Author

D. Singh

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#artificial intelligence Conference Open access Oct 2026

SARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning

SARATHI achieves competitive perplexity and matches or exceeds existing structured pruning baselines on zero-shot accuracy, and corrects systematic activation drift in post-LayerNorm architectures that would otherwise cause perplexity collapse at high sparsity.

Sankar Behera, Dhruv Singh, Satyadev Ahlawat et al. · Proceedings of the 2026 Conf... · 0 citations

#machine learning Preprint Sep 2026

LILA: Calibration-Free Structured Pruning of Large Language Models via Latent Spectral Geometry

Extending LILA to dynamically allocate sparsity budgets via KS-scores yields state-of-the-art generative preservation at moderate compression, while uncovering fundamental single-layer architectural bottlenecks at higher compression regimes.

Sankar Behera, Dhruv Singh, Anshika Agnihotri et al. · 1 citation