Publications & Research Library

Architectural synthesis comparing the 5 foundational AI paradigms (Symbolic, Probabilistic, Connectionist, Evolutionary, and Hybrid Neuro-Symbolic)—tracing classical state space search, heuristic admissibility, and MCTS directly to modern inference-time compute rollouts, constrained logit masking, and deterministic agent control planes.

Architecting enterprise agent control planes: bridging Agent Protocol REST/SSE execution lifecycles with Model Context Protocol multi-server tool, resource, and prompt federation.

Engineering million-token context windows: circular peer-to-peer Ring Attention, 2D rotary embedding extrapolation (YaRN & NTK-aware), and FP8/FP4 KV-cache memory budgeting.

Mathematical foundations of Actor-Critic PPO vs. critic-free Group Relative Policy Optimization (GRPO), verifiable rule-based reward engineering, and actor-critic memory budgeting for reasoning models.

Deconstructing draft model speculative decoding, Medusa/Eagle tree-based speculative heads, parallel candidate verification kernels, lossless rejection sampling, and memory bandwidth scaling.

A comprehensive reference guide on blockchain state, EVM execution, smart contract security, Layer-2 rollups, EIP-4844 blobs, ZK-SNARKs/STARKs, RISC-V zkVMs, zkML inference, decentralized GPU compute, and secure autonomous on-chain AI agents.

A comprehensive guide on ontology engineering—analyzing formal triples, W3C standards (RDF/OWL/SHACL), Domain-Driven Design integration, enterprise Data Mesh virtualization, and Neuro-Symbolic GraphRAG.

A deep dive into NVIDIA H100/H200, AMD MI300X, and Google TPU v5p hardware architectures, HBM3e memory bandwidth, NVLink interconnects, and Roofline model execution.

Production reference architecture guide covering NVIDIA H100/H200/B200 GPU hardware microarchitectures, TFLOPS/AIFLOPS precision math, TPS/TTFT/TPOT/MBU performance metrics, CUDA kernel optimization, and AI Infrastructure Engineering roles.

Production architecture blueprint for multi-query research decomposition, iterative web crawling, evidence synthesis, agent swarm topologies, scratchpad state isolation, and Postgres checkpointing.

Production architecture blueprint for GraphRAG pipelines—covering entity-relation extraction, community detection, sub-graph summarization, and hybrid graph-vector retrieval.

Production architecture blueprint for multi-stage RAG pipelines—covering chunking strategies, hybrid search fusion (RRF math), cross-encoder re-ranking, vector database indexing, and evaluation guardrails.

A production guide for FP8, INT4, AWQ, GPTQ, and Unsloth model quantization, scaling factors, and Tensor Core GEMM kernel execution on H100 and A100 GPUs.

Production reference architecture teardown of sandboxed SuperAgent execution harnesses—analyzing Docker container isolation, declarative SKILL.md dynamic parsing, bash guardrails, and durable state persistence.

Production architecture blueprint for multi-agent supervisor routing, Pregel state isolation, Postgres checkpointer persistence, human-in-the-loop breakpoints, and failure defenses.

A telemetry and response architecture for tracing model, retrieval, tool, policy, quality, cost, and user-outcome failures.

A control system for optimizing cost per successful task while preserving quality, latency, safety, capacity, and fallback behavior.

A bounded agent control plane for state, tool authorization, delegated credentials, approvals, verification, recovery, and audit.

An evaluation architecture connecting capability claims, versioned datasets, deterministic checks, validated graders, release gates, and production outcomes.

A workload-first method for sizing accelerators, KV cache, admission, batching, redundancy, autoscaling, and failure reserve.

An end-to-end isolation architecture spanning identity, retrieval, caches, tools, prompts, traces, evaluations, exports, and deletion.

An execution boundary for untrusted model proposals, typed validation, authorization, credentials, sandboxing, egress, approvals, and verification.

Operational trade-offs must be measured at the successful user outcome, inside the same security and quality boundary.

A production LLM system is a chain of contracts, not a model wrapped in a chat box.

An evaluation is useful only when it changes a release, routing, or product decision.

Evaluation should connect product risks and user outcomes to measurable evidence, release policy, monitoring, and corrective action.

The right intervention follows the type of gap: instructions, knowledge, behavior, or action.

RAG quality is determined by the whole evidence path, not by adding a vector database.

Four boundaries explain much of an LLM application's behavior: encoding, representation, finite context, and sequential generation.

A transformer becomes operationally understandable when architecture, training, inference, context, and serving constraints are traced as one system.