AgentOmnia: Scaling Agentic Models for Full-Scenario Applications | Paper Citation Record

Observation b0b46990-f0b4-41ba-953d-f508e842a4ab · outbound

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

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications ReAct: Synergizing reasoning and acting in language models

Reference 1

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Observation c162e5f0-028f-48c7-b1a4-74ad6b27c090 · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Toolformer: Language models can teach themselves to use tools

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:13.606154Z

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Observation 8b9fee35-ceaf-4190-9c58-8a1681dd2817 · outbound

This paper cites ToolLLM: Facilitating large language models to master 16000+ real-world APIs.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications ToolLLM: Facilitating large language models to master 16000+ real-world APIs

Reference 3

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Observation 72be1729-e266-4c5b-bc66-5ac7c0c6902b · outbound

This paper cites Patil, Tianjun Zhang, Xin Wang, and Joseph E.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Patil, Tianjun Zhang, Xin Wang, and Joseph E

Reference 4

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Observation 74fe678d-b19d-406a-ad87-d5cfacca27cc · outbound

This paper cites ToolTalk: Evaluating Tool-Usage in a Conversational Setting.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications ToolTalk: Evaluating Tool-Usage in a Conversational Setting

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:14.110286Z

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Observation ceb3b68c-bc60-4a3a-9cb4-8172151e9d01 · outbound

This paper cites API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:14.153015Z

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Observation 3e5136d4-e21a-434a-804b-8516150b1205 · outbound

This paper cites AgentBench: Evaluating LLMs as agents.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications AgentBench: Evaluating LLMs as agents

Reference 7

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Observation 8117e4d2-e992-46ff-b9a7-35d4df4de73f · outbound

This paper cites WebArena: A realistic web environment for building autonomous agents.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications WebArena: A realistic web environment for building autonomous agents

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:14.251202Z

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Observation 03565b86-67cb-4d32-8ca2-0a25ac95d186 · outbound

This paper cites AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:14.426866Z

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Observation 8ab59d87-cc19-4419-b918-02e7f37ce06a · outbound

This paper cites OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:14.544686Z

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Observation f47bff33-85fe-4a00-9617-e374f7e86c6d · outbound

This paper cites $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:14.718397Z

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Observation 346c5fb3-7925-4d70-8c01-c90025e0a818 · outbound

This paper cites DeepPlanning: Benchmarking long-horizon agentic planning with verifiable constraints.arXiv preprint arXiv:2601.18137, 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications DeepPlanning: Benchmarking long-horizon agentic planning with verifiable constraints.arXiv preprint arXiv:2601.18137, 2026

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:14.917091Z

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Observation e8dfb898-edcd-459f-82b7-316fe344fb7f · outbound

This paper cites VitaBench: Benchmarking LLM agents with versatile interactive tasks in real-world applications.arXiv preprint arXiv:2509.26490, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications VitaBench: Benchmarking LLM agents with versatile interactive tasks in real-world applications.arXiv preprint arXiv:2509.26490, 2025

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:15.027046Z

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Observation ce215630-2872-4aa2-adb8-2c1cc5b5fa23 · outbound

This paper cites The tool decathlon: Benchmarking language agents for diverse, realistic, and long-horizon task execution.arXiv preprint arXiv:2510.25726, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications The tool decathlon: Benchmarking language agents for diverse, realistic, and long-horizon task execution.arXiv preprint arXiv:2510.25726, 2025

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:15.219994Z

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Observation 8e92d2a9-5bc0-41f5-b6e8-ca8469372fe7 · outbound

This paper cites Gaia2: Benchmarking LLM agents on dynamic and asynchronous environments.arXiv preprint arXiv:2602.11964, 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Gaia2: Benchmarking LLM agents on dynamic and asynchronous environments.arXiv preprint arXiv:2602.11964, 2026

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:15.349911Z

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Observation 80af2bea-ba60-4879-8095-bc5b3e427a4e · outbound

This paper cites AgentTuning: Enabling generalized agent abilities for LLMs.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications AgentTuning: Enabling generalized agent abilities for LLMs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:15.498465Z

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Observation 44eaf730-8743-45e2-82bb-d3dd4f6c71e6 · outbound

This paper cites AgentSkiller: Scaling generalist agent intelligence through semantically integrated cross-domain data synthesis.arXiv preprint arXiv:2602.09372, 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications AgentSkiller: Scaling generalist agent intelligence through semantically integrated cross-domain data synthesis.arXiv preprint arXiv:2602.09372, 2026

