Architecture175 articles

Architecture

Articles

Multi-Token Prediction (MTP): Mathematical Foundations, Shared Trunk Architectures, Sequential Future Verification, and Speculative Decoding Dynamics
The standard training objective for autoregressive large language models is next-token prediction (NTP), where model parameters $\theta$ are trained via maximum likelihood estimation to forecast a single subsequent token given all previous context. While this paradigm has driven modern foundation models, it enforces a myopic local optimization: the model learns transition probabilities strictly between adjacent tokens without explicit incentives to plan multi-step syntactic or semantic trajector
August 29, 20261 min
Model Context Protocol (MCP) in Production AI Agents: Architecture, Transport Layers, Security Sandboxing, and Tool Federation
Model Context Protocol (MCP) in Production AI Agents: Architecture, Transport Layers, Security Sandboxing, and Tool Federation The transition from standalone large language models to autonomous agentic systems has introduced an integration scaling problem. Early agent implementations relied on proprietary, ad hoc function-calling wrappers written specifically for each model provider or orchestration framework. Connecting $M$ distinct agent runtimes to $N$ enterprise data stores and developer to
August 28, 20261 min
Multi-Head Latent Attention (MLA): Mathematical Foundations, Low-Rank KV Compression, Decoupled Rotary Positional Embeddings, and Inference Matrix Absorption
Autoregressive transformer inference in production is fundamentally bounded by memory bandwidth rather than compute capability during the decoding phase. As context lengths scale from 8,000 to 128,000 tokens and beyond, storing and loading the Key-Value (KV) cache across tens of attention layers consumes the vast majority of GPU High-Bandwidth Memory (HBM) and degrades serving throughput. While earlier architectures mitigated this bottleneck via Multi-Query Attention (MQA) or Grouped-Query Atte
August 28, 20261 min
Rotary Position Embeddings (RoPE) and Context Window Extension: Mathematical Foundations, Complex Rotations, NTK-Aware Scaling, and YaRN Dynamics
Autoregressive transformers process input sequences as permutation-invariant collections of token vectors. Without explicit positional encoding, the self-attention mechanism cannot distinguish between different token orderings. While early transformer architectures relied on additive absolute position embeddings (such as learned position tables or fixed sinusoidal encodings) or additive relative position biases, modern frontier large language models have almost universally converged on Rotary Po
August 28, 20261 min
PagedAttention: Mathematical Foundations, Virtual Memory Block Allocation, Non-Contiguous KV-Cache Management, and Prefix Sharing Dynamics
PagedAttention is an attention algorithm and memory management architecture designed to eliminate memory waste and fragmentation in autoregressive Large Language Model (LLM) serving. Introduced by researchers at UC Berkeley and published at SOSP 2023 alongside the vLLM serving engine, PagedAttention adapts the classic operating system principles of virtual memory, paging, and copy-on-write to the management of dynamic Key-Value (KV) caches in GPU High Bandwidth Memory (HBM). During autoregressi
August 28, 20261 min
Multi-Agent Orchestration Frameworks in Production: Comparing LangGraph, AutoGen, CrewAI, and LlamaIndex Workflows
Production AI agent architectures have evolved past single-prompt loops and linear chains into complex multi-agent systems. When systems require multiple specialized models, tools, and validation gates to collaborate, selecting an orchestration framework determines the application's runtime latency, fault tolerance, state persistence, and debugging overhead. Four major frameworks dominate modern production multi-agent design: LangGraph, Microsoft AutoGen, CrewAI, and LlamaIndex Workflows. Each
August 28, 20261 min
Agentic Memory and Context Management Systems in Production: Comparing Letta, Zep, Mem0, and LangMem
Agentic Memory and Context Management Systems in Production: Comparing Letta, Zep, Mem0, and LangMem Stateless large language model APIs present a fundamental bottleneck for autonomous agents operating across extended multi-turn sessions: context window exhaustion, quadratic attention overhead, and memory drift. While standard Retrieval-Augmented Generation (RAG) retrieves static document chunks based on semantic similarity, autonomous agents require dynamic, stateful memory capable of updating
August 28, 20261 min
Mixture-of-Depths (MoD): Mathematical Foundations, Dynamic Token-Level Compute Routing, Top-k Capacity Budgeting, and FLOP-Optimal Transformer Architectures
