Research Papers

Peer-reviewed publications, technical whitepapers, and production engineering research focused on AI systems, agentic workflows, large language models, distributed inference, memory architectures, and autonomous systems.

From Multi-Head to Multi-Head Latent Attention: An Engineering Analysis of Attention Mechanisms for Large Language Model Inference

Trisham Patil <trishampatil@gmail.com>

Published: July 2026 | Technical Report

Attention Mechanisms | Transformer Architecture | LLM Inference | KV Cache | Multi-Head Attention | Multi-Query Attention | Grouped Query Attention | Multi-Head Latent Attention | GPU Architecture | Memory Bandwidth

Modern large language models have become increasingly constrained by memory bandwidth rather than compute during autoregressive inference. This technical report presents a systems-level engineering analysis of the evolution of attention mechanisms—from Multi-Head Attention (MHA) through Multi-Query Attention (MQA), Grouped Query Attention (GQA), and Multi-Head Latent Attention (MLA)—covering their mathematical foundations, KV cache optimization, GPU memory behavior, and inference trade-offs in modern LLM serving systems.

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A Scalable Memory Lifecycle Architecture for Agentic Legal Document Intelligence Systems

Trisham Bharat Patil <trishampatil@gmail.com>

Published: July 10, 2026 | Technical Research Paper

Agentic AI | Memory Architecture | Legal AI | LangGraph | Vector Compression | Multi-Tenant Systems | Document Intelligence | Long-Term Memory

Enterprise legal document intelligence pipelines face compound memory problems spanning hundreds of files and weeks-long drafting sessions. This paper presents a scalable memory lifecycle architecture for an eleven-node LangGraph-based legal AI pipeline, introducing legal-specific memory gating, tenant-isolated retrieval, importance-aware memory aging, and TurboVec vector compression.

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Research Articles

Research-first writeups on AI systems, domain-specific NLP, model architecture, and production lessons from real deployments.

Domain-Adaptive Multi-Task NLP for Financial and Geopolitical Intelligence Extraction

Published: April 7, 2026

Tags: NLP | NER | Multi-task Learning | Financial AI | DistilBERT | LLM | Weak Supervision

We present QuantBridge/energy-news-classifier-ner-multitask, a unified architecture performing Named Entity Recognition and multi-label topic classification in a single forward pass over a shared DistilBERT encoder, designed for real-time structured signal extraction from financial and geopolitical news.

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