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A New Way to Read the DNA of AI Models
The artificial intelligence race is no longer only about who can build the biggest model. It is increasingly about a harder question: who actually built the model from the ground up, and who built on top of someone else’s foundation?
That distinction matters.
Using an open-weight model such as Qwen, Llama, DeepSeek, or Mistral as a starting point is completely legitimate. Fine-tuning, continued pretraining, modifying architectures, and adapting models for a particular language or industry are all normal parts of modern AI development.
But there is a meaningful difference between adapting an existing foundation and independently developing one.
Model Genome Korea attempts to make that difference measurable.
The project describes itself as a kind of DNA lineage test for Korean large language models (LLMs) and vision-language models (VLMs). Instead of relying on marketing language or company announcements, it examines publicly available model files, including config.json, tokenizer data, and model weights.
The goal is straightforward: identify fingerprints that can reveal whether a model appears to be native, adapted, mixed, or effectively ported from an existing foreign foundation.
And the results point toward a much more complicated Korean AI landscape than a simple “built here” versus “copied there” debate.
Why AI Lineage Matters
Modern foundation models are extraordinarily expensive to develop.
Training a competitive model requires enormous computing resources, huge datasets, specialized researchers, infrastructure, evaluation systems, and months of experimentation. As a result, open-weight foundations have become incredibly valuable starting points for companies that want to build their own AI products without recreating the entire research stack.
There is nothing inherently wrong with that.
In fact, much of the AI industry depends on this ecosystem.
The problem begins when technical lineage becomes difficult to understand.
A company may release a model with a new name, a Korean tokenizer, additional training data, a different instruction-tuning strategy, or a modified architecture. To users, the resulting system may look entirely new.
Underneath the surface, however, parts of its genetic structure may still resemble an existing model.
Model Genome Korea attempts to investigate that hidden layer.
The Core Idea Behind Model Genome Korea
The project combines several different measurements into what can be thought of as an AI genotype.
The primary evidence comes from three areas:
Architecture configuration
Tokenizer vocabulary
Model-weight representations
These measurements are then examined together rather than treated as independent proof.
That distinction is important because no single measurement can reliably establish model lineage.
An architecture can be reused while the weights are independently trained.
A tokenizer can be rebuilt while the underlying architecture remains almost identical to a foreign model.
And model weights can change dramatically during continued pretraining.
The project’s central argument is therefore not that one number can expose a model’s origin. Instead, multiple fingerprints together can provide a much stronger picture.
config.json Is an Architectural Fingerprint
Every Hugging Face Transformers checkpoint generally exposes a config.json file containing important information about the model’s structure.
Several fields are especially useful:
model_type
vocab_size
hidden_size
intermediate_size
num_hidden_layers
num_attention_heads
num_key_value_heads
Individually, these values are not necessarily remarkable.
Together, they can become surprisingly distinctive.
Consider a simplified architecture fingerprint:
hidden_size · intermediate_size · layers · attention_heads · key_value_heads
If five major architectural dimensions match another public model exactly, the probability of that being a meaningless coincidence becomes much smaller.
The project therefore treats simultaneous architectural matches as a fingerprint rather than focusing on one parameter.
Several Models Showed Exact Architectural Matches
The analysis describes examples where commercial or publicly released models matched recognizable foreign architectures.
One reported 7B model matched the Qwen2.5-7B configuration:
3584 · 18944 · 28 · 28 · 4
A reported 72B model matched Qwen2.5-72B:
8192 · 29568 · 80 · 64 · 8
A 14B vision-language model matched Qwen3-14B:
5120 · 17408 · 40 · 40 · 8
Another 8B model matched Llama-3.1-8B:
4096 · 14336 · 32 · 32 · 8
A mixture-of-experts configuration also showed a close match to DeepSeek-V3-style architecture.
These observations do not automatically prove that the model’s weights were copied.
That distinction is crucial.
An organization can legitimately adopt an existing architecture while training its own parameters from scratch.
Architecture tells us about the skeleton. It does not necessarily tell us where the brain came from.
The Tokenizer Reveals Another Layer of Lineage
The tokenizer provides a different kind of fingerprint.
