Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
Compared to this week’s papers
Verification pending
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Page Freshness
Canonical route: /signal-canvas/leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-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 leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-systems | Route /signal-canvas/leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-systems
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-systemsMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "leveraging-gauge-freedom-for-learning-non-gradient-population-dynamics-of-stochastic-systems",
"query_text": "Summarize Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems",
"normalized_query": "2605.25107",
"route": "/signal-canvas/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",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Claims: 1
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems
PDF: https://arxiv.org/pdf/2605.25107v1
Source count: 3
Coverage: 50%
Last proof check: 2026-05-27T01:09:38.535Z
Signal Canvas 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.
Preparing verified analysis
Dimensions overall score 0.0
No public code linked for this paper yet.
{"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"
Implication not extracted yet.
partial
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
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Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
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.
Pending verification refs / 3 sources / Verification pending
repo_url
references