LLM DNA: Tracing Functional Genotype

• LLM DNA is a computational representation that encodes LLM functional outputs into low-dimensional vectors, capturing key properties like inheritance and genetic determinism.
• It employs a training-free pipeline involving prompt sampling, semantic embedding, random projection, and normalization to preserve functional distances.
• Empirical analysis demonstrates strong clustering of related models and reliable evolution tracing, facilitating model routing, provenance tracking, and safety evaluations.

LLM DNA refers to a mathematical and computational framework for representing, tracing, and interpreting the functional “genotype” of LLMs—analogous to biological DNA encoding the inheritable properties of living organisms. The concept, as formulated in "LLM DNA: Tracing Model Evolution via Functional Representations" (Wu et al., 29 Sep 2025), defines LLM DNA as a low-dimensional, bi-Lipschitz embedding that encapsulates an LLM’s functional output behavior in a Euclidean space, enabling the reconstruction of model evolutionary relationships, efficient model management, and systematic comparison across heterogeneous architectures. This representation adheres to principles of inheritance and genetic determinism within the landscape of LLM development.

1. Mathematical Definition and Theoretical Properties

LLM DNA is mathematically formalized as a vector $\tau_f \in \mathbb{R}^L$ for an LLM $f$, which captures functional behavior over an input distribution $\mu$ via semantic embedding and random projection. The extraction procedure induces a bi-Lipschitz mapping between the functional distance $d_H$ (typically a Hilbert space metric over the model’s semantic response distribution) and the DNA space’s Euclidean metric $d_\tau$:

$$c_1 \cdot d_H(f_1, f_2) \leq d_\tau(\tau_{f_1}, \tau_{f_2}) \leq c_2 \cdot d_H(f_1, f_2)$$

where $c_1$, $c_2$ are positive constants, $f_1, f_2$ are LLMs, and $\tau_{f_1}, \tau_{f_2}$ their respective DNA vectors. The mapping is constructed to guarantee two key properties:

• Inheritance: Small functional perturbations (e.g., fine-tuning, incremental adaptation) yield small DNA changes; inherited traits are preserved in the DNA space.
• Genetic Determinism: Models with close DNA vectors exhibit similar functional responses under arbitrary prompt distributions.

This construction ensures the DNA embedding is stable, robust across input choices, and universally comparable.

2. General DNA Extraction Pipeline

DNA extraction is achieved through a scalable, training-free pipeline:

• Prompt Sampling: Select a set $S_t$ of $t$ input prompts sampled from a distribution $\mu$ (e.g., drawn for broad coverage).
• Semantic Embedding: For each prompt $x_i$, obtain the model’s response and encode it with a sentence-embedding model $\varphi$, resulting in a fixed-size vector per response.
• Functional Vectorization: Concatenate all response embeddings to form a functional representation $E_f \in \mathbb{R}^{p \cdot t}$, where $p$ is the embedding dimension.
• Random Projection: Apply a random Gaussian projection matrix $A \in \mathbb{R}^{L \times (p \cdot t)}$ (target DNA dimension $L$) to compute the DNA vector $\tau_f = A \cdot E_f$.
• Normalization: Optional postprocessing or normalization ensures consistently scaled DNA vectors.

This algorithm is agnostic to model architecture (encoder–decoder, decoder-only, etc.), tokenizer, or internal layout, requiring no retraining or parameter sharing. By virtue of the Johnson–Lindenstrauss lemma, the procedure empirically preserves the pairwise distances between models in functional space within controllable bounds.

3. Empirical Analysis, Validation, and Relationship Extraction

Across 305 LLMs from 153 organizations, LLM DNA demonstrates:

• Family and Derivation Detection: Models derived via fine-tuning or distillation consistently cluster closely in DNA space; related models are easily identified. Area-under-the-curve (AUC) for “related”/“unrelated” classification reaches $\sim0.957$, surpassing baseline methods.
• Stability: DNA numbering is strongly correlated (Pearson $r>0.75$) across independent prompt sets ($p<0.001$), demonstrating representation invariance with respect to input selection.
• Model Routing: Query routing (selecting optimal model for a task) using DNA embeddings and SVM classifiers achieves competitive or superior performance to end-to-end learned router embeddings, with the added benefit of being fully model-agnostic.
• Visualization: t-SNE and phylogenetic tree plots reveal clear groupings by organization, architectural shifts, and evolutionary speed, confirming both temporal progression and adaption strategies.

4. Phylogenetic Model Tree Construction

LLM DNA provides a principled basis for tracing model evolution and understanding developmental trajectories:

• Tree Construction: Pairwise Euclidean DNA distances are input to classical phylogenetic algorithms (e.g., neighbor-joining).
• Inter-family Relationships: The resulting tree branches segregate encoder–decoder (e.g., Flan-T5) and decoder-only (e.g., Llama, Qwen, Gemma) architectures, matching observed historical transitions in the field.
• Temporal Dynamics: As new models are released, their position within the tree follows chronological inference; branch lengths encode functional divergence and evolutionary speed.
• Discovery of Undocumented Relationships: Previously unknown derivations and adaptations are revealed through DNA proximity, enabling autonomous, evidence-based model genealogy.

5. Implications for Model Management, Safety, and Research

LLM DNA (as an Editor's term: “functional genotype”) constitutes a foundational tool for:

• Model Provenance: Auditing lineage, detecting provenance, and mitigating risks of backdoor introduction or unauthorized derivative models.
• Repository Organization: Systematic indexing of model families for large-scale catalogues, including licensing or intellectual property tracing.
• Multi-Agent Coordination: Dynamic query routing, co-model comparison, and ensemble decisions based on functional DNA proximity.
• Benchmarking and Evaluation: Task-independent comparison of model capabilities, robustness, and adaptation via a unified, interpretable embedding.
• Safety and Regulatory Compliance: Facilitating detection and prevention of adversarial transfer or undesired inheritance in model outputs by leveraging intrinsic DNA signatures for forensic or security examination.

A plausible implication is that future LLM governance frameworks may adopt DNA-based indexing and verification as the standard for model tracing and validation.

6. Directions for Further Research

• Domain Adaptation: Specializing DNA extraction pipelines to scientific, programming, or specialist domains could enhance representational fidelity and contextual relevance.
• Theoretical Refinements: Further work on bounding distortion, optimizing dimensionality, and extending to interactive/task-based dialogs is warranted.
• Integration with Safety Frameworks: DNA analysis may serve as a backbone for comprehensive safety alignment, automatic risk assessment, and dual-use detection as discussed in synergistic research on DNA model jailbreaking (Zhang et al., 28 May 2025).
• Expansion to Non-Textual Modalities: Extension to multimodal models—possibly incorporating graph, image, or biological data—is suggested as complex integration tasks gain prominence.

7. Comparison with Related Frameworks

LLM DNA fundamentally differs from approaches that track model lineage by external metadata, provenance logs, or parameter fingerprinting. Directly encoding functional behavior rather than parameter configurations, it is not constrained by architecture, tokenizer, or training history, and is universally applicable to any model that can produce outputs on a set of prompts. Its rigorous mathematical guarantees and empirical validity (Wu et al., 29 Sep 2025) position it as a distinctive tool for large-scale model analysis, evolution tracing, and systematic comparison—analogous to biological DNA sequencing for living organisms.

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LLM DNA provides a robust, mathematically grounded foundation for representing the functional “genotype” of LLMs, enabling scalable, architecture-agnostic tracing of evolution, inheritance, and specialization across the rapidly developing landscape of foundation models. Its development catalyzes advances in model management, scientific inquiry, and the analytic interpretation of learning systems.