SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/safeadapt-provably-safe-policy-updates-in-deep-reinforcement-learning
- Proof freshness
- stale
- Proof status
- partial
- Display score
- 7/10
- Last proof check
- 2026-04-13
- Score updated
- 2026-04-13
- Score fresh until
- 2026-05-13
- References
- 0
- Source count
- 4
- Coverage
- 83%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
Canonical ID safeadapt-provably-safe-policy-updates-in-deep-reinforcement-learning | Route /signal-canvas/safeadapt-provably-safe-policy-updates-in-deep-reinforcement-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/safeadapt-provably-safe-policy-updates-in-deep-reinforcement-learningMCP example
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Route status: buildingClaims: 0
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
PDF: https://arxiv.org/pdf/2604.09452v1
Repository: https://github.com/maxanisimov/provably-safe-policy-updates
Source count: 4
Coverage: 83%
Last proof check: 2026-04-13T20:33:10.950Z
Signal Canvas receipt window
Ready for execution: SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
/buildability/safeadapt-provably-safe-policy-updates-in-deep-reinforcement-learning
Subject: SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
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Insufficient data
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Compute envelope
Structured compute envelope
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No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Evidence ids
Receipt path
/buildability/safeadapt-provably-safe-policy-updates-in-deep-reinforcement-learning
Paper ref
safeadapt-provably-safe-policy-updates-in-deep-reinforcement-learning
arXiv id
2604.09452
Freshness
Generated at
2026-04-13T20:33:10.950Z
Evidence freshness
stale
Last verification
2026-04-13T20:33:10.950Z
Sources
4
References
0
Coverage
83%
Hash state
Lineage hash
35b9d8550a714d298fea821aa4199c9686651de646d092a00aa7466ad3abf800
Canonical opportunity-kernel lineage hash.
Signature state
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.
Blockers
- Missing: references
Pending verification refs / 4 sources / Verification pending
references
Paper Conversation
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SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
Canonical Paper Receipt
Last verification: 2026-04-13T20:33:10.950ZFreshness: stale
Proof: partial
Repo: active
References: 0
Sources: 4
Coverage: 83%
- - references
No unresolved unknowns recorded.
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Dimensions overall score 7.0
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Related Resources
- Just-In-Time Reinforcement Learning(glossary)
- Multi-Agent Test-Time Reinforcement Learning (MATTRL)(glossary)
- Maximum Entropy Reinforcement Learning(glossary)
- How does PRISM improve reinforcement learning?(question)
- What is the significance of reinforcement learning in AI?(question)
- How does RetroAgent improve reinforcement learning?(question)
- Reinforcement Learning – Use Cases(use_case)
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