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ARXIV:2604.00986 · AGENTS · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.00986AGENTSSUBMITTED 03 APR · 20:30 UTCFRESHNESS STALEZhengyang Tang · Ke Ji · Xidong Wang · Zihan Ye · Xinyuan Wang · Yiduo Guo · +16 at arXiv
This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment.
Opportunity summary
Pain This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment.
Evidence 10 refs | 4 sources | 83% coverage
Blocker Evidence partial
This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment. This question has remained hard to answer because privacy-compliant behavior…
We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that privacy failures arise from over-helpful execution of benign tasks, and that success-only evaluation overestimates the deployment readiness of current phone-use…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Continue into Read for claims, analysis, references, and neighboring papers.
Opportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment.
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WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.
Paper Pack
10.48550/arXiv.2604.00986This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment.
Abstract
We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal exactly what data agents type into which form entries during execution. To make this question measurable, we introduce MyPhoneBench, a verifiable evaluation framework for privacy behavior in mobile agents. We operationalize privacy-respecting phone use as permissioned access, minimal disclosure, and user-controlled memory through a minimal privacy contract, iMy, and pair it with instrumented mock apps plus rule-based auditing that make unnecessary permission requests, deceptive re-disclosure, and unnecessary form filling observable and reproducible. Across five frontier models on 10 mobile apps and 300 tasks, we find that task success, privacy-compliant task completion, and later-session use of saved preferences are distinct capabilities, and no single model dominates all three. Evaluating success and privacy jointly reshuffles the model ordering relative to either metric alone. The most persistent failure mode across models is simple data minimization: agents still fill optional personal entries that the task does not require. These results show that privacy failures arise from over-helpful execution of benign tasks, and that success-only evaluation overestimates the deployment readiness of current phone-use agents. All code, mock apps, and agent trajectories are publicly available at~ https://github.com/tangzhy/MyPhoneBench.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
partial10 refs; 4 sources; 83% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Dimensions overall score 7.0
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that privacy failures arise from over-helpful execution of benign tasks, and that success-only evaluation overestimates the deployment readiness of current phone-use agents. A public re...
PROBLEM
This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment. This question has remained hard to answer because privacy-compliant behavior is not operationalized for ph...
METHOD
We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal exactly what data agents type into wh...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal exactly what data agents type into which form entries during execution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operationalized for phone-use agents, and ordinary apps do not reveal exactly what data agents type into which form entries during execution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that privacy failures arise from over-helpful execution of benign tasks, and that success-only evaluation overestimates the deployment readiness of current phone-use agents. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Preview the source document here, or use the hero PDF action for a new tab.
Reference metadata is not materialized in the public index yet. The source PDF remains the authority; cache refresh is optional.
CITED BY
No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.
Foundation
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Concepts
Methods
Materials
Markets
Competitors
This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment.
Segment
Agents
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2604.00986 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.
Hacker News
Not indexed yet
Not indexed yet
Bluesky
Not indexed yet
Conflicting
/api/v1/paper/do-phone-use-agents-respect-your-privacy/paper-pack/api/v1/paper/do-phone-use-agents-respect-your-privacy/build-passport/api/openapi.json/api/mcpsciencetostartup://surfaces/paper-workspacepaper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state{
"contract_version": "paper-r2",
"paper_id": "34c02fa5-0a14-43ed-a8cb-833703063be7",
"arxiv_id": "2604.00986",
"canonical_route": "/paper/do-phone-use-agents-respect-your-privacy",
"active_tab": "synced from current hash by the drawer client",
"selected_artifact": "do-phone-use-agents-respect-your-privacy",
"endpoints": {
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"build_passport": "/api/v1/paper/do-phone-use-agents-respect-your-privacy/build-passport",
"mcp_resource": "sciencetostartup://surfaces/paper-workspace"
}
}Canonical route, proof status, last verified, refs, sources, and coverage.
Page Freshness
Canonical route: /paper/do-phone-use-agents-respect-your-privacy
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Endpoint list, payload shape, route context, and copyable handoff data.