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:15.997564Z

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Observation a627f7a2-73de-40ac-8c8a-79704e0755bc · outbound

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

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:16.200638Z

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Observation 0f5d9833-9ae2-41bf-a710-9838f18f47cd · outbound

This paper cites Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:16.385740Z

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Observation be09b4b2-6c86-462a-96d8-d929d5ace596 · outbound

This paper cites Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:16.523167Z

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Observation 2bc7ea6a-b277-4acd-9cb3-21332b85d0d0 · outbound

This paper cites OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:16.681296Z

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Observation f48550a9-3235-439c-aa50-415a3e26592b · outbound

This paper cites AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:16.855650Z

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Observation 3b0b79ad-c691-4f48-90d0-8aa8eded79ea · outbound

This paper cites WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:16.973028Z

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Observation c8eb2476-a876-4d4f-95e6-f9b0399c63aa · outbound

This paper cites CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.064902Z

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Observation 01b64d77-1f89-4df4-bee8-bb8f6d0802f3 · outbound

This paper cites GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.216965Z

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Observation afff5545-726f-45fd-b48d-709ba2dcfc7c · outbound

This paper cites OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.347721Z

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Observation a742f357-078a-4605-9334-f3c07a1bafd2 · outbound

This paper cites SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.484022Z

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Observation dcf146c0-347d-498b-9e88-49c6e859863d · outbound

This paper cites Agentsynth: Scalable task generation for generalist computer-use agents.arXiv (Cornell University), abs/2506.14205, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Agentsynth: Scalable task generation for generalist computer-use agents.arXiv (Cornell University), abs/2506.14205, 2025

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.556821Z

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Observation 8456650f-93fc-4216-ae8d-4fd70d04dc28 · outbound

This paper cites EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.639212Z

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Observation 9c1dfdd8-e6a3-4b99-8a1b-2ba7d0f56717 · outbound

This paper cites Genenv: Difficulty-aligned co-evolution between LLM agents and environment simulators.CoRR, abs/2512.19682, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Genenv: Difficulty-aligned co-evolution between LLM agents and environment simulators.CoRR, abs/2512.19682, 2025

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.756270Z

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Observation 86e028c6-c714-43f2-bf1d-30298431ad08 · outbound

This paper cites Graph2eval: Automatic multimodal task generation for agents via knowledge graphs.CoRR, abs/2510.00507,.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Graph2eval: Automatic multimodal task generation for agents via knowledge graphs.CoRR, abs/2510.00507,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.867436Z

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Observation 41372fac-44ca-4f1e-8b00-6793d416097a · outbound

This paper cites Close the loop: Synthesizing infinite tool-use data via multi-agent role-playing.arXiv preprint arXiv:2512.23611, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Close the loop: Synthesizing infinite tool-use data via multi-agent role-playing.arXiv preprint arXiv:2512.23611, 2025

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.030301Z

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Observation 59e699cd-a0e1-41a2-9172-f486aff1133b · outbound

This paper cites URLhttps://doi.org/10.48550/arXiv.2510.00507.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications URLhttps://doi.org/10.48550/arXiv.2510.00507

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:17.949905Z

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Observation 48851208-f0aa-4801-ac9d-96c8f695195a · outbound

This paper cites Reasoning with language model is planning with world model.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Reasoning with language model is planning with world model

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.260403Z

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Observation c2137ae4-8cd9-4130-9e2b-8fff699f522f · outbound

This paper cites The false promise of imitating proprietary language models.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications The false promise of imitating proprietary language models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.123309Z

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Observation ce1a7cbe-9b13-46be-83eb-785e2466843c · outbound

This paper cites Qwen3-30B-A3B-Thinking-2507.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Qwen3-30B-A3B-Thinking-2507

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.427087Z

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Observation 7bdb8e19-14cb-472a-9274-677ad70d5535 · outbound

This paper cites Qwen3 Technical Report.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Qwen3 Technical Report

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.341721Z

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Observation 585af319-8973-4832-a8b6-1b105f2f3657 · outbound

This paper cites Weak-to-strong generalization: Eliciting strong capabilities with weak supervision.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Weak-to-strong generalization: Eliciting strong capabilities with weak supervision

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.626242Z

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Observation 0217fff4-900d-4dba-b088-db438dbf0b1b · outbound

This paper cites Nex-N2-mini.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Nex-N2-mini

