--- title: "The Archive - AI Research Papers & Engineering | Commune9" description: "Explore peer-level AI research papers, mathematical derivations, transformer architectures, production RAG systems, and computer vision guides on Commune9." canonical: "https://commune9.org/blogs/" --- # The Archive - Machine Learning Research Publications | Commune9 > Explore peer-level AI research papers, mathematical derivations, transformer architectures, production RAG systems, and computer vision guides on Commune9. ## What technical research papers are published in The Archive? The Archive is Commune9's flagship research publication containing peer-reviewed technical deep dives, mathematical derivations, and runnable PyTorch implementations across frontier AI systems. Published papers cover hardware-aware transformer scaling, deterministic RAG architectures, autonomous agent state machines, NVIDIA Blackwell GPU microarchitectures, QLoRA 4-bit fine-tuning, and production YOLO11 vision systems. ## Which featured AI engineering guides are available to read? The Archive provides comprehensive, code-backed engineering guides across core machine learning domains. Our current publications include in-depth technical analyses of rotary position embeddings, hybrid BM25 and vector search retrieval, supervisor-worker multi-agent swarms, B200 FP4 Tensor Core acceleration, 24GB consumer GPU fine-tuning, and edge TensorRT deployments. - Modern Transformer Architectures & Hardware-Aware Scaling (RoPE, FlashAttention-3, GQA, MoE) - Building Production-Grade RAG Systems: Deterministic AST Chunking, Hybrid BM25 & Vector Search - The Anatomy of Autonomous AI Agents: State Graphs, Deterministic Tool Calling & Multi-Agent Hierarchies - NVIDIA Blackwell B200 Deep Dive: Microarchitectural Analysis & FP4 Tensor Core Acceleration - Fine-Tuning Open-Source LLMs with QLoRA 4-bit NormalFloat on Consumer GPUs - Ethics, Safety & Guardrails of Autonomous AI Systems in Production - Object Detection with YOLO11: Architecture, Loss Formulations, Training & TensorRT Deployment Research Publication Author Domain Focus Key Hardware / Framework Modern Transformer Architectures Akshit Joshi Attention Mechanisms & Scaling FlashAttention-3, GQA, RoPE Production RAG Systems Priya Shah Hybrid Search & AST Chunking BM25, Qdrant, Cross-Encoders Autonomous Agent Swarms Akshit Joshi Cyclical State Machines LangGraph, Structured JSON, Guardrails NVIDIA Blackwell B200 Dhruv Makwana GPU Microarchitecture NVLink 5, FP4 Tensor Cores QLoRA Fine-Tuning Dhruv Makwana Quantized PEFT Workflows 4-bit NF4, Paged AdamW, 24GB VRAM Ethics & Guardrails Priya Shah AI Safety & Governance AST Policy Filters, Merkle Trails YOLO11 Object Detection Akshit Joshi Real-Time Computer Vision C3k2, C2PSA, TensorRT INT8 ## How are research papers reviewed and verified in The Archive? The Archive peer-review process is a double-blind technical evaluation conducted by collective machine learning leads and systems architects. Every publication requires verified empirical benchmarks, complete mathematical formulations for loss functions, and runnable PyTorch repositories to guarantee reproducible research across production deployments. --- *Commune9 - The Premier AI Community & Engineering Collective.* *Canonical URL: https://commune9.org/blogs/*