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ARXIV:2603.08533 · MOBILE GUI AUTOMATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.08533MOBILE GUI AUTOMATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
SecAgent is a 3B-scale mobile GUI agent that automates smartphone tasks using a novel semantic context mechanism and a new multilingual dataset, offering efficient and accurate performance.
Opportunity summary
Pain SecAgent is a 3B-scale mobile GUI agent that automates smartphone tasks using a novel semantic context mechanism and a new multilingual dataset, offering efficient and accurate performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SecAgent is a 3B-scale mobile GUI agent that automates smartphone tasks using a novel semantic context mechanism and a new multilingual dataset, offering efficient and accurate performance. However, existing approaches face two critical limitations:…
Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, existing approaches face two critical limitations: the scarcity of high-quality multilingual datasets,…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Through supervised and reinforcement fine-tuning, SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks.
Mobile GUI Automation moved forward this cycle; last verified April 2026. Public score 8.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
SecAgent is a 3B-scale mobile GUI agent that automates smartphone tasks using a novel semantic context mechanism and a new multilingual dataset, offering efficient and accurate performance.
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Paper Pack
10.48550/arXiv.2603.08533SecAgent is a 3B-scale mobile GUI agent that automates smartphone tasks using a novel semantic context mechanism and a new multilingual dataset, offering efficient and accurate performance.
Abstract
Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, existing approaches face two critical limitations: the scarcity of high-quality multilingual datasets, particularly for non-English ecosystems, and inefficient history representation methods. To address these challenges, we present SecAgent, an efficient mobile GUI agent at 3B scale. We first construct a human-verified Chinese mobile GUI dataset with 18k grounding samples and 121k navigation steps across 44 applications, along with a Chinese navigation benchmark featuring multi-choice action annotations. Building upon this dataset, we propose a semantic context mechanism that distills history screenshots and actions into concise, natural language summaries, significantly reducing computational costs while preserving task-relevant information. Through supervised and reinforcement fine-tuning, SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks. We will open-source the training dataset, benchmark, model, and code to advance research in multilingual mobile GUI automation.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
SecAgent is a 3B-scale mobile GUI agent that automates smartphone tasks using a novel semantic context mechanism and a new multilingual dataset, offering efficient and accurate performance. However, existing approaches face two critical limitations: the scarcity of high-quality...
METHOD
Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, existing approaches face two critical limitations: the scarcity of high-quality multilingual dataset...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Through supervised and reinforcement fine-tuning, SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks.
WHY NOW
Mobile GUI Automation moved forward this cycle; last verified April 2026. Public score 8.0/10.
existing approaches face two critical limitations: the scarcity of high-quality multilingual datasets, particularly for non-English ecosystems
Directly and explicitly stated in the abstract as a key problem being addressed
partial
existing approaches face two critical limitations: ... and inefficient history representation methods
Directly and explicitly stated in the abstract as a key problem being addressed
partial
We first construct a human-verified Chinese mobile GUI dataset with 18k grounding samples and 121k navigation steps across 44 applications
Specific numeric details provided directly in the abstract
partial
along with a Chinese navigation benchmark featuring multi-choice action annotations
Directly stated in the abstract with specific details
partial
we propose a semantic context mechanism that distills history screenshots and actions into concise, natural language summaries
Directly stated in the abstract as a core technical contribution
partial
significantly reducing computational costs while preserving task-relevant information
Directly stated benefit of the proposed method, though specific cost reduction numbers not provided
partial
SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks
Directly stated performance claim, though specific metrics not provided in abstract
partial
achieves performance comparable to 7B-8B models on our and public navigation benchmarks
Directly stated performance comparison, though specific benchmark results not provided in abstract
partial
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Concepts
Methods
Materials
Markets
Competitors
SecAgent is a 3B-scale mobile GUI agent that automates smartphone tasks using a novel semantic context mechanism and a new multilingual dataset, offering efficient and accurate performance.
Segment
Mobile GUI Automation
Adoption evidence
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Commercial read
8.0/10 public viability
Direct
Adjacent
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CITED BY
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Commercially relevant
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 17% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Evidence
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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People
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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BUZZ
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