Paper Citation Record · LEDGER

MASPRM: Multi-Agent System Process Reward Model

As of 23 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2510.24803.

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

pith.paper-citation-record.v1
2510.24803 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:56:48.047690Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

27 of 27 outbound references displayed

verified exact0
verified fuzzy0
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b7692c4d-85f9-4910-a5a7-d62e1a7e7240 · outbound

This paper cites online" 'onlinestring :=.

MASPRM: Multi-Agent System Process Reward Model online" 'onlinestring :=

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:45.789075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 684fef4c-a351-48f4-868c-d894a4bc037f · outbound

This paper cites write newline.

MASPRM: Multi-Agent System Process Reward Model write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:45.833177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3f51e71-208f-4874-9e3b-34949f3ee422 · outbound

This paper cites an unresolved cited work.

MASPRM: Multi-Agent System Process Reward Model Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:45.894456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb73fb84-8b1e-4f77-bbaf-95cbc41138c7 · outbound

This paper cites Scaling Autonomous Agents via Automatic Reward Modeling And Planning.

MASPRM: Multi-Agent System Process Reward Model Scaling Autonomous Agents via Automatic Reward Modeling And Planning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:45.944867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69ce9a63-a132-439c-b4ef-6b5382d7eb0c · outbound

This paper cites Process Reward Models for LLM Agents: Practical Framework and Directions.

MASPRM: Multi-Agent System Process Reward Model Process Reward Models for LLM Agents: Practical Framework and Directions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.023170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53283494-a6e1-490e-9314-547247250b80 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

MASPRM: Multi-Agent System Process Reward Model Training Verifiers to Solve Math Word Problems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.107061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 881e3b73-1110-4772-9f13-8601f252e676 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

MASPRM: Multi-Agent System Process Reward Model Process Reinforcement through Implicit Rewards

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.211072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43d7f39d-9f5c-48a5-833c-98136f934a23 · outbound

This paper cites an unresolved cited work.

MASPRM: Multi-Agent System Process Reward Model Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.312568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c1c197b-2910-421a-b4f6-af84b593fac8 · outbound

This paper cites rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking.

MASPRM: Multi-Agent System Process Reward Model rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.376327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b70fef98-0b36-4922-815d-b009d1471623 · outbound

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

MASPRM: Multi-Agent System Process Reward Model Measuring Mathematical Problem Solving With the MATH Dataset

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.446556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 021f8d04-102a-46b9-8d57-c22f8d372532 · outbound

This paper cites an unresolved cited work.

MASPRM: Multi-Agent System Process Reward Model Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.502349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a8fa645-9e8a-43c9-b9ee-0c03bf070808 · outbound

This paper cites Six Challenges for Neural Machine Translation.

MASPRM: Multi-Agent System Process Reward Model Six Challenges for Neural Machine Translation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.579280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c744a8-7fc4-4fc7-a50f-d1ac819ee512 · outbound

This paper cites MARFT: Multi-Agent Reinforcement Fine-Tuning.

MASPRM: Multi-Agent System Process Reward Model MARFT: Multi-Agent Reinforcement Fine-Tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.663818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccd2f73c-dac3-476f-90d9-542b1c7f04b8 · outbound

This paper cites an unresolved cited work.

MASPRM: Multi-Agent System Process Reward Model Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.737622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61540494-045a-46b4-b6e9-baa27c71ad69 · outbound

This paper cites Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning.

MASPRM: Multi-Agent System Process Reward Model Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.797195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48877fb9-e9cd-4017-825f-afe11f9ab73f · outbound

This paper cites Improve Mathematical Reasoning in Language Models by Automated Process Supervision.

MASPRM: Multi-Agent System Process Reward Model Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.894742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ef2ecb1-3502-4295-9f9a-d9a99d76b874 · outbound

This paper cites Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment.

MASPRM: Multi-Agent System Process Reward Model Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:46.965356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa185488-4eb0-48e8-8ae1-8d51f677c548 · outbound

This paper cites Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

MASPRM: Multi-Agent System Process Reward Model Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.011678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 479e194e-4b08-45f6-b0b4-2035b59a8ee8 · outbound

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

MASPRM: Multi-Agent System Process Reward Model Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.083055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e81255b8-ca8b-4fa4-a25f-17e737f2a235 · outbound

This paper cites SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attribution.

MASPRM: Multi-Agent System Process Reward Model SPA-RL: Reinforcing LLM Agents via Stepwise Progress Attribution

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.157332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf0f3b42-89d9-4fe8-b040-d0d9f591fd22 · outbound

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

MASPRM: Multi-Agent System Process Reward Model Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.291212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1525c82c-fdbb-405e-a361-e760530203a1 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

MASPRM: Multi-Agent System Process Reward Model Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.435790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f56fec8-7d61-4596-9512-7fee015ef8da · outbound

This paper cites Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation.

MASPRM: Multi-Agent System Process Reward Model Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.604970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4314658c-e392-409d-8aa4-778ee98c19c7 · outbound

This paper cites an unresolved cited work.

MASPRM: Multi-Agent System Process Reward Model Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.764276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4f44dfe-ee49-448d-9142-681886b99854 · outbound

This paper cites VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data.

MASPRM: Multi-Agent System Process Reward Model VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.849322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 525660d8-24af-4269-b8ea-866c6bd7e667 · outbound

This paper cites G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks.

MASPRM: Multi-Agent System Process Reward Model G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.964740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efbc73d8-16ad-4a69-b59b-a06923dfcd0d · outbound

This paper cites an unresolved cited work.

MASPRM: Multi-Agent System Process Reward Model Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:48.047690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.