Paper Citation Record · LEDGER

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

As of 18 August 2026, this Paper Citation Record lists 100 of 210 outbound references and 17 inbound Pith citation observations for arXiv:2508.13167.

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

pith.paper-citation-record.v1
2508.13167 v1

Coverage vector

measured 100 of 210 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:57:38.586633Z

measured 117 of 117 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:55:37.907213Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-10T12:15:01.137692Z

Reference resolution

100 of 210 outbound references displayed

verified exact0
verified fuzzy0
unresolved100
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03a0143b-e32f-42b7-8b9a-3f79642a9777 · outbound

This paper cites Towards Effective Code-Integrated Reasoning.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Towards Effective Code-Integrated Reasoning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:31.528964Z

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Observation aec18274-6b2c-4ac8-8320-231110c0218b · outbound

This paper cites Multi-agent reinforcement learning: A review of challenges and applications.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Multi-agent reinforcement learning: A review of challenges and applications

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:31.628867Z

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Observation 5d9241b2-eeb8-4ea6-b3a8-f5ced24ab1b6 · outbound

This paper cites ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:31.713985Z

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Observation 78de714f-8f74-4c57-b429-3147a784ba24 · outbound

This paper cites Process Reinforcement through Implicit Rewards.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Process Reinforcement through Implicit Rewards

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:31.801869Z

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Observation 6d12bb68-7528-4280-9e78-e7c7d7d238ba · outbound

This paper cites Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:31.860107Z

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Observation f938d6e4-c9ab-470b-bbbe-fa16b4fd35a4 · outbound

This paper cites Multi-agent systems: A survey.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Multi-agent systems: A survey

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:31.976176Z

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Observation b2afc82b-2a1a-495a-a462-851c017dea1c · outbound

This paper cites ReTool: Reinforcement Learning for Strategic Tool Use in LLMs.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.038564Z

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Observation d7c323eb-4fe7-4925-8e5c-cd209bf84eb6 · outbound

This paper cites AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.134517Z

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Observation 1ebec6f0-8c81-4910-8bcf-7fee5a73e4fa · outbound

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

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.221011Z

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Observation 446c71b1-1cc7-47ac-bd20-23c2c81e9da5 · outbound

This paper cites How we built our multi-agent research system.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL How we built our multi-agent research system

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.315489Z

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Observation 94b0014d-7d06-4d5c-aa20-005f30600bee · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 12

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Observation d13b30e1-d8ff-4535-975a-5062af49d861 · outbound

This paper cites Skywork Open Reasoner 1 Technical Report.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Skywork Open Reasoner 1 Technical Report

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.471543Z

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Observation b85af062-d1d0-499d-b050-0154e419afa7 · outbound

This paper cites Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.528248Z

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Observation c6f539eb-1a58-4bed-a208-5a9535f3dc05 · outbound

This paper cites OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.626007Z

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Observation f0fcd6a6-61df-4133-afe5-f7e729f6796b · outbound

This paper cites AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.689715Z

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Observation 569bcbb2-9b69-42af-8c16-e24c18f1e10a · outbound

This paper cites Qwen2.5-Coder Technical Report.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Qwen2.5-Coder Technical Report

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.723395Z

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Observation 5ba5575f-73c2-4c8c-b042-4dddfe5900d2 · outbound

This paper cites Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez

Reference 18

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Observation ab7cd166-2d4f-424c-b04a-ff638f752448 · outbound

This paper cites Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.820793Z

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Observation 56debdf8-3f47-45cf-abac-8b59149eb863 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.881563Z

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Observation 0e843709-2b3a-4e48-b8a1-38af653cd782 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.948738Z

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Observation f375e221-b36a-49db-b778-fcbd77e91648 · outbound

This paper cites Reveal: Self-evolving code agents via iterative generation-verification, 2025.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Reveal: Self-evolving code agents via iterative generation-verification, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:32.984142Z

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Observation 8b88e872-4291-459d-b147-7b7809f15a36 · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.011052Z

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Observation 491219c8-5731-4cf3-9ead-c526dd628817 · outbound

This paper cites Sequence-level knowledge distillation.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Sequence-level knowledge distillation

Reference 24

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Observation 4d480276-2117-48db-bed9-e2b3747df3f8 · outbound

