proof blockPage 668%
This equation captures one of the core mathematical components of the system. unknown scalar state θ ∈R. Time is discrete and runs from t = 0 to infinity. Each agent i observes
Page and bbox are available; crop image is pending.
How AI Aggregation Affects Knowledge explores Extends the DeGroot model to analyze how AI aggregation affects social learning, identifying a critical threshold in update speed for robust learning improvement. in AI Theory.
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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/how-ai-aggregation-affects-knowledge
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Canonical ID how-ai-aggregation-affects-knowledge | Route /paper/how-ai-aggregation-affects-knowledge
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/how-ai-aggregation-affects-knowledgeMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.04906"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "How AI Aggregation Affects Knowledge",
"normalized_query": "2604.04906",
"route": "/paper/how-ai-aggregation-affects-knowledge",
"paper_ref": "how-ai-aggregation-affects-knowledge",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Paper proof page receipt window
/buildability/how-ai-aggregation-affects-knowledge
Subject: How AI Aggregation Affects Knowledge
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
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Insufficient data
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Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
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Dimensions overall score 0.0
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This equation captures one of the core mathematical components of the system. unknown scalar state θ ∈R. Time is discrete and runs from t = 0 to infinity. Each agent i observes
Page and bbox are available; crop image is pending.
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Receipt path
/buildability/how-ai-aggregation-affects-knowledge
Paper ref
how-ai-aggregation-affects-knowledge
arXiv id
2604.04906
Generated at
2026-04-08T00:52:05.511Z
Evidence freshness
fresh
Last verification
2026-04-08T00:52:05.511Z
Sources
0
References
0
Coverage
0%
Lineage hash
88fd597cee824114f1c10fb243e0d3468e3adaaa861b5ceb5d558ea41f3c944b
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.
Verification pending / evidence receipt incomplete
paper_evidence_receipts.references_count
paper_evidence_receipts.coverage
This equation captures one of the core mathematical components of the system. population that it places positive training weight on. Thereafter, this output a(t) ∈R evolves according
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This equation captures one of the core mathematical components of the system. Theorem 1. Suppose that ρ ∈(0, 1) and βi ∈(0, 1) for all i. Then, the consensus with an AI aggregator
Page and bbox are available; crop image is pending.
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