proof blockPage 768%
This equation captures one of the core mathematical components of the system. k X Ptotal ≈ i=1 ωi · P(failure | εi) ≤δ where εi represents a specific perturbati
Page and bbox are available; crop image is pending.
Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation explores A statistical certification framework for AI risk regulation that provides auditable bounds on system failure rates without requiring model internals.. Commercial viability score: 4/10 in AI Risk Regulation.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/bounding-the-black-box-a-statistical-certification-framework-for-ai-risk-regulation
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID bounding-the-black-box-a-statistical-certification-framework-for-ai-risk-regulation | Route /paper/bounding-the-black-box-a-statistical-certification-framework-for-ai-risk-regulation
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/bounding-the-black-box-a-statistical-certification-framework-for-ai-risk-regulationMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.21854"
}
}source_context
{
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"query": "Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation",
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"topic_slug": null,
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}Paper proof page receipt window
/buildability/bounding-the-black-box-a-statistical-certification-framework-for-ai-risk-regulation
Subject: Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
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.
Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
Research neighborhood
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Preparing verified analysis
Dimensions overall score 4.0
Visual citation anchors from the paper document graph.
Owned Distribution
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References are not available from the internal index yet.
Receipt path
/buildability/bounding-the-black-box-a-statistical-certification-framework-for-ai-risk-regulation
Paper ref
bounding-the-black-box-a-statistical-certification-framework-for-ai-risk-regulation
arXiv id
2604.21854
Generated at
2026-04-24T20:30:59.192Z
Evidence freshness
fresh
Last verification
2026-04-24T20:30:59.192Z
Sources
3
References
0
Coverage
50%
Lineage hash
2e869163dce3b33bb3e1beadaa72311690d83febe62952101ca2934f99fe1000
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.
Pending verification refs / 3 sources / Verification pending
repo_url
references
This equation captures one of the core mathematical components of the system. k X Ptotal ≈ i=1 ωi · P(failure | εi) ≤δ where εi represents a specific perturbati
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. i=1 ωi · P(failure | εi) ≤δ where εi represents a specific perturbation
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. δ = 10−9 with a confidence level of 1−α = 0.99, Hoeffding’s
Page and bbox are available; crop image is pending.
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