CV
Pavel Golikov
AI Researcher & Agent Engineer
Summary
AI Researcher and agent engineer specializing in robust machine reasoning, retrieval-augmented generation (RAG), LLM evaluation, and alignment. Builds grounded agentic workflows, vector-retrieval systems, and dataset generators for context-management evaluation in LLM agents. Former Military Intelligence Operator who held a Top Secret clearance, bringing a threat-modeling mindset to AI security and model capability evaluation.
Education
- PhD in Computer Science (leave of absence beginning September 2026)PresentUniversity of Toronto
- Master of Science in Computer Science2022University of Toronto
- Bachelor of Science in Mathematics and Philosophy (Formal Logic)2011University of Toronto
Work Experience
- Graduate Researcher (PhD, on leave) — ML Systems & Agent Evaluation2022 - PresentUniversity of Toronto, EcoSystem Research Group
- Conducted research spanning ML systems and efficient computation, with later work focused on robust machine reasoning, agent evaluation, context management, and AI alignment, in the EcoSystem Research Group and as a member of the Vector Institute.
- Developed open-source evaluation frameworks, agentic RAG systems, and mechanistic-interpretability analyses, resulting in first-author research on context management and reasoning robustness.
- Graduate Researcher (MSc) — Distributed Systems2020 - 2022University of Toronto, EcoSystem Research Group
- Engineered a flexible IoT distributed data-streaming framework from scratch, designed to automatically partition computational streaming queries between edge devices and cloud instances.
- Built the full software stack: programmed Arduino/C++ sensors for real-time biological data collection (EMG/ECG), developed custom socket networking protocols, and deployed cloud infrastructure using AWS and Apache Flink.
- Intelligence Operator2013 - 2018Canadian Armed Forces
- Formerly held a Top Secret security clearance while conducting rigorous analysis of classified information streams to produce actionable intelligence reports for command elements.
- Developed a strong adversarial threat-modeling mindset, emphasizing operational security, rigorous data validation, and the identification of logical vulnerabilities in complex, multi-agent scenarios.
- Mathematics Teacher2012 – 2013 & 2018 – 2019 -Blyth Academy
- Taught foundational mathematics to students in Grades 10, 11, and 12, developing the ability to distill and communicate complex quantitative concepts.
Skills
Languages
- Python
- C++
- Java
- SQL
- LaTeX
AI & Agents
- PyTorch
- Reinforcement Learning (RL)
- RLVR
- LangGraph
- LangChain
- RAG
- Hugging Face
- vLLM
- Transformers
- Gemini/OpenAI/Anthropic APIs
RAG & Evals
- Vector storage
- embeddings
- BM25
- hybrid retrieval
- cross-encoder reranking
- MRR/nDCG/recall
- groundedness
- validation
Systems
- Linux
- SQLite
- GitHub Actions CI
- AWS
- Apache Flink
- Distributed GPU Clusters
Publications
- ArbiGraph: Arbitrarily Scalable Verifiable Task Graphs for Evaluating Context Management2026arXivA benchmark generator for exact evaluation of state management across controllable, executable task graphs. The public implementation also includes reinforcement learning with verifiable rewards (RLVR) environments with dataset-backed and on-demand episodes, hidden verifier state, exact binary rewards, and per-node diagnostics.
- Robust Reasoning Benchmark2026
- Fusing Adds and Shifts for Efficient Dot Products2026IEEE Computer Architecture LettersAn algorithmic and hardware optimization for efficient dot-product computation.
- GPUPool: A Holistic Approach to Fine-Grained GPU Sharing in the Cloud2022
- Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training2021
Portfolio
- ArbiGraph2026Agent evaluationOpen-source benchmark and dataset generator for evaluating context management in tool-assisted LLM agents across arbitrarily scalable, long-horizon task graphs. It includes dataset-backed and on-demand reinforcement learning with verifiable rewards (RLVR) environments with hidden verifier state, exact binary rewards, and per-node diagnostics.
- arXiv Research Agent2026Agentic ai / rag / llm evalsEvaluation-first LangGraph literature-review agent with persistent Chroma vector storage, dense/BM25/hybrid/cross-encoder retrieval, citation grounding, resumable SQLite checkpoints, hand-labeled eval datasets, and 163 offline CI tests.