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Patent Pending Technology  ·  model-surgery.com

Transplant Knowledge.
Not Weights.

We built the surgical toolkit for AI knowledge transfer. Extract any capability from any foundation model and implant it precisely into any other — with zero retraining, zero GPU cost, and verified 91.7% alignment.

Request Early Access → See How It Works
13
Live MRI Tools
91.7%
Transplant Alignment
<1s
Per Concept Scan
$0
GPU Cost · Runs Local
Scroll
See It In Action

Every Tool. Real Data.
Real Time.

Runs on-premise with zero external dependencies. No data leaves your network. Every result computed in under one second. Hover to pause.

Diagnostic Scan — 34 concepts, 98% avg gap
Diagnostic Scan — 34 concepts scanned, 34 critical gaps, 98% avg gap
Attention entropy heatmap
Attention — Head entropy heatmap across 6 layers × 12 heads
Trace — gravity concept flow
Trace — "gravity" flowing through 18 layers with semantic shifts
SAE — sparse autoencoder features
SAE — 4,140 active features decomposed from model activations
Abliterate — concept removal
Abliterate — "hate" removed from model weights, 3 layers edited
Step — logit lens debugger
Step / Logit Lens — predictions at layer 17, 91.8% confidence
Diagnostic Scan
Diagnostic Scan — 34 concepts scanned, 34 critical gaps, 98% avg gap
Attention entropy heatmap
Attention — Head entropy heatmap across 6 layers × 12 heads
Trace
Trace — "gravity" flowing through 18 layers with semantic shifts
SAE
SAE — 4,140 active features decomposed from model activations
Abliterate
Abliterate — "hate" removed from model weights, 3 layers edited
Step
Step / Logit Lens — predictions at layer 17, 91.8% confidence
Attention head matrices
Attention — Per-head attention matrices, 12 heads at Layer 0
Post-transplant validation
Validate — Perplexity 112.77, QA 27%, Integrity 96.97
Transplant view
Transplant — Donor → Patient with safety threshold controls
Gap analysis
Gap Analysis — angry 93.7%, anxiety 97.5%, atom 99.0%
Diagnostics
Diagnostics — 7-test automated health check on loaded model
Compare
Compare — Side-by-side model diagnostics and concept overlap
Attention head matrices
Attention — Per-head attention matrices, 12 heads at Layer 0
Post-transplant validation
Validate — Perplexity 112.77, QA 27%, Integrity 96.97
Transplant view
Transplant — Donor → Patient with safety threshold controls
Gap analysis
Gap Analysis — angry 93.7%, anxiety 97.5%, atom 99.0%
Diagnostics
Diagnostics — 7-test automated health check on loaded model
Compare
Compare — Side-by-side model diagnostics and concept overlap

A new class of neural tool. Purpose-built for teams who need to see inside models, not just prompt them.

Fig 0.1

Concept Mapping

Every concept has a mathematical address inside the model. We find it in under one second across every weight matrix.

Fig 0.2

Cross-Model Alignment

Different models store the same knowledge in different coordinate systems. We compute the exact rotation between them.

Fig 0.3

Surgical Precision

Write knowledge directly into model weights via rank-k conjugation. Interference detection prevents collateral damage.

Diagnose

Ask a question.
Get the MRI.

Type a concept, click send, and watch the platform scan your model's internals in real time. Every bar is a real measurement. Every gap is a transplant target.

Click the message in the chat to trigger a scan. →

Model Surgery MRI
Layer Map
Trace
Graph
Attention
Diagnostics
SAE
Step
System GPT-2 loaded. 124M parameters. Ready for analysis.
You Scan all 34 concepts in GPT-2
Model Surgery Scanning 34 concepts... 34 critical gaps found. Average gap: 98.1%.
⊕ Click a query to run diagnostics
Diagnostic Scan
Complete
34
Concepts
34
Critical Gaps
98.1%
Avg Gap
time
110.3%
morality
107.0%
joy
104.2%
gravity
100.8%
emotion
98.6%
abstract
97.3%
Model Surgery MRI — Transplant
Diagnostics
Transplant
Validate
Abliterate
GPT-2
124M · Donor
→
DistilGPT-2
82M · Patient
Concept: gravity  ·  24 layers
Mapping concept in donor... ✓
Computing Procrustes alignment... ✓
Interference check: 0.12 cos-sim — SAFE
Transplanting via rank-k conjugation... ✓
Post-graft verification: 91.7% alignment

✓ Knowledge Implanted Successfully

"gravity" transplanted into DistilGPT-2 · 24 layers · δ = 3.71 · 91.7% alignment verified

Transplant

One click.
Knowledge transferred.

