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Paper Citation Record · LEDGER

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning

As of 7 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 2 inbound Pith citation observations for arXiv:2607.04425.

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

pith.paper-citation-record.v1
2607.04425 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:16:37.302399Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:20:19.104036Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

68 of 68 outbound references displayed

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  • verified fuzzy0
  • unresolved67
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

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Outbound references

Observation 8161e85c-1527-4bf5-9592-ecdb220c3205 · outbound

This paper cites Introducing claude opus 4.6.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Introducing claude opus 4.6

Reference 1

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:2043a5ea6fd4136360186b019d5365073c6491ae5641cbfb52d9e9f1c7d6b827

Observation d62fe1b8-d2df-43d6-9c16-65295eb0383e · outbound

This paper cites Qwen3-VL Technical Report.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Qwen3-VL Technical Report

Reference 2

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:9e5875ead7f5bf5dcb99f14c7edba2e1fc9465a20830049865367a7a5cea887c

Observation fbeb1a9b-6677-4b65-9965-dd75bed08d7b · outbound

This paper cites Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale

Reference 3

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Observation d21a3e25-35ac-4e3e-93da-237130938ff5 · outbound

This paper cites Seed2.0 model card: Towards intelligence frontier for real-world complexity.arXiv preprint arXiv:2603.11103, 2026.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Seed2.0 model card: Towards intelligence frontier for real-world complexity.arXiv preprint arXiv:2603.11103, 2026

Reference 4

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:91a52fe63c0a12ce4a2ac211070537233875cd1b6a5ad87ba6ca339aab135fa2

Observation c7102f04-7de1-4df9-9231-0e8589c87870 · outbound

This paper cites Xiaomi-GUI-0 Technical Report.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Xiaomi-GUI-0 Technical Report

Reference 5

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:11d3c0be6fa72787db35c09790ee1b82c28e653a1d03749d796aed85cd2c81e3

Observation 01cc85d9-8a50-40df-a209-25f77f0816ae · outbound

This paper cites KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation

Reference 6

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Observation 28c997f6-88d2-42ee-8ceb-c7c2cabccc10 · outbound

This paper cites OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis

Reference 7

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Observation 958ab685-f1ac-4763-8078-a11d6c5bfaa5 · outbound

This paper cites Audio-Oscar: A Multi-Agent System for Complex Audio Scene Generation, Orchestration, and Refinement.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Audio-Oscar: A Multi-Agent System for Complex Audio Scene Generation, Orchestration, and Refinement

Reference 8

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Observation fad5d392-d3ef-4da6-b6da-9f7e2192cdbd · outbound

This paper cites Gemini 3.1 pro model card.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Gemini 3.1 pro model card

Reference 9

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Observation eb471240-ce53-48c8-8a72-c9aca56b42df · outbound

This paper cites Seed1.5-VL Technical Report.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Seed1.5-VL Technical Report

Reference 10

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Observation 7b7bb050-e1e9-4ea0-8aef-afe9dd742b0d · outbound

This paper cites Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe

Reference 11

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Observation ab2a7b33-d13c-4bb7-b242-17ec6af0cd28 · outbound

This paper cites Visualwebarena: Evaluating multimodal agents on realistic visual web tasks.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Visualwebarena: Evaluating multimodal agents on realistic visual web tasks

Reference 12

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Observation b51f6ffc-e2f3-4979-9ae6-6d34c0d6b41f · outbound

This paper cites Mobileworld: Benchmarking autonomous mobile agents in agent-user interactive and mcp-augmented environments.arXiv preprint arXiv:2512.19432, 2025.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Mobileworld: Benchmarking autonomous mobile agents in agent-user interactive and mcp-augmented environments.arXiv preprint arXiv:2512.19432, 2025

Reference 13

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:9ee4a9e470e9ea6f062071cf2a9f15785ad5b9373154b195137315499b6e2f06

Observation 7ac64117-ea55-4cc6-9811-3d20e71d757f · outbound

This paper cites Efficient memory management for large language model serving with pagedattention.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Efficient memory management for large language model serving with pagedattention

Reference 14

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Observation 17c9b607-1a47-4c68-8e68-662cc4bb5810 · outbound