This paper cites Natural questions: a benchmark for question answering research.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Natural questions: a benchmark for question answering research

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.161310Z

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Observation 1a6955b5-0238-40d6-8a10-fabf57a2607e · outbound

This paper cites Camel: Communicative agents for "mind" exploration of large language model society.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Camel: Communicative agents for "mind" exploration of large language model society

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.222898Z

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Observation 35eff444-9688-4119-b2fc-d4120c69eaff · outbound

This paper cites WebSailor: Navigating Super-human Reasoning for Web Agent.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL WebSailor: Navigating Super-human Reasoning for Web Agent

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.317703Z

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Observation d45b9d34-84d0-4829-ab88-b378f61b7e81 · outbound

This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.383273Z

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Observation b7497d46-9b24-4489-a3a3-658154dbf6b9 · outbound

This paper cites WebThinker: Empowering Large Reasoning Models with Deep Research Capability.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.450894Z

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Observation 0824de45-a673-4ba2-a2a3-3a113de407a4 · outbound

This paper cites ToRL: Scaling Tool-Integrated RL.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL ToRL: Scaling Tool-Integrated RL

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.485908Z

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Observation d780e1ec-75b0-4e6b-a57a-888b514102d9 · outbound

This paper cites Competition-level code generation with alphacode.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Competition-level code generation with alphacode

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.551858Z

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Observation 9af51948-67fd-470a-bb5f-24d2555930fa · outbound

This paper cites Let's Verify Step by Step.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Let's Verify Step by Step

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.617991Z

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Observation 56168054-a414-4018-b0d5-23cf642948e2 · outbound

This paper cites Inference-time scaling for generalist reward modeling.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Inference-time scaling for generalist reward modeling

Reference 33

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Observation daeccf5a-4e54-4b1d-99e2-96006c7d481b · outbound

This paper cites Decoupled Weight Decay Regularization.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Decoupled Weight Decay Regularization

Reference 34

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Observation 82b74efe-b908-4be7-8a9d-f20ce2219763 · outbound

This paper cites Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.803769Z

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Observation c9526066-f0ec-4fa6-9041-eb7abad91742 · outbound

This paper cites When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Reference 36

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Observation c3dda23f-b55c-45f0-a2ee-e71e1aa333ff · outbound

This paper cites Gaia: a benchmark for general ai assistants.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Gaia: a benchmark for general ai assistants

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.961254Z

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Observation 12f83a6e-a7c5-4102-801f-ee5284d61ecb · outbound

This paper cites American invitational mathematics examination (aime) 2024.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL American invitational mathematics examination (aime) 2024

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:33.994732Z

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Observation 71e45e29-ac31-4ee9-a15a-c1cf165fd34a · outbound

This paper cites American invitational mathematics examination (aime) 2025.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL American invitational mathematics examination (aime) 2025

Reference 39

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Observation dd56ec39-9d2e-4dd0-9d70-fd0e6f2ab48b · outbound

This paper cites Codeforces.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Codeforces

Reference 40

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Observation 57664f53-66ae-40ee-bf40-62956e47ff95 · outbound

This paper cites Humanity's Last Exam.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Humanity's Last Exam

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.124997Z

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Observation ce665dea-d3f5-4d73-860c-e3fdff5debb1 · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Measuring and Narrowing the Compositionality Gap in Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.183382Z

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Observation 9f7c568a-a605-4617-984f-dbfdab6eaa81 · outbound

This paper cites ToolRL: Reward is All Tool Learning Needs.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL ToolRL: Reward is All Tool Learning Needs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.215415Z

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Observation bc2447ea-9700-4597-8a65-008dda813d42 · outbound

This paper cites Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.272995Z

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Observation 05afe5d6-a392-4423-9300-045722a72b91 · outbound

This paper cites Qwen2.5 Technical Report.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Qwen2.5 Technical Report

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.308026Z

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Observation 4f0b6d47-7d03-47a1-b67e-6530e49c7431 · outbound

This paper cites ‘smolagents‘: a smol library to build great agentic systems.https://github.com/huggingface/smolagents, 2025.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL ‘smolagents‘: a smol library to build great agentic systems.https://github.com/huggingface/smolagents, 2025

