proof blockPage 468%
This equation captures one of the core mathematical components of the system. S(t) = w · CSM(t) + (1 −w) · DF(t)
Page and bbox are available; crop image is pending.
ReLeVAnT: Relevance Lexical Vectors for Accurate Legal Text Classification explores A framework for accurate and efficient legal document classification using n-gram processing and shallow neural networks, achieving 99.3% accuracy.. Commercial viability score: 7/10 in Legal Document Classification.
Use This Via API or MCP
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/relevant-relevance-lexical-vectors-for-accurate-legal-text-classification
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID relevant-relevance-lexical-vectors-for-accurate-legal-text-classification | Route /paper/relevant-relevance-lexical-vectors-for-accurate-legal-text-classification
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/relevant-relevance-lexical-vectors-for-accurate-legal-text-classificationMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.22292"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "ReLeVAnT: Relevance Lexical Vectors for Accurate Legal Text Classification",
"normalized_query": "2604.22292",
"route": "/paper/relevant-relevance-lexical-vectors-for-accurate-legal-text-classification",
"paper_ref": "relevant-relevance-lexical-vectors-for-accurate-legal-text-classification",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Paper proof page receipt window
/buildability/relevant-relevance-lexical-vectors-for-accurate-legal-text-classification
Subject: ReLeVAnT: Relevance Lexical Vectors for Accurate Legal Text Classification
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
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
Interactive graph renders after load.
Preparing verified analysis
Dimensions overall score 7.0
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. S(t) = w · CSM(t) + (1 −w) · DF(t)
Page and bbox are available; crop image is pending.
Owned Distribution
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References are not available from the internal index yet.
Receipt path
/buildability/relevant-relevance-lexical-vectors-for-accurate-legal-text-classification
Paper ref
relevant-relevance-lexical-vectors-for-accurate-legal-text-classification
arXiv id
2604.22292
Generated at
2026-04-27T20:15:30.344Z
Evidence freshness
fresh
Last verification
2026-04-27T20:15:30.344Z
Sources
3
References
0
Coverage
50%
Lineage hash
40294f6dfeb20a6140ef4e45bf5ed00b7cb237176fb1faaa5c3055a38ed8ca7b
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
This equation captures one of the core mathematical components of the system. DF(t) = r+ t r+ t + r− t + ϵ where r+ t = d+ t / N+ represents the fraction o
Page and bbox are available; crop image is pending.
This equation defines the score or evaluation function that determines model quality.
Page and bbox are available; crop image is pending.
No public competitor map is available for this paper yet.