A tokenizer determines how text is broken into tokens before it reaches the neural network.
If two models use extremely similar vocabularies, especially when the vocabulary is effectively identical, that can provide stronger evidence of technical inheritance than architecture alone.
Model Genome Korea compares vocabulary sets extracted from tokenizer.json.
Its overlap measurement is:
|A ∩ B| / min(|A|, |B|)
This is deliberately different from a conventional Jaccard similarity.
The minimum vocabulary size is used because a smaller vocabulary that is effectively carved out of a larger vocabulary can score close to 1.0.
That is useful when investigating whether one tokenizer represents a reduced or adapted version of another.
The “Foreign Brain, Own Language” Scenario
One of the most interesting findings described by the project is the possibility of separating architecture inheritance from tokenizer inheritance.
A model can have the exact architecture of Qwen2.5-7B while sharing only around 38 percent of its vocabulary.
That creates an intriguing technical profile.
The architecture appears familiar, but the tokenizer looks substantially different.
For a Korean-focused model, this makes practical sense.
A research team may take advantage of a proven architecture while designing a tokenizer optimized for Korean text, multilingual data, domain-specific terminology, or a different vocabulary distribution.
The result could therefore be described as a model with a foreign architectural skeleton and a locally designed linguistic interface.
That is very different from simply downloading an existing checkpoint and fine-tuning it.
Tokenizer Reuse Can Tell a Different Story
The opposite situation is equally revealing.
Some VLMs reportedly showed tokenizer overlap of 1.000 with a reference model.
That means the measured vocabulary was effectively a complete subset or match under the project’s metric.
When an architecture and tokenizer both line up closely with an existing foundation, the evidence of technical inheritance becomes considerably stronger.
Still, even this does not automatically establish that the model’s actual weights were inherited.
It demonstrates lineage in the publicly observable components.
The training history remains a separate question.
Why Comparing Embeddings Sounds Better Than It Works
At first glance, comparing embedding weights seems like the obvious way to detect model ancestry.
If two models share the same vocabulary, one might expect their embedding vectors to look similar.
The project tested exactly that idea.
The result was a warning for anyone attempting model forensics.
Row-by-row cosine similarity between embedding vectors can be almost useless for this purpose.
Why?
Because neural representations are rotationally invariant.
Two models can encode essentially the same information while representing that information in different coordinate systems.
Imagine two maps of the same city.
One map points north at the top.
Another rotates the entire map by 90 degrees.
The roads have not changed.
The city has not changed.
But comparing the coordinates directly would suggest that everything is different.
The same fundamental problem can appear inside neural representations.
Linear CKA Provides a Better Comparison
The project therefore turns to Linear Centered Kernel Alignment, or CKA.
CKA is designed to compare representations while being invariant to transformations such as rotation and isotropic scaling.
In simplified form, the method centers two representation matrices and compares their cross-covariance structure against their individual self-similarity.
The project reports an important result.
A known from-scratch model produced near-zero CKA against its candidate base.
That is exactly what researchers would hope to see from independently trained weights.
But a known continued-pretraining derivative produced a CKA score of approximately 0.25.
That number sounds meaningful until it is compared with the baseline.
Two unrelated models from the same general family reportedly produced a score around 0.21.
Suddenly, the distinction becomes much less impressive.
The Weight Test Has a Fundamental Limitation
This is one of the most important conclusions of the entire project.
The weights axis appears useful for confirming strong evidence of independent pretraining when similarity is extremely low.
But it is not a reliable standalone detector of model derivation.
Large-scale continued pretraining can significantly reshape representations.
A derivative model may inherit a substantial amount of technical lineage while eventually producing internal representations that look considerably different from its starting point.
Therefore, CKA should be treated as supporting evidence rather than a final verdict.
That methodological honesty strengthens the overall project.
Attention Diversity Adds Another Clue
The investigation also examines how many different attention mechanisms appear inside a model’s configuration.
Most models use a relatively consistent attention mechanism throughout their architecture.
More experimental models can mix multiple mechanisms.
