ScienceToStartup
Product
Proof
DevelopersTrends
Resources
Company

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs

Product, Proof, and developer surfaces share one public navigation contract.

Product

  • Daily Dashboard
  • Signal Canvas
  • Build Loop
  • Evidence
  • Workspace
  • Terminal
  • Talent Layer
  • GitHub Velocity

Proof

  • Foresight
  • Proof Layer
  • Proof Homepage
  • Freshness Hub
  • Example Paper Page
  • Topic Proof Layer
  • Benchmark Scorecard
  • Public Dataset

Developers

  • Overview
  • Start Here
  • REST API
  • MCP Server
  • SDKs
  • Examples
  • Keys
  • Docs

Trends

  • Live Desk
  • Archive
  • Entities
  • Narratives
  • Topics
  • Methodology

Resources

  • All Resources
  • Benchmark
  • Dataset
  • Database
  • Glossary
  • Directory
  • Templates
  • Topics

Company

  • Company Hub
  • About
  • Articles
  • Changelog
  • Careers
  • Enterprise
  • Scout
  • RFPs
  • FAQ
  • Legal
  • Privacy
  • Contact
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy|Legal
  1. Home
  2. Signal Canvas
  3. Frugal Knowledge Graph Construction with Local LLMs: A Zero-
← Back to Paper

Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds

Stale6d agoPending verification refs / 4 sources / Verification pending
Clone RepoExport BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Verification pending

Use This Via API or MCP

Use Signal Canvas as the narrative proof surface

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.

Signal Canvas APIPaper Proof PageOpen Build LoopLaunch Pack Example

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/frugal-knowledge-graph-construction-with-local-llms-a-zero-shot-pipeline-self-consistency-and-wisdom-of-artificial-crowd

ready
Proof freshness
fresh
Proof status
unverified
Display score
6/10
Last proof check
2026-04-15
Score updated
2026-04-15
Score fresh until
2026-05-15
References
0
Source count
4
Coverage
67%

Page-specific freshness sourced from this paper's evidence receipt and score bundle.

Agent Handoff

Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds

Canonical ID frugal-knowledge-graph-construction-with-local-llms-a-zero-shot-pipeline-self-consistency-and-wisdom-of-artificial-crowd | Route /signal-canvas/frugal-knowledge-graph-construction-with-local-llms-a-zero-shot-pipeline-self-consistency-and-wisdom-of-artificial-crowd

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/frugal-knowledge-graph-construction-with-local-llms-a-zero-shot-pipeline-self-consistency-and-wisdom-of-artificial-crowd

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "frugal-knowledge-graph-construction-with-local-llms-a-zero-shot-pipeline-self-consistency-and-wisdom-of-artificial-crowd",
    "query_text": "Summarize Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds",
  "normalized_query": "2604.11104",
  "route": "/signal-canvas/frugal-knowledge-graph-construction-with-local-llms-a-zero-shot-pipeline-self-consistency-and-wisdom-of-artificial-crowd",
  "paper_ref": "frugal-knowledge-graph-construction-with-local-llms-a-zero-shot-pipeline-self-consistency-and-wisdom-of-artificial-crowd",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds

PDF: https://arxiv.org/pdf/2604.11104v1

Repository: https://github.com/jourlin/synsynth

Source count: 4

Coverage: 67%

Last proof check: 2026-04-15T16:47:03.320Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

Frugal Knowledge Graph Construction with Local LLMs: A Zero-Shot Pipeline, Self-Consistency and Wisdom of Artificial Crowds

Overall score: 6/10
Lineage: d8acc2e9576d…
Cmd/Ctrl+K
Search the latest paper corpus with startup-focused AI synthesis.

Canonical Paper Receipt

Last verification: 2026-04-15T16:47:03.320Z

Freshness: fresh

Proof: unverified

Repo: active

References: 0

Sources: 4

Coverage: 67%

Missingness
  • - references
  • - proof_status
Unknowns
  • - proof verification has not been recorded yet

Mode Notes

  • Corpus mode searches the research corpus broadly.
  • Paper mode pins trust state to the canonical paper kernel.
  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

Preparing verified analysis

Dimensions overall score 6.0

GitHub Code Pulse

Stars
0
Health
C
Last commit
4/16/2026
Forks
0
Open repository

Claim map

No public claim map is available for this paper yet.

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Keep exploring

Prior Work
Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
Score 6.0stable
Higher Viability
Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
Score 7.0up
Higher Viability
Shattering the Shortcut: A Topology-Regularized Benchmark for Multi-hop Medical Reasoning in LLMs
Score 9.0up
Higher Viability
KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Score 8.0up
Higher Viability
KG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning
Score 7.0up
Higher Viability
MMKG-RDS: Reasoning Data Synthesis via Deep Mining of Multimodal Knowledge Graphs
Score 7.0up
Higher Viability
CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering
Score 8.0up
Higher Viability
$π^2$: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models
Score 7.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

Related Resources

Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Recommended Stack

PyTorchML Framework
NVIDIA CUDAGPU
TensorRTInference
ONNXModel Format
VerilogHardware

Startup Essentials

Render

Deploy Backend

Railway

Full-Stack Deploy

Supabase

Backend & Auth

Vercel

Deploy Frontend

Firebase

Google Backend

Hugging Face Hub

ML Model Hub

Banana.dev

GPU Inference

Antigravity

AI Agent IDE

Estimated $9K - $13K over 6-10 weeks.

MVP Investment

$9K - $13K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

0.5-1x

3yr ROI

6-15x

GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Talent Scout

View Repository

Find Builders

Knowledge experts on LinkedIn & GitHub

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.