Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:42:37.887634Z
Paper Citation Record · LEDGER
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
As of 17 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 30 inbound Pith citation observations for arXiv:2505.23885.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:13:39.194980Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
96 of 96 outbound references displayed
- verified exact2
- verified fuzzy16
- unresolved78
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation 91aee782-fe76-4f72-b9ce-8ac7637705a9 · outbound
This paper cites Claude 3.7 sonnet and claude code, February 2025.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Claude 3.7 sonnet and claude code, February 2025
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:31.702131Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4386a7ad-296b-4200-b9ef-3a7323a9189e · outbound
This paper cites TapeAgents: a Holistic Framework for Agent Development and Optimization.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation TapeAgents: a Holistic Framework for Agent Development and Optimization
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:31.777686Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation da351dd0-93da-4672-b144-ced5533be68a · outbound
This paper cites FireAct: Toward Language Agent Fine-tuning.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation FireAct: Toward Language Agent Fine-tuning
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:31.904774Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bda087bc-2891-45a7-a225-ac8ad12ee88a · outbound
This paper cites AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.044010Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fa3b2a13-dba9-4065-a0a5-b92123ebd0cd · outbound
This paper cites Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.124907Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c46db65-b8d0-4681-a708-b13a788ca2e7 · outbound
This paper cites ReTool: Reinforcement Learning for Strategic Tool Use in LLMs.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.207734Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77d3cccf-c2da-4ed5-87af-d3af8e4d903e · outbound
This paper cites Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.348935Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f88df7a-31fb-4d08-b7c7-73940e30d757 · outbound
This paper cites Langfun.https://github.com/google/langfun, 2023.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Langfun.https://github.com/google/langfun, 2023
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.425251Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 12ed2e58-d17a-4825-87f8-63a45ae8839a · outbound
This paper cites Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.510127Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37afe2b6-6ea4-4e8a-8763-f4d626727099 · outbound
This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Large Language Model based Multi-Agents: A Survey of Progress and Challenges
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.554886Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e459dc5d-abb2-4c98-9da1-e2a1eeec238d · outbound
This paper cites Autonomous agentic ai: execute multi-step workflows autonomously.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Autonomous agentic ai: execute multi-step workflows autonomously
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.597993Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 99748e46-905a-4a1c-8fab-6c0baf071b5b · outbound
This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.641011Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 90fa0509-8035-4ec1-8f23-c1bdc9561f78 · outbound
This paper cites AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.678440Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 08308c6d-c82d-42e8-9cbb-8c033428e52d · outbound
This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.707939Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 00c026a0-f708-4fc7-8f69-67e30790592c · outbound
This paper cites Camel: Communicative agents for" mind" exploration of large language model society.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Camel: Communicative agents for" mind" exploration of large language model society
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.742215Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 637e62a7-8182-468c-bd52-8c8395c28202 · outbound
This paper cites Coevolving with the other you: Fine-tuning llm with sequential cooperative multi-agent reinforcement learning.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Coevolving with the other you: Fine-tuning llm with sequential cooperative multi-agent reinforcement learning
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.817064Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e84025b-699c-4ad7-9582-1f4391f9f117 · outbound
This paper cites Gaia: a benchmark for general ai assistants.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Gaia: a benchmark for general ai assistants
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:32.917216Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 050dd69a-8ee2-4fe3-ade6-b551ebdc6e78 · outbound
This paper cites Multi-agent experiment v0.1 msr ai frontiers (autogen team members).