The analysis looks for configuration keys such as:
layer_types
linear_attn_config
sliding_window
mamba2_d_state
husda_filter_order
mla_kv_lora_rank
attention_cls
A model using several fundamentally different mechanisms may indicate a more customized architecture.
For example, one hybrid configuration reportedly combines full attention with sliding-window attention.
Another model is described as combining mechanisms associated with Mamba2, Husda, MLA, linear attention, gated-delta networks, native sparse attention, and sliding-window attention.
That level of architectural diversity is an interesting signal.
It does not prove independence, but it makes the model’s technical design substantially more distinctive.
Four Proposed Model Genotypes
Model Genome Korea ultimately collapses architecture and weight evidence into four broad categories.
🟢 Native
A Native model uses a self-developed architecture and independently trained weights.
This represents the strongest form of technical independence under the project’s framework.
🔵 Adapted
An Adapted model is mostly self-developed but appears to borrow one major technical axis.
For example, the architecture may be customized while some weight or representation lineage remains connected to another foundation.
🟡 Mixed
A Mixed model sits between the two extremes.
Its architecture may contain inherited components while the weights show partial inheritance or transformation.
This category is particularly important because real-world AI development often happens this way.
🔴 Ported
A Ported model shows a foreign architectural match together with evidence of inherited weights.
This represents the strongest lineage signal in the framework.
The tokenizer and attention-diversity measurements are kept alongside the genotype instead of being hidden inside the final label.
That allows researchers to inspect the underlying evidence.
Nine Korean Organizations Were Examined
The same methodology was reportedly applied to publicly available foundation models from nine Korean organizations, spanning large enterprises, telecommunications companies, mid-sized businesses, and startups.
The resulting picture was not uniform.
Some models appeared to have strong foreign lineage.
Others showed characteristics consistent with independent development.
Many occupied the middle ground.
That is arguably the most interesting conclusion.
Korean AI development cannot be accurately described as one homogeneous strategy.
Different organizations are making different technical choices.
Some are building highly customized systems.
Some are adapting existing open-weight foundations.
Others appear to combine external foundations with significant internal engineering.
Why This Matters for Korean AI
South Korea has invested heavily in becoming a major AI power.
Companies are competing not only in consumer applications but also in enterprise AI, robotics, semiconductors, telecommunications, search, manufacturing, and national AI infrastructure.
The ability to distinguish genuine foundation-model development from foundation-model adaptation therefore has economic and strategic importance.
A model built from scratch may represent enormous research investment.
A model derived from an open-weight foundation can reach market faster and potentially at a lower cost.
Neither strategy is automatically superior.
The important thing is transparency.
Users, investors, researchers, regulators, and enterprise customers should be able to understand what they are actually evaluating.
Open-Weight Foundations Are Not a Dirty Secret
There is an important nuance that should not be lost in the debate.
Using Qwen, Llama, DeepSeek, or Mistral as a starting point is not inherently deceptive.
Open-weight ecosystems exist precisely so researchers and companies can build upon them.
The open-source and open-weight model ecosystem has accelerated AI progress by allowing organizations to reuse architectures, training techniques, evaluation frameworks, and model parameters.
The real issue is how that relationship is described.
Calling a fine-tuned derivative a completely independent foundation model can create a misleading impression.
Calling an extensively modified model “just a copy” can be equally misleading.
The technical reality is usually somewhere between those extremes.
The Debate Around “Self-Developed” Models
The project emerged amid renewed attention around models described as self-developed or DeepSeek-rivaling.
The debate extended beyond Korea and into Chinese technology communities, where discussions about model ancestry attracted enormous attention.
That reaction illustrates why lineage analysis matters.
When AI models become symbols of national technological competitiveness, questions about their origin become politically and economically sensitive.
But technical analysis can move the conversation away from speculation.
Instead of asking whether a company “really built AI,” researchers can ask measurable questions.
What architecture does the model use?
Which tokenizer does it use?
How much vocabulary overlap exists?
Do its weights show representation-level similarities?
Does its configuration contain distinctive architectural mechanisms?
Those are much more productive questions.