Select a donor model, pick a concept, and press transplant. The system maps, aligns, checks for interference, and writes the knowledge directly into model weights. Verified alignment before you close the tab.

Click "Begin Transplant" to watch the surgery. →

The Problem We Solved

We Changed the Economics
of AI Development.

For years, teams had two choices when they needed a new AI capability: retrain from scratch, or distill. Both cost a fortune. Both take months. We built the third option — and it costs nothing.

The Old Way

Retrain. Distill. Wait.

  • ✕ $50,000–$500,000 per training run
  • ✕ Weeks to months of wall-clock time
  • ✕ Entire ML teams required
  • ✕ Catastrophic forgetting risk
  • ✕ Everything changes — precision impossible
  • ✕ Cannot transfer a single capability
Average cost: $200,000+
→
The Model Surgery Way

Map. Align. Transplant.

  • ✓ $0 GPU cost — no training compute required
  • ✓ Concept fingerprinted in under 1 second
  • ✓ Single API call to transplant
  • ✓ Interference detection prevents damage
  • ✓ Surgical precision — one concept at a time
  • ✓ 91.7% verified post-graft alignment
Average cost: $0
The Methodology

Three Breakthroughs.
One Surgery.

Our 12-stage pipeline distils to three novel scientific contributions — each independently publishable, together forming the first complete system for cross-model knowledge transplantation.

1
∇

Gradient-SVD Concept Mapping

Every concept has a precise mathematical address inside a model's weight space. We compute it in under one second using a novel gradient-decomposition technique — producing a rank-k fingerprint that uniquely identifies where and how any piece of knowledge is stored. No training required.

2
↻

Layerwise Orthogonal Alignment

GPT-2 and LLaMA store the same concept in different coordinate systems. We solve the orthogonal Procrustes problem independently at each network depth — computing the exact rotation matrix between any two models' internal geometries. Residuals approach zero at all layers.

3
✦

Rank-K Conjugation Transplant

We write knowledge directly into model weights via rank-k conjugation: RTΔR — where R is the Procrustes rotation and Δ is the concept delta. Before any edit, interference detection scans for concept collisions. After surgery, an independent probe verifies the graft took at 91.7% alignment.

13
Live diagnostic & surgery tools in one platform
91.7%
Verified alignment on real models post-surgery
0
External dependencies — deploys fully air-gapped
<6GB
VRAM minimum — scales from workstation to data center
The MRI Toolkit

13 Tools. Every One Live.
No Mockups. No Simulations.

A complete operating room for neural networks — from real-time generation debugging to surgical knowledge transplantation. Every tool runs on-premise with zero cloud dependencies — from workstation to data center.

Diagnostic & Interpretability Tools

⌘

Chat Debugger

Watch a model think token-by-token. Confidence scores, entropy, and per-layer predictions for every generated token. Catch hallucinations at the layer they originate.

Real-Time
▶

Step (Logit Lens)

VCR controls to step through layers one at a time. Watch predictions form in slow motion — early layers guess, middle layers refine, final layers commit.

Layer-by-Layer
◧

Layer Map

X-ray any concept across every weight matrix. Heatmap reveals exactly which layers encode which knowledge — answers where information lives inside a network.

Concept X-Ray
⊕

Trace

Follow a concept flowing through every layer. Track meaning shifts, semantic transformations, and information propagation — like injecting dye into neural arteries.

Flow Analysis
⬡

Graph

Visual network of competing token predictions across all layers. Watch dozens of hypotheses compete — like seeing a chess player consider every possible move simultaneously.

Prediction Network
◉

Attention

Entropy heatmaps + per-head attention matrices. See exactly which tokens attend to which — identify syntax heads, position heads, and redundant heads for pruning.

Head Analysis
⧫

Causal Patching

The gold standard from mechanistic interpretability. Corrupt specific layers and measure output divergence to find exactly where factual knowledge is stored.

Causal Tracing
△

Diagnostics

7-test automated health check: activation magnitude, token entropy, attention specialization, gradient flow, dead neurons, temporal regularity, contamination.

