This equation captures one of the core mathematical components of the system. acyclic graph (DAG) G = (V, E), where V = {c1, . . . , cn} is a set of concepts
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Pedagogical Safety in Educational Reinforcement Learning: Formalizing and Detecting Reward Hacking in AI Tutoring Systems explores This research formalizes and detects reward hacking in AI tutoring systems, proposing a framework and index to measure pedagogical safety.. Commercial viability score: 2/10 in Educational AI.
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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/pedagogical-safety-in-educational-reinforcement-learning-formalizing-and-detecting-reward-hacking-in-ai-tutoring-systems
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 pedagogical-safety-in-educational-reinforcement-learning-formalizing-and-detecting-reward-hacking-in-ai-tutoring-systems | Route /paper/pedagogical-safety-in-educational-reinforcement-learning-formalizing-and-detecting-reward-hacking-in-ai-tutoring-systems
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/pedagogical-safety-in-educational-reinforcement-learning-formalizing-and-detecting-reward-hacking-in-ai-tutoring-systemsMCP example
{
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"arxiv_id": "2604.04237"
}
}source_context
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"query": "Pedagogical Safety in Educational Reinforcement Learning: Formalizing and Detecting Reward Hacking in AI Tutoring Systems",
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}Paper proof page receipt window
/buildability/pedagogical-safety-in-educational-reinforcement-learning-formalizing-and-detecting-reward-hacking-in-ai-tutoring-systems
Subject: Pedagogical Safety in Educational Reinforcement Learning: Formalizing and Detecting Reward Hacking in AI Tutoring Systems
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
Time to first demo
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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Research neighborhood
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Preparing verified analysis
Dimensions overall score 2.0
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Visual citation anchors from the paper document graph.
This equation captures one of the core mathematical components of the system. acyclic graph (DAG) G = (V, E), where V = {c1, . . . , cn} is a set of concepts
Page and bbox are available; crop image is pending.
Owned Distribution
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References are not available from the internal index yet.
Receipt path
/buildability/pedagogical-safety-in-educational-reinforcement-learning-formalizing-and-detecting-reward-hacking-in-ai-tutoring-systems
Paper ref
pedagogical-safety-in-educational-reinforcement-learning-formalizing-and-detecting-reward-hacking-in-ai-tutoring-systems
arXiv id
2604.04237
Generated at
2026-04-07T20:14:09.513Z
Evidence freshness
fresh
Last verification
2026-04-07T20:14:09.513Z
Sources
0
References
0
Coverage
0%
Lineage hash
5ea13ffc853d4b73a03284640fa4b36a332a4d60b314e3d540fa5c453b2882d4
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. {ci ∈V : (ci, cj) ∈E} and depth(cj) as the longest path from any root to cj.
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. and E ⊆V × V defines prerequisite relationships. We define prereq(cj) =
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No public competitor map is available for this paper yet.