Buildability / Receipt
This public receipt window renders only fields present in the canonical receipt object, deterministic fixture receipt, or canonical evidence receipt. Missing compute, demo, hash, signature, approval, telemetry, and adoption fields stay explicit.
Public buildability page receipt window
/buildability/safe-embodied-ai-for-long-horizon-tasks-a-cross-layer-analysis-of-robotic-manipulation
Subject: Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation
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.
Data
Agia C, Sinha R, Yang J, et al (2024) Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress. arXiv:2410.04640 Agrawal S, Seo J, Nakamura K
Truth Boundary
Buildability surfaces only report computed viability and proof receipts. They do not claim live production usage, pilot outcomes, founder sign-off, public Brier calibration, judge divergence, or external adoption unless explicitly sourced.
Compute
Agia C, Sinha R, Yang J, et al (2024) Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress. arXiv:2410.04640 Agrawal S, Seo J, Nakamura K, et al (2025) Anysafe: Adapting latent safety filters at runtime via safety constraint parameterization in the latent space. arXiv:2509
Inference
Agia C, Sinha R, Yang J, et al (2024) Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress. arXiv:2410.04640 Agrawal S, Seo J, Nakamura K, et al (2025) Anysafe: Adapting latent safety filters at runtime via safety constraint parameterization in the latent space. arXiv:2509
Hardware
Agia C, Sinha R, Yang J, et al (2024) Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress. arXiv:2410.04640 Agrawal S, Seo J, Nakamura K, et al (2025) Anysafe: Adapting latent safety filters at runtime via safety constraint parameterization in the latent space. arXiv:2509
Receipt path
/buildability/safe-embodied-ai-for-long-horizon-tasks-a-cross-layer-analysis-of-robotic-manipulation
Paper ref
safe-embodied-ai-for-long-horizon-tasks-a-cross-layer-analysis-of-robotic-manipulation
arXiv id
2606.05660
Generated at
2026-06-06T03:22:02.709Z
Evidence freshness
fresh
Last verification
2026-06-06T03:22:02.709Z
Sources
3
References
0
Coverage
50%
Lineage hash
119833223b9ad0a3cbaff97de87b2385b19ab7102d54ecf12540aa0e880e7bff
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.
Canonical opportunity-kernel evidence is available for this receipt window.
repo_url
references