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Multi-agent experiment v0.1 msr ai frontiers (autogen team members)
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.008594Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a6cd953f-9550-4a7f-ba27-7d1b232836c1 · outbound
This paper cites Malt: Improving reasoning with multi-agent llm training.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Malt: Improving reasoning with multi-agent llm training
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.102768Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2cd434fb-0b0e-48fa-82ef-31053255e72b · outbound
This paper cites Hello gpt-4o, May 2024.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Hello gpt-4o, May 2024
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.206895Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7811ea08-64bc-4985-8730-2a51d5f903dc · outbound
This paper cites Introducing deep research.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Introducing deep research
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.286796Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b4ae600-625e-40c2-9a32-87312edfaa3d · outbound
This paper cites Why Do Multiagent Systems Fail? In ICLR 2025 Workshop on Building Trust in Language Models and Applications, 2025.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Why Do Multiagent Systems Fail? In ICLR 2025 Workshop on Building Trust in Language Models and Applications, 2025
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.386804Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f70b9856-ce02-4eab-a0b3-197e7b6d15fd · outbound
This paper cites Compositional Semantic Parsing on Semi-Structured Tables.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Compositional Semantic Parsing on Semi-Structured Tables
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.502831Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b08b5f1c-dc3f-4049-b9ba-cc3b23103658 · outbound
This paper cites HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.615592Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 14a271ba-3cbd-410d-abf3-89156620648a · outbound
This paper cites WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.721752Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f374a80b-a4b7-46d3-9b62-7693524f1ce4 · outbound
This paper cites ChatDev: Communicative Agents for Software Development.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation ChatDev: Communicative Agents for Software Development
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.775904Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54043157-3dca-4002-9dac-941a09066168 · outbound
This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.819868Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3fbc137-c57a-4061-a594-c8a1a694b32f · outbound
This paper cites Direct preference optimization: Your language model is secretly a reward model.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Direct preference optimization: Your language model is secretly a reward model
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.874138Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 07ad8e3a-8360-4905-bf61-18e36a20a821 · outbound
This paper cites Open- source deepresearch – freeing our search agents.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Open- source deepresearch – freeing our search agents
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.924231Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f385ab2-c488-4c5b-a6dc-342e8a1d6438 · outbound
This paper cites ‘smolagents‘: a smol library to build great agentic systems.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation ‘smolagents‘: a smol library to build great agentic systems
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:33.992909Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 82d13071-9405-4b8f-8947-cac063cfa783 · outbound
This paper cites Leveraging large language models for optimised coordination in textual multi-agent reinforcement learning.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Leveraging large language models for optimised coordination in textual multi-agent reinforcement learning
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.051475Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5172e71-5f28-4917-a73f-ff581a50d963 · outbound
This paper cites Debategpt: Fine-tuning large language models with multi-agent debate supervision.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Debategpt: Fine-tuning large language models with multi-agent debate supervision
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.091536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4c042a0f-f30e-41e7-a719-ba6232585ac4 · outbound
This paper cites Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.120413Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6eec2b2-2592-4b39-b503-e74ae565abd7 · outbound
This paper cites Autoagent: A fully-automated and zero-code framework for llm agents.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Autoagent: A fully-automated and zero-code framework for llm agents
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.148422Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f1da812b-7ebd-4ae0-91eb-a0030f5f9db0 · outbound
This paper cites Meet trase systems, the ai agent platform.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Meet trase systems, the ai agent platform
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:46.235885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bc00e0a5-f09b-4c93-bb93-e8adefc65fb4 · outbound
This paper cites AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.204950Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81e151c8-d8cf-465b-9403-a16f994fa712 · outbound
This paper cites Ag2: Open-source agentos for ai agents, 2024.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Ag2: Open-source agentos for ai agents, 2024
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:46.103066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9d1c98a6-6d6b-427d-bac5-14c57871a8ce · outbound
This paper cites SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.258962Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd86ebdf-3f9b-48ff-bda0-38e3e97c457a · outbound
This paper cites Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.314187Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 320a8591-5093-4ca7-b203-c5a3bbbede80 · outbound
This paper cites OS-Copilot: Towards Generalist Computer Agents with Self-Improvement.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.335079Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 902e570e-25d7-4ebe-a253-43e089e37ec5 · outbound
This paper cites OpenAgents: An Open Platform for Language Agents in the Wild.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation OpenAgents: An Open Platform for Language Agents in the Wild
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.419064Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c104d0af-66fc-4703-99c6-1003c8fa6cf3 · outbound
This paper cites MPO: Boosting LLM Agents with Meta Plan Optimization.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation MPO: Boosting LLM Agents with Meta Plan Optimization
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.489727Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 034eb0ae-7300-403b-bf08-cd425df0f5be · outbound
This paper cites Qwen2.5 Technical Report.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Qwen2.5 Technical Report
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.542867Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 48edb476-918d-4471-aea4-149100c2085d · outbound
This paper cites AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.577875Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2060af3c-b622-4d3c-9333-8c2cdf530e56 · outbound
This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.629997Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4eefef7b-74e7-4ac0-9a53-136341c6f1b8 · outbound
This paper cites StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.711360Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 86f2020c-077f-4619-949a-b0aad02c7da8 · outbound
This paper cites AgentTuning: Enabling Generalized Agent Abilities for LLMs.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation AgentTuning: Enabling Generalized Agent Abilities for LLMs
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.822811Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e1708a38-668a-4001-89a7-703b547e44f6 · outbound
This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.868457Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ffc4f05-8eee-49a8-b12c-1c574339e80a · outbound
This paper cites DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:34.919672Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1454204d-6f50-4207-9c68-4aac41fc2c92 · outbound
This paper cites SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:35.042447Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a20d0831-495a-47c0-9289-f04e15dd4f26 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 52
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:46.015881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a028e0c3-e09c-4c13-aad2-0b88f91ffaad · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 53
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:45.852175Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 20c6a4dd-df1a-403f-bbb7-16180491f7d5 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 54
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:45.768865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 054d4313-7284-4a19-aaab-9cf23cc90ab0 · outbound
This paper cites How many applicants for the job in the PDF are only missing a single qualification?.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation How many applicants for the job in the PDF are only missing a single qualification?