Agent Handoff
Canonical ID do-phone-use-agents-respect-your-privacy | Route /paper/do-phone-use-agents-respect-your-privacy
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/do-phone-use-agents-respect-your-privacyMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.00986"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Do Phone-Use Agents Respect Your Privacy?",
"normalized_query": "2604.00986",
"route": "/paper/do-phone-use-agents-respect-your-privacy",
"paper_ref": "do-phone-use-agents-respect-your-privacy",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Verdict, compute envelope, blockers, signature state, and receipt links.
Paper proof page receipt window
/buildability/do-phone-use-agents-respect-your-privacy
Subject: Do Phone-Use Agents Respect Your Privacy?
Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Visual citations from the paper document graph.
Visual citation anchors from the paper document graph.
This equation captures one of the core mathematical components of the system. Trap resistance (TR). TR(t) = max(0, 1.0 −|violations|/|traps|), where a violation means
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. Over-permissioning (OP). OP(t) = max(0, 1.0 −∑penalties), computed from the access
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. Form minimization (FM). FM(t) = max(0, 1.0 −∑field penalties), where each unneces-
Page and bbox are available; crop image is pending.
The application/ld+json payload rendered for agents.
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "WebPage",
"@id": "https://sciencetostartup.com/paper/do-phone-use-agents-respect-your-privacy#webpage",
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"name": "Ke Ji"
},
{
"@type": "Person",
"name": "Xidong Wang"
},
{
"@type": "Person",
"name": "Zihan Ye"
},
{
"@type": "Person",
"name": "Xinyuan Wang"
},
{
"@type": "Person",
"name": "Yiduo Guo"
},
{
"@type": "Person",
"name": "Ziniu Li"
},
{
"@type": "Person",
"name": "Chenxin Li"
},
{
"@type": "Person",
"name": "Jingyuan Hu"
},
{
"@type": "Person",
"name": "Shunian Chen"
},
{
"@type": "Person",
"name": "Tongxu Luo"
},
{
"@type": "Person",
"name": "Jiaxi Bi"
},
{
"@type": "Person",
"name": "Zeyu Qin"
},
{
"@type": "Person",
"name": "Shaobo Wang"
},
{
"@type": "Person",
"name": "Xin Lai"
},
{
"@type": "Person",
"name": "Pengyuan Lyu"
},
{
"@type": "Person",
"name": "Junyi Li"
},
{
"@type": "Person",
"name": "Can Xu"
},
{
"@type": "Person",
"name": "Chengquan Zhang"
},
{
"@type": "Person",
"name": "Han Hu"
},
{
"@type": "Person",
"name": "Ming Yan"
},
{
"@type": "Person",
"name": "Benyou Wang"
}
],
"codeRepository": "https://github.com/tangzhy/MyPhoneBench",
"additionalProperty": [
{
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},
{
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},
{
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"value": "code, repo url"
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]
},
{
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"name": "Do Phone-Use Agents Respect Your Privacy? - Source Code",
"description": "This research introduces a framework to evaluate and improve the privacy-preserving capabilities of phone-use AI agents, addressing a critical gap in current AI deployment.",
"codeRepository": "https://github.com/tangzhy/MyPhoneBench",
"url": "https://github.com/tangzhy/MyPhoneBench"
},
{
"@type": "BreadcrumbList",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Home",
"item": "https://sciencetostartup.com"
},
{
"@type": "ListItem",
"position": 2,
"name": "Agents",
"item": "https://sciencetostartup.com/topics"
},
{
"@type": "ListItem",
"position": 3,
"name": "Do Phone-Use Agents Respect Your Privacy?",
"item": "https://sciencetostartup.com/paper/do-phone-use-agents-respect-your-privacy"
}
]
}
]
}Receipt path
/buildability/do-phone-use-agents-respect-your-privacy
Paper ref
do-phone-use-agents-respect-your-privacy
arXiv id
2604.00986
Generated at
2026-04-03T20:30:37.886Z
Evidence freshness
stale
Last verification
2026-04-03T20:30:37.886Z
Sources
4
References
10
Coverage
83%
Lineage hash
b2170db8fd525df3e8dd62ea402938ece9477de43a77ec15e6791fa45ecbb537
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
10 refs / 4 sources / Verification pending
distribution_readiness_scores
distribution readiness has not been computed yet
3/3 checks · 100%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
10 refs / 4 sources / 83% 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
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
Evidence
10 references, 4 sources, 83% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
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
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
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.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path