Practical deployment guides for multi-agent workflows, semantic vector stores, speculative decoding, and quantization.
Architecting low-latency Retrieval-Augmented Generation systems with hybrid BM25 search, Hypothetical Document Embeddings, and cross-encoders.
Empirical analysis of Generative Engine Optimization (GEO) strategies, measuring citation frequency in ChatGPT, Claude, and Perplexity using standardized llms.txt manifests and Schema.org JSON-LD graphs.
Empirical benchmarks comparing speculative decoding performance across vLLM, TensorRT-LLM, and SGLang using Llama-3.3-70B and quantized draft models.
A deep empirical comparison of Hierarchical Navigable Small World graphs versus Inverted File Product Quantization across 100M vector workloads.
Evaluating activation-aware weight quantization versus generalized post-training quantization on edge accelerators and consumer workstations.
Empirical latency distribution, chain-of-thought token generation ratios, and dollar-per-1M-token pricing curves comparing DeepSeek-R1 against OpenAI o3-mini across 50,000 reasoning prompts.
Measuring VRAM footprint, throughput degradation, and perplexity shifts under aggressive KV cache quantization (FP8 vs INT4) and token pruning at 128k context lengths.
Dynamic PagedAttention memory management versus static TensorRT engine compilation
Lexical BM25 rank scoring versus semantic approximate nearest neighbor (ANN) retrieval
Automatic KV cache reuse across complex prompt trees versus general-purpose linear batch throughput
Salient weight protection in 4-bit GEMM kernels versus one-shot Hessian inverse optimization
Large-scale reinforcement learning test-time compute versus frontier pre-training and steering
Production-scale memory-mapped vector search with payload filtering versus lightweight local prototyping
Separate draft model verification versus multi-head residual prediction on base weights
Continuous batching with PagedAttention versus static TensorRT engine compilation
Low-rank latent vector compression versus grouped key-value projection heads
Dynamic sparse expert routing versus uniform parameter activation per token