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Daniel Agyei Asante

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Preprint Oct 2026

Augmentation-Guided Adversarial Training via Local Stability-Based Sample Selection

Adversarial training remains one of the most effective defenses against adversarial attacks, but its standard PGD-based training is computationally expensive because adversarial examples are generated for every training sample. Selective adversarial training reduces this cost by perturbing only a subset of samples. How...

Daniel Agyei Asante, M. Tetteh, Deborah Asamoah et al. · 0 citations
#machine learning Preprint Aug 2026

TopoCompress: Long Context Compression via Graph-Wired Semantic Trajectories

TopoCompress is introduced, a training-free and model-agnostic framework that compresses long contexts by selecting coherent semantic spans by selecting coherent semantic spans and achieves performance comparable to the strongest baseline while using a 4x smaller compression budget.

Daniel Agyei Asante, Yang Li · 1 citation

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