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:35.302464Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f0c6579-f11a-4e45-97fb-444742dab660 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 56
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:45.678598Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 00e0b3ff-8d4f-44cf-9414-323dd3683202 · outbound
This paper cites It functions without requiring an internet connection.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation It functions without requiring an internet connection
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:45.583971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 28c47b34-e8ae-4f35-bb9c-207ce5b6618d · outbound
This paper cites How many applicants for the job in the PDF are only missing a single qualification?.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation How many applicants for the job in the PDF are only missing a single qualification?
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:45.437372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fd5f2977-110d-4257-9edf-6f71240ef6f3 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 60
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:45.506872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ddbad318-9d73-4d76-8ec1-c1d5e67cf749 · outbound
This paper cites How many applicants for the job in the PDF are only missing a single qualification?.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation How many applicants for the job in the PDF are only missing a single qualification?
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:45.359173Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c44088ad-5b9e-4961-8011-5768bb93f392 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 62
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:45.263327Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5e6a0f42-6755-4e64-b075-7ac3acd2ee79 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 63
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:45.124736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bbb289a6-82a9-418e-902f-f9ab4f68c5fb · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 64
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:44.960643Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4ae8dde4-5725-41d5-b73f-b83202217433 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 65
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:44.831880Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c57aed5f-e039-416d-b824-182d78ed02bc · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 66
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:44.749898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 26ffb137-fa57-465c-8ffa-a257b0bd2bfe · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 67
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:44.658550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 756b8076-5b6d-49bf-b956-9ea3a0e504f3 · outbound
This paper cites Applicants.xlsx.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Applicants.xlsx
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:44.551273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f8650be0-976a-4108-95cb-cb38959d7e92 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 69
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:44.355218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation cb2dad32-d75f-4373-979d-5c814cdc48a4 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 70
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:44.213073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1ef3eba0-9a2b-4f85-8485-b73eec35da82 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 71
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:44.109528Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b21eb60d-26fb-4953-a9c0-f341975eead7 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 72
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:43.967388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation dffe4413-e887-425a-a277-b9e104e75679 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 73
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:43.897025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 69584173-cc88-42df-9e90-90282f0308d0 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 74
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:43.733996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 237f46ab-6029-4985-8644-09606399958b · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 75
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:43.549168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 27ab7018-759e-48f0-9dcf-26a9cc68da60 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 76
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:43.366737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d6e30ca6-a8a8-4f75-bc41-caf6a22a1466 · outbound
This paper cites Second Language.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Second Language
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:43.166743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f04c0e84-1e7d-4220-a504-a054cd4e7b40 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 78
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:43.003770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 33ccdeaf-f694-4d68-a4e5-f00be9bd3b26 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 79
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:42.799330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c68792c4-99b7-4567-8e98-eaab92191fd8 · outbound
This paper cites Trans fatty acid contents in chocolates and chocolate wafers in Turkey.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Trans fatty acid contents in chocolates and chocolate wafers in Turkey
Reference 80
- Resolution
- verified exact
- raw_fallback, observed 2026-08-07T12:42:38.547798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 16b9ada8-8e9d-4fba-ba6d-b77e9badfc81 · outbound
This paper cites The closest Metrorail stop is at L’Enfant Plaza.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation The closest Metrorail stop is at L’Enfant Plaza
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:42.571189Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2b385274-5f78-4b8f-8733-dff1292383f8 · outbound
This paper cites National Airport.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation National Airport