Model Lineage Is Better Viewed as a Spectrum
The biggest conceptual lesson from Model Genome Korea may be that AI ancestry is not binary.
A model is not necessarily either “original” or “copied.”
There can be many layers of inheritance.
A company might adopt an architecture.
Then create a new tokenizer.
Then initialize from an open-weight checkpoint.
Then perform massive continued pretraining.
Then replace selected layers.
Then add proprietary data.
Then develop a new multimodal encoder.
Then perform extensive alignment and instruction tuning.
What should that model be called?
The answer depends on which layer of lineage is being discussed.
That is why a multi-axis genome is more informative than a single label.
The Three Core Functions Make the Method Accessible
The
An architecture fingerprint can be extracted with a simple Python request to a public Hugging Face repository.
A tokenizer can be downloaded and compared programmatically.
A representation-level comparison can then be performed when compatible model weights are available.
The basic concept is therefore reproducible.
Researchers can point the methodology at different repositories and independently inspect the resulting fingerprints.
What This Could Mean for AI Transparency
Model lineage analysis could eventually become a normal part of AI documentation.
Today, model cards commonly describe training data, benchmarks, capabilities, limitations, and licensing.
But the ancestry of the underlying architecture and weights is not always presented with the same level of detail.
That could change.
Future model documentation may include a formal lineage section.
It could identify:
Base architecture
Initial checkpoint
Tokenizer origin
Continued-pretraining relationship
Major architectural modifications
Multimodal components
Parameter inheritance
Proprietary training stages
Such documentation would make model ecosystems much easier to understand.
The Bigger Industry Question
The deeper question is not whether Korean companies use foreign foundations.
The deeper question is whether the AI industry is entering an era where model ancestry becomes a measurable technical property.
If so, the implications go far beyond Korea.
The same approach could be applied to models from China, the United States, Europe, Japan, India, and elsewhere.
Researchers could build global model family trees.
A model could be represented as a node.
Architectural inheritance could form one edge.
Tokenizer inheritance another.
Weight initialization another.
Continued pretraining another.
Fine-tuning could form another branch.
Over time, the AI ecosystem could begin to look less like a collection of isolated models and more like an evolutionary tree.
What Undercode Say:
The AI Industry Needs a Better Vocabulary
Model Genome Korea addresses a problem that the AI industry has quietly carried for years: the word “model” can describe radically different things.
One model may be trained entirely from random initialization.
Another may begin from billions of inherited parameters.
A third may reuse only an architecture.
A fourth may combine an external language model with a proprietary vision encoder.
All four can be marketed as a new model.
Technically, they are not equivalent.
Architecture Is the Skeleton
Configuration fingerprints are powerful because architecture is expensive to reinvent.
When hidden dimensions, layer counts, attention heads, intermediate dimensions, and other structural characteristics align perfectly, researchers have a concrete starting point for investigating lineage.
It does not prove copied weights.
But it establishes a structural relationship worth examining.
Tokenizers Are Often Underrated
The tokenizer may be one of the most useful forensic artifacts in a public checkpoint.
A company can change training data.
It can change instruction tuning.
It can alter model behavior.
But a tokenizer leaves a remarkably visible signature.
Vocabulary overlap can therefore expose relationships that a model’s marketing description does not reveal.
The 1.000 Overlap Signal Is Especially Interesting
A perfect or near-perfect overlap with a known tokenizer deserves attention.
It does not prove inherited weights.
But it strongly suggests that the linguistic preprocessing layer was not independently reinvented.
That is particularly relevant when the architecture is also an exact match.
CKA Should Not Become a Magic Number
The project is correct to avoid treating CKA as an absolute lineage detector.
A single similarity score cannot explain an entire training history.
Training can transform representations.
Optimization can rotate or reshape internal spaces.
Fine-tuning can push models into substantially different regions.
Therefore, CKA should support a conclusion rather than dictate it.
Model Forensics Must Remain Evidence-Based
The strongest aspect of the methodology is the emphasis on multiple signals.
Architecture provides one fingerprint.
Tokenizer similarity provides another.
Weights provide another.
