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.

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.