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Paper Citation Record · LEDGER

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

As of 8 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 9 inbound Pith citation observations for arXiv:2505.21496.

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

pith.paper-citation-record.v1
2505.21496 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:34:16.540169Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:38:36.511631Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T05:17:40.282692Z

Reference resolution

45 of 45 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved43
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 227e2ae3-3a85-4962-9ffa-e7834f866702 · outbound

This paper cites Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents

Reference 1

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source=pdf_text observed=2026-08-07T13:34:12.202353Z digest=sha256:db9e04aa02e60b8e7f03d21f5efe36a6757ddcc2f99d9dd1dcedf3286d929380

Observation a2f36f7f-c2e9-4ecd-b9a8-939e3a2ed610 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

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source=pdf_text observed=2026-08-07T13:34:12.249449Z digest=sha256:791a56644ac3ec4a932dd07a39b2d9c9af73c7d9747522c39536124656038c1c

Observation 7465ed76-0ae9-4e79-ae52-f65a0c47afa5 · outbound

This paper cites Qwen2.5-VL Technical Report.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Qwen2.5-VL Technical Report

Reference 3

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source=pdf_text observed=2026-08-07T13:34:12.316549Z digest=sha256:2ccf0ff7206abfdaf1091d434ac4de5a38e2d9b08820ab6e576db75243beb848

Observation e7f68e8e-48ee-48c7-b024-8ce6c8907cfa · outbound

This paper cites A survey of monte carlo tree search methods.IEEE Transactions on Computational Intelligence and AI in games, 4(1):1–43, 2012.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents A survey of monte carlo tree search methods.IEEE Transactions on Computational Intelligence and AI in games, 4(1):1–43, 2012

Reference 4

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source=pdf_text observed=2026-08-07T13:34:12.379422Z digest=sha256:f7c68fb039b7cc4c8ecaedb94bdfe44258dde7913aebc796e9403d839ab1c8f7

Observation 9f0b76e8-ea5d-4239-af90-8675f023a612 · outbound

This paper cites AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents

Reference 5

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source=pdf_text observed=2026-08-07T13:34:12.433269Z digest=sha256:4227cf2d237ba712a20217b665dc33981924a69153f4437d91f1a7ea7fb5f0c3

Observation 31233573-e7a7-4112-8222-849a623d1057 · outbound

This paper cites A3: Android agent arena for mobile gui agents.arXiv preprint arXiv:2501.01149, 2025.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents A3: Android agent arena for mobile gui agents.arXiv preprint arXiv:2501.01149, 2025

Reference 6

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source=pdf_text observed=2026-08-07T13:34:12.506033Z digest=sha256:6b10233912b733a1925054c414d4e04da745f1e3ffc89c824beba1ae8935b7d1

Observation 0d1cd788-394a-43d5-bbad-e5da891f9fc6 · outbound

This paper cites AlphaMath Almost Zero: Process Supervision without Process.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents AlphaMath Almost Zero: Process Supervision without Process

Reference 7

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source=pdf_text observed=2026-08-07T13:34:12.576689Z digest=sha256:8c472837cd64055f2c108470133a4ad63505520d5f061b93e21ed6b863ae42fc

Observation f8d6c2ca-21f7-4c57-a9b2-d1fed7883993 · outbound

This paper cites SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents

Reference 8

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source=pdf_text observed=2026-08-07T13:34:12.653482Z digest=sha256:7aa79b578fbe5a38dd859bef59bf068e79e588b929b43e3294fc92a7d32ec3f0

Observation f779bc17-e800-4b2d-bdc9-34e5539ba275 · outbound

This paper cites rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking

Reference 9

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source=pdf_text observed=2026-08-07T13:34:12.734899Z digest=sha256:1f3f2eba20a4ea51b32960fdcde44c0c3acb2cb366d7350c7b6d1a68b510710f

Observation ca5dbac5-7c7b-4248-94c3-41f5904e5db8 · outbound

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

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

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source=pdf_text observed=2026-08-07T13:34:12.797680Z digest=sha256:2910ce88142c290b59ac5be29d1a67f95a1e7a2322a1f390702d8cfbf1d8ad54

