Intelligent search over 50+ neuroscience-inspired mechanisms discovers lightweight adapters that improve foundation models beyond state-of-the-art parameter-efficient fine-tuning.
Sentence transformer enhanced with bio-inspired adapters discovered through intelligent search. Validated on STS, pair classification, and clustering benchmarks. Each adapter adds up to ~1% of model parameters.
This is a hard-mode validation: MiniLM is a 22M-param, 6-layer model already distilled and heavily optimized by the sentence-transformers team. Larger models with more layers offer substantially more room for bio-adapter improvement.
View on HuggingFace →all-MiniLM-L6-v2 + GENbAIs Bio Adapters vs. baseline sentence-transformers/all-MiniLM-L6-v2
| Task | Dataset | Metric | Baseline | Finetuned | Δ | Δ% |
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General Efficient Neural bio-Adapter Intelligent Search
A library of 50+ computational primitives inspired by neuroscience — lateral inhibition, predictive coding, Hebbian learning, cortical column dynamics, and more. Each is implemented as a lightweight adapter module.
The search space is ~10²² configurations. Thompson sampling with Bayesian pruning finds winning combinations in ~1,000 experiments — exploring 0.00000000000000001% of the space.
We first grid-search for the optimal LoRA configuration, merge it into the base model, then stack bio-adapters on top. This ensures bio features provide genuine additive improvement over the best available PEFT.
The winning configuration is evaluated on 20 held-out benchmark metrics spanning semantic textual similarity, adversarial pair classification, and clustering.
We don't build models from scratch — instead we modify existing models with novel bio-inspired adapters.
Even though the configuration space is astronomical, intelligent pruning finds strong results in ~1,000 experiments — exploring 0.00000000000000001% of the search space while discovering configurations that outperform LoRA.
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