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:42.381421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ee9e416c-2d7a-4ba7-88d9-52f097b585a5 · outbound
This paper cites James Cheater Cheater Beater CFM Season 4.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation James Cheater Cheater Beater CFM Season 4
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:42.171569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d7931d0a-1233-4e9d-8be2-1ddb221ca486 · outbound
This paper cites ocean liner floating prop The Last V oyage.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation ocean liner floating prop The Last V oyage
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:42.021742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2643cb67-8b34-4476-9efc-dfd9c6070275 · outbound
This paper cites gamegrumps mario kart 8 deluxe may 14 2017.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation gamegrumps mario kart 8 deluxe may 14 2017
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:41.951903Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1df58ee4-d111-4dad-b48f-76322a93e3bd · outbound
This paper cites GCN Yoshi Circuit,.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation GCN Yoshi Circuit,
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:41.856543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9b628f79-c5a7-40cb-8e8d-135832beef80 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 87
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:41.723118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7fe4b522-1a3f-43d5-9e57-d3824aec3554 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 88
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:41.615882Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 79f79231-97a4-430d-a9a2-0208fd0f1cea · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 89
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:41.496535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a019e003-ad9e-4b68-98c0-d86d9c3b2c6f · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 90
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:41.329037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a6b7111d-aa94-4c44-95e3-b424c1a82507 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 91
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:41.115926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7cc5bfb5-fc3d-401c-8e58-b0ad78896a8a · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 92
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:40.987115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0e98760a-456d-4af4-b43b-b9e093b2c1aa · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 93
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:40.843093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2c6d7a7e-7452-4d41-aa85-777ebd98ebc2 · outbound
This paper cites an unresolved cited work.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Unresolved cited work
Reference 94
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T12:42:40.695972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 26c0b9bf-ed67-4b91-92c4-0bff43fc3e34 · outbound
This paper cites Find the Wikipedia page for the 2019 game that won the British Academy Games Awards.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Find the Wikipedia page for the 2019 game that won the British Academy Games Awards
Reference 95
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:40.483539Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c67173d2-ee05-4565-b4cc-a056f4995ed2 · outbound
This paper cites 2019 game that won the British Academy Games Awards.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation 2019 game that won the British Academy Games Awards
Reference 96
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:40.242081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bf4947fc-6205-4d19-bd67-2533e5ca443b · outbound
This paper cites Olga Tapia hafnia alvei.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Olga Tapia hafnia alvei
Reference 97
- Resolution
- verified exact
- raw_fallback, observed 2026-08-07T12:42:38.278791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 355c8086-fe48-488a-982f-cf80e901fa10 · outbound
This paper cites Pie Menus or Linear Menus, Which Is Better?.
OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Pie Menus or Linear Menus, Which Is Better?
Reference 98
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T12:42:40.033311Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
Observation ffe8e6dd-af23-482a-be2f-377ca11b373a · inbound
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models cites this paper.
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 278
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T08:40:41.643460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2f2376da-c7ce-4ee9-97d7-a0d8e0782a53 · inbound
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes cites this paper.
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:13:39.194980Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df8f58ff-efd3-46f5-bbb4-55f4a002f89c · inbound
MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications cites this paper.
MATE: LLM-Powered Multi-Agent Translation Environment for Accessibility Applications OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:10:12.502664Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c1742524-cab3-455d-ad94-c969dd422554 · inbound
PyVision: Agentic Vision with Dynamic Tooling cites this paper.
PyVision: Agentic Vision with Dynamic Tooling OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:31:28.376138Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1dee2c63-bca1-4dd7-b125-4e2ccb52c189 · inbound
Aime: Towards Fully-Autonomous Multi-Agent Framework cites this paper.
Aime: Towards Fully-Autonomous Multi-Agent Framework OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:59:40.057408Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d397e1b-5d6a-4635-8b91-3f7a10240f06 · inbound
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T22:23:15.462600Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c497974f-a139-4110-857e-29b97e572122 · inbound
Agentic Web: Weaving the Next Web with AI Agents cites this paper.
Agentic Web: Weaving the Next Web with AI Agents OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 96
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:05:36.367389Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 89b0f690-1830-4613-b407-6e923d38572a · inbound
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent cites this paper.
