ScienceToStartup
TrendsTopicsSavedArticlesChangelogCareersAbout

113 Cherry St #92768

Seattle, WA 98104-2205

Backed by Research Labs
All systems operational

Product

  • Dashboard
  • Workspace
  • Build Loop
  • Research Map
  • Trends
  • Topics
  • Articles

Enterprise

  • TTO Dashboard
  • Scout Reports
  • RFP Marketplace
  • API

Resources

  • All Resources
  • Benchmark
  • Database
  • Dataset
  • Calculator
  • Glossary
  • State Reports
  • Industry Index
  • Directory
  • Templates
  • Alternatives
  • Changelog
  • FAQ
  • Docs

Company

  • About
  • Careers
  • For Media
  • Privacy Policy
  • Legal
  • Contact

Community

  • Open Source
  • Community
ScienceToStartup

Copyright © 2026 ScienceToStartup. All rights reserved.

Privacy Policy|Legal
  1. Home
  2. Signal Canvas
  3. Discovery of interaction and diffusion kernels in particle-t
← Back to Paper

Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems

Fresh2d ago
Export BriefOpen in Build LoopConnect with Author
View PDF ↗
Viability
0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems

PDF: https://arxiv.org/pdf/2603.15927v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Paper Conversation

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

Paper Mode

Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems

Overall score: 4/10
Lineage: dccae6f10ae2…
Cmd/Ctrl+K
Search the latest paper corpus with startup-focused AI synthesis.

Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

Missingness
  • - repo_url
  • - references
  • - proof_status
  • - distribution_readiness_scores
  • - paper_extraction_scorecards
Unknowns
  • - distribution readiness has not been computed yet
  • - 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.

Starting…

Dimensions overall score 4.0

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Claim extraction is still pending for this paper. Check back after the next analysis run.

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Keep exploring

Builds On This
Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling
Score 3.0down
Builds On This
A theory of learning data statistics in diffusion models, from easy to hard
Score 2.0down
Builds On This
Manifold Generalization Provably Proceeds Memorization in Diffusion Models
Score 2.0down
Prior Work
Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems
Score 4.0stable
Prior Work
From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers
Score 4.0stable
Prior Work
Factorized Neural Implicit DMD for Parametric Dynamics
Score 4.0stable
Higher Viability
Physics-informed diffusion models in spectral space
Score 6.0up
Higher Viability
Behavioral Score Diffusion: Model-Free Trajectory Planning via Kernel-Based Score Estimation from Data
Score 6.0up

Startup potential card

Startup potential card preview
Share on XLinkedIn

Related Resources

  • multi-agent systems(glossary)
  • What advantages do multi-agent systems provide in AI?(question)
  • What role does AI play in multi-agent systems?(question)
  • What role does AI play in multi-agent systems?(question)

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
OpenAI APILLM API
Anthropic ClaudeLLM API
LangChainAgent Framework
CrewAIAgent Framework

Startup Essentials

Antigravity

AI Agent IDE

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

Estimated $10K - $14K over 6-10 weeks.

MVP Investment

$10K - $14K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

1-2x

3yr ROI

10-25x

Automation tools have long sales cycles but high retention. Expect $5K MRR by 6mo, accelerating to $500K+ ARR at 3yr as enterprises adopt.

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

7-day free trial. Cancel anytime.

Talent Scout

Find Builders

Multi-Agent experts on LinkedIn & GitHub

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.