This equation captures one of the core mathematical components of the system. Return (/20) 19.8±0.2 18.2±0.4 15.0±0.7 19.6±0.3 16.0±0.6 12.5±0.8 18.0±0.5 13.5±0.6 9.0±0.7 18.6±0.4 18.0±
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KD-MARL: Resource-Aware Knowledge Distillation in Multi-Agent Reinforcement Learning explores A framework for resource-aware knowledge distillation in multi-agent reinforcement learning to enable practical deployment on edge devices.. Commercial viability score: 4/10 in Multi-Agent Reinforcement Learning.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/kd-marl-resource-aware-knowledge-distillation-in-multi-agent-reinforcement-learning
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 kd-marl-resource-aware-knowledge-distillation-in-multi-agent-reinforcement-learning | Route /paper/kd-marl-resource-aware-knowledge-distillation-in-multi-agent-reinforcement-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/kd-marl-resource-aware-knowledge-distillation-in-multi-agent-reinforcement-learningMCP example
{
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"arxiv_id": "2604.06691"
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}Paper proof page receipt window
/buildability/kd-marl-resource-aware-knowledge-distillation-in-multi-agent-reinforcement-learning
Subject: KD-MARL: Resource-Aware Knowledge Distillation in Multi-Agent Reinforcement Learning
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.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
Research neighborhood
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Preparing verified analysis
Dimensions overall score 4.0
No public claim map is available for this paper yet.
Visual citation anchors from the paper document graph.
This equation captures one of the core mathematical components of the system. Return (/20) 19.8±0.2 18.2±0.4 15.0±0.7 19.6±0.3 16.0±0.6 12.5±0.8 18.0±0.5 13.5±0.6 9.0±0.7 18.6±0.4 18.0±
Page and bbox are available; crop image is pending.
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References are not available from the internal index yet.
Receipt path
/buildability/kd-marl-resource-aware-knowledge-distillation-in-multi-agent-reinforcement-learning
Paper ref
kd-marl-resource-aware-knowledge-distillation-in-multi-agent-reinforcement-learning
arXiv id
2604.06691
Generated at
2026-04-10T00:16:06.597Z
Evidence freshness
stale
Last verification
2026-04-10T00:16:06.597Z
Sources
3
References
42
Coverage
67%
Lineage hash
ec23ebace12f6ca25a731e247cc189fa1d7618dfcfe1b5428b985026ff068936
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.
42 refs / 3 sources / Verification pending
repo_url
proof_status
This equation captures one of the core mathematical components of the system. Return (/20) 18.0±0.6 16.5±0.5 13.0±0.7 19.1±0.3 14.0±0.8 10.0±1.0 16.0±0.7 11.0±0.8 7.0±0.9 16.8±0.5 16.5±
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. Return (/20) 18.5±0.5 16.8±0.5 13.5±0.7 18.7±0.4 15.0±0.7 10.5±0.9 16.5±0.6 11.0±0.7 7.0±0.8 17.2±0.5 16.5±
Page and bbox are available; crop image is pending.
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