Standard autoregressive Transformers allocate an identical compute budget to every token in a sequence. Regardless of whether a token represents a trivial punctuation mark, a common grammatical function word, or a complex semantic reasoning step, the model applies the exact same sequence of multi-head self-attention and multilayer perceptron (MLP) operations across all $L$ layers. In Mixture-of-Depths: Dynamically allocating compute in transformer-based language models, researchers at Google De
August 28, 20261 min
Grouped-Query Attention (GQA) and Multi-Query Attention (MQA): Mathematical Foundations, KV-Cache Bandwidth Reduction, Uptraining Recipes, and Tensor Parallelism Implications
Grouped-Query Attention (GQA) and Multi-Query Attention (MQA): Mathematical Foundations, KV-Cache Bandwidth Reduction, Uptraining Recipes, and Tensor Parallelism Implications The KV-Cache Bandwidth Wall Autoregressive decoder inference is bottlenecked by memory bandwidth, not compute. At each decoding step, the model must reload the entire key-value (KV) cache from high-bandwidth memory (HBM) into the compute units. For a model with $H$ attention heads, sequence length $n$, head dimension $d_
August 28, 20261 min
Multi-Token Prediction (MTP): Mathematical Foundations, Sequential Latent Stacking, Auxiliary Loss Schedules, and Speculative Inference Acceleration
Multi-Token Prediction (MTP): Mathematical Foundations, Sequential Latent Stacking, Auxiliary Loss Schedules, and Speculative Inference Acceleration Autoregressive language models have traditionally been trained under a single-token objective: predicting the immediate next token $x_{t+1}$ given the causal context $x_{1:t}$. While this next-token prediction (NTP) paradigm scales predictably with parameter count and dataset volume, it suffers from severe structural limitations. NTP optimizes excl
August 28, 20261 min
Real-Time Voice AI Agent Frameworks in Production: Comparing LiveKit Agents, Pipecat, OpenAI Realtime API, and Ultravox
Real-Time Voice AI Agent Frameworks in Production: Comparing LiveKit Agents, Pipecat, OpenAI Realtime API, and Ultravox Deploying conversational voice agents in production requires solving a fundamental physics and networking problem: human conversational turn-taking occurs within an average gap of 200 to 300 milliseconds. When an artificial conversational agent exceeds 600 to 800 milliseconds of round-trip latency, users perceive the interaction as sluggish, talk over the assistant, or experie
August 27, 20261 min
Semantic Caching Engines in Production LLM Systems: Comparing GPTCache, RedisVL, Portkey, and Cloudflare AI Gateway
Semantic Caching Engines in Production LLM Systems: Comparing GPTCache, RedisVL, Portkey, and Cloudflare AI Gateway Standard exact-match caching strategies fail in large language model applications. Because natural language is inherently variable, users asking the exact same question will use different phrasing, punctuation, sentence structures, or synonyms. Exact string hashing (such as MD5 or SHA-256 caching over raw prompt strings) rarely yields hit rates above 3% to 5% in production convers
August 27, 20261 min
RMSNorm and SwiGLU: Mathematical Foundations of Scaling-Invariant Normalization, Gated Activations, and FFN Architectures in Modern LLMs
The architectural baseline of modern autoregressive large language models has converged on a distinct set of mathematical primitives. While early Transformer architectures relied on standard Layer Normalization, Post-LN residual routing, and two-layer Multi-Layer Perceptrons with ReLU or GELU activations, state-of-the-art open-weights models such as LLaMA, Mistral, Gemma, Qwen, and DeepSeek utilize a different combination: Root Mean Square Layer Normalization (RMSNorm), Pre-LN residual connectio
August 27, 20261 min
Chunking Strategies in Production RAG: Comparing Fixed-Size, Semantic Chunking, Late Chunking, and Contextual Retrieval
In production Retrieval-Augmented Generation (RAG) pipelines, the chunking strategy determines the theoretical ceiling of retrieval quality. Splitting documents into discrete text spans transforms continuous discourse into isolated segments. When chunks are indexed in isolation, critical context disappears: pronoun antecedents lose their referents, domain-specific acronyms lose their definitions, and propositions spanning arbitrary token boundaries become fragmented. Selecting an appropriate ch
August 27, 20261 min
Prompt and Context Caching in Production: Comparing Anthropic, OpenAI, DeepSeek, Google Gemini, and RadixAttention KV Reuse
Modern LLM serving workloads spend a disproportionate share of computational budget and time-to-first-token (TTFT) latency on prompt prefill. In agentic loops, retrieval-augmented generation (RAG), and multi-turn chat applications, repeated prompts often share 80% to 95% of their token sequences across requests. Without caching, inference engines recompute key-value (KV) attention tensors across every input token on every turn, driving quadratic compute overhead and memory bandwidth saturation.
