Paper Citation Record · LEDGER

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving

As of 20 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2506.10674.

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

pith.paper-citation-record.v1
2506.10674 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:24:24.555370Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T11:23:34.256279Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T11:28:14.621818Z

Reference resolution

15 of 15 outbound references displayed

verified exact1
verified fuzzy8
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0cae8cc0-f3a5-41ce-b008-9906393038d2 · outbound

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

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:25.895388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a3236643-599e-4bb5-8ae4-3cae5588d4c5 · outbound

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

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving Least-to-Most Prompting Enables Complex Reasoning in Large Language Models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:25.759566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f1abe2a5-c25e-4e2e-84b2-20d402ea6ce2 · outbound

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

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:24:23.326028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7416337b-c870-44a6-a202-d889c205e44e · outbound

This paper cites Hermes: A Large Language Model Framework on the Journey to Autonomous Networks.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving Hermes: A Large Language Model Framework on the Journey to Autonomous Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:24:23.423809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22d93a5c-73a8-42ef-ac87-3838dd027056 · outbound

This paper cites LLM-Based Emulation of the Radio Resource Con- trol Layer: Towards AI-Native RAN Protocols,.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving LLM-Based Emulation of the Radio Resource Con- trol Layer: Towards AI-Native RAN Protocols,

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:24:24.839485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4d11f59-64b8-4842-846e-ee689bd5c758 · outbound

This paper cites NetConfEval: Can LLMs Facilitate Network Configuration?.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving NetConfEval: Can LLMs Facilitate Network Configuration?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:24:23.635262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c839ae91-1872-4ee2-a1a6-e52917723911 · outbound

This paper cites What do LLMs need to Synthesize Correct Router Configurations?.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving What do LLMs need to Synthesize Correct Router Configurations?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:25.633581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87aea58b-546e-4c7f-b7fc-495bb4c4b3ed · outbound

This paper cites Large Language Models as Optimizers,.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving Large Language Models as Optimizers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:25.472314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a18cecd-cf04-4f94-9784-b7b047fcc944 · outbound

This paper cites TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:24:23.983494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9947d540-7a0e-433b-8b0a-d76c9fef43b9 · outbound

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

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving Measuring Mathematical Problem Solving With the MATH Dataset,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:25.327467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation df19336b-d9ad-4432-9315-12bcd14d74cb · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving Training Verifiers to Solve Math Word Problems

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:24:24.178303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f1b25c9-8a2c-4c40-9429-de13494db947 · outbound

This paper cites SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis,.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:25.218128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3d5ed636-b9e5-457e-b477-c6df12e7a89a · outbound

This paper cites TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:24:24.384682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 587ada48-ce04-4589-8c9f-ed66ad052b0d · outbound

This paper cites TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge,.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:25.095777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d8bbf9fc-b1d5-4533-b30e-8bf4e74a159b · outbound

This paper cites WirelessMathBench: A Mathematical Modeling Bench- mark for LLMs in Wireless Communications,.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving WirelessMathBench: A Mathematical Modeling Bench- mark for LLMs in Wireless Communications,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:24:24.975807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation a76ac13d-1749-4901-be2c-ee5b6fe1e79d · inbound

TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications? cites this paper.

TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications? TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:28:14.623191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.