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.515646Z

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Observation 8eacd945-76f1-463c-93e5-fdfb59c3d38c · outbound

This paper cites AgentGym: Evolving Large Language Model-based Agents across Diverse Environments.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.754752Z

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Observation 76835c3d-022c-4f24-9eb7-1d6585ad0c3d · outbound

This paper cites DigiRL: Training in-the-wild device-control agents with autonomous reinforcement learning.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications DigiRL: Training in-the-wild device-control agents with autonomous reinforcement learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:18.685301Z

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Observation 8c6db000-1089-4673-8124-45dec2a3da51 · outbound

This paper cites Self-instruct: Aligning language models with self-generated instructions.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Self-instruct: Aligning language models with self-generated instructions

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.201348Z

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Observation b1bb2712-7032-43aa-84fb-fa6502ce6bdc · outbound

This paper cites MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.076593Z

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Observation 334a7181-e49d-49e4-b66d-25f024adc106 · outbound

This paper cites Self-Refine: Iterative Refinement with Self-Feedback.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Self-Refine: Iterative Refinement with Self-Feedback

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.596894Z

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Observation b8d99ba1-4b7f-4a51-9d8a-8337277723db · outbound

This paper cites Self- rewarding language models.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Self- rewarding language models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.314234Z

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Observation 18253b20-bf5a-4eb0-bab1-a413842c19d4 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.429471Z

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Observation 209537c7-edf2-4958-943b-a557f7aa13da · outbound

This paper cites SEAL: Synergistic Co-Evolution of Agents and Learning Environments.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications SEAL: Synergistic Co-Evolution of Agents and Learning Environments

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.956424Z

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Observation 0abd943a-9804-4312-a9be-7e7980d303c5 · outbound

This paper cites SELF: Self-Evolution with Language Feedback.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications SELF: Self-Evolution with Language Feedback

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.769347Z

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Observation d5082c61-da55-4f70-a9ed-170ea7fe5c11 · outbound

This paper cites AgentEvolver: Towards efficient self-evolving agent system.arXiv preprint arXiv:2511.10395, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications AgentEvolver: Towards efficient self-evolving agent system.arXiv preprint arXiv:2511.10395, 2025

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:19.886098Z

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Observation 0a105275-95f9-411f-a631-721dad76a4fb · outbound

This paper cites Towards general agentic intelligence via environment scaling.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Towards general agentic intelligence via environment scaling

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.303673Z

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Observation 59420964-1d3e-47cc-ae8a-bc09c3c3abae · outbound

This paper cites OpenSkill: Open-World Self-Evolution for LLM Agents.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications OpenSkill: Open-World Self-Evolution for LLM Agents

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.065815Z

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Observation adcffc99-241e-48fb-906a-1595971cb7e4 · outbound

This paper cites Autoforge: Automated environment synthesis for agentic reinforcement learning.arXiv preprint arXiv:2512.22857, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Autoforge: Automated environment synthesis for agentic reinforcement learning.arXiv preprint arXiv:2512.22857, 2025

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.209285Z

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Observation 4ae3ba75-c77d-4a70-b1b5-4f1b98544de4 · outbound

This paper cites Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.507514Z

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Observation 7037e72a-afae-4e29-a103-9b047dfadae7 · outbound

This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.360761Z

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Observation 7bc54ac1-4cbe-4f58-b605-8775741ba252 · outbound

This paper cites EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.430907Z

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Observation d49ec8bb-2568-4669-84d7-ecc02e36ebe4 · outbound

This paper cites Let it flow: Agentic crafting on rock and roll, building the rome model within an open agentic learning ecosystem.arXiv preprint arXiv:2512.24873, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Let it flow: Agentic crafting on rock and roll, building the rome model within an open agentic learning ecosystem.arXiv preprint arXiv:2512.24873, 2025

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.865271Z

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Observation 1fc5d40f-f130-4a86-9e41-b585abd7572d · outbound

This paper cites DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.625282Z

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Observation 59aa6f80-54ff-4a90-8364-1a9dd8dc955f · outbound

This paper cites Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.733283Z

Source-reported events for the cited work

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Observation 6b45b8d9-83f7-4e13-8e04-eb09843a4dd2 · outbound

This paper cites The Verification Horizon: No Silver Bullet for Coding Agent Rewards.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications The Verification Horizon: No Silver Bullet for Coding Agent Rewards

