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Igor Bogdanov

Publications

Publications

Newest first. Workshop papers are marked as workshop papers. Each entry links to the paper and, where I have them, the code, artifacts, and data.

Theme
Venue

7 of 7 publications

2026ACM proceedings paper

Context, Reasoning, and Hierarchy: A Cost–Performance Study of Compound LLM Agent Design in an Adversarial POMDP

Igor Bogdanov, C.-H. Lung, T. Kunz, J. Gao, A. Taylor, M. Zaman

ACM Conference on AI and Agentic Systems (CAIS), 2026

3,475 episodes across five model families: structured state abstraction improves return by up to 76%, while distributed deliberation across an agent hierarchy can be up to 3.4× worse.

  • Agent architecture
  • Evaluation
2026ACM proceedings paper

FORGE: Self-Evolving Agent Memory With No Weight Updates via Population Broadcast

Igor Bogdanov, C.-H. Lung, T. Kunz, J. Gao, A. Taylor, M. Zaman

ACM Conference on AI and Agentic Systems (CAIS), 2026

A population-based protocol that turns failed trajectories into reusable prompt memory and broadcasts champion memories, improving return 1.7–7.7× over zero-shot without weight updates.

  • Adaptation
  • Agent architecture
2025Workshop paper

Delay-of-Gratification as a Multi-Agent Survival Micro-Benchmark for Long-Horizon LLMs: Social Exposure, Personas, and Tool Use Budgets

O. Manakina, Igor Bogdanov, C.-H. Lung

NeurIPS 2025 Workshop on Multi-Turn Interactions in Large Language Models

A text-based multi-agent gym modelled as an MDP/POMDP for studying long-horizon LLM behaviour under social exposure, personas, and tool-use budgets.

  • Evaluation
  • Agent architecture
2025Applied-ML paper

Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring

O. Manakina, Igor Bogdanov

EDM 2025 Workshop on Leveraging LLMs for Innovative Educational Data Mining (WLIEDM)

A multi-armed bandit selects among four grading recipes per essay, cutting LLM calls by 78.4% while tracking cost and reliability.

  • Applied ML
  • Adaptation
2025Dataset paper

Infant Care Video Dataset for Classification of Interventions Using Transformers

Igor Bogdanov, J. Green

IEEE COMPSAC 2025, MediComp Symposium

A 4,144-video, 12-class, privacy-compliant NICU intervention dataset with TimeSformer and MotionFormer baselines reaching up to 94% top-1 accuracy.

  • Applied ML

Contact

Building reliable AI systems requires both research and engineering.

I’m interested in research engineering, applied research, agent infrastructure, evaluation, reliability, interpretability, and research-to-production work.

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