Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
Compared to this week’s papers
Verification pending
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Route this paper proof surface into REST, MCP, or developer workflows while preserving the same evidence receipt and related-resource context.
Page Freshness
Canonical route: /signal-canvas/learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios | Route /signal-canvas/learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/learning-of-robot-safety-policies-via-adversarial-synthetic-scenariosMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios",
"query_text": "Summarize Learning of Robot Safety Policies via Adversarial Synthetic Scenarios"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Learning of Robot Safety Policies via Adversarial Synthetic Scenarios",
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"route": "/signal-canvas/learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios",
"paper_ref": "learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Claims: 1
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
PDF: https://arxiv.org/pdf/2606.05952v1
Source count: 3
Coverage: 50%
Last proof check: 2026-06-06T03:20:15.259Z
Signal Canvas receipt window
/buildability/learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios
Subject: Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
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": 8, "author": "Nikolai Dorofeev; Alexey Odinokov; Rostislav Yavorskiy", "title": "Learning of Robot Safety Policies via Adversarial Synthetic Scenarios"
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/learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios
Paper ref
learning-of-robot-safety-policies-via-adversarial-synthetic-scenarios
arXiv id
2606.05952
Generated at
2026-06-06T03:20:15.259Z
Evidence freshness
fresh
Last verification
2026-06-06T03:20:15.259Z
Sources
3
References
0
Coverage
50%
Lineage hash
f48f492bf9a689948253735b4994096baae43f9d087ecbb260ce6162c745ef4c
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