7 papers
LLMs can Compress LLMs: Adaptive Pruning by Agents
Sai Varun Kodathala, Rakesh Vunnam
As Large Language Models (LLMs) continue to scale, post-training pruning has emerged as a promising approach to reduce computational costs while preserving performance. Existing me…
Can Large Language Models Solve Engineering Equations? A Systematic Comparison of Direct Prediction and Solver-Assisted Approaches
Sai Varun Kodathala, Rakesh Vunnam
Transcendental equations requiring iterative numerical solution pervade engineering practice, from fluid mechanics friction factor calculations to orbital position determination. W…
Temporal vs. Spatial: Comparing DINOv3 and V-JEPA2 Feature Representations for Video Action Analysis
Sai Varun Kodathala, Rakesh Vunnam
This study presents a comprehensive comparative analysis of two prominent self-supervised learning architectures for video action recognition: DINOv3, which processes frames indepe…
Six Sigma For Neural Networks: Taguchi-based optimization
Sai Varun Kodathala
The optimization of hyperparameters in convolutional neural networks (CNNs) remains a challenging and computationally expensive process, often requiring extensive trial-and-error a…
Fast OTSU Thresholding Using Bisection Method
Sai Varun Kodathala
The Otsu thresholding algorithm represents a fundamental technique in image segmentation, yet its computational efficiency is severely limited by exhaustive search requirements acr…
The Describe-Then-Generate Bottleneck: How VLM Descriptions Alter Image Generation Outcomes
Sai Varun Kodathala, Rakesh Vunnam
With the increasing integration of multimodal AI systems in creative workflows, understanding information loss in vision-language-vision pipelines has become important for evaluati…