ConQuR is a post-training rotation calibration technique that aligns activations to hypercube corners via Procrustes optimization and online updates, delivering competitive LLM quantization performance without end-to-end training or offline activation storage.
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A genetic algorithm evolves CLIP token vectors to optimize aesthetic quality and prompt alignment in diffusion models, outperforming Promptist and random search by up to 23.93% on a combined fitness score.
IO-SVD performs SVD-based LLM compression by constructing a KL-aware double-sided whitening space and using first-order loss estimates for heterogeneous rank allocation.
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ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs
ConQuR is a post-training rotation calibration technique that aligns activations to hypercube corners via Procrustes optimization and online updates, delivering competitive LLM quantization performance without end-to-end training or offline activation storage.
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Evolutionary Token-Level Prompt Optimization for Diffusion Models
A genetic algorithm evolves CLIP token vectors to optimize aesthetic quality and prompt alignment in diffusion models, outperforming Promptist and random search by up to 23.93% on a combined fitness score.
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IO-SVD: Input-Output Whitened SVD for Adaptive-Rank LLM Compression
IO-SVD performs SVD-based LLM compression by constructing a KL-aware double-sided whitening space and using first-order loss estimates for heterogeneous rank allocation.