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.186892Z

Source-reported events for the cited work

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Observation b045e45c-a13b-4d2c-aa64-f9ffe4eb635d · outbound

This paper cites A survey on llm-as-a-judge.The Innovation, 7(6), 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications A survey on llm-as-a-judge.The Innovation, 7(6), 2026

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:20.980602Z

Source-reported events for the cited work

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Observation 1642f806-1407-43b7-932d-b2d76280d4d5 · outbound

This paper cites From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.094327Z

Source-reported events for the cited work

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Observation f14d2dbb-355a-46d1-aea5-cc29494eef8d · outbound

This paper cites When speed kills stability: Demystifying RL collapse from the training-inference mismatch, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications When speed kills stability: Demystifying RL collapse from the training-inference mismatch, 2025

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.614609Z

Source-reported events for the cited work

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Observation 71c07a45-7329-4c20-9d61-7033f69c75d2 · outbound

This paper cites Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.327505Z

Source-reported events for the cited work

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Observation d623d54c-601b-4c6f-8a44-d37fc9746f12 · outbound

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

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.472971Z

Source-reported events for the cited work

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Observation ce937021-a1b1-49a4-9986-4ce4513e2750 · outbound

This paper cites Dapo: An open-source llm reinforcement learning system at scale.Advances in Neural Information Processing Systems, 38:113222–113244, 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Dapo: An open-source llm reinforcement learning system at scale.Advances in Neural Information Processing Systems, 38:113222–113244, 2026

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.984566Z

Source-reported events for the cited work

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Observation 1d6b70bc-be54-4d67-ac3e-5d865921adc4 · outbound

This paper cites Your efficient rl framework secretly brings you off-policy rl training, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Your efficient rl framework secretly brings you off-policy rl training, 2025

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.755573Z

Source-reported events for the cited work

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Observation 45f972cb-c5c8-469d-a433-3caa3928a06b · outbound

This paper cites Stabilizing moe reinforcement learning by aligning training and inference routers.arXiv preprint arXiv:2510.11370, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Stabilizing moe reinforcement learning by aligning training and inference routers.arXiv preprint arXiv:2510.11370, 2025

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:21.856470Z

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Observation 71d2a21f-1f4b-4d43-8854-dbd176969f0b · outbound

This paper cites Skyrl gym generator tutorial.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Skyrl gym generator tutorial

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.201149Z

Source-reported events for the cited work

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Observation 1dac5146-acca-4ef0-9663-3156e108243d · outbound

This paper cites an unresolved cited work.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.044662Z

Source-reported events for the cited work

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Observation 049c48b6-adf4-4adf-a08c-aef8cbed8868 · outbound

This paper cites Save, load and learn: Boosting agentic llms via rollback-based curriculum learning.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Save, load and learn: Boosting agentic llms via rollback-based curriculum learning

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.123009Z

Source-reported events for the cited work

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Observation 73ceaa56-5d7d-4383-aadc-dbe1a5b1bd46 · outbound

This paper cites Gemini 3.5 Flash model card.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Gemini 3.5 Flash model card

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.441669Z

Source-reported events for the cited work

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Observation d762cc4a-975b-44be-b6d3-44d9895a98ab · outbound

This paper cites GPT-5.5 system card.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications GPT-5.5 system card

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.230205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6919cec2-94fc-4609-a02b-dbaba7868672 · outbound

This paper cites Claude Opus 4.7 model report.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Claude Opus 4.7 model report

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.345133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c20310d6-02de-41b2-a7c0-a763af8da3a3 · outbound

This paper cites Qwen3-235B-A22B-Thinking-2507.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Qwen3-235B-A22B-Thinking-2507

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.713763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1936f663-173b-4fa7-9287-0d0a388b17ed · outbound

This paper cites Qwen3.7: The agent frontier.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Qwen3.7: The agent frontier

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.520967Z

Source-reported events for the cited work

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Observation 4f7ac7fd-cb34-4c22-ac17-d0d7e60d119c · outbound

This paper cites DeepSeek-V4: Towards highly efficient million-token context intelligence, 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications DeepSeek-V4: Towards highly efficient million-token context intelligence, 2026

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.622942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaea734f-b5ec-46b2-a9fb-99fbe2f44502 · outbound

This paper cites Nex-N1: Agentic models trained via a unified ecosystem for large-scale environment construction.arXiv preprint arXiv:2512.04987, 2025.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Nex-N1: Agentic models trained via a unified ecosystem for large-scale environment construction.arXiv preprint arXiv:2512.04987, 2025

