Author

Kasun Dewage

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#artificial intelligence Feb 2026

LORA-CRAFT: Cross-layer Rank Adaptation via Frozen Tucker Decomposition of Pre-trained Attention Weights

CRAFT achieves competitive performance with existing methods while requiring only extremely low Tucker adaptation parameters, and its efficiency advantage grows with model scale, as the frozen Tucker factors better capture the richer cross-layer structure of larger pre-trained models.

Kasun Dewage, Marianna Pensky, Suranadi De Silva et al. · arXiv.org · 1 citation

#machine learning Preprint Aug 2026

Component Type, Not Reconstruction Error, Predicts Attention Quantization Sensitivity

Many post-training quantization (PTQ) methods use layer-wise reconstruction, second-order proxy objectives, or activation-aware transformations to reduce quantization-induced error. Whether that error signal predicts the downstream functional impact of quantizing an individual attention projection has not been directly...

Kasun Dewage, Marianna Pensky, Suranadi De Silva · 0 citations

#artificial intelligence Preprint Aug 2026

Magnitude Profile Pruning: Calibration-Free Structured Attention Head Removal for Transformer Compression

The results show that weight-only statistical scoring can match or outperform data-dependent methods for structured head pruning, providing a practical, zero-cost criterion for Transformer compression.

Kasun Dewage, Marianna Pensky, Heranga K. Rathnasekara et al. · 0 citations

Preprint Aug 2026

Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction

A comprehensive study of hybrid neural-classical correction for adapting frozen TimesFM to stock return prediction during the volatile opening trading hour, with classical methods playing a crucial complementary role.

Kasun Dewage, Suranadi De Silva, Shankhadeep Mondal · 0 citations