Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
Compared to this week’s papers
Verification pending
Use This Via API or MCP
Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.
Use This Via API or MCP
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/mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-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 mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning | Route /signal-canvas/mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learningMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning",
"query_text": "Summarize Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning",
"normalized_query": "2603.22198",
"route": "/signal-canvas/mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning",
"paper_ref": "mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Claims: 0
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
PDF: https://arxiv.org/pdf/2603.22198v1
Repository: https://github.com/mahmoodlab/mammoth
Source count: Pending verification
Coverage: 50%
Last proof check: 2026-03-24T21:26:50.348Z
Signal Canvas receipt window
/buildability/mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning
Subject: Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
Verdict
Preparing verified analysis
Dimensions overall score 7.0
CLAIM MAP
No public claim map is available for this paper yet.
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
Use an AI coding agent to implement this research.
Lightweight coding agent in your terminal.
Agentic coding tool for terminal workflows.
AI agent mindset installer and workflow scaffolder.
AI-first code editor built on VS Code.
Free, open-source editor by Microsoft.
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
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/mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning
Paper ref
mixture-of-mini-experts-overcoming-the-linear-layer-bottleneck-in-multiple-instance-learning
arXiv id
2603.22198
Generated at
2026-03-24T21:26:50.348Z
Evidence freshness
stale
Last verification
2026-03-24T21:26:50.348Z
Sources
0
References
0
Coverage
50%
Lineage hash
a4d2982854d9e44e6bd63db742d1a40f7344b8b38c015374bb5bb841cdc5d36c
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.
Verification pending / evidence receipt incomplete
references
distribution_readiness_scores