BSCA BGE-Micro-v2 Gold v3 Multidomain

Embedding model for stripped pseudo-C → source retrieval.

• Model ID: Labradorlabs/bsca-bge-micro-v2-contrastive-v15-gold-v3-multidomain-384
• Backbone: TaylorAI/bge-micro-v2
• Initialization / training mode: weights-restart from artifacts/reference_v2/models/gold-scale-v2-seed44; optimizer, scheduler, and RNG state were reinitialized (fresh-or-weights-restart)
• Dimension / maximum tokens: 384 / 384
• Development-only selected seed: 44 (R@1 0.3186, MRR 0.3553)
• Frozen test result: R@1 0.3294, R@10 0.6488, MRR 0.4431
• Training data: Labradorlabs/bsca-binary-source-gold-v3-multidomain

Embed query pseudo-C and source code with attention-mask mean pooling followed by L2 normalization. Use the exact model revision recorded by the associated ReferenceDB vector manifest; do not combine it with vectors from another model revision.

Scope is limited to the accompanying Gold distribution: ELF/Linux and PE x86_64 Windows-GNU direct-TU views. The model is not promoted by an external blind SCA gate yet.