August 27, 20261 min
Mixture-of-Experts (MoE) Routing and Load Balancing: Mathematical Foundations of Noisy Top-k Gating, Expert Capacity, Auxiliary Balancing Losses, and Loss-Free Load Balancing
Mixture-of-Experts (MoE) Routing and Load Balancing: Mathematical Foundations of Noisy Top-k Gating, Expert Capacity, Auxiliary Balancing Losses, and Loss-Free Load Balancing Scaling dense transformer models incurs computational costs that grow proportionally with parameter count. In dense architectures, every token in a sequence activates all feed-forward network (FFN) parameters across every layer. Sparse Mixture-of-Experts (MoE) architectures decouple parameter count from per-token compute b
August 27, 20261 min
Document Parsing Engines in Production RAG: Comparing Docling, Marker, MinerU, and Unstructured Architecture, Layout Analysis, Table Extraction, and Serving Economics
Retrieval-Augmented Generation (RAG) systems frequently fail not in their embedding models or vector databases, but at the initial document ingestion layer. When enterprise documents (including financial 10-K filings, clinical trial reports, technical manuals, and multi-column academic papers) are parsed using legacy text scrapers such as basic pypdf or pdfminer, structural semantics are lost. Multi-column reading orders interleave across paragraphs, header and footer noise pollutes vector indic
August 27, 20261 min
Grouped-Query Attention (GQA) and Multi-Query Attention (MQA): Mathematical Foundations, Arithmetic Intensity, and KV-Cache Scaling Mechanics
Grouped-Query Attention (GQA) and Multi-Query Attention (MQA): Mathematical Foundations, Arithmetic Intensity, and KV-Cache Scaling Mechanics Autoregressive large language model inference is governed by a fundamental hardware asymmetry. During the initial prompt processing phase (prefill), computation is compute-bound because matrix multiplications process entire sequences in parallel. During sequential token generation (decoding), execution becomes strictly memory-bandwidth bound. For each gen
August 27, 20261 min
RingAttention and Context Parallelism: Mathematical Foundations, Distributed Blockwise Attention, Circular Communication Topologies, and Million-Token Context Scaling
RingAttention and Context Parallelism: Mathematical Foundations, Distributed Blockwise Attention, Circular Communication Topologies, and Million-Token Context Scaling Standard Transformer architectures face a quadratic memory and computational barrier in their self-attention mechanism. While FlashAttention solved the high-bandwidth memory (HBM) IO bottleneck on single devices by tiling matrices within SRAM, scaling sequence lengths beyond hundreds of thousands or millions of tokens quickly exce
August 27, 20261 min
State Space Models (SSMs) and Mamba: Mathematical Foundations of Continuous-Time Discretization, Selective Scan Mechanics, and Structured State Space Duality
State Space Models (SSMs) and Mamba: Mathematical Foundations of Continuous-Time Discretization, Selective Scan Mechanics, and Structured State Space Duality Autoregressive Large Language Models (LLMs) built on the standard Transformer architecture rely on softmax self-attention. While self-attention provides expressive in-context learning and dense token-to-token associative recall, it exhibits fundamental algorithmic scaling limits: prefill compute scales quadratically with sequence length ($
August 27, 20261 min