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Paper Citation Record · LEDGER

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

As of 18 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 2026-08-18T06:34:40.430872+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:fad71d08ab2f4345928d0e7c9e62d1e9d6f4cae26d4ba54aa219c33d30227333

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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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=sha256:118fd9fee86d606df64ee03b448f1a659422de4500aed05f90659d406c9aae6b

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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source=pdf_text observed=2026-08-07T05:15:46.241395Z digest=sha256:74e000c32d159c52b9ca4c0bfb65f8a417dc9a6e983b58c1cff1b81ac509b171

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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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=sha256:67605afe5102e67ee48375c3a97c1ffb98d1774bbb13357622a6db5aba29df25

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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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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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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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=sha256:814796a6f145cd641cbe3fab94ca73fa29da2c5365990d67a5aca6d8e00606fe

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=sha256:79332b031bbc3824f7748b9b3647e5aba5273eff64a40a522448a99a07d6a6fc

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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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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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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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=sha256:7783e27053ba6b99075c864a6149834857a12c973db690a61216c86deeaea0bf

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:cafc75979fb2673f4e64f9d3c008e67c34ef9bd7d1ea533aae52ba75aa492127

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=sha256:41e8ae3f7d95d37c4eb7583a0de37a40864f6706f441eeb0b3d70c27387213fb

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:c9df512a282e1e7ea18fa1c956a75dbf5e3d11664b55acef303f54a1e17759ef

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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:15:46.332652Z digest=sha256:08f5e3d10c022f0074bdbd2be8cf396721e9c227dc59e1afd48327549daa1390

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=sha256:029546b9248f7484176c4a0cac3a425ec19adef74a98a27d689628691ce32f8b

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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source=pdf_text observed=2026-08-07T05:15:46.341641Z digest=sha256:f9e93f85280e6a20165f67b9f43a2170a13889a52e50a154e777465a931b9b57

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=sha256:427ffd5e52c8ec4747bd6fecdbb5081384e1761f6a5d1cda0f38dbc13b5eafa5

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.351234Z digest=sha256:c5b9be30c09d01350694363be6f61ff732c5f8d4859b1eb6dd0f91d6f8e4d7f4

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.355522Z digest=sha256:85ff32c0fbb4ecfb52c17d3fa423884dae88243806dcbde2f06e7c3e06763324

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.359847Z digest=sha256:5d3c2646d74d92704f9ba728580b5c9947c3b074d7c8a10ad7eaa77e164d6eb9

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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unresolved
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=sha256:469fcd8b05d0069dcb0e8fba6bdc2c1eb51b09d0fd8c06a8492e502cc6e80764

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.369706Z digest=sha256:459397f4d0c8e924f871951c861524a705c21b7c0e7c5cf1f90c4ee9b3c05a0b

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.374277Z digest=sha256:f6de079a36904bbfbe89e3493be18ee7893c005016060a6a88653fc1c0712255

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.379327Z digest=sha256:54151147159c8be118ec92e6e15fe11cf31ec39bec5c48ffa9a659935ab79fe5

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

Resolution
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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:15:46.383758Z digest=sha256:0db36ea1970c328e34ecf2a47edba714ea6fd522672901e39645cb39831dd9b9

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:15:46.388287Z digest=sha256:c93cd2aaf642bdf6f90ad232658930140fe1186d578b89d70ffc0fea706af8a0

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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unresolved
no resolver link, observed 2026-08-07T05:15:46.393258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.393258Z digest=sha256:8c738641dfdf61ad015c330d137b0c4bd2b0e6e3c3f27757e8804b68345ee26b

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T05:15:46.397717Z digest=sha256:3d871c1d0a690ed615c91278c24ff199007ebe9902f78463da479076cb742e04

Pith citing papers

No inbound Pith citation observations are available.