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.367351Z

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Observation 3067b16b-3f58-4d30-8537-c6cba9e32084 · outbound

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

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.486689Z

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Observation d21dd24e-8f78-4877-99fa-aac28be7692b · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL HybridFlow: A Flexible and Efficient RLHF Framework

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.579121Z

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Observation 6813e7ef-1462-4ad8-84b5-999c92740970 · outbound

This paper cites TaskCraft: Automated Generation of Agentic Tasks.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL TaskCraft: Automated Generation of Agentic Tasks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.611121Z

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Observation ad93aa0b-f9c9-45de-b47f-7ae4d9c8160f · outbound

This paper cites R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.684171Z

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Observation 5d5138e2-3aec-4a06-852f-36d9df1b15c8 · outbound

This paper cites ZeroSearch: Incentivize the Search Capability of LLMs without Searching.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.744588Z

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Observation b0e531ab-4bc4-429e-996f-fb502cc01dd8 · outbound

This paper cites Simpledeepsearcher: Deep information seeking via web-powered reasoning trajectory synthesis.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Simpledeepsearcher: Deep information seeking via web-powered reasoning trajectory synthesis

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.819776Z

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Observation 2b63bb7a-bc4c-493c-a671-6eb18354746d · outbound

This paper cites Agent kb: Leveraging cross-domain experience for agentic problem solving.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Agent kb: Leveraging cross-domain experience for agentic problem solving

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:34.891354Z

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Observation a0fc01ef-b2d6-40a4-bf48-752f4cd7f1bf · outbound

This paper cites WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization

Reference 54

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Observation 997d1e1f-332b-40b3-8ef6-f4b208246bd0 · outbound

This paper cites Qwq: Reflect deeply on the boundaries of the unknown, November 2024.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Qwq: Reflect deeply on the boundaries of the unknown, November 2024

Reference 55

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Verl-tool: A version of verl to support tool use, 2025

Reference 56

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Observation dd42928b-5df2-4c13-9dca-a8e7060461e5 · outbound

This paper cites Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 57

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Observation 978d58b2-0f67-45da-9586-f4a534701f94 · outbound

This paper cites Musique: Multihop questions via single-hop question composition.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Musique: Multihop questions via single-hop question composition

Reference 58

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Observation d91c3373-7184-4626-9b61-c92451bd1110 · outbound

This paper cites Otc: Optimal tool calls via reinforcement learning.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Otc: Optimal tool calls via reinforcement learning

Reference 59

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Observation 47a1e444-233c-42af-bd0b-a92e31b51857 · outbound

This paper cites StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization

Reference 60

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Observation ff451199-ebf8-41dd-a9cf-d0eae2555210 · outbound

This paper cites Corag: A cost-constrained retrieval optimization system for retrieval-augmented generation.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Corag: A cost-constrained retrieval optimization system for retrieval-augmented generation

Reference 61

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Observation 81e9dadf-6486-4031-b452-4f976bd78665 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Chain-of-thought prompting elicits reasoning in large language models

Reference 62

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Observation c4d45d17-24ac-4d7b-9ab3-b79be03868be · outbound

This paper cites BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Reference 63

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Observation be2f730c-502f-49ba-afd5-8c9677e23336 · outbound

This paper cites AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning

Reference 64

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Observation e5141165-c921-4a66-8436-6ff4afbefcc0 · outbound

This paper cites WebDancer: Towards Autonomous Information Seeking Agency.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL WebDancer: Towards Autonomous Information Seeking Agency

Reference 65

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Observation ad09509b-22a5-476b-9da2-a04b43cfa779 · outbound

This paper cites Simpletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning.https://simpletir.notion.site/report, 2025.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Simpletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning.https://simpletir.notion.site/report, 2025

Reference 66

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Observation 265c1ce6-2293-4363-80f6-774048b00e63 · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 67

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Observation b4dd1e80-a7f9-410c-8c91-6a50e0b59175 · outbound

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

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL React: Synergizing reasoning and acting in language models

Reference 68

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Observation a7267780-6bf5-4d8c-954b-797bca6902e2 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 69

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Observation 55570e4e-d987-4a9d-890f-b5f8c7a3f39c · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models

Reference 70

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Observation 5acf09dd-76c4-4cb9-8a9a-2a43bec7bf88 · outbound