Attention diversity provides additional context.
When several independent signals point in the same direction, confidence increases.
When they disagree, the disagreement itself becomes useful evidence.
“Original” Is Not Always the Same as “Better”
A model trained from scratch is not automatically superior to an adapted model.
A carefully adapted Qwen or Llama model can outperform a poorly designed independent foundation.
Engineering quality matters.
Training data matters.
Optimization matters.
Evaluation matters.
Deployment matters.
The lineage question should therefore be treated as a transparency question, not a quality ranking.
Adaptation Is a Legitimate Engineering Strategy
Companies often need models optimized for local languages and industries.
Starting from a proven foundation can dramatically reduce development costs and shorten the time required to reach production.
For smaller organizations, this may be the only practical route to competitive AI.
The industry should not stigmatize adaptation.
It should simply describe it accurately.
Korean Tokenization Is a Particularly Interesting Case
Korean presents linguistic characteristics that make tokenization choices important.
A tokenizer optimized for Korean text can produce different segmentation behavior from one designed primarily around English or multilingual corpora.
That means an organization can meaningfully customize the linguistic layer while retaining an established transformer architecture.
The resulting model is neither purely foreign nor purely independent.
That is exactly where lineage analysis becomes useful.
VLMs Make the Question More Complicated
Vision-language models add another layer of complexity.
A VLM may inherit a language backbone while using a new vision encoder.
It may reuse a tokenizer but introduce proprietary multimodal components.
It may inherit the language model weights but train the cross-modal projection from scratch.
Calling the entire system “inherited” would oversimplify its actual architecture.
Model genealogy therefore needs to account for components, not just model names.
Open-Weight Ecosystems Are Creating AI Family Trees
Qwen, Llama, DeepSeek, Mistral, and other major foundations are increasingly becoming ancestors of downstream models.
Some descendants remain close to their parents.
Others evolve dramatically.
Some acquire new tokenizers.
Some gain new attention mechanisms.
Some become multimodal.
Some undergo massive continued pretraining.
The family-tree metaphor is becoming technically useful.
Public Checkpoints Are Becoming Forensic Evidence
Every publicly downloadable configuration file and tokenizer is potentially a piece of evidence.
That changes the role of model repositories.
They are not simply distribution platforms.
They are also technical records.
Researchers can inspect configuration history, tokenizer structures, parameter shapes, licenses, and published model cards.
Transparency therefore increasingly depends on what developers choose to expose.
Model Genome Could Become a Broader Standard
The methodology could eventually evolve into a standardized reporting system.
Imagine a model card that says:
Architecture lineage: Qwen-derived.
Tokenizer lineage: custom.
Weight lineage: uncertain.
Attention architecture: heavily modified.
Training: continued pretraining.
That would be far more informative than simply stating “7B Korean foundation model.”
Regulators May Eventually Care About This
AI regulation is increasingly concerned with provenance, transparency, intellectual property, safety, and accountability.
Model genealogy could eventually become relevant to those discussions.
If a system is trained from an external checkpoint, licensing conditions may matter.
If its tokenizer or architecture is inherited, documentation may matter.
If proprietary weights are involved, provenance becomes even more important.
Technical lineage analysis could therefore become part of compliance tooling.
Investors Should Pay Attention Too
The difference between building a foundation model and adapting one can have significant implications for valuation.
Training a competitive model from scratch requires enormous capital.
Adapting an existing foundation can require far less.
Neither business model is inherently bad.
But they represent different technological assets.
Investors evaluating AI companies should increasingly distinguish model ownership, model development, model adaptation, and model deployment.
The Biggest Risk Is False Certainty
There is also a danger in the opposite direction.
A forensic tool can become misleading if its output is treated as an accusation rather than evidence.
An exact architecture match does not prove stolen weights.
A tokenizer match does not prove copied training.
A CKA score does not reveal an entire training history.
The correct approach is probabilistic and evidence-based.
Model Genome Korea is most valuable when used in precisely that way.
Reproducibility Is the Key
A major strength of the project is that its basic tests can be repeated.
Researchers do not need to accept a
They can retrieve public files.
