MIT CSAIL AI Artist Unlearning & Attribution Lab

Research Model: Style Latent Space Indeterminacy

Experimental Setup

MIT CSAIL Core Insight: Pruning 100% of an artist's direct training data leaves stylistic synthesis intact due to proxy visual features distributed across adjacent datasets.

Latent Style Manifold (D3 Simulation)

Training Points Active
Style Retention
100.0%
Attribution Entropy
1.18
Manifold Drift
0.000

Synthesis Probe & Attribution Trace

Baseline Synthesis
Post-Pruning Output
Multi-Source Proxy Decomposition
Proxy Source Cluster Shared W Attribution
Tracing generated output to a single training source remains statistically indeterminate without targeted weight nullspace projection.
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