This paper cites Computerrl: Scaling end-to-end online reinforcement learning for computer use agents.arXiv preprint arXiv:2508.14040, 2025.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Computerrl: Scaling end-to-end online reinforcement learning for computer use agents.arXiv preprint arXiv:2508.14040, 2025

Reference 15

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:44e2a8bdc2c7b1796ce75c23e3aec47244ac9e6210313571b6f68a2649ae176c

Observation 50188716-f718-4acc-aaa6-6a9101dd0220 · outbound

This paper cites Screenspot-pro: Gui grounding for professional high-resolution computer use.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Screenspot-pro: Gui grounding for professional high-resolution computer use

Reference 16

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Observation 9869d23c-bf29-49ac-9e73-252291a43cf3 · outbound

This paper cites On the effects of data scale on ui control agents.Advances in Neural Information Processing Systems, 37: 92130–92154, 2024.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning On the effects of data scale on ui control agents.Advances in Neural Information Processing Systems, 37: 92130–92154, 2024

Reference 17

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Observation baf01ff1-873c-48ed-839b-1b9b7c85d152 · outbound

This paper cites From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents

Reference 18

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Observation 3f8044eb-8c2b-4618-860c-41afa8ba8c4e · outbound

This paper cites Ui-r1: Enhancing efficient action prediction of gui agents by reinforcement learning.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Ui-r1: Enhancing efficient action prediction of gui agents by reinforcement learning

Reference 19

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Observation aef8a658-fd6e-49bc-a6b3-356dd1da25c8 · outbound

This paper cites Self-Distilled Agentic Reinforcement Learning.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Self-Distilled Agentic Reinforcement Learning

Reference 20

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Observation 43e6f612-65a8-4b64-bb92-5daedcbbc9e6 · outbound

This paper cites Computer-using agent.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Computer-using agent

Reference 21

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Observation 6b3408f5-74dd-4e0a-8ba8-773366ab3e19 · outbound

This paper cites UI-TARS: Pioneering Automated GUI Interaction with Native Agents.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning UI-TARS: Pioneering Automated GUI Interaction with Native Agents

Reference 22

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Observation 420b80a8-1bfe-4130-a07c-bc12570a67f9 · outbound

This paper cites Androidworld: A dynamic benchmarking environment for autonomous agents.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Androidworld: A dynamic benchmarking environment for autonomous agents

Reference 23

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Observation dae7c779-a17c-4b27-bc63-85d3b2c619c4 · outbound

This paper cites Self-Distillation Enables Continual Learning.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Self-Distillation Enables Continual Learning

Reference 24

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Observation 8356ccac-91f4-4c3c-bb16-9f2fd1d517f5 · outbound

This paper cites Hybridflow: A flexible and efficient rlhf framework.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Hybridflow: A flexible and efficient rlhf framework

Reference 25

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Observation c66a4991-0c9d-4fa5-8fc6-3494d37f4708 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 26

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Observation 78ef349e-514a-4e67-9658-3710b5a250b7 · outbound

This paper cites Scaling laws for optimal data mixtures.Advances in Neural Information Processing Systems, 38:129554–129579, 2026.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Scaling laws for optimal data mixtures.Advances in Neural Information Processing Systems, 38:129554–129579, 2026

Reference 27

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Observation 2da4a4da-4a7c-4ce4-90a5-ac11cc56b030 · outbound

This paper cites ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents

Reference 28

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Observation 495bffc9-4bf9-4a20-a1ee-092e3bf742b6 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Kimi K2: Open Agentic Intelligence

Reference 29

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Observation 9f3e1f25-cd14-463a-a0ea-714207e9d60b · outbound

This paper cites Ui-venus-1.5 technical report.arXiv preprint arXiv:2602.09082, 2026.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Ui-venus-1.5 technical report.arXiv preprint arXiv:2602.09082, 2026

Reference 30

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Observation bdb7bd18-2f5e-433a-a0ab-c480c5ed2ef8 · outbound

This paper cites Consensus-driven multi-agent cognitive reasoning for enhancing the emotional intelligence of large language models.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Consensus-driven multi-agent cognitive reasoning for enhancing the emotional intelligence of large language models

Reference 31

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Observation 3d42dae9-7f89-4a89-8db2-12c2e3b14198 · outbound