They can calculate architecture fingerprints.
They can compare vocabulary sets.
They can inspect configurations.
They can reproduce the measurements.
That turns an argument about model identity into something closer to an empirical investigation.
AI Lineage Could Become Competitive Intelligence
Companies will likely use these techniques internally as well.
Before adopting a third-party model, an enterprise could investigate its lineage.
Before licensing a model, a company could determine whether it appears heavily dependent on another foundation.
Before releasing a new system, an AI developer could document its own lineage more clearly.
The same tools could serve researchers, buyers, regulators, and developers.
The Future May Be About Provenance, Not Just Performance
For years, AI benchmarks dominated public discussion.
Who has the highest reasoning score?
Who wins coding tests?
Who has the best vision benchmark?
Who uses the fewest tokens?
Those measurements remain important.
But as models become more interconnected, provenance may become equally important.
A model’s ancestry can influence licensing, reproducibility, cost, safety, intellectual property, and strategic independence.
Model Genome Korea Is a Sign of Things to Come
The project does not solve model genealogy completely.
No static checkpoint can reveal every detail of its training history.
But it demonstrates something important: public model artifacts contain far more information about lineage than many people realize.
Architecture files, tokenizers, and weights can tell a story.
Sometimes that story is obvious.
Sometimes it is ambiguous.
Sometimes the most interesting conclusion is that the evidence points in different directions.
That is still valuable.
The Real Victory Is Transparency
The strongest future for this technology is not creating a public “copied” label.
It is creating better technical transparency.
A model should be allowed to say:
“We used Qwen as our base.”
Or:
“We designed a new architecture but initialized from an open-weight checkpoint.”
Or:
“We trained our weights independently while adopting an established transformer design.”
Those statements are not weaknesses.
They are precise descriptions of engineering choices.
And precision is exactly what the AI industry needs as foundation models become increasingly difficult to distinguish from their descendants.
Deep Analysis
Inspecting a Model Configuration
The first practical step is to retrieve the public configuration and extract its architectural signature.
python - <<'PY' import requests
repo = "Qwen/Qwen3-14B"
url = f"https://huggingface.co/{repo}/resolve/main/config.json"
cfg = requests.get(url).json()
keys = [ "model_type", "vocab_size", "hidden_size", "intermediate_size", "num_hidden_layers", "num_attention_heads", "num_key_value_heads" ]
for key in keys:
print(f"{key}: {cfg.get(key)}")
PY
Comparing Two Architectural Fingerprints
A researcher can turn the configuration into a compact fingerprint.
python - <<'PY' import requests
def fingerprint(repo):
url = f"https://huggingface.co/{repo}/resolve/main/config.json"
c = requests.get(url).json()
return (
c.get("hidden_size"),
c.get("intermediate_size"),
c.get("num_hidden_layers"),
c.get("num_attention_heads"),
c.get("num_key_value_heads")
)
models = [ "Qwen/Qwen3-14B", ]
for model in models: print(model, fingerprint(model)) PY
If two unrelated repositories produce the same complete structural tuple, the result deserves investigation.
It should not, however, be treated as proof of inherited weights.
Measuring Vocabulary Overlap
Tokenizer analysis can reveal another layer of model ancestry.
python - <<'PY' import requests
def vocab(repo):
url = f"https://huggingface.co/{repo}/resolve/main/tokenizer.json"
data = requests.get(url).json()
return set(data["model"]["vocab"].keys())
def overlap(a, b): A = vocab(a) B = vocab(b) return len(A & B) / min(len(A), len(B))
a = "MODEL_A" b = "Qwen/Qwen3-14B"
print(Vocabulary overlap:, overlap(a, b))
PY
A score approaching 1.0 indicates that the smaller vocabulary is almost entirely contained within the larger vocabulary under this metric.
That is an especially useful signal when combined with architecture data.