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.028748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 517845ba-380c-4cea-81c9-70074976db40 · outbound

This paper cites Qwen3.5: Towards native multimodal agents, February 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Qwen3.5: Towards native multimodal agents, February 2026

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.833799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01eec249-9f14-4d82-b16e-0ba0b0bd6b48 · outbound

This paper cites Qwen3.6-35B-A3B: Agentic coding power, now open to all, April 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Qwen3.6-35B-A3B: Agentic coding power, now open to all, April 2026

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:22.943979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39305907-6306-4c25-b190-45003a673ea5 · outbound

This paper cites Large language models are zero-shot reasoners.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Large language models are zero-shot reasoners

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.329682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50763220-3970-4cbc-b5f6-6a717be71f7c · outbound

This paper cites ScaleEnv: Scaling environment synthesis from scratch for generalist interactive tool-use agent training.arXiv preprint arXiv:2602.06820, 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications ScaleEnv: Scaling environment synthesis from scratch for generalist interactive tool-use agent training.arXiv preprint arXiv:2602.06820, 2026

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.142866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 812280a8-01c6-4a44-96f6-462f8c2f0e9c · outbound

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

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Chain-of-thought prompting elicits reasoning in large language models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.259458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 565a8b08-9384-422e-ad3b-a35d686578ef · outbound

This paper cites A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.650158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 539346c2-1d56-43f4-a67b-77659e158fa4 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Training Verifiers to Solve Math Word Problems

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.470230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed1aab7e-681f-4ae5-a243-7c29b2de7278 · outbound

This paper cites Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.547518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e55a1835-c145-4bb4-a7dd-eb52d2e2b00c · outbound

This paper cites Mind2Web: Towards a generalist agent for the web.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Mind2Web: Towards a generalist agent for the web

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.967520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1439f165-08f4-4164-ae8c-f9b30856d0b9 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.751972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1eb5563-8512-41b4-a39d-15e0fd76cc29 · outbound

This paper cites WebShop: Towards scalable real-world web interaction with grounded language agents.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications WebShop: Towards scalable real-world web interaction with grounded language agents

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:23.823649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5372a40-724e-4a77-b7b8-3f229399fa68 · outbound

This paper cites $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.233099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a24fb7e-a20d-429f-ad71-cfe29319e4e2 · outbound

This paper cites VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.033281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f22ee88-a561-4cf2-b1c8-a8a810309a7c · outbound

This paper cites SWE-bench: Can language models resolve real-world GitHub issues? InThe Twelfth International Conference on Learning Representations, 2024.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications SWE-bench: Can language models resolve real-world GitHub issues? InThe Twelfth International Conference on Learning Representations, 2024

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.126811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a507565d-4f33-4990-8bea-37749c6dbdec · outbound

This paper cites Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets.Advances in Neural Information Processing Systems, 37:54463–54482, 2024.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets.Advances in Neural Information Processing Systems, 37:54463–54482, 2024

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.533446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12c4df0b-504b-4e39-9fdb-cfa386ec6143 · outbound

This paper cites Berkeley function-calling leaderboard.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Berkeley function-calling leaderboard

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.324952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb24fb35-4630-4374-b4ac-65dc984e9a53 · outbound

This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.402366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb950eb9-0a9c-4159-b89a-c75b664dd962 · outbound

This paper cites Dive: Scaling diversity in agentic task synthesis for generalizable tool use.arXiv preprint arXiv:2603.11076, 2026.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Dive: Scaling diversity in agentic task synthesis for generalizable tool use.arXiv preprint arXiv:2603.11076, 2026

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.815851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03eaa09c-825a-43cb-86a4-46d96c0616e0 · outbound

This paper cites Toolace: Winning the points of llm function calling.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications Toolace: Winning the points of llm function calling

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.644166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a85bb155-9246-44e6-b63f-b37966f4c4e4 · outbound

This paper cites EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL.

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.740632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2766b60f-5bd6-4d97-89bf-b8b2e19eea6d · outbound

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

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications ToolRL: Reward is All Tool Learning Needs

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:25.069775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d0f22f7-6959-45aa-88ee-69e804db4ee0 · outbound

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

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.888398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d448022d-1539-4953-a0b3-593549b6988a · outbound

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

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 102

Resolution: unresolved; no resolver link, observed 2026-08-01T03:38:24.974260Z

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