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Paper Citation Record · LEDGER

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2506.08507.

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

pith.paper-citation-record.v1
2506.08507 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:15:46.397717Z

measured 47 of 47 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

47 of 47 outbound references displayed

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  • verified fuzzy5
  • unresolved42
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation b2306a7d-840b-4c49-9282-2b69f62b89d4 · outbound

This paper cites GPT-4 Technical Report.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning GPT-4 Technical Report

Reference 1

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source=pdf_text observed=2026-08-07T05:15:46.177624Z digest=sha256:b87e5d8d5997d0275f4dece57f676ab99205107e1d4a1bb06d1eb747c63ddcb0

Observation 17845117-7d98-45f7-9fa7-a0bcced6e1db · outbound

This paper cites Program Synthesis with Large Language Models.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Program Synthesis with Large Language Models

Reference 2

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Observation 914c045a-0591-4866-a91a-0a4fc1262488 · outbound

This paper cites AutoAgents: A Framework for Automatic Agent Generation.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning AutoAgents: A Framework for Automatic Agent Generation

Reference 3

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Observation 99538ea8-fa36-419e-9393-96eab618fd42 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Evaluating Large Language Models Trained on Code

Reference 4

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Observation 8fa7a4e5-96c7-4595-8a94-4e6f5be402b6 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Training Verifiers to Solve Math Word Problems

Reference 5

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source=pdf_text observed=2026-08-07T05:15:46.198600Z digest=sha256:85bd6dba6039b062e38397a72ad141daee6380522456c5cfe3d55f9ea14a8795

Observation fafd95a7-c04b-42c5-82f5-d63377ef481b · outbound

This paper cites Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning

Reference 6

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Observation 46752285-6f86-4489-912c-1556515930a9 · outbound

This paper cites Improving factuality and reasoning in language models through multiagent debate.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Improving factuality and reasoning in language models through multiagent debate

Reference 7

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Observation 9cb85eeb-2b74-47bd-b360-0339b3ea2e5c · outbound

This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Reference 8

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Observation 06173c9d-0bef-4e06-913b-e9990739c465 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Measuring Massive Multitask Language Understanding

Reference 9

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Observation a5e52074-8336-4651-9be7-688ad3dff401 · outbound

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

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 10

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Observation caafe439-f005-4cf5-97c2-fa0c359d0b74 · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 11

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Observation 08fae210-a4c8-4711-89fe-2efa0b18fd26 · outbound

This paper cites Automated Design of Agentic Systems.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Automated Design of Agentic Systems

Reference 12

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source=pdf_text observed=2026-08-07T05:15:46.231681Z digest=sha256:83672ecfe0c1e5cc0df10f9ac30a5c8eb084569f5a359fb3187d1e9f2791209f

Observation 9c6a1a9d-baff-4b6c-90ee-1f66fe0d349d · outbound

This paper cites Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization

Reference 13

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Observation 438e905b-3bd6-48c5-ba3c-0303f6b86fcd · outbound

This paper cites Reinforcement learning: A survey.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Reinforcement learning: A survey

Reference 14

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Observation 77742588-3936-4750-8ead-8178dd7e8acd · outbound

This paper cites The Dawn of Natural Language to SQL: Are We Fully Ready?.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning The Dawn of Natural Language to SQL: Are We Fully Ready?

Reference 15

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source=pdf_text observed=2026-08-07T05:15:46.246281Z digest=sha256:c06b4c8fefc02f91a59967987799f3e6b9db6a7e7a84f89828b2289e7528d5f6

Observation 785a8e32-aa35-43f0-9e6e-5a24d3735d4d · outbound

This paper cites CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models

Reference 16

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source=pdf_text observed=2026-08-07T05:15:46.251381Z digest=sha256:1e8c0be85c433525b1bff138300403678aa3a128c88e9d7ec77827931a836cea

Observation dcecd16d-815b-490e-ac94-88ef76c87f6f · outbound

This paper cites Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

Reference 17

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source=pdf_text observed=2026-08-07T05:15:46.256745Z digest=sha256:c2306775fec00dc5cecf927decb821e4839c1f0ce316dcc4b8dc3f10643c3d9b

Observation 2891d2bc-6913-48a1-8b03-b4a1c52651e7 · outbound

This paper cites Deep Reinforcement Learning: An Overview.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Deep Reinforcement Learning: An Overview

Reference 18

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Observation 0a85782b-b7fd-4d4d-9b6b-8e4d485ddd4d · outbound

This paper cites A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration

Reference 19

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source=pdf_text observed=2026-08-07T05:15:46.266505Z digest=sha256:134f65175679a62f30d24ee28a765ed8ea9d3bb34047ad2b0661cff97b0058bb

Observation 574c3976-111a-4a8b-aba3-93900e025b94 · outbound

This paper cites Large Language Model Agent: A Survey on Methodology, Applications and Challenges.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Reference 20