Observation 5291f4ed-3582-48b4-a0a5-fe1bc8e8c58c · outbound

This paper cites Cogagent: A visual language model for gui agents.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Cogagent: A visual language model for gui agents

Reference 11

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source=pdf_text observed=2026-08-07T13:34:12.869363Z digest=sha256:f70fb7914b64d9d45e37f5193225da6e4aa3064694408348142dcad6ae5b3b20

Observation ede2a85a-43d5-41a2-8f87-252065a26fc8 · outbound

This paper cites 3D-LLM: Injecting the 3D World into Large Language Models.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents 3D-LLM: Injecting the 3D World into Large Language Models

Reference 12

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source=pdf_text observed=2026-08-07T13:34:12.942509Z digest=sha256:fc161181bb3207093b29df74e0ecb48ad9ed9ca487fddcc1cea3d1186f3886c9

Observation 2311d38f-86cf-4f67-87ed-853bfaf5705d · outbound

This paper cites On the effects of data scale on computer control agents.arXiv e-prints, pages arXiv–2406, 2024.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents On the effects of data scale on computer control agents.arXiv e-prints, pages arXiv–2406, 2024

Reference 13

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source=pdf_text observed=2026-08-07T13:34:13.035312Z digest=sha256:e955939c5ca1709104fa92f993c40ddd81c932cb147fd7ecf735edf6fb39df99

Observation 887cdd4c-5205-4652-a939-da5b5e711103 · outbound

This paper cites Let’s verify step by step.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Let’s verify step by step

Reference 14

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source=pdf_text observed=2026-08-07T13:34:13.140041Z digest=sha256:e364f83cbcdc1f631f1be33087a4172097b2f14a0406f2186a5e4f22da6d96d1

Observation 92a97f96-647a-453c-a8a7-13773936f429 · outbound

This paper cites SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 15

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source=pdf_text observed=2026-08-07T13:34:13.235887Z digest=sha256:529eca07c203ab7b89e760ba76225181336fa9919bc80e9b9560dc0b7862a3eb

Observation 292911e5-8523-4bf0-a20d-6ab532f11507 · outbound

This paper cites DeepSeek-V3 Technical Report.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents DeepSeek-V3 Technical Report

Reference 16

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source=pdf_text observed=2026-08-07T13:34:13.339552Z digest=sha256:82219b367c97e41c29305b43b358c3f695006a5d711f00ecee2bbf84f2523d76

Observation c5cd0a30-8150-4219-9a2a-8804e503146f · outbound

This paper cites InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners

Reference 17

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source=pdf_text observed=2026-08-07T13:34:13.481997Z digest=sha256:253f68df95e6e0ba7c5f4c52bae5d2057a49c645efdbbc342494df5ed6f03a88

Observation 4c982ce9-de55-414f-b99b-e78a0d8b3a49 · outbound

This paper cites UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning

Reference 18

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source=pdf_text observed=2026-08-07T13:34:13.620018Z digest=sha256:1d570ff0904b8948d94122e4b4706226cfeb3263293461ccc2ae1475100b8f58

Observation 0f33a703-4705-4b47-8a72-5a2f45a5edd8 · outbound

This paper cites Improve Mathematical Reasoning in Language Models by Automated Process Supervision.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Improve Mathematical Reasoning in Language Models by Automated Process Supervision

Reference 19

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source=pdf_text observed=2026-08-07T13:34:13.755616Z digest=sha256:168a511371e6d23c540f1e5ed9502ba204e3b7c0fac7a3529bbf5a7f6d565566

Observation c2f9b3b7-b026-492d-aae0-ca092bc084fc · outbound

This paper cites Chatgpt.https://chat.openai.com, 2023.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Chatgpt.https://chat.openai.com, 2023

Reference 20

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source=pdf_text observed=2026-08-07T13:34:13.862753Z digest=sha256:81e51bf038449730496cb39345c5c0ff98a32e5275a732b72e21f786caa85507

Observation 8f64ac00-d58b-4ca5-b8a6-74c2aa79dc80 · outbound

This paper cites GPT-4 Technical Report.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents GPT-4 Technical Report

