AI Model Distillation Evidence & Knowledge Transfer Workbench

Distillation Architecture SYS-FORENSICS

American Frontier Model (Teacher)

Distilled Target Model (Student)

Hyperparameters TUNING

Softmax Temperature (T) 2.5 Smooths logit distributions to expose dark knowledge vectors.

KL-Divergence Loss Weight (α) 0.75 Ratio between teacher soft-logit mimicking vs hard ground truth.

Query Batch Volume (Queries) 50,000

Synthetic Tokens Harvested 12.0M

Forensic Distillation Metrics & Transfer Integrity LIVE LOGS

Audit Status

Industrial-scale distillation pattern confirmed

Detection Risk Index

High (NSA/CISA Flagged)

Fidelity Score

94.8%

Param Compression

16.4x

Est. Transfer Cost

$420,000

Probability Vector Approximation: Soft Logit Alignment

24 Output Tokens Measured

Teacher Soft-Logits (US Frontier Model)

Student Mimicry (DeepSeek / Moonshot)

Residual KL Divergence Gap

NSA TAO / CISA BULLETIN INDICATOR #01

Unusual automated high-entropy query spikes observed querying US Frontier API endpoints via distributed proxy nodes with deterministic temperature forcing (T > 2.0).

FBI CYBER DIVISION INDICATOR #02

Targeted reverse synthetic dataset curation harvesting proprietary chain-of-thought tokens, replicating proprietary reasoning paths at ~2-5% of initial training capex.

Forensic Audit Output (RFC-8259 Compliant)

Model Distillation and Soft Logit Extraction Dynamics

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Read the explanation

Distillation extracts knowledge from frontier teacher models by sampling softened probability distributions rather than solitary hard labels. Increasing softmax temperature spreads peak probabilities, exposing the dark knowledge vectors that student networks mimic. During training, student weights align to teacher outputs as Kullback-Leibler divergence loss penalizes differences between their logits. Selecting presets updates query volumes and tokens, projecting transfer cost, compression, and government advisory detection risk.