This paper cites SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

Reference 71

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Observation 976deefd-64fb-4a82-a025-15911957bbc9 · outbound

This paper cites Flowmind: automatic workflow generation with llms.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Flowmind: automatic workflow generation with llms

Reference 72

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Observation 5cecdfa7-9a73-4d04-8df6-fbb414618969 · outbound

This paper cites EvolveSearch: An Iterative Self-Evolving Search Agent.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL EvolveSearch: An Iterative Self-Evolving Search Agent

Reference 73

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Observation 2bdecd40-5aca-4a54-9bbc-091346685d9a · outbound

This paper cites AFlow: Automating Agentic Workflow Generation.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL AFlow: Automating Agentic Workflow Generation

Reference 74

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Observation 3e140002-0657-44be-bb0f-5d16e054be44 · outbound

This paper cites Process vs.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Process vs

Reference 75

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Observation aab162f6-2a18-4c07-9bd7-a58fc8dc8939 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 76

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Observation 594b156b-bb91-45a3-a408-e3da399ca22e · outbound

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

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 77

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Observation f0758f8d-b297-46e9-8cc1-d30b5fcd370d · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL OpenResearcher: Unleashing AI for Accelerated Scientific Research

Reference 78

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Observation a1e83de9-b3f6-4826-8813-6dc0726be362 · outbound

This paper cites DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

Reference 79

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Observation 9e66a4e0-1668-4f1f-8d1c-035c6775baf3 · outbound

This paper cites Agents: An Open-source Framework for Autonomous Language Agents.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Agents: An Open-source Framework for Autonomous Language Agents

Reference 80

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Observation 3d81235d-624b-42cd-af34-ed281e30b754 · outbound

This paper cites Symbolic Learning Enables Self-Evolving Agents.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Symbolic Learning Enables Self-Evolving Agents

Reference 81

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Observation bea47f24-507f-4a7c-aacb-ab5965079b03 · outbound

This paper cites OAgents: An Empirical Study of Building Effective Agents.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL OAgents: An Empirical Study of Building Effective Agents

Reference 82

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Observation 4bc80da3-53d8-4761-b32d-2fd4cf67c913 · outbound

This paper cites Scaling Test-time Compute for LLM Agents.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Scaling Test-time Compute for LLM Agents

Reference 83

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Observation 692098ca-827b-401b-8f0c-3d73f7049b76 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 84

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Observation 249683ff-7b58-4757-983b-819eff462a8a · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 85

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Observation 1cd27c36-f15d-4f28-8e14-c1db3961abfa · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 86

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Observation a60e1ba7-42e8-4d3f-a083-84864fc73b46 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 87

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Observation 873d6a4c-b7d7-498f-ba4f-5dfc30521fd8 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 88

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Observation 905295ae-3d75-4b20-9f22-39e4d96e6773 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 89

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Observation 0919bf46-5b23-41f7-90d4-74524349be45 · outbound

This paper cites An archive of all existing APOD pages (current date through.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL An archive of all existing APOD pages (current date through

Reference 90

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Observation 7e8e6feb-dc51-4b6c-aead-2bb9fbc372e6 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 91

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Observation 06be36e4-0a7b-4720-932d-6f6bc3e8dac4 · outbound

This paper cites an unresolved cited work.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 92

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Observation 2b93abf2-062e-4341-a327-adacede45e6c · outbound

This paper cites 1, 2015 (Credit: NASA/Bill Ingalls).

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL 1, 2015 (Credit: NASA/Bill Ingalls)

Reference 93

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Observation e796f871-e906-41de-aff2-01168f04413e · outbound

This paper cites </observation> Step 3 <think> Step 1 of the task is to identify the NASA Astronomy Picture of the Day (APOD) from the first week of August 2015 showing city lights on the horizon.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL </observation> Step 3 <think> Step 1 of the task is to identify the NASA Astronomy Picture of the Day (APOD) from the first week of August 2015 showing city lights on the horizon

Reference 94

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Observation 36753cd6-d66e-4f7a-bae2-1df7277ade9b · outbound

This paper cites Marquette had a population of 20,629 at the.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Marquette had a population of 20,629 at the

Reference 95

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Observation 43afb520-cf77-4d5f-8a29-315a88ecbeff · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 96

