Paper Citation Record · LEDGER

Data Diversification Methods In Alignment Enhance Math Performance In LLMs

As of 7 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2507.02173.

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

pith.paper-citation-record.v1
2507.02173 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:43:29.427195Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

52 of 52 outbound references displayed

verified exact5
verified fuzzy2
unresolved45
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d92ffc23-8a64-46af-bfa4-d4db85d4754d · outbound

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

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:25.297350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7157dea3-8c15-41b8-9bb5-25ef58a14369 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Constitutional AI: Harmlessness from AI Feedback

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:25.373440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c42a73a5-7816-488d-88f3-0f7fe511240e · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:25.475454Z

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Unavailable: canonical work link unavailable.

Observation 1fca4070-72b2-4e17-945a-71322eb9b07a · outbound

This paper cites Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:25.630857Z

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Unavailable: canonical work link unavailable.

Observation bd415e0a-8f97-4623-bc54-0cedd279b351 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:25.745977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f435057-e261-4bd1-b10b-2051be45f099 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:32.694358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c98a11c4-9a41-44d3-8c36-fba22a53a96e · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:32.483099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fd487ea-fefa-48ab-8426-a33c9be58a96 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:25.973794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a1f5f4c-bd4b-4007-af55-873c9248df71 · outbound

This paper cites Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.040552Z

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Unavailable: canonical work link unavailable.

Observation 03c7ab66-53b6-4fd8-af83-8aca3e33e2d6 · outbound

This paper cites Machine Learning Driven Biomarker Selection for Medical Diagnosis.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Machine Learning Driven Biomarker Selection for Medical Diagnosis

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:30.543205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78ca6f3f-a653-4381-9165-dc2028650198 · outbound

This paper cites Magnetic ground state of monolayer CeI$_{2}$: occupation matrix control and DFT+U calculations.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Magnetic ground state of monolayer CeI$_{2}$: occupation matrix control and DFT+U calculations

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:30.416067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a140d1b5-77dc-4353-9028-07de6ffee18f · outbound

This paper cites Direct Language Model Alignment from Online AI Feedback.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Direct Language Model Alignment from Online AI Feedback

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.226540Z

Source-reported events for the cited work

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Observation b5d6526b-a6de-4671-adf1-a677ebe22893 · outbound

This paper cites In-Context Learning for Extreme Multi-Label Classification.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs In-Context Learning for Extreme Multi-Label Classification

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.295700Z

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Observation 9f34cb8c-8df1-4872-9ef5-ba94cc628c77 · outbound

This paper cites GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.369435Z

Source-reported events for the cited work

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Observation 64e5b573-db6f-472b-a8b3-7281bd471371 · outbound

This paper cites ORPO: Monolithic Preference Optimization without Reference Model.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs ORPO: Monolithic Preference Optimization without Reference Model

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.410004Z

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Unavailable: canonical work link unavailable.

Observation d58765e0-60b8-41ca-8842-80620c7b26f3 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:32.320909Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c13c74ba-0510-45eb-b4da-2f6fa9eccded · outbound

This paper cites Human-centric Dialog Training via Offline Reinforcement Learning.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Human-centric Dialog Training via Offline Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.565724Z

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Observation 41b2e59f-0d9b-41ee-b8b3-496fed02fd9b · outbound

This paper cites Top-philic Machine Learning.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Top-philic Machine Learning

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:30.166636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9ae8b89-1084-4bca-951c-0fd403558898 · outbound

This paper cites DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.719336Z

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Unavailable: canonical work link unavailable.

