Paper Citation Record · LEDGER

Teaching LLMs to Refine with Tools

As of 13 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2412.16871.

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

pith.paper-citation-record.v1
2412.16871 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T06:05:31.251081Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy0
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4463aa3d-661d-4037-8523-8456ed561e41 · outbound

This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

Teaching LLMs to Refine with Tools Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.141813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee7d540f-6902-4cc8-aed4-e71f0156c536 · outbound

This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

Teaching LLMs to Refine with Tools Training Language Models to Self-Correct via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.151684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76d6981f-b18b-48d3-bb40-535598651d93 · outbound

This paper cites Let's Verify Step by Step.

Teaching LLMs to Refine with Tools Let's Verify Step by Step

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.156593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99907d5d-b151-473e-b0e7-32f1c5d08195 · outbound

This paper cites Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation.

Teaching LLMs to Refine with Tools Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.171365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c75c41e-3a5a-4e49-b466-6577666deebf · outbound

This paper cites LLM Critics Help Catch LLM Bugs.

Teaching LLMs to Refine with Tools LLM Critics Help Catch LLM Bugs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.176033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d9aa4fb-1abf-4a20-ad00-75503eaf4df3 · outbound

This paper cites Iterative Reasoning Preference Optimization.

Teaching LLMs to Refine with Tools Iterative Reasoning Preference Optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.181251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488e115b-c050-4395-bec6-db881bbb78a9 · outbound

This paper cites Self-critiquing models for assisting human evaluators.

Teaching LLMs to Refine with Tools Self-critiquing models for assisting human evaluators

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.191300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2b20b61-e17f-4fef-987a-e6e465ab953a · outbound

This paper cites BOND: Aligning LLMs with Best-of-N Distillation.

Teaching LLMs to Refine with Tools BOND: Aligning LLMs with Best-of-N Distillation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.198228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1b41003-4031-4cb1-ba6f-19133195b372 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Teaching LLMs to Refine with Tools Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.203217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1984b41d-d237-4ffe-9ef8-0cc92945c2f8 · outbound

This paper cites Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing.

Teaching LLMs to Refine with Tools Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.207636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8cea3ad-7e1c-4cdc-a79f-96fd14e23a31 · outbound

This paper cites MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning.

Teaching LLMs to Refine with Tools MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.212295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38231b56-f0fe-4a7c-8444-93464939d40d · outbound

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

Teaching LLMs to Refine with Tools CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.216495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 398eefc4-0f30-462d-83cd-4fde728dc9c0 · outbound

This paper cites Generating Sequences by Learning to Self-Correct.

Teaching LLMs to Refine with Tools Generating Sequences by Learning to Self-Correct

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.220595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1cfb3a5-3345-450a-a9e7-aa6f0da57552 · outbound

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

Teaching LLMs to Refine with Tools ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.224575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 369021d9-523d-4905-b6bf-6b9e9fd5e8b6 · outbound

This paper cites Qwen2 Technical Report.

Teaching LLMs to Refine with Tools Qwen2 Technical Report

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.229765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95eb5713-a90a-483e-8b1c-fe8659182d85 · outbound

This paper cites SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models.

Teaching LLMs to Refine with Tools SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.234167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ce726e7-ce31-42b3-af25-d3b4b9123604 · outbound

This paper cites DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search.

Teaching LLMs to Refine with Tools DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.238102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a233914d-be8c-45f4-8dff-6a0500e2f22c · outbound

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

Teaching LLMs to Refine with Tools Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.242190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5453ccd-5ad8-449a-be72-0ad08986aee8 · outbound

This paper cites Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems.

Teaching LLMs to Refine with Tools Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.246208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24eda0b8-b944-46fc-a3c0-ef5c5af74b96 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

Teaching LLMs to Refine with Tools LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.251081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d216fba5-44b4-4519-8c64-70ecabdfa56a · outbound

This paper cites Recursive Introspection: Teaching Language Model Agents How to Self-Improve.

Teaching LLMs to Refine with Tools Recursive Introspection: Teaching Language Model Agents How to Self-Improve

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.186318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9be5e479-3a9c-4a3b-bf5e-6bd433228d9b · outbound

This paper cites Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors.

Teaching LLMs to Refine with Tools Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.146889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab72e31-c006-48d2-a228-b9f56114878b · outbound

This paper cites Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer.

Teaching LLMs to Refine with Tools Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.165998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12851464-d289-43a9-bc89-b1a639642103 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Teaching LLMs to Refine with Tools Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T06:05:31.136611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.