Paper Citation Record · LEDGER

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs

As of 17 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.02851.

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

pith.paper-citation-record.v1
2507.02851 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:24:00.072659Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

21 of 21 outbound references displayed

verified exact0
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d1ab79bf-bfa9-4f86-8244-f623cc80202b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Training Verifiers to Solve Math Word Problems

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.425741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bbb6bad-8780-49ae-8cff-76d35cbd5203 · outbound

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

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.618190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10d88f10-faae-461e-92be-b5b33ae3038e · outbound

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

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Measuring Mathematical Problem Solving With the MATH Dataset

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.701438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeac5b9b-b6d6-4df1-8850-e86650132768 · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Lost in the Middle: How Language Models Use Long Contexts

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.003617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25bb37db-d3ba-485c-b145-102e33531c4c · outbound

This paper cites Distributed Mixture-of-Agents for Edge Inference with Large Language Models.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Distributed Mixture-of-Agents for Edge Inference with Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.097832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13df14a6-0ba3-4bfd-9742-b2bafe2e196f · outbound

This paper cites s1: Simple test-time scaling.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs s1: Simple test-time scaling

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.192668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e128e2a-9d15-41db-8ed4-447e90630a8e · outbound

This paper cites Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.260212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54dd50c3-df24-42f5-b8f8-ec4744002cf9 · outbound

This paper cites Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.391394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f21235f-aaf2-4b8b-93d7-aa0cc0404c4c · outbound

This paper cites Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.459779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5624c9c5-cef8-4112-969c-fec3d4d8411b · outbound

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

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.546311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4acbca54-faff-4b52-9264-555fd99b6fe8 · outbound

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

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.612071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec8f81c2-c71e-48f4-8be7-a5f134b74053 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Gemini: A Family of Highly Capable Multimodal Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.704363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c107b09-0a26-4ad1-b454-e6d62fdca359 · outbound

This paper cites Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.774601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5b6da0e-5c72-4e63-9fd5-00f508c0bc21 · outbound

This paper cites Mixture-of-Agents Enhances Large Language Model Capabilities.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Mixture-of-Agents Enhances Large Language Model Capabilities

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.868672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e115bb4-420d-4c02-b88a-a10199752927 · outbound

This paper cites Inftythink: Breaking the length limits of long-context reasoning in large language models.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Inftythink: Breaking the length limits of long-context reasoning in large language models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:59.964450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19b1f7ff-1647-42a6-8aef-00e65e33485f · outbound

This paper cites Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T20:24:00.072659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d697b007-2cf2-4794-be4f-2283440f5ce2 · outbound

This paper cites Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.328473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1673fa13-dfda-421b-8440-b4887d3289ba · outbound

This paper cites OpenAI o1 System Card.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs OpenAI o1 System Card

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.821730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd220da0-e9ad-43e9-b6fd-e1996fb0e4de · outbound

This paper cites Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.523176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f67615ef-5366-4947-a05b-f5a7327e10e0 · outbound

This paper cites SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.917129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 127a6973-4793-4e5f-bb46-1190510a15f9 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs Evaluating Large Language Models Trained on Code

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T20:23:58.197865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.