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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Princy Randhawa

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Conference Aug 2026

Beyond CLIP: A Critical Analysis of Representational Misalignment When LLMs Replace Text Encoders in Diffusion-based Image Generation

Recent text-to-image diffusion systems have begun replacing CLIP and T5 text encoders with decoder-only large language models (LLMs), motivated by their stronger language understanding. This substitution, however, does not straightforwardly improve image-text alignment: naively using an LLM as the prompt encoder can su...

Yogesh Kakde, Jitendra Jaiswal, B. Sahoo et al. · 0 citations
Conference Aug 2026

Federated Learning Architectures and Communication-Efficient Optimisation for Privacy-Preserving Distributed AI Systems

Federated learning (FL) trains a shared model across data holders that cannot pool their records, but deployments remain bounded by three coupled costs: uplink traffic from repeated model exchange, accuracy loss under statistically heterogeneous clients, and the information that updates still leak. These are usually at...

Harshavardhan Peddireddy, Sandeep Kumar Gadde, Prasad Bheemavarapu et al. · 0 citations
Conference Aug 2026

Autonomous Multi-Agent Systems Orchestrated via n8n: Infrastructure Bottlenecks and Self-Healing Architectures

The transition from single-shot generative models to autonomous, goal-directed agents represents a structural departure from fixed-pipeline automation. Low-code orchestration platforms such as n8n increasingly supply the operational substrate for this transition, handling function invocation, state persistence, and int...

Swapnil Mohan Gaikwad, Sourav Saha, Ranjit Kumar Reddy Ponugoti et al. · 0 citations
Review Open access Aug 2026

Skin Lesion Classification in Low-Resource Settings Using Lightweight CNNs with Uncertainty Estimation

A computationally efficient skin lesion classification framework for seven classes using EfficientNet-B0, complemented by Monte Carlo (MC) Dropout for uncertainty quantification is introduced, and the only lightweight method in the comparison providing calibrated uncertainty estimates.

Princy Randhawa, S. Suddala, M. Hemal et al. · 0 citations

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