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Observation 856fc924-0d4e-4a7a-a21b-72606be3a7a2 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 97

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Observation 6c50ade7-1dde-42f3-8be2-eaef972b8c75 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL “Back in the 1600’s he set up several missions, including

Reference 98

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Observation 51893399-7f40-4f42-8504-10392a842094 · outbound

This paper cites an unresolved cited work.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 99

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Observation 06e14bd8-408b-471c-ba65-a08968c95961 · outbound

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Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Completed in 1894, the Marquette Building brings Chicago’s early history to life in an artistic and elegant setting

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-05T23:57:38.557553Z

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Observation 80c25442-ad78-4c32-9ada-b5f4885910b9 · outbound

This paper cites an unresolved cited work.

Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL Unresolved cited work

Reference 101

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Pith citing papers

Observation dcdc812e-5f22-4df7-b1b8-81b57d114aa0 · inbound

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SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 18

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Observation 1a7eaf6c-332a-4b96-a4a8-af5f03ecb7d2 · inbound

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A Survey of Reinforcement Learning for Large Reasoning Models Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 283

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Observation f01437bd-012f-4fca-8e25-4f829e0cd832 · inbound

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Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 16

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Observation e54dbf78-fc94-44f5-8dbc-82d62a9e7f40 · inbound

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling cites this paper.

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-17T21:45:17.898438Z

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Observation fcd7cc45-b888-4cd7-b8ac-6651321df2f3 · inbound

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Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 2

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Source-reported events for the cited work

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Observation 45d13e58-7e23-4823-89be-325b8f3bb308 · inbound

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LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-05T15:11:10.916430Z

Source-reported events for the cited work

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Observation 3c6f6425-b17e-4b45-aa4c-911cbb85099b · inbound

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence cites this paper.

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:25:54.213509Z

Source-reported events for the cited work

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Observation 61c22ce0-a9eb-4916-b90d-779314c5c6a1 · inbound

MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate cites this paper.

MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:41:20.540725Z

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.

Observation 0c4865a5-0294-4186-b4c5-e2124955073d · inbound

SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States cites this paper.

SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:41:06.769144Z

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.

Observation 1e23cc51-8da2-4836-a10b-219e68569a74 · inbound

SOD: Step-wise On-policy Distillation for Small Language Model Agents cites this paper.

SOD: Step-wise On-policy Distillation for Small Language Model Agents Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:40:54.388059Z

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.

Observation ee513b98-3b27-4c01-8302-c504f50af3bd · inbound

SOD: Step-wise On-policy Distillation for Small Language Model Agents cites this paper.

SOD: Step-wise On-policy Distillation for Small Language Model Agents Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T05:20:45.013251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7b11d48-501a-4101-8a2e-0be63c69dd0f · inbound

Scaling Mobile Agent Systems: From Capability Density to Collective Intelligence cites this paper.

Scaling Mobile Agent Systems: From Capability Density to Collective Intelligence Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:24.624276Z

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.

Observation f1c0f03c-60f7-42d7-9d7c-12631d1e739a · inbound

ICRL: Learning to Internalize Self-Critique with Reinforcement Learning cites this paper.

ICRL: Learning to Internalize Self-Critique with Reinforcement Learning Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T18:02:42.426375Z

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.

Observation d80bed98-4296-410e-bd21-44b6724818ba · inbound

Efficient Agentic Reasoning Through Self-Regulated Simulative Planning cites this paper.

Efficient Agentic Reasoning Through Self-Regulated Simulative Planning Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-22T06:34:41.023546Z

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.

Observation 7dae01a7-445c-4944-b479-069ec03d4324 · inbound

Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning cites this paper.

Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:07:28.234364Z

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.

Observation 9aa5ba37-f362-4f0e-8428-9d85470c07af · inbound

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application cites this paper.

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 262

Resolution: verified exact; arxiv_id, observed 2026-06-27T09:50:48.446656Z

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.

Observation b1b9892c-b075-46dc-8e03-96c7a9adf5cc · inbound

Mathematical methods of reinforcement learning cites this paper.

Mathematical methods of reinforcement learning Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

Reference 121

Resolution: verified exact; local_arxiv, observed 2026-07-09T22:56:37.743625Z

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.