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source=pdf_text observed=2026-08-07T05:15:46.271094Z digest=sha256:b9babcd2f175d3771301fba540c17e50968a99025210d2a19a7781d13270b927

Observation c6a7b146-9077-46f3-ac73-8b87b1a0b477 · outbound

This paper cites Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Reference 21

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source=pdf_text observed=2026-08-07T05:15:46.275717Z digest=sha256:88c659a81b42addb934eb701d6820eec6c2ced1a300dc9bc0abb05e0e23b1296

Observation aaba72c0-e0e2-4604-b289-bb60c9465f40 · outbound

This paper cites Gpt-4o mini: Advancing cost-efficient intelligence.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Gpt-4o mini: Advancing cost-efficient intelligence

Reference 22

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source=pdf_text observed=2026-08-07T05:15:46.280402Z digest=sha256:c2fc5ea49f27f6b15defadcbc5bc86a1fd4a98bcff2ccf1bb6505fe2b34468b4

Observation 15f84a89-e88d-486c-b381-cf2b65eddfa9 · outbound

This paper cites Gpqa: A graduate-level google-proof q&a benchmark.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Gpqa: A graduate-level google-proof q&a benchmark

Reference 23

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Observation d0441e11-3d83-42d4-ae56-577172fe9880 · outbound

This paper cites Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering

Reference 24

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Observation 9c009297-d3c7-4dea-aa5c-d99d7fc736c5 · outbound

This paper cites Proximal Policy Optimization Algorithms.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 25

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source=pdf_text observed=2026-08-07T05:15:46.294794Z digest=sha256:e017409391460e91f939ff985c218e187a8b4b7a334d4c6ce5bd524cd50ab035

Observation 1fbed30d-3835-4c7b-a58a-e29281af6da5 · outbound

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

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 26

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source=pdf_text observed=2026-08-07T05:15:46.299563Z digest=sha256:960f22463315ec0eb7e29df71829ea7ac6a4fda8c8278a2d45e3810a7eea0394

Observation 88d880d4-c7b5-4a44-ba30-ab6ce57a7306 · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023

Reference 27

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Observation a7fc2418-0f88-4bb5-bb36-b00c7f139ffc · outbound

This paper cites Llm- planner: Few-shot grounded planning for embodied agents with large language models.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Llm- planner: Few-shot grounded planning for embodied agents with large language models

Reference 28

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source=pdf_text observed=2026-08-07T05:15:46.308865Z digest=sha256:b5527737f5ab0b5579275f5a66b2f7458fc99dbc0e9402fceebf20d97037055c

Observation 6dfa02b4-64d2-4034-907e-24d1ff83c84a · outbound

This paper cites LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 29

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source=pdf_text observed=2026-08-07T05:15:46.313451Z digest=sha256:eef67bc5546317dce6dcad5d1d2e84b021dc3d19e7ae7e1d055a974d6f746583

Observation 6d19bffc-3658-4fb8-8914-5bf61c01349e · outbound

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

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 30

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source=pdf_text observed=2026-08-07T05:15:46.318627Z digest=sha256:6e89e11beab73b466092a59671340e8403fbdc5d8f50a1886c7c082c4b997a59

Observation 6da77bcb-433c-4cf7-84c1-d7085f949c87 · outbound

This paper cites Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration

Reference 31

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source=pdf_text observed=2026-08-07T05:15:46.323581Z digest=sha256:6e64b050e2dc3aa6dbefcc2f5e9bd5cd48bdfc320d3cf1fb9bb58211739d3845

Observation d9caf22b-906b-4baf-a4c5-b97fb0ac96cc · outbound

This paper cites Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

Reference 32

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source=pdf_text observed=2026-08-07T05:15:46.328070Z digest=sha256:f05c669cdf03f650cb0f56998df48fb8901d4e207b83edea3a570c9cd3b22e31

Observation 1c87dbdb-1f92-420f-96f3-cf7ed6400928 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 33

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source=pdf_text observed=2026-08-07T05:15:46.332652Z digest=sha256:612a24efd86ea72f1747c5461391680bb2768bf004d0427e4f2329017c07e20c

Observation 1582b37b-1c27-4af3-88f0-31f8fff4eb9e · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 34

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source=pdf_text observed=2026-08-07T05:15:46.336932Z digest=sha256:bde147289a3c420f99cdd371e378493fc1a9836fdc7e7e238778d1480a6dab31

Observation 5fb0a280-7783-4f11-889f-dd962fea3173 · outbound

This paper cites TravelPlanner: A Benchmark for Real-World Planning with Language Agents.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning TravelPlanner: A Benchmark for Real-World Planning with Language Agents

Reference 35

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Observation a470c992-a3a4-497c-b6e5-8127f8093c98 · outbound

This paper cites Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions

Reference 36

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source=pdf_text observed=2026-08-07T05:15:46.346163Z digest=sha256:7861cdac9b3b6d006b9e57ef8c9af23265e44be6acc81b1d7c024b532aa635e5

