Definitions
alpha: risk budget. delta: confidence (1-delta). Acceptance fraction: share of inputs certified. tau_acc: internal acceptance threshold.
Guaranteed AI Gate
Only pay for certified predictions. Everything else safely abstains.
Pick your guarantee (95% or 99%). We deliver coverage at that bar — abstentions are free and routed to review.
- 89.1% CIFAR-10zero-shot from class prompts
- 94% MNISTwith only 100 labels
- 100–1000× lower energyvs. deep training
- Seconds on CPUno training, no fine‑tuning
Guarantees
Accepted-subset error ≤ alpha with confidence ≥ 1-delta (one-sided Clopper–Pearson). We expose presets so you can pick your risk and confidence.
How we certify
RCPS calibrates acceptance so accepted-subset error ≤ alpha with probability ≥ 1-delta. MFA accepts early when confident and escalates otherwise.
Presets
Strict (alpha=1%, delta=5%), Balanced (alpha=5%, delta=5%), Fast (alpha=10%, delta=5%).
Tech Note & Reproducibility
CSV artifacts, charts, and a minimal evaluation recipe for CIFAR‑10 and AG News to support apples‑to‑apples benchmarking.
- CIFAR‑10 zero‑shot with CLIP text prototypes; CPU wall‑time and memory reported
- Few‑shot Soft‑KAN on CIFAR‑10 (1/5/10 per class)
- AG News label‑efficiency and encoder trade‑offs (mpnet vs MiniLM)
- Coverage vs certified accuracy curves with L=5 and L=10
CSV Artifacts
Methods Note
Request the tech note (PDF). Includes baselines, hardware, and ablations.
Code
Repository link (site + artifacts). Eval repo coming soon.
Try it (CLI):
python vc_bundle_2025-10-06/code/kan_infty_speedrun_v2.py --task agnews --method softkan --subset 10000 --labels-per-class 5 --auto-tau --rcps-enable --rcps-alpha 0.05 --rcps-delta 0.05Trust & Guarantees
Accepted-subset error ≤ alpha with confidence ≥ 1-delta (one-sided Clopper–Pearson). We expose presets so you can pick your risk and confidence.
Definitions
alpha: risk budget. delta: confidence (1-delta). Acceptance fraction: share of inputs certified. tau_acc: internal acceptance threshold.
How we certify
RCPS calibrates acceptance so accepted-subset error ≤ alpha with probability ≥ 1-delta. MFA accepts early when confident and escalates otherwise.
Presets
Strict (alpha=1%, delta=5%), Balanced (alpha=5%, delta=5%), Fast (alpha=10%, delta=5%).
Ship accurate models without training
Cut energy and time‑to‑value. Certify decisions with abstention when inputs are unfamiliar.
What is KAN-Infinity?
KAN-Infinity is a universal extension law for learning. Given known examples, it fills in the unknowns by solving a simple, stable equation - no training, no fragile optimization, and no black-box guesswork. The result is unique, certifiable, and explainable.
From a small set of known examples or class prompts, we compute a stable solution that generalizes across the space, with a calibrated confidence signal for abstention when inputs are unfamiliar.
No gradient training — CPU‑friendly
Millisecond decision step on CPU
Guaranteed confidence via one threshold
Built‑in safety — abstain when unsure
Simple Pricing - pay only for certified outputs
A Single Law. Many Tasks.
- Classification with minimal labels
- Image restoration and inpainting
- Regression with smoothness certificates
- Control, interpolation, and reasoning
94% MNISTwith only 100 labels
85% inpaintingreconstructed from 15% pixels
89.1% CIFAR‑10zero‑shot from class prompts
Why KAN‑Infinity
Accuracy from less data, fast on commodity hardware, greener by design, and transparent by default.
Accuracy
94% MNIST with 100 labels; edge‑preserving inpainting.
Speed
No training. Solutions via averaging or closed‑form.
Efficiency
100–1000× lower energy use vs. deep training.
Transparency
Certificates of smoothness and reliability.
Abstention
Use distance-to-boundary to abstain on out-of-distribution inputs and route to review.
Active Learning
Suggests top-N next labels that most reduce worst-case error.
Three high‑value wedges
Fraud Detection in Payments
Set a 95% target. We charge only when decisions meet that bar; otherwise we abstain and route to review.
Value: fewer manual reviews and disputes; pay only for trustable outputs.
Content Moderation
Confident labels are charged; low‑confidence items abstain to Trust & Safety with audit trail and coverage dashboards.
Value: meet policy thresholds with auditability; stop paying for guesses.
Medical Triage
Doctors set 99%. Routine cases are certified and automated; ambiguous cases abstain and escalate.
Value: faster answers, reduced clinician load; pay only for certified outputs.
Disclaimer: decision‑support only; not medical advice. See Trust.
Why Teams Care
“Zero-shot is no longer a party trick. Start from prompts, add a few examples, and you’re production‑ready — without training.”
“Governance teams finally get a confidence knob: abstain when unsure, act when certain. It’s certified caution, not a heuristic.”
Demos
Zero/few‑shot results, coverage curves, and sample tiles.
Click charts to zoom. CSVs available in /website_copy/data.
Applications
Product & Content Moderation
Launch a new category from class names; abstain when unsure.
Retail & Catalog Intelligence
Cold-start recognition for new SKUs without annotation.
Security & Compliance
Threshold confidence to filter out-of-distribution inputs and escalate to humans.
RAG / Tool Routing
Training-free router from text-only labels (e.g., use OCR, search docs, summarize) with certificates.
Healthcare
Predict outcomes from minimal data with reliability bounds.
Climate & Energy
Fill satellite gaps and optimize grids with extreme efficiency.
Robotics & Safety
Learn safe behaviors quickly with certified robustness.
Finance
Forecast and score risk with fewer assumptions and tighter bounds.
Public Services
Transparent, accountable AI for policy and operations.
How It Works
From boundary knowledge to universal extension - simple, stable, and certifiable.
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1) Pose the Boundary
Encode known examples as boundary conditions on a graph/geometry.
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2) Solve the Law
Compute the minimal-complexity extension - often via averaging or closed form.
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3) Get Certificates
Obtain smoothness and reliability bounds for transparent decisions.
In practice: We derive a simple, stable solution from boundary examples and provide a usable confidence signal to support abstention and human review.
Pricing - pay only for certified outputs
Starter
$0.0008 per certified decision; up to 5M certified/month; shared cloud.
Growth
$0.0005 per certified decision; up to 50M certified/month; VPC deploy.
Enterprise
Custom per certified decision; on‑prem / air‑gapped; SLA and audit pack.
- Certified decision = charged event (meets your target)
- Abstentions = free, routed to review
- Choose your guarantee (e.g., 95% or 99%)
FAQ — certified predictions and abstention
Is abstention a weakness?
No. It is a safety valve and cost shield. You only pay for certified predictions; abstentions route to your queue with full context.
How do you compute a certified prediction?
We compute a confidence bound relative to a threshold. If the bound meets your target, we certify; otherwise we abstain.
What happens under distribution shift?
Coverage drops first (more abstentions). Accuracy on certified outputs remains high. You can alert & switch into conservative mode.
Do you train on my data?
No gradient training by default. You can add labels anytime to increase coverage; the system updates without backprop.
How do I deploy?
Cloud API, VPC, or on‑prem SDK — same certification semantics and audit trail.
What’s the ROI?
Use the calculator below to estimate savings with abstentions versus legacy review rates.
Cost & Coverage Calculator
Get Involved
Join the early access, request a demo, or explore research collaboration.