Research & Technical Notes

4 studies
Latest

APEE: Adaptive Poly-Agentic Evaluation Ecosystem

A comprehensive framework for evaluating multi-agent AI systems using LLM-as-a-Judge methodology. Tested 12 collaborative scenarios across 6 collaboration patterns with ensemble judges providing nuanced evaluation scores.

7.3/10 Research synthesis — sequential pattern excels
5.6 vs 8.3 L2 collaborative bottleneck vs L3 performance
20-24B Parameter judges evaluating 3B agent outputs
Multi-Agent AI LLM Evaluation Benchmarking Ollama

Vision Model Quantization: Research to Production

Complete analysis of quantization performance across 16 vision models spanning 1.3M–632M parameters. 64 experiments revealing deployment strategies for production environments with real-world ROI analysis.

2.50× Speedup with ViT-Huge + FP16
75% Memory reduction via INT8
100% Success rate across all configurations
Quantization Vision Transformers MLOps Production

TensorFlow Performance: Eliminating Retracing

Deep analysis of TensorFlow's @tf.function behavior revealing persistent retracing issues affecting production trading models. Developed optimization strategies and a weight-swapping cache system.

72.6% Performance improvement achieved
Zero Retrace operation with cache system
TF 2.19 Validated on latest stack
TensorFlow Performance Graph Optimization

Multi-GPU Training: Hardware Topology Analysis

Why more GPUs doesn't always mean better performance. 120+ hours of testing with dual RTX 4070 Ti SUPER reveals critical hardware topology considerations for distributed training decisions.

<10M Parameters threshold for single-GPU
PCIe Host Bridge prevents P2P communication
Negative ROI scenarios identified
Multi-GPU Hardware Distributed Training Cost Analysis

Research Focus

Performance Analysis

System bottleneck identification, optimization strategies, and rigorous benchmarking methodologies for production environments.

ML Infrastructure

Production ML systems, model serving architectures, and scalable training pipeline design.

Multi-Agent Systems

Evaluation frameworks, collaboration patterns, and LLM-as-a-Judge methodologies for agent assessment.

Model Optimization

Quantization techniques, pruning strategies, and efficient inference deployment for resource-constrained environments.