9f1f808afb3927a8bfee42810

Observation bc57096e-daec-494d-9a4e-e599ef340c00 · outbound

This paper cites An essential goal in evaluating mathematical reasoning models is to assess whether they exhibit capabilities comparable to, or exceeding, those of humans.

A Survey on Large Language Models for Mathematical Reasoning An essential goal in evaluating mathematical reasoning models is to assess whether they exhibit capabilities comparable to, or exceeding, those of humans

Reference 118

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source=pdf_text observed=2026-08-07T05:14:47.632619Z digest=sha256:e8686b3c4cf45e544810097db374b2ccc609514ab334446536ca3de84873dc46

Observation da6e38ee-19e5-4a19-a50e-bab43e9ae6f5 · outbound

This paper cites an unresolved cited work.

A Survey on Large Language Models for Mathematical Reasoning Unresolved cited work

Reference 119

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source=pdf_text observed=2026-08-07T05:14:47.638235Z digest=sha256:2a3100ce64ddba759548cd1f299b5e0d783a362dfb1667d7f35b09545439cae1

Observation 8bd2bb36-4635-4235-80c5-9c500646881b · outbound

This paper cites Why Can Large Language Models Generate Correct Chain-of-Thoughts?.

A Survey on Large Language Models for Mathematical Reasoning Why Can Large Language Models Generate Correct Chain-of-Thoughts?

Reference 1950

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source=pdf_text observed=2026-08-07T05:14:47.475664Z digest=sha256:256a428f05655d5c1b614eeec3d2dd08f03e812ac9d99ee40f218f7c9e8a713e

Observation 981061a7-936c-4eff-8bc3-69c427dbbfae · outbound

This paper cites CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?.

A Survey on Large Language Models for Mathematical Reasoning CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?

Reference 1962

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source=pdf_text observed=2026-08-07T05:14:47.498407Z digest=sha256:d79a2c7465a10ad745c1e3a2c4056eb47722d9ffcbb06dd2ee3bbf06b558f558

Pith citing papers

Observation a467bf4f-b6ee-4efa-906b-c9030f76d49d · inbound

Proof2Hybrid: Automatic Mathematical Benchmark Synthesis for Proof-Centric Problems cites this paper.

Proof2Hybrid: Automatic Mathematical Benchmark Synthesis for Proof-Centric Problems A Survey on Large Language Models for Mathematical Reasoning

Reference 36

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no resolver link, observed 2026-08-06T05:11:55.947139Z

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source=arxiv_source observed=2026-08-06T05:11:55.947139Z digest=sha256:0d4cc89394e260e13265d77ff314540946a09b4af69911ce6752291ae0caa624

Observation 4884c081-abb6-484b-97b8-4ca30140d06d · inbound

Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving cites this paper.

Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving A Survey on Large Language Models for Mathematical Reasoning

Reference 33

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no resolver link, observed 2026-08-04T06:40:26.346269Z

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source=pdf_text observed=2026-08-04T06:40:26.346269Z digest=sha256:3b34961448f104402d3da6d1e62953c853507d43f73d191f2242d81243ff59b6

Observation 3de7106d-8b8b-40ea-8e1f-b7a454f6b5a1 · inbound

MMR-GRPO: Accelerating GRPO-Style Training through Diversity-Aware Reward Reweighting cites this paper.

MMR-GRPO: Accelerating GRPO-Style Training through Diversity-Aware Reward Reweighting A Survey on Large Language Models for Mathematical Reasoning

Reference 5

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source=pdf_text observed=2026-08-03T10:46:46.689284Z digest=sha256:fd4598c677a56d95a65983952ceea2da1bafac033629510124390662bfc0c976

Observation 42edb151-c342-42d8-acae-edab792b795b · inbound

From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning cites this paper.

From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning A Survey on Large Language Models for Mathematical Reasoning

Reference 38

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source=arxiv_source observed=2026-08-03T06:52:56.005103Z digest=sha256:d8eb6186ffeb59b6cb97ad3e578fa2d0757b7fe229492af29d188ee25313c285

Observation bbd43a06-46e9-4f69-bbbe-4fb9553c514d · inbound

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$\Delta$ Integration into Upcycled MoE cites this paper.

A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$\Delta$ Integration into Upcycled MoE A Survey on Large Language Models for Mathematical Reasoning

Reference 7

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arxiv_id, observed 2026-05-20T11:13:13.553569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-20T11:09:22.027588Z digest=sha256:d6cf83da887a040beb55c203152c8e4d62ab9e1eeb9e3600f7fbc8163a8d6052

Observation a805b500-b73e-4516-b016-59450b8ef7cc · inbound

Benchmarking Large Language Models on Floating-Point Error Classification cites this paper.

Benchmarking Large Language Models on Floating-Point Error Classification A Survey on Large Language Models for Mathematical Reasoning

Reference 29

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arxiv_id, observed 2026-07-01T10:15:44.914578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-07-01T05:39:01.180338Z digest=sha256:83f012da9afac7443ead738a90e71511c700012842f45a058c7a5cb29fcfb3ee

Observation 70393a12-af5c-44e1-9f0c-a4d44f2ab2b1 · inbound

FormalRx: Rectify and eXamine Semantic Failures in Autoformalization cites this paper.

FormalRx: Rectify and eXamine Semantic Failures in Autoformalization A Survey on Large Language Models for Mathematical Reasoning

Reference 136

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source=arxiv_source observed=2026-07-11T15:42:50.296348Z digest=sha256:d44bcff11520260b5b303029291b48e8dbe136167c132e0ccf3bcfd7d3f341a5

Observation 49ef8e72-1f4e-4c2f-8b03-d4e5f60927ba · inbound

MedCalc-R1: Knowledge-Guided Reward Framework for Medical Mathematical Reasoning cites this paper.

MedCalc-R1: Knowledge-Guided Reward Framework for Medical Mathematical Reasoning A Survey on Large Language Models for Mathematical Reasoning

Reference 35

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source=arxiv_source observed=2026-08-14T04:33:18.492951Z digest=sha256:0b88d4e1a0d0d9268ea894340a8da9f197577e1b34e88c938cfe2d03580f3de1