Health Check
◈

SAE

Train sparse autoencoders to decompose hidden activations into interpretable features. The same technique Anthropic uses to understand Claude — in a point-and-click interface.

Feature Discovery
⊞

Clusters

Reveals how the model organizes knowledge internally. Concepts grouped by representational similarity — the model's internal ontology made visible.

Concept Mapping
⇋

Compare

Side-by-side diagnostics + concept overlap between two models. Essential for distillation teams: see exactly what knowledge was lost during compression.

Model Diff

Neural Surgery Suite

⊘

Diagnostic Scan

Full-body MRI. Scan entire concept packs and measure knowledge gaps between donor and patient models. Prioritize surgery targets with quantitative gap percentages.

Scan
⊹

Transplant

Extract concept representations from a donor model and inject them into the patient. Safety system detects interference and blocks dangerous transfers automatically.

Transfer
◇

Validate

Post-operative verification: domain perplexity (coherence), QA accuracy (knowledge works), general integrity (no damage). Every surgery gets verified.

Verify
⊖

Abliterate

Surgical concept removal. Erase specific knowledge from model weights — not prompt filtering, actual weight-level deletion. For AI safety teams who need precision.

Remove
Built For

Every Team Working With LLMs.

◈

AI Safety Teams

Abliterate harmful capabilities at the weight level, not the prompt level. Causal tracing shows exactly where dangerous knowledge lives. Precision removal, not blunt RLHF.

⊕

Research Labs

Logit lens, activation patching, sparse autoencoders, attention analysis — every interpretability technique from the literature in one unified interface. No custom scripts.

⬡

Distillation Teams

Compare shows exactly what knowledge was lost during compression. Diagnostic Scan quantifies gaps. Transplant restores what was lost — without retraining.

△

Production ML Engineers

7-test diagnostics catches dead neurons, exploding activations, and contamination before deployment. Validation confirms model health after any modification.

◉

Fine-Tuning Teams

Before/after concept comparisons, quantitative auditing, and the ability to surgically correct mistakes instead of restarting expensive training runs.

▷

AI Educators

The Step view and Chat Debugger make "how transformers work" tangible and visual. Watch predictions form layer by layer. The best teaching tool for neural networks.

Standing on Giants

Grounded in Peer-Reviewed Science.

Model Surgery builds on, extends, and in some areas supersedes the best existing work in neural editing and mechanistic interpretability. We are transparent about our intellectual lineage.

Prior Art · Model Editing

ROME: Locating and Editing Factual Associations in GPT

Meng et al., 2022. Proved facts can be located and surgically edited in transformer weights. We generalize this to arbitrary capabilities.

Read Paper →
Prior Art · Adapter Methods

LoRA: Low-Rank Adaptation of Large Language Models

Hu et al., 2021. The adapter architecture we repurpose for concept fingerprinting — used to extract geometry, not to fine-tune.

Read Paper →
Prior Art · Mass Editing

MEMIT: Mass-Editing Memory in a Transformer

Meng et al., 2022. Extended ROME to simultaneous multi-edits. We extend this concept to full capability transplantation.

Read Paper →
Prior Art · Cross-Lingual Alignment

MUSE: Multilingual Unsupervised Embeddings

Conneau et al., 2018. Showed that embedding spaces align across languages — directly validating our cross-model Procrustes approach.

Read Paper →
Why This Matters

This Changes the Economics
of AI — For Everyone.

"Training a 7B model costs $500,000. With Model Surgery, transplanting that capability costs $0. For the first time in history, AI capability is not a function of how much money you spent training it."

Language Equity

Extract French fluency from a 70B multilingual model and transplant it into a 7B English model. No bilingual data. No fine-tuning. The geometry transfers — verified.

Enterprise Savings

Companies spending millions on domain-specific model training can instead surgically transplant domain knowledge. A single procedure replaces months of training expense.

Scientific Transparency

For the first time: observe exactly where knowledge lives in neural networks, compare locations across architectures, verify transfers mechanistically. A microscope for AI.

Speed to Market

From "we need this capability" to deployed in minutes, not months. From capability need to production deployment in minutes — not months.

$500M+

Estimated annual industry savings once teams replace retraining with Model Surgery

Early Access

Be First to Transplant
Neural Knowledge.

Model Surgery is in private research beta. We are onboarding a select group of teams who want to reshape the economics of their AI development.

Request Access →

Patent pending. By requesting access you agree to our research terms.  ·  [email protected]