Peer-reviewed research notes, benchmarks, and technical analyses from the DLYog Lab
An academic-style research paper on how enterprises can use AI to defend against AI-enabled threats through identity-centric controls, agent guardrails, continuous asset visibility, and carefully governed open-source or open-weight AI systems.
A deployment-focused research note comparing Gemma 4 E2B on RTX 3090 against Gemma 4 E4B with 4-bit Unsloth quantization on GB10 DGX Spark, covering throughput, latency, pass rate, and observed power and thermal behavior.
A practical benchmark comparing Gemma 4 E2B and Phi-4 Multimodal on a single RTX 3090, covering throughput, latency, memory efficiency, qualitative output structure, and the major architectural gap between the two systems: Gemma 4 supports video, while Phi-4 does not.
A prompt-configurable video analysis platform that combines classical image processing, person localization, motion heatmaps, and a multimodal language model to support real-time edge surveillance with explainable outputs and natural-language interaction.
Meta's LLAMA3 language model can be used for content moderation by leveraging carefully crafted system prompts, providing a cost-effective and flexible solution that reduces the need for additional compute resources and operational overhead. This approach can be generalized to other models, offering a streamlined method for integrating content moderation into AI applications and increasing resource efficiency.
GemmaCure turns drug discovery into a crowdsourced 3D battle game where student players collectively explore chemical space. Built on a fine-tuned Gemma 4-E2B LLM (5.2B parameters, 4-bit quantized), it achieves 100% SMILES validity and a 5× drug-likeness improvement (QED 0.591 vs. 0.116 base) on five held-out targets, with IBM MAMMAL providing live pKd binding-affinity scoring. Extends the Deep2Lead platform for the Kaggle Gemma 4 Good Hackathon 2026.
As cyber threats evolve, the need for robust endpoint security solutions becomes paramount. This paper introduces a novel generative AI-based architecture for endpoint security agents, named "AI4Falcon," designed to enhance their predictive, detection, and response capabilities. We propose a comprehensive framework that integrates generative adversarial networks (GANs) and transformer models to create dynamic threat models capable of anticipating and mitigating zero-day vulnerabilities.
A comprehensive analysis of Ilya Sutskever's NeurIPS 2024 presentation, examining the diminishing returns of current large-scale pre-training strategies and exploring emerging methodologies such as synthetic data generation, biologically inspired architectures, and ethical considerations for superintelligence.