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ARXIV:2603.11935 · MOBILE KERNEL GENERATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.11935MOBILE KERNEL GENERATIONSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance.
Opportunity summary
Pain MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the…
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile de- vices remains largely unexplored. In this work, we extend the scope of automated…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross-framework interoperability, coupled with an automated…
Mobile Kernel Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance.
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Paper Pack
10.48550/arXiv.2603.11935MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance.
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile de- vices remains largely unexplored. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write efficient kernels for mobile devices? To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross-framework interoperability, coupled with an automated pipeline that bridges the host-device gap for on-device verification. Leveraging this framework, we conduct extensive evaluation on the CPU backend of Mobile Neural Network (MNN), revealing that current LLMs struggle with the engineering complexity and data scarcity inher-ent to mobile frameworks; standard models and even fine-tuned variants exhibit high compilation failure rates (over 54%) and negligible performance gains due to hallucinations and a lack of domain-specific grounding. To overcome these limitations, we propose the Mobile K ernel A gent (MoKA), a multi-agent system equipped with repository-aware reasoning and a plan-and-execute paradigm.Validated on MobileKernelBench, MoKA achieves state-of-the-art performance, boosting compilation success to 93.7% and enabling 27.4% of generated kernelsto deliver measurable speedups over native libraries.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 33% 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 7.0
PROBLEM
MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write...
METHOD
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile de- vices remains largely unexplored. In this work, we extend the scope of automated kernel generation to the mobile doma...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross-framework interoperability, couple...
WHY NOW
Mobile Kernel Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write efficient kernels for mobile devices?
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile de- vices remains largely unexplored. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write efficient kernels for mobile devices?
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. To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross-framework interoperability, coupled with an automated pipeline that bridges the host-device gap for on-device verification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Mobile Kernel Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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Materials
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MobileKernelBench enables LLMs to generate efficient kernels for mobile devices, significantly improving compilation success and performance.
Segment
Mobile Kernel Generation
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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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
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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
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stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Evidence
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Build Passport does not name an implementer.
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People
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ARTIFACTS
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DEFENSIBILITY
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WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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BUZZ
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