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Paper Citation Record · LEDGER

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach

As of 11 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2501.04425.

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

pith.paper-citation-record.v1
2501.04425 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:36:40.332557Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

16 of 16 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2bb0e081-ff39-4c5d-9db7-e4aa955c24ef · outbound

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

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.255920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.255920Z digest=sha256:50856dd6f9bc446f7e17ab45d24a3be2050eca602fea4b885e349458af1ca499

Observation 9f4a04d1-7530-4fdb-b530-3ef7fe345547 · outbound

This paper cites MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.261108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.261108Z digest=sha256:b733a51c6cccc2ae94cc1c109942d9a23fc23408be241f55628023d3b9f0dcea

Observation 507daac7-be1b-4e77-939b-ef08457eeaad · outbound

This paper cites ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.265989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.265989Z digest=sha256:e816aff2576e9320cd5c31cbb9d50185ab72ea6ea9b5a8746562c0589450bbfa

Observation 0a029dfd-5b88-4b2b-8d11-2ec4940cb5dd · outbound

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

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.271057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.271057Z digest=sha256:0ea7ef3a3f1474edc87b2d2bc1f4631216a6e4e865cc49936a488f386965a26e

Observation e3ea84d4-bbcc-4bc0-9f81-2bf3a29ce502 · outbound

This paper cites Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-10T21:36:40.495025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T21:36:40.276449Z digest=sha256:897a48cfa09ea67891bb99c3c3efeb984aafc409428b245ffc9a09fc3384d2f3

Observation 072f8dad-d8ee-4634-b4bb-36ffcd031618 · outbound

This paper cites ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.281697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.281697Z digest=sha256:f02961922a88752c03a5f0395a040161d71e50b755d1589cd938120506225066

Observation e5967104-67d1-49e0-9ff1-fb85e6b451ed · outbound

This paper cites Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.287160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.287160Z digest=sha256:a5e83c447a216ba788a49b72e537c1fb073a5532ba9a9b3874e9a17d66ab1acc

Observation b604734c-5b22-4684-86c7-f132863c451a · outbound

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

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.291941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.291941Z digest=sha256:e3c3a2a56e06cfb25aa39ddfa1d8061fabcb6f26553e52e8cbc81d253024ede5

Observation 3deebd06-7f0a-46e9-bf6c-b7532562f6a7 · outbound

This paper cites Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.297600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.297600Z digest=sha256:aa8f32fa20971cb1bf1b2dc4cdd32bbf3a31274425fa724fff72887ed3fe7f7a

Observation 6b3e754b-f4e9-4dde-97a3-2be4292e5872 · outbound

This paper cites BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.302161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.302161Z digest=sha256:c00ea45c6196cbf6d7265e129c8dccf3610ebaef2949ced34c0d4eecf16eb3f3

Observation 48fc4fea-dc5c-4fc8-bb17-f96532661c14 · outbound

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

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.308069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.308069Z digest=sha256:752fec22544c6a864288a7ea9cd320cd9fda76be574eb5ad1f671aaef3f7cd1a

Observation 8267c3c3-d3f1-486d-91bd-a5c7ef091d1b · outbound

This paper cites & Polu, S.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach & Polu, S

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:36:40.582117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T21:36:40.313932Z digest=sha256:01afce6bcc4dc8a1286937f273b228ceae1b8c0e4ae80a44a6a4352d9e51d51c

Observation 21f17f7a-1388-41d3-bb9d-32487527bd62 · outbound

This paper cites Qwen2.5 Technical Report.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach Qwen2.5 Technical Report

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.318877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.318877Z digest=sha256:0ad14e47277b9905b230488b16edd717ae85e0b36753b5744cf9f8baa00019df

Observation 9d2f4d00-9cf8-4fd2-bd86-c181b10fcb06 · outbound

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

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.323674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.323674Z digest=sha256:992a2c40ba535cacf7d738b93a8683f2b63b3306b1185c3b825e485bedc6182d

Observation 49b09acd-7c46-4297-819e-6b48e6ed5e11 · outbound

This paper cites ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-10T21:36:40.328027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:40.328027Z digest=sha256:cde63068e2e077ecc23fc8c6c5c5d56e18ed5c813f75954f5a9692b252e14f5a

Observation 60c7da0c-a65d-49e5-bce3-d11f6fba0143 · outbound

This paper cites & Kiela, D.

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach & Kiela, D

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:36:40.567443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T21:36:40.332557Z digest=sha256:46e9d838de161d53dece92e58e0ee747a3ce2db72ba0a58aacae407e3563765f

Pith citing papers

No inbound Pith citation observations are available.