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SatarovMOCR

Where model behavior forms

Research direction, the lifecycle it instruments, and the published work it sits beside.

I am a Ph.D. researcher at the University of Cincinnati, advised by Prof. Justin Zhan. My work is on how behavior emerges inside preference-based training, how to attribute it back to the updates that produced it, and how to keep tool-using agents inside their permissions once they are deployed.
Channels
10 active
Lifecycle stages
7 instrumented
Reading list
6 by others
Own papers
None yet

Research channels

  • CH01

    Trustworthy machine learning

    Making model behavior predictable, auditable, and safe to depend on.

  • CH02

    RLHF & preference optimization

    How preference data and policy optimization shape model behavior.

  • CH03

    AI alignment & model safety

    Training-time attacks and safety evaluation for aligned models.

  • CH04

    Learning dynamics & influence analysis

    Per-update gradient tracing to attribute behavior to training data.

  • CH05

    LLM security

    Jailbreaks, data poisoning, and securing third-party agent extensions.

  • CH06

    Computer vision

    Detection and pose pipelines (YOLO, ViT-Pose) in research and production.

  • CH07

    Multimodal learning

    Models and pipelines that cross text, image, and video.

  • CH08

    Retrieval-augmented generation

    Grounded assistants with vector search and honest citations.

  • CH09

    Agentic AI systems

    Tool-using agents, orchestration graphs, and safe third-party extensions.

  • CH10

    Scalable model deployment

    GPU serving, containerized inference, and reproducible training infra.

The lifecycle under the microscope

I study the whole lifecycle of an aligned model: how behavior forms during preference-based training, how training-time attacks can corrupt it, how influence analysis can audit it, and what changes once a model acts through tools and third-party extensions. These are open problems across the field — the published work below frames the questions.

Aligned-model lifecycle pipelineSeven connected stages: Data feeds supervised fine-tuning, then a reward model, then PPO reinforcement learning, then evaluation, safety analysis, and finally deployment as tool-using agents with third-party skills. The reward-model, PPO, and agent stages are highlighted as attack surfaces documented in the published literature; influence tracing feeds back from safety analysis to the training stages.Dataprompts + preferencesSFTsupervised fine-tuneReward Modelpreference learningPPO / RLHFpolicy optimizationEvaluationsafety benchmarksSafety Analysisinfluence tracingAgents & Toolsthird-party skillspoisoned data enters hereuntrusted extensionsinfluence tracing → attribute model behavior to training data
The lifecycle my research instruments — from training data to deployed, tool-using agents. The highlighted attack surfaces are documented in the published literature cited below.

Framed by the open literature

These papers are by other researchers, listed as the context this work builds on — never as his own. Every arXiv identifier was checked against the arXiv API.

Publications

Papers will be listed here as they become citable. Until then, the channels above and the systems on the systems station are the accurate picture of the work.

A Google Scholar profile will be linked alongside the first listed paper.

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