Fine-tuned AI models deployed in regulated decisions need a signed technical record of what they actually learned. The EU AI Act requires it. Most organizations don't have one. We produce it — layer by layer, feature by feature, in language a regulator can read and a scientist can verify.
A complete technical record of what your fine-tuned model learned — what it added, what it removed, what changed between the base model and the deployed version. Signed by a PhD researcher. Defensible in front of a regulator.
We classify every feature in your model as suppressed, shared, amplified, new, or eliminated — layer by layer. The first directional audit of exactly what a LoRA removes versus what it creates, with statistical validation at each step. Kill:create ratio documented. Nothing left unmeasured.
A layer-by-layer reconstruction ratio profile showing where fine-tuning effects concentrate in your model's architecture. Early layers, mid-network, output reorganization — the shape of the curve carries real information about how the model was changed. We document that shape with precision.
Full-rank SVD projection onto the base model's complete representational geometry produces a mathematically stable version signature. Any two model versions can be compared. Documented modifications can be verified against the original. Validated cross-architecture: Llama 3.x and Qwen 2.5.
Side-by-side completions on domain-representative prompts with statistical measurement of output divergence. Because internal representation change does not reliably predict behavioral change — both measurements are required. Neither alone is sufficient. This is one of our core research findings.
This is the finding that changes how compliance auditing works. A model can show dramatic internal reorganization while producing nearly identical outputs — or show stable internal structure while behavioral outputs diverge significantly. Neither measurement alone is sufficient to characterize what the fine-tuning actually did. Our methodology requires both.
The methodology underlying every Compliance Map was developed over 18 months of original research at the Awakened Intelligence AI Laboratory — coordinated by John Holman and executed by lead researcher Arshavir Blackwell, PhD. The findings were submitted to NeurIPS 2026. Every technique is implemented against a publicly documented, independently verifiable neutral evaluation corpus. A signed document that cannot be independently verified by a regulator is not worth the paper it is printed on.
We train sparse autoencoders on both the base model and the fine-tuned model to extract monosemantic feature dictionaries from each architecture. Features are classified by their behavior in base vs. fine-tuned contexts: suppressed, shared, amplified, new, or eliminated.
A crosscoder trained jointly on both model versions aligns the feature spaces for direct comparison. The pipeline includes dead-neuron auxiliary loss — a critical methodological requirement that prevents artifactually zero feature counts from corrupting the audit record.
Reconstruction ratios measured at every transformer layer produce a depth fingerprint unique to each model version. The shape of the curve — where effects accelerate, plateau, and concentrate — carries structural information about how the LoRA reorganized the model's processing.
Our pipeline is calibrated specifically for attention-targeted LoRA fine-tuning — the most common fine-tuning approach for deployed models. Adapter weights analyzed. Modifications mapped. Content preservation confirmed through permutation testing at p ≈ 0.9.
Independent geometric verification: principal component analysis in the SAE feature space produces trajectory compression ratios that confirm representational divergence findings through a separate mathematical lens. Both methods must agree. Discordant results trigger additional investigation before the report is signed.
Side-by-side completions on 50–200 domain-representative prompts provided by the client. Statistical measurement of output divergence alongside internal measurement. The two-measurement requirement is a methodological non-negotiable — not an upsell.
Every Compliance Map is backed by original published methodology. We publish our findings, explain our techniques, and do not ask clients to trust a black box. Read the research. Verify the approach. Then decide.
Mechanistic interpretability and artificial psycholinguistics in large language models. Written by Arshavir Blackwell, PhD and John Holman. Thousands of subscribers. Every technique underlying the Compliance Map has been explained in plain language for working researchers, legal practitioners, and compliance officers.
Scale-Dependent Representational Dynamics in Attention-Targeted LoRA Fine-Tuning. Co-authored by John Holman and Arshavir Blackwell, PhD. Two primary findings: a large-scale specialization surge at 70B/72B parameters, and the decorrelation of internal representation divergence from output divergence — the finding that motivates the two-measurement requirement.
Compliance Labs is the product of 18 months of coordinated original research between a founder with a gift for building from first principles and a cognitive scientist with deep roots in the computational foundations of language. Every audit is a collaboration between them.
Every engagement produces a Compliance Map — a signed technical record of what your model learned. The scope of the engagement determines how much of the process we own. Pricing is scoped per engagement. Start with a conversation.
You provide the model. We produce the Compliance Map — a signed, independent third-party audit of what the model learned. The canonical case: your legal team needs documentation of a model that already exists and is already deployed.
Everything in the Independent Audit, plus identification of specific features responsible for compliance concerns, a targeted remediation plan, retraining execution, and a new Compliance Map issued on completion. Before-and-after comparison report included.
We prepare the training data, fine-tune to your objectives with interpretability built in from the start, and deliver the Compliance Map as part of the engagement. The "Compliance Map with receipt" — documents the process as well as the outcome. Disclosed as an integrated engagement in all documentation.
For teams that iterate. Re-audit triggered automatically by any material model change: new training data exceeding 10% of original corpus volume, any architecture or adapter change, any model version pushed to production, or annual refresh regardless of changes.
Compliance Labs operates as a technical expert retained through outside counsel. When retained in this capacity, findings and technical analysis may be protected under attorney-client privilege and the work product doctrine — shielding the audit record from discovery while preserving its value as a compliance artifact.
For law firms advising clients on EU AI Act compliance: this is the technical documentation component your clients need. We produce what Articles 11 and 13 and Annex IV require. GPAI model documentation obligations have been in force since August 2025 and are untouched by any proposed extension. High-risk Annex III system obligations remain subject to the current August 2 deadline until trilogue concludes. You provide the legal strategy and regulatory filing. We give your team the technical foundation to file with confidence — protected when the engagement warrants it.
Tell us about your fine-tuned model — the architecture, the training objective, and your timeline. August 2, 2026 remains the current legal deadline. Trilogue negotiations may extend high-risk Annex III obligations to December 2, 2027. Either way, the documentation work is the same — and the organizations who complete it early are in the strongest position regardless of which date applies. We'll scope the engagement and respond within one business day.
No forms. No call schedulers. A real conversation with the team that will do the work.
john@compliance-labs.ai