This paper cites UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

Reference 32

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Observation 839a3956-249e-4029-8b32-69b0475a801c · outbound

This paper cites Opencua: Open foundations for computer-use agents.Advances in Neural Information Processing Systems, 38:139756–139806, 2026.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Opencua: Open foundations for computer-use agents.Advances in Neural Information Processing Systems, 38:139756–139806, 2026

Reference 33

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Observation aca5a806-9a51-46fd-b9cc-7fe5b5a02594 · outbound

This paper cites Milestone-Guided Policy Learning for Long-Horizon Language Agents.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Milestone-Guided Policy Learning for Long-Horizon Language Agents

Reference 34

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Observation 5bbdc4ad-ad9a-45fb-8653-8ba75ac8113d · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 35

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Observation 71d2110c-3fa3-4f32-866f-ae4de3def13a · outbound

This paper cites Os-atlas: Foundation action model for generalist gui agents.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Os-atlas: Foundation action model for generalist gui agents

Reference 36

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:fffab311468dd1763a483008f7d3eab6fecbe24cd75e561bd3e38d8c099a9be9

Observation de43ff55-1967-475c-b553-1b29d4c39c6b · outbound

This paper cites MiMo-V2-Flash Technical Report.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning MiMo-V2-Flash Technical Report

Reference 37

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:fac6ba9fb15816a661c72e0aaf99227301c7b7726d64acffcd64a166409fd2a8

Observation 86e06eef-20c4-47c0-b8d3-a8fb0ebb1d5c · outbound

This paper cites Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024

Reference 38

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:ed1f7f810bd07d7e60ba75672f3424f4bb0f0b9df6af561eac6ce6478757cf56

Observation 062f9356-b29c-4bb3-8998-39b858ef9f07 · outbound

This paper cites Scaling computer-use grounding via user interface decomposition and synthesis.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Scaling computer-use grounding via user interface decomposition and synthesis

Reference 39

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:a6325fa740fe14d2978aa67a1e5c37d885ed3e9092803075172aeda042aacda0

Observation 4a54aafa-5ad6-4a7b-8b88-0793ef4c83dc · outbound

This paper cites Deepseek-v4: Towards highly efficient million-token context intelligence.arXiv preprint arXiv:2606.19348, 2026.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Deepseek-v4: Towards highly efficient million-token context intelligence.arXiv preprint arXiv:2606.19348, 2026

Reference 40

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:6e0216191b7afdfd40815fd27f707ce5db4cae92e6bfd2d11c3277e9c7c7d101

Observation 49dae88b-7396-40b6-ade7-007e870f21b3 · outbound

This paper cites Mobile-agent-v3.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Mobile-agent-v3

Reference 41

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:5cdf0d05268b23511a4de18fc4c7356927bdc2f333241e799bdfe4039c591898

Observation 770eb853-1fa2-4fb8-92bf-dbdea335b8f5 · outbound

This paper cites Evocua: Evolving computer use agents via learning from scalable synthetic experience.arXiv preprint arXiv:2601.15876, 2026.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Evocua: Evolving computer use agents via learning from scalable synthetic experience.arXiv preprint arXiv:2601.15876, 2026

Reference 42

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:601cf6bcb28ff872a301fed1729986dbfc8e49ca74d75ac5077279cd51b7cd3f

Observation 67cfe5ba-a0a1-4594-a63b-02e6f8c3e79c · outbound

This paper cites Ties-merging: Resolving interference when merging models.Advances in neural information processing systems, 36:7093–7115, 2023.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Ties-merging: Resolving interference when merging models.Advances in neural information processing systems, 36:7093–7115, 2023

Reference 43

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:9b679bcf25a0a77325d0799de33ee0ab64eba376b36594f664b2cb828c118cad

Observation 5d72dd75-2191-4764-8f95-398c08c9060e · outbound

This paper cites Step-gui technical report.arXiv preprint arXiv:2512.15431, 2025.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Step-gui technical report.arXiv preprint arXiv:2512.15431, 2025

Reference 44

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:0bfe0d7c93ec8640b190a4ce1b483c89f5f75b64200652c37afbc5d2db646564

