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/evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat
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 evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat | Route /signal-canvas/evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combatMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat",
"query_text": "Summarize Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat",
"normalized_query": "2605.25091",
"route": "/signal-canvas/evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat",
"paper_ref": "evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Claims: 1
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
PDF: https://arxiv.org/pdf/2605.25091v1
Source count: 3
Coverage: 50%
Last proof check: 2026-05-27T01:09:42.084Z
Signal Canvas receipt window
/buildability/evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat
Subject: Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat
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": "Chengwei Li; Junlin Liu; Yang Gao", "title": "Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat", "creation date": null
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/evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat
Paper ref
evolutionary-enhanced-multi-agent-reinforcement-learning-for-cooperative-air-combat
arXiv id
2605.25091
Generated at
2026-05-27T01:09:42.084Z
Evidence freshness
stale
Last verification
2026-05-27T01:09:42.084Z
Sources
3
References
0
Coverage
50%
Lineage hash
2e394cea2fa39740240cd30d55abede664a10569a4f1040f16b0b76b7d22e2d2
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