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ARXIV:2603.03672 · AI MODEL VALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.03672AI MODEL VALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation.
Opportunity summary
Pain Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a…
The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in…
AI Model Valuation moved forward this cycle; last verified April 2026. Public score 4.0/10.
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Score4.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation.
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Paper Pack
10.48550/arXiv.2603.03672Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation.
Abstract
The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points influences the prediction. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in GNNs), showing that Shapley computation can be projected onto these supports without loss when locality is exact. This reframes Shapley evaluation as a structured data processing problem over overlapping support-induced subset families rather than exhaustive coalition enumeration. We prove that the intrinsic complexity of Local Shapley is governed by the number of distinct influential subsets, establishing an information-theoretic lower bound on retraining operations. Guided by this result, we propose LSMR (Local Shapley via Model Reuse), an optimal subset-centric algorithm that trains each influential subset exactly once via support mapping and pivot scheduling. For larger supports, we develop LSMR-A, a reuse-aware Monte Carlo estimator that remains unbiased with exponential concentration, with runtime determined by the number of distinct sampled subsets rather than total draws. Experiments across multiple model families demonstrate substantial retraining reductions and speedups while preserving high valuation fidelity.
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
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Preparing verified analysis
Dimensions overall score 4.0
PROBLEM
Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points infl...
METHOD
The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small sub...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in GNNs), showing that Shapley computa...
WHY NOW
AI Model Valuation moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points influences the prediction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points influences the prediction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in GNNs), showing that Shapley computation can be projected onto these supports without loss when locality is exact.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Model Valuation moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
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Efficiently compute Shapley values locally by leveraging model-induced locality with LSMR for faster data valuation.
Segment
AI Model Valuation
Adoption evidence
No public code link in the paper record yet
Commercial read
4.0/10 public viability
Direct
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CITED BY
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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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.
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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
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
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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
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Defensibility signals are missing.
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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
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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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
No named person assigned.
Gaps
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People
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Regulatory need unclassified.
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People
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Gaps
Next verification path
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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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
Buzz trend pending.