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/leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-systems
Subject: Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems
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
{"file name": "input.pdf", "number of pages": 16, "author": "Jules Berman; Tobias Blickhan; Benjamin Peherstorfer"
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
{"file name": "input.pdf", "number of pages": 16, "author": "Jules Berman; Tobias Blickhan; Benjamin Peherstorfer", "title": "Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems", "creation date": null, "modification date": null, "kids": []}
Inference
{"file name": "input.pdf", "number of pages": 16, "author": "Jules Berman; Tobias Blickhan; Benjamin Peherstorfer", "title": "Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems", "creation date": null, "modification date": null, "kids": []}
Hardware
{"file name": "input.pdf", "number of pages": 16, "author": "Jules Berman; Tobias Blickhan; Benjamin Peherstorfer", "title": "Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems", "creation date": null, "modification date": null, "kids": []}
Receipt path
/buildability/leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-systems
Paper ref
leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-systems
arXiv id
2605.25107
Generated at
2026-05-27T01:09:38.535Z
Evidence freshness
stale
Last verification
2026-05-27T01:09:38.535Z
Sources
3
References
0
Coverage
50%
Lineage hash
750a352a6df05161e2e21a8c7d1947200b3226c27463b2c873163ae116fc98d8
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.
Some score or evidence fields are outside the preferred freshness window.
repo_url
references