#training-free-optimization News & Analysis

2 articles tagged with #training-free-optimization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 297/10

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OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning

Researchers introduce OccamToken, a training-free method for compressing vision-language models by pruning unnecessary visual tokens while maintaining accuracy. The approach reduces visual token sequences by 98.6% (from 2,880 to 40 tokens) on LLaVA-NeXT while preserving over 93% accuracy, addressing computational bottlenecks in VLM inference.

AIBullisharXiv – CS AI · Jun 96/10

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Correcting Mean Bias in Text Embeddings: A Refined Renormalization with Training-Free Improvements on MMTEB

Researchers identify a systematic mean bias in sentence-embedding models where all embeddings share a near-identical mean component. They propose two training-free corrections, with the projection-based method (R2) demonstrating consistent improvements across 38 models on MMTEB benchmarks by better canceling mean-estimation errors than direct subtraction.