LoRAHunter: Decoding Semantic Representations from Low-Rank Adapter Weights

The rapid growth of community-trained Low-Rank Adaptation (LoRA) adapters creates a need for scalable adapter understanding and retrieval, yet existing systems rely heavily on noisy metadata, example images, or expensive generation-based profiling. We investigate whether LoRA weights can support text-aligned adapter representations learned from metadata and offline generation feedback. We propose LoRAHunter, a structured parameter representation framework that queries low-rank weight updates with learnable probes, projects module-specific responses into a shared token space, and aggregates them into a weight-derived adapter embedding. We align this space with language using noisy metadata as weak supervision and further calibrate it for generation-oriented retrieval using offline rewards. At retrieval time, database-side adapter embeddings are computed from LoRA parameters and support semantic clustering, text-to-LoRA retrieval, and multi-adapter selection. Experiments on Stable Diffusion and Qwen-Image show that the learned parameter representations exhibit meaningful semantic structure, support text-to-LoRA retrieval, and provide a useful signal for generation-oriented adapter selection. These results support supervised weight-derived representations as a complementary signal for adapter analysis and selection.