Searching Attention Configuration
Researchers can also inspect whether a model contains unusual combinations of attention mechanisms.
python - <<'PY' import requests
repo = "MODEL_A"
url = f"https://huggingface.co/{repo}/resolve/main/config.json"
cfg = requests.get(url).json()
keys = [ "layer_types", "linear_attn_config", "sliding_window", "mamba2_d_state", "husda_filter_order", "mla_kv_lora_rank", "attention_cls" ]
for key in keys:
if key in cfg:
print(f"{key}: {cfg[key]}")
PY
This does not establish lineage by itself.
Instead, it helps distinguish conventional configurations from heavily customized architectures.
Why Naive Embedding Cosine Fails
A direct embedding comparison can be misleading.
cosine(E_A[token], E_B[token])
looks intuitive, but it assumes that both models use the same coordinate system.
Neural representation spaces do not require that assumption.
A rotation can transform one representation space into another without destroying the information encoded inside it.
Therefore, low row-wise cosine similarity does not necessarily mean unrelated models.
CKA Provides a More Robust Representation Comparison
A simplified Linear CKA implementation looks like this:
import torch
def linear_cka(X, Y): X = X - X.mean(0, keepdim=True) Y = Y - Y.mean(0, keepdim=True)
numerator = (X.T @ Y).norm() 2 denominator = (X.T @ X).norm() (Y.T @ Y).norm()
return (numerator / denominator).item()
The critical requirement is that the representations being compared correspond to the same tokens and compatible observations.
Even then, the result should be interpreted cautiously.
A low score can provide evidence for independent representations.
A moderately higher score does not automatically establish inherited weights.
Building a Practical Genome Report
A useful automated report could combine all signals into one table:
Model
├── Architecture fingerprint
├── Closest known architecture
├── Architecture match score
├── Vocabulary size
├── Tokenizer overlap
├── Attention diversity
├── Weight CKA
├── Candidate base
└── Genome classification
That structure makes the analysis easier to audit.
It also prevents a single metric from dominating the conclusion.
The Most Important Rule
The strongest forensic workflow is:
Architecture
↓
Tokenizer
↓
Weights
↓
Attention diversity
↓
Cross-check
↓
Lineage assessment
The answer should emerge from the combined evidence.
Not from one suspicious number.
Architecture Fingerprints
✅ Supported: Model configuration fields such as hidden dimensions, layer counts, attention heads, and vocabulary size can provide highly distinctive architectural fingerprints. An exact multi-field match is meaningful evidence of architectural similarity.
Representation Analysis
✅ Supported: The explanation of rotational invariance is technically sound, and CKA is more appropriate than simple row-wise cosine similarity for comparing representation spaces under certain conditions. However, CKA should not be treated as definitive proof of model ancestry.
Model Lineage Conclusions
✅ Supported with qualification: Architecture and tokenizer similarities can provide strong evidence of technical inheritance, but neither alone proves that model weights were copied or that a model was not independently pretrained. The strongest conclusions require multiple independent signals and careful interpretation.
Prediction
(+1) Model Lineage Analysis Will Become More Common
(+1) As open-weight models continue producing hundreds or thousands of derivatives, automated lineage detection will become increasingly valuable. Model cards may eventually include standardized architecture, tokenizer, initialization, and training-provenance information.
- AI companies will have stronger incentives to disclose whether their models are native, adapted, continued-pretrained, or heavily derived from an existing foundation.
-
Researchers will build larger model family trees linking major foundations such as Qwen, Llama, DeepSeek, and Mistral to their downstream descendants.
-
Tokenizer and configuration fingerprints are likely to become routine components of AI forensic research.
-
VLM genealogy will become increasingly important as companies combine language backbones, proprietary vision encoders, and multimodal adapters.
(-1) A Single “Original vs. Copied” Score Will Remain Unreliable
(-1) No single metric is likely to completely reconstruct a model’s training history from a public checkpoint.
- Continued pretraining will continue to make weight-based lineage detection difficult.
-
Architecture reuse will remain common even among genuinely independent training efforts.
-
Sophisticated developers can modify several components without eliminating all traces of the original foundation.
The most realistic future is therefore not a universal “AI DNA test” that produces one unquestionable answer. It is a layered forensic system that combines architecture, tokenizer, weights, training documentation, and public provenance into a much clearer picture of where an AI model came from.
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