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Mikhail Mozikov

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4 papers
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4

AAAI Conference 2026 System Paper

RESPOND: Realistic Environment Simulation of Population and Natural Disasters with LLM-Driven Agents

  • Roman Sultimov
  • Mikhail Mozikov
  • Dmitrii Abramov
  • Mariia Kovalchuk
  • Maksim Malykh
  • Ilya Makarov
  • Andrei Osiptsov
  • Aleksandr Volkov

Climate change is driving more frequent and severe disasters, putting people and infrastructure at risk. Protecting communities requires models that capture both natural disasters dynamics and how people behave under extreme