Observation e3919420-d76a-4d7c-aedb-c87a1e8f1385 · outbound

This paper cites macosworld: A multilingual interactive benchmark for gui agents.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning macosworld: A multilingual interactive benchmark for gui agents

Reference 45

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:d253790362cbfd563878c22382af14b9d9e90705b91a357e60c10aa5e20139e5

Observation 63250040-fa70-48c4-a92a-9434f657eac2 · outbound

This paper cites Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation

Reference 46

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:55ab8392f49c8eb970ef7537963936cd8b7cfbed69cfe8f01ce482685d191c07

Observation 4e9dcf68-63e4-4d72-90ae-4d960d6be6e2 · outbound

This paper cites Nemotron-cascade 2: Post-training llms with cascade rl and multi-domain on-policy distillation.arXiv preprint arXiv:2603.19220, 2026.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Nemotron-cascade 2: Post-training llms with cascade rl and multi-domain on-policy distillation.arXiv preprint arXiv:2603.19220, 2026

Reference 47

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:7b5fd247ad8a1364f415cd2e8b68a1ddf80988f163411aa48fab0dc43bef771d

Observation 612f8354-fea9-48b4-80af-fa284954663f · outbound

This paper cites Mobile-Agent-v3: Fundamental Agents for GUI Automation.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Mobile-Agent-v3: Fundamental Agents for GUI Automation

Reference 48

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:d45772acb95c8521b6416bedc65c7366d14e7b1db28829bf44ec5753626fc5d0

Observation 05a204fb-e7ea-40a7-8d71-d51effaa3e4d · outbound

This paper cites On-Policy Context Distillation for Language Models.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning On-Policy Context Distillation for Language Models

Reference 49

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:a7ddbe2fb4ff33b0e6c6c8abeb84b00a22317a19daaf3e36f2242ea1f8e4dd6a

Observation 3c634df0-d68a-4d94-b65c-4d9869bc24a9 · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning GLM-5: from Vibe Coding to Agentic Engineering

Reference 50

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:1b30012442ebd8104ba95d82814e9f41e4eade9406cc3e5dee9cdcd8921d0d93

Observation 8a9cc120-ac5c-4c25-8eef-e6204e4c2bf3 · outbound

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

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models

Reference 51

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:79ab64fce69394a9e83e31089030602b30385a77fa0e8a7287e53139dc8c0bf6

Observation f69572a5-1359-4b39-b224-72537d74d371 · outbound

This paper cites Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37:62557–62583, 2024.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37:62557–62583, 2024

Reference 52

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:2cf8f2f81900aeb0a3a0656d599479fc519f50931241fc973db88c016318ac18

Observation a2cf2296-561d-4790-9a87-ac6718b6b447 · outbound

This paper cites price.docx\.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning price.docx\

Reference 53

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no resolver link, observed 2026-07-11T19:16:37.302399Z

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:83f6f44ff6d6a8675d925c21a607e0f31bea9f80260e402b305bc290de393773

Observation 5ac95a3e-c6bb-4cd4-a559-79e2cbabfc01 · outbound

This paper cites an unresolved cited work.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Unresolved cited work

Reference 54

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:f77db82587aba50b28f5924404a45bbd16539e99b4aa5a2bf9d2576a95cc74da

Observation 5f1ad482-5143-4371-b3ad-30ea0c0e8b14 · outbound

This paper cites name": <function-name>,.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning name": <function-name>,

Reference 55

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:e42f57d2910c44ee8fc5f3dbb179b588a0af262a0e7e9773957b53ef0b824c8e

Observation 3ede704c-d5a3-4620-a172-8d18e5e248b1 · outbound

This paper cites an unresolved cited work.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Unresolved cited work

Reference 56

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:45546db28558354b5e2ed9bdd03f129d8fbca3f3c8919098cf47a1dff5ecdea9

Observation 2fce0700-0ed2-4c70-95dd-5c24f5367c88 · outbound

This paper cites an unresolved cited work.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Unresolved cited work

Reference 57

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:8cd948c0d27564128052d81f609fa583acb7ec3e3aca26ca0890aab0b0e2f0b1

Observation 96bfa400-1c10-4337-aec2-bd36bbf83500 · outbound

This paper cites name": <function-name>,.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning name": <function-name>,

Reference 58

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:70edcb08d59b5b9c28ed9fca4efc9c43e5a44647788d00c5a41f607e4802cbfe

Observation 1974f2ec-083d-444d-ab69-4f53ad2b1106 · outbound

This paper cites - Follow the user instruction strictly, e.g., only return a single number, only return True or False, or only return items separated by comma.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning - Follow the user instruction strictly, e.g., only return a single number, only return True or False, or only return items separated by comma

Reference 59

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:44713e36a19a7fb88fd801206b9431e7bf28354d290072df3fb09a5e2a74f3c7

Observation 8f078a95-f8d6-46cc-8ba5-28aed14c7a9b · outbound

This paper cites - If an action fails twice, try alternatives, e.g., long_press instead of click.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning - If an action fails twice, try alternatives, e.g., long_press instead of click

Reference 60

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:cea138cbc0abbdd6882f3609c21f0a3ccf2e98e06ec7b470cb948a02107473d7

Observation de680007-44c7-413e-99fd-a53bca7fdbc3 · outbound

This paper cites - For scrolling, scroll direction is inverse to swipe direction.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning - For scrolling, scroll direction is inverse to swipe direction

Reference 61

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:9ea8b41e99cc6970bd197bcb352c93adb601d4e1f0ca4d1fc2ee638006333ef3

Observation 69da12cb-17be-4cf1-9136-3d87138e2510 · outbound

This paper cites - For text manipulation, long press to select, use selection bar options, and delete by selecting then cutting.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning - For text manipulation, long press to select, use selection bar options, and delete by selecting then cutting

Reference 62

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:2c2231136d3feef06c4635bb3a7315f6d7852d74cd135e2fa4d902abc2f84257

Observation 7627ab6f-cd0d-4e3e-b7bd-61bcd5b494d2 · outbound

This paper cites # Decision Process.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning # Decision Process

Reference 63

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:256b569dbeb249fae386bc1a8992f5ae296c04c58cddad75ba79a13495b72cdc

Observation a7b62370-f99f-46c7-a96b-576b630a0c3c · outbound

This paper cites an unresolved cited work.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Unresolved cited work

Reference 64

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:c7e29eb9e28320a3497618a4a5430414f7214fbbe63a049eaa9bc68a2bca05d4

Observation 223410a4-d497-498c-a7f0-830ab9ba4f37 · outbound

This paper cites an unresolved cited work.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Unresolved cited work

Reference 65

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:2fff07c80df1abeb0c3d0660bf1f45e4a3ec53ea0a570f1df95fc2f46ea6307e

Observation 6a1d48cf-078e-4e7e-8ca5-84a9b0b6dabf · outbound

This paper cites an unresolved cited work.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning Unresolved cited work

Reference 66

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:324b73dc9da395c6e620660071b8585d5813d48ecc85a04b3d21bb4260395231

Observation d1155179-dd92-4853-89a8-ff7b45f1da40 · outbound

This paper cites The action must be a valid JSON string.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning The action must be a valid JSON string

Reference 67

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:1b115e4af34847691215dca41bbe713fc7d46c90ef94bf1b9b665ef967ffffc4

Observation ca26e53c-7927-49c7-bb5a-0d7af0bf718f · outbound

This paper cites action_type.

UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning action_type

Reference 68

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source=pdf_text observed=2026-07-11T19:16:37.302399Z digest=sha256:851bfd0bc68848676773b8d1e6449fc76854f69c6fa08af355a5a898840e29f8

Pith citing papers

Observation 4a2cd700-446b-4d0c-9771-9a76942551eb · inbound

Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents cites this paper.

Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning

Reference 29

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no resolver link, observed 2026-07-31T14:04:39.216655Z

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source=arxiv_source observed=2026-07-31T14:04:39.216655Z digest=sha256:555e0543f828a23a402172d3c978ca3e5fabc6b9710f167e5964ca55f4d4a4ef

Observation 56214e94-25c1-4180-8070-851175630d4b · inbound

MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation cites this paper.

MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning

Reference 70

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source=arxiv_source observed=2026-08-03T09:20:19.104036Z digest=sha256:d61907863c1d8b6910cf5cfc98d9e172cb70cfaf776daf6e8f1c5fcee49b9049