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 13
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T18:56:23.944685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 060a0a30-0e08-4484-85b8-b9de86eb0618 · inbound
SimViews: An Interactive Multi-Agent System Simulating Visitor-to-Visitor Conversational Patterns to Present Diverse Perspectives of Artifacts in Virtual Museums cites this paper.
SimViews: An Interactive Multi-Agent System Simulating Visitor-to-Visitor Conversational Patterns to Present Diverse Perspectives of Artifacts in Virtual Museums OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 1962
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:58:53.771704Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6f539eb-1a58-4bed-a208-5a9535f3dc05 · inbound
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL cites this paper.
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd5fd225-1cce-4510-83b4-7298b75cac67 · inbound
SWIRL: A Staged Workflow for Interleaved Reinforcement Learning in Mobile GUI Control cites this paper.
SWIRL: A Staged Workflow for Interleaved Reinforcement Learning in Mobile GUI Control OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:18:52.500880Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0eb961a3-8564-4129-b000-36a1d4aaa251 · inbound
PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation cites this paper.
PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T20:22:50.834297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 07fbba1b-dd98-46de-8e1d-69af1e45cf4b · inbound
PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation cites this paper.
PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:02:49.621878Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9a501a8a-bc0f-4015-ab05-68363163a4ea · inbound
Latent Collaboration in Multi-Agent Systems cites this paper.
Latent Collaboration in Multi-Agent Systems OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T20:17:49.197736Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65f5c482-76e6-4966-aecd-6c0ee9f27bfa · inbound
Latent Collaboration in Multi-Agent Systems cites this paper.
Latent Collaboration in Multi-Agent Systems OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T06:52:32.093332Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 320fa6ae-0d2a-4fdd-8427-d44471c32f25 · inbound
Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei cites this paper.
Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T11:51:52.436356Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a757264d-dc11-4f7f-ad7c-989a71b44a19 · inbound
ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation cites this paper.
ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T03:30:06.633654Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aad4bbe9-662a-4f18-88ec-1a866a9f8e4b · inbound
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems cites this paper.
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T22:58:07.278993Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df2ec206-1c2b-478d-9f77-c4499b245904 · inbound
Transition from Statistical to Hardware-Limited Scaling in Photonic Quantum State Reconstruction cites this paper.
Transition from Statistical to Hardware-Limited Scaling in Photonic Quantum State Reconstruction OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T22:25:15.261445Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea923e4e-650a-466f-a3e7-19e88e69a850 · inbound
GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces cites this paper.
GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T17:33:02.375040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 46a2a182-8ae1-49a6-858c-f2708a87e9fc · inbound
EigentSearch-Q+: Enhancing Deep Research Agents with Structured Reasoning Tools cites this paper.
EigentSearch-Q+: Enhancing Deep Research Agents with Structured Reasoning Tools OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T00:15:55.718783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ae772254-e14d-4969-af34-a3d88abe6637 · inbound
From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company cites this paper.
From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 19
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T19:21:09.456893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 50e4793a-ff87-4971-9761-b80886000ae9 · inbound
OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction cites this paper.
OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T23:41:20.377840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c6ad22fa-6416-4151-967c-fe5f73f0421c · inbound
Learning Agent Routing From Early Experience cites this paper.
Learning Agent Routing From Early Experience OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:35:57.423223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7c339105-f20f-4faa-aa81-9c3ef04ef737 · inbound
Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent cites this paper.
Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T07:16:44.621286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 107a24f6-4dcc-424c-862a-8db13b3244fd · inbound
Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seeking cites this paper.
Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seeking OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:47:10.213293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 53f3ac0a-8054-429b-bcd4-6e065ce7131b · 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 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 248
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-27T09:50:48.551642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1be00bf4-9fb8-4eb5-9aac-7caffa9eb5e7 · inbound
GS-Agent: Creating 4D Physical Worlds With Generative Simulation cites this paper.
GS-Agent: Creating 4D Physical Worlds With Generative Simulation OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T07:14:42.076735Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e52e806d-7d05-446c-92ef-eab78f4db8fe · inbound
Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity cites this paper.
Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:07:42.028159Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37c71dc2-58c2-4409-866f-41579c5d1722 · inbound
Architectural Implications of Agentic AI Workflows cites this paper.
Architectural Implications of Agentic AI Workflows OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:49:15.590641Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.