Observation 9fda36b6-16e5-4f65-a47e-93d2b28a15bd · outbound

This paper cites A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.773582Z

Source-reported events for the cited work

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Observation 98e6a46a-df0f-406b-9770-48f65a56ac2e · outbound

This paper cites Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:26.891310Z

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Observation 74a78612-4639-4436-9307-225e8bbe64e3 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:32.132088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d3766c4b-fd3b-449d-82f3-0af9f8b682a6 · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs RewardBench: Evaluating Reward Models for Language Modeling

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.030856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca30404d-f858-4923-bbef-4c3b8c65c32b · outbound

This paper cites Scalable agent alignment via reward modeling: a research direction.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Scalable agent alignment via reward modeling: a research direction

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.141756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce9cb4d-caeb-4049-8170-27981c03d074 · outbound

This paper cites Let's Verify Step by Step.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Let's Verify Step by Step

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.242716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3acae069-a9e5-4ad9-8660-402d0202892d · outbound

This paper cites Spectrally Pruned Gaussian Fields with Neural Compensation.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Spectrally Pruned Gaussian Fields with Neural Compensation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.365315Z

Source-reported events for the cited work

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Observation e02f8e1a-08f2-4517-b09a-dca55508671e · outbound

This paper cites What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.467789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c82d6083-6cac-458f-819f-695fa29c96b4 · outbound

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

Data Diversification Methods In Alignment Enhance Math Performance In LLMs WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.578947Z

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Observation a5119672-470d-427a-b83d-82a4addb89ff · outbound

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

Data Diversification Methods In Alignment Enhance Math Performance In LLMs SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:27.665291Z

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Observation d09995e2-1c1d-4ead-961f-e0b58834e731 · outbound

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Reference 30

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9839e3ad-6891-4d57-b5ee-0c4de2a60fa7 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:31.821340Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 11f834ca-61ef-4c14-aff7-3377728ac151 · outbound

This paper cites Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:31.704902Z

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Observation d864c01a-0f98-44b6-99a3-28f70c0cf0be · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs Plackett

Reference 33

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Observation 67846956-50c1-45ea-862d-80a9695cf0ef · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 34

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Observation 02c90f97-bf16-44f3-b895-e3ded481a023 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:31.522941Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e264d90a-da9f-4b84-8963-2048a6a9c5df · outbound

This paper cites Proximal Policy Optimization Algorithms.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Proximal Policy Optimization Algorithms

Reference 36

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Observation 2876bc76-70a6-4968-821d-33b64aea6876 · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs Small Solutions of generic ternary quadratic congruences

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:29.866506Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44fbde2e-45aa-4645-8be3-64f31345cb7f · outbound

This paper cites an unresolved cited work.

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Reference 38

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 268ee180-92b5-45fb-93da-c996e3e89ae8 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 39

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Observation 145d718e-7b02-4278-98be-c7ed15b2070a · outbound

This paper cites A Survey on Human Preference Learning for Large Language Models.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs A Survey on Human Preference Learning for Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:28.561126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d16e4121-2391-4386-8d22-9d14fe6d3555 · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:31.218132Z

Source-reported events for the cited work

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Observation 505e5c89-3fd9-492e-bbe6-ef669eb24bae · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs Rush, and Thomas Wolf

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:31.075825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c61b6799-576f-4998-82e0-c0fa5ae3da00 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:30.918375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fd07e8cb-16c9-47e2-aeb9-a5b319f6dc64 · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4a752a6-7d73-4165-b915-105a3afcc6c9 · outbound

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

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:28.888089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 881ee8d8-e4dc-400c-b1cd-98c7b7f2914f · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:28.969383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7455639d-3a6d-4ee7-8b6f-1bbbd092c814 · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 47

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Unavailable: canonical work link unavailable.

Observation 5f87e13e-c8d8-4dfd-a5da-8bf382cdd869 · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:29.154180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1acab3fa-9085-402c-b881-dd1951581835 · outbound

This paper cites an unresolved cited work.

Data Diversification Methods In Alignment Enhance Math Performance In LLMs Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:43:30.757053Z

Source-reported events for the cited work

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Observation 22e057b2-25da-4681-a6f7-b422f0d6d6c3 · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs On widely degenerate \textit{p}-Laplace equations with symmetric data

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:29.615209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 15160f95-381a-409c-b1ac-e71fd46fb900 · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs URL: " 'urlintro :=

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Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:29.350602Z

Source-reported events for the cited work

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Observation 50750e9d-5cb9-4e02-a835-690459f00bae · outbound

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Data Diversification Methods In Alignment Enhance Math Performance In LLMs write newline

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:29.427195Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.