Observation 47d0df47-f349-4cc8-9a84-7a390d432d61 · outbound

This paper cites React: Synergizing reasoning and acting in language models.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning React: Synergizing reasoning and acting in language models

Reference 37

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.351234Z digest=sha256:061ca861874ad43e1799132bb971e0c0261441bbf9b59905a629d0247a05dbc4

Observation e4a104ff-2598-47a9-8b26-713f04bac4b0 · outbound

This paper cites MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 38

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no resolver link, observed 2026-08-07T05:15:46.355522Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T05:15:46.355522Z digest=sha256:4bf9314c79d0bda45faa6d2a68ea3e05dfd57a183c2162f8b791cbca3c6d108a

Observation 81e18702-0380-4c05-9387-8926490a336e · outbound

This paper cites MasRouter: Learning to Route LLMs for Multi-Agent Systems.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning MasRouter: Learning to Route LLMs for Multi-Agent Systems

Reference 39

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no resolver link, observed 2026-08-07T05:15:46.359847Z

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source=pdf_text observed=2026-08-07T05:15:46.359847Z digest=sha256:589cc7ff1f191d5863c5002fea0bbf456da94018f7a3d7a282a93faf6c5fedd2

Observation 11de2d7d-f878-4b39-9c02-a7075fe1f524 · outbound

This paper cites TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

Reference 40

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no resolver link, observed 2026-08-07T05:15:46.364482Z

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source=pdf_text observed=2026-08-07T05:15:46.364482Z digest=sha256:4be2032356123b294079654ee30befda190110540c35d313ae994d015dba879b

Observation caf2a6f2-e40a-4fc9-afb3-9ac3e194fb70 · outbound

This paper cites Multi-agent Architecture Search via Agentic Supernet.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Multi-agent Architecture Search via Agentic Supernet

Reference 41

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no resolver link, observed 2026-08-07T05:15:46.369706Z

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source=pdf_text observed=2026-08-07T05:15:46.369706Z digest=sha256:f1bdd2d0aa884b1a5ac14a72e35b2e16eb87e1b5a6c68616fed96698f2456d05

Observation eab37f38-d52f-44c3-a0c7-5614cae9a874 · outbound

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

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 42

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no resolver link, observed 2026-08-07T05:15:46.374277Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T05:15:46.374277Z digest=sha256:6dd4f80da1971b056da8bfb5e6741cdb802f285ff978a9dda1d63d8a6cc75134

Observation 1f7bc27b-54e8-4f01-9eb1-b81910fa998c · outbound

This paper cites AFlow: Automating Agentic Workflow Generation.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning AFlow: Automating Agentic Workflow Generation

Reference 43

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no resolver link, observed 2026-08-07T05:15:46.379327Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T05:15:46.379327Z digest=sha256:996657c0880b8001ecd99ab5e921b4cf194ce66784472fafa5a661a98021e185

Observation 53735eb0-40f9-4b71-892a-1ac96110671b · outbound

This paper cites Achieving> 97% on gsm8k: Deeply understanding the problems makes llms perfect reasoners.arXiv e-prints, pages arXiv–2404, 2024.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Achieving> 97% on gsm8k: Deeply understanding the problems makes llms perfect reasoners.arXiv e-prints, pages arXiv–2404, 2024

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-07T05:15:46.972608Z

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.

source=pdf_text observed=2026-08-07T05:15:46.383758Z digest=sha256:c2af0aebf4cea3ab9d6aa2318dc2380774bbb6586ef184377505c7f0c03491d4

Observation 6bd22a3a-090e-4673-aff8-1cbbccc2d43c · outbound

This paper cites Star-agents: Automatic data optimization with llm agents for instruction tuning.Advances in Neural Information Processing Systems, 37:4575–4597, 2024.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Star-agents: Automatic data optimization with llm agents for instruction tuning.Advances in Neural Information Processing Systems, 37:4575–4597, 2024

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:15:46.958212Z

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.

source=pdf_text observed=2026-08-07T05:15:46.388287Z digest=sha256:b93b96a8074d2d2119393d62d46fe0ed52892ecf7bfe4d04ad352b88294c8bfb

Observation ad330fb1-de3a-42de-910c-5564b69fdb7c · outbound

This paper cites Are Large Language Models Good Statisticians?.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Are Large Language Models Good Statisticians?

Reference 46

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source=pdf_text observed=2026-08-07T05:15:46.393258Z digest=sha256:5aec26d2939e8cea437d20631517c6176297c913bddbf446e1e63638ca850cc6

Observation 754a0e84-7138-4a4d-862d-c7837060706f · outbound

This paper cites Gptswarm: Language agents as optimizable graphs.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning Gptswarm: Language agents as optimizable graphs

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:15:46.942530Z

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.

source=pdf_text observed=2026-08-07T05:15:46.397717Z digest=sha256:4978a8d245437b153566a406cc112d060a3a88ddc2d3a2b849415de974db547b

Pith citing papers

No inbound Pith citation observations are available.