Reference 21

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source=pdf_text observed=2026-08-07T13:34:13.981674Z digest=sha256:58ee3a1ff8019b7f25bb242aa93c7ae002edec7071a24f76333411e67a331ca5

Observation a4ea2b57-ebf4-42ff-9979-5c8891bc4380 · outbound

This paper cites Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents

Reference 22

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source=pdf_text observed=2026-08-07T13:34:14.113020Z digest=sha256:6dc73cecb5961936405dd6f81c4c2445fed3ec1b5d1082bb43bf53c8f4cf327c

Observation 92256e3c-d1ad-4ea4-a686-baab33ea19aa · outbound

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

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents UI-TARS: Pioneering Automated GUI Interaction with Native Agents

Reference 23

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source=pdf_text observed=2026-08-07T13:34:14.257156Z digest=sha256:424de41d2474153f66139454a26b06c53f4eb91a3158f0d047a564fbb8ac49d2

Observation 18e99542-9edb-404b-922c-7bd8fd6b195b · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 24

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source=pdf_text observed=2026-08-07T13:34:14.409660Z digest=sha256:fbb31d6105e9e265036a132875abf6a2ea9d31a835968e9c6cc7ba75a713d951

Observation 4299b27c-2464-471b-8631-fd47de7faa97 · outbound

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

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 25

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source=pdf_text observed=2026-08-07T13:34:14.554994Z digest=sha256:9343098390eeace738aaaa5c6218abda59162ccc160451d424e4ac8c527e0937

Observation 0b45da9d-dea5-4d83-a679-ceb0e656aa83 · outbound

This paper cites Mas- tering the game of go with deep neural networks and tree search.nature, 529(7587):484–489, 2016.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Mas- tering the game of go with deep neural networks and tree search.nature, 529(7587):484–489, 2016

Reference 26

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source=pdf_text observed=2026-08-07T13:34:14.699428Z digest=sha256:a74b428daaccfcd0954960ccadb2f948af7d4c15fba9958be54fda4a6f95a75a

Observation 16db5226-a409-4b9c-864a-bf6255dbe2ed · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 27

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source=pdf_text observed=2026-08-07T13:34:14.819538Z digest=sha256:a30e353575884bf73360ac424c7099a955f49914998b5b348dc4851b87f976ad

Observation 7297932f-4faf-4bc3-ab0f-fd3a169a502c · outbound

This paper cites OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis

Reference 28

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source=pdf_text observed=2026-08-07T13:34:14.968525Z digest=sha256:ed19efdc84e8d702216aefb509d3e341b45d1aa38c92fdb5280241174b288c7f

Observation 44e31cf1-f544-4aeb-a2eb-5ddf837cb190 · outbound

This paper cites Solving math word problems with process- and outcome-based feedback.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Solving math word problems with process- and outcome-based feedback

Reference 29

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source=pdf_text observed=2026-08-07T13:34:15.089504Z digest=sha256:943f210c5d15389e47ad88be951ca5f3802c7c95bf530e192616553cd0c31d82

Observation fe1316b3-e451-4395-ae3c-764650b0e91a · outbound

This paper cites Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration

Reference 30

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source=pdf_text observed=2026-08-07T13:34:15.163897Z digest=sha256:5fdf64c8fd0623a6a56cc487eae47d7901da4395b9816f360e59f2789b3ca208

Observation bbef74c7-fff6-4cc0-980b-e343c0d65e40 · outbound

This paper cites Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception

Reference 31

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source=pdf_text observed=2026-08-07T13:34:15.236161Z digest=sha256:a9f1b5411f054c4d99a891f7f40f0d21e2852c82066e8cd81797a3d198be2bf6

Observation def6758d-5dbf-4975-9f7d-8f9d65e82240 · outbound

This paper cites Mathcoder-VL: Bridging vision and code for enhanced multimodal mathematical reasoning.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Mathcoder-VL: Bridging vision and code for enhanced multimodal mathematical reasoning

Reference 32

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raw_fallback, observed 2026-08-07T13:34:17.521569Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:34:15.316016Z digest=sha256:bbc76f79d5624008d63076d2a1c7934237f7d10dbd990917a5b94f5171412615

Observation 1df33d67-42ef-4230-8368-26f8a79366c6 · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 33

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source=pdf_text observed=2026-08-07T13:34:15.423628Z digest=sha256:5c970a7c77c1339afa499b78b31209ec3639c41e623f22f89e60852ab4f54a96

Observation 1aa3ba3e-bc83-4e45-923a-36e8737cec09 · outbound

This paper cites VisualPRM: An Effective Process Reward Model for Multimodal Reasoning.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents VisualPRM: An Effective Process Reward Model for Multimodal Reasoning

Reference 34

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source=pdf_text observed=2026-08-07T13:34:15.560978Z digest=sha256:f426be7baefaaf65651617a8c4e414afc02c60e5d8268b404f7819d7dc91b6ed

Observation 8af82fd7-ea28-4370-a825-150a1c1f3d2e · outbound

This paper cites Unified Reward Model for Multimodal Understanding and Generation.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Unified Reward Model for Multimodal Understanding and Generation

Reference 35

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no resolver link, observed 2026-08-07T13:34:15.647874Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:15.647874Z digest=sha256:9466a80e9ecb92890baff9b0146091f6fb032c9f988f09c184bad93ed7704c66

Observation 8fb54bdf-7891-4fab-8e5a-d249069a88b9 · outbound

This paper cites OS-ATLAS: A Foundation Action Model for Generalist GUI Agents.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents OS-ATLAS: A Foundation Action Model for Generalist GUI Agents

Reference 36

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no resolver link, observed 2026-08-07T13:34:15.744658Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:15.744658Z digest=sha256:806ca9c66063a0450a5c640f1202524a34949e469785f7072e2f31d59ba99123

Observation cf9fdf4f-a4ba-4e57-b9c9-f3686dbf2bf3 · outbound

This paper cites AgentRM: Enhancing Agent Generalization with Reward Modeling.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents AgentRM: Enhancing Agent Generalization with Reward Modeling

Reference 37

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no resolver link, observed 2026-08-07T13:34:15.862250Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:15.862250Z digest=sha256:984f1f7bf833548131380a34a92c5daed663b62f20c145670c1da7fc4abed4fc

Observation af4cb082-3624-408f-8885-41b813c97ed9 · outbound

This paper cites AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents

Reference 38

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no resolver link, observed 2026-08-07T13:34:15.971321Z

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source=pdf_text observed=2026-08-07T13:34:15.971321Z digest=sha256:a591afb2bf74b9d7937793d779a8e88a39ed063ffae54fca9a6a4f48ef2ea36b

Observation 3725fa04-4eae-44f7-9e8d-9e1c688ce59e · outbound

This paper cites Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction

Reference 39

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no resolver link, observed 2026-08-07T13:34:16.052993Z

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source=pdf_text observed=2026-08-07T13:34:16.052993Z digest=sha256:a63d4c95de193dd4eee9c5ecb7101a03040521e43b19beb7edade914fac2c1e0

Observation c048bd41-ef28-4f99-888c-ac0ddaf58a90 · outbound

This paper cites OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning

Reference 40

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no resolver link, observed 2026-08-07T13:34:16.142382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:16.142382Z digest=sha256:8c5b96148b82f42931c83afba496b69e3c7fc463d2b427f4d373776454ae7207

Observation 0390a426-2071-44cc-81f4-329ffa836446 · outbound

This paper cites Free Process Rewards without Process Labels.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Free Process Rewards without Process Labels

Reference 41

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unresolved
no resolver link, observed 2026-08-07T13:34:16.228199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:16.228199Z digest=sha256:4901abf49507ba173df7b36cc3df72a51e86ee72c5976c77f94d64c61d931feb

Observation 38988312-0153-4080-8c93-3efae3d66d08 · outbound

This paper cites InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model

Reference 42

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no resolver link, observed 2026-08-07T13:34:16.325870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:16.325870Z digest=sha256:6da2561c13319293c586f81fe9a61a491055764b1375ee24c8451ead9f9831fd

Observation c7a15394-1998-4c38-84fa-f5a389b6a23f · outbound

This paper cites Appagent: Multimodal agents as smartphone users.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Appagent: Multimodal agents as smartphone users

Reference 43

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unresolved
no resolver link, observed 2026-08-07T13:34:16.413731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:16.413731Z digest=sha256:c89d5fc4cd15249f20e6864c20f97c0a0bcd58bb86f8ac3cc057d1444d8cf169

Observation 289121d4-a544-4f0d-92f6-41ef672b1dfa · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 44

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unresolved
no resolver link, observed 2026-08-07T13:34:16.465747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:16.465747Z digest=sha256:83720aa56d220c5887aa4b978d268b1942c16cd7e745e4a20c1a9eaa0a0d8099

Observation c0c5c59e-0ee5-4ba8-94f0-7349e1a74c8a · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 45

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unresolved
no resolver link, observed 2026-08-07T13:34:16.540169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:34:16.540169Z digest=sha256:9f4a3dff0a8aaab3015056b3094c7f2247417d2fa25f7de8015a562c1883a19e

Pith citing papers

Observation 5289f811-e60a-44fc-aef3-8421e4c95f59 · 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 UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 156

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metadata mismatch
arxiv_id, observed 2026-05-14T22:23:14.891136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-14T22:23:14.621091Z digest=sha256:de0d8b2e9785b2e51bbc27fd8bc8be22221ce3c5161ec9b1924d26851c1f8ef8

Observation 4d2494aa-d860-460c-af02-942321710354 · inbound

MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents cites this paper.

MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:01:20.732591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-16T22:59:16.413568Z digest=sha256:f532f080b734fe59122d2d1b4e3ed9c54f75d0e175d84786f8f6935f93e4fe02

Observation 57332581-d9cf-46d9-8dae-1575766256bf · inbound

MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents cites this paper.

MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 42

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unresolved
no resolver link, observed 2026-08-03T16:38:36.511631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:38:36.511631Z digest=sha256:5114566d8f49cd0248530b1660c34b24855c50a459c6862298fda69475728d05

Observation 0eb2fcd5-bef3-4ff5-b6ed-4384ad800f34 · inbound

Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents cites this paper.

Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 34

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metadata mismatch
arxiv_id, observed 2026-05-11T10:31:00.883961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-10T15:28:07.981488Z digest=sha256:82c1c2d8c6991c11e42b1f95d5b3d3764eee0487419efe41d266bfca22cfbe3a

Observation c2fb5109-690a-4177-92e8-b942e493da8f · inbound

SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning cites this paper.

SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 20

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verified exact
arxiv_id, observed 2026-05-11T19:21:08.791996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-08T12:09:24.371878Z digest=sha256:15d7ababfc04991523c05daa694ea09e3179f139d2d70c37b762606aeb8014a0

Observation 11271b9d-07e3-4faf-b2bd-ec8727698671 · inbound

Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability cites this paper.

Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 116

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metadata mismatch
arxiv_id, observed 2026-05-11T04:35:56.386591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-11T01:15:19.239355Z digest=sha256:7be2a3732617f57b4fd048938afa3bc58a9eebc7917fac0b72850be8bd50582f

Observation 493f2919-3491-4dea-8426-4b60a7a5116e · inbound

Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment cites this paper.

Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 118

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verified exact
arxiv_id, observed 2026-05-15T01:58:28.784779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-15T01:58:19.295247Z digest=sha256:c76e74425ecf64d495261382d23fb55b84fc8aa30f265e2bb990d9b27a2d6718

Observation 91a71e88-db5d-424b-aeeb-791f91cc0643 · inbound

Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment cites this paper.

Beyond Binary: Reframing GUI Critique as Continuous Semantic Alignment UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 118

Resolution
verified exact
arxiv_id, observed 2026-05-19T16:47:40.442080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-19T16:45:16.963802Z digest=sha256:a6400e2a7cbf409bf78b259a25fa9700a036a211ffe2f5d2b59ed8674577a086

Observation 2ae10624-8280-4103-b73a-fe9c95337637 · inbound

A History-Aware Visually Grounded Critic for Computer Use Agents cites this paper.

A History-Aware Visually Grounded Critic for Computer Use Agents UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents

Reference 28

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T05:17:40.284425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T13:20:32.432002Z digest=sha256:4611c3c06bc49579f69d17821fcfdbee4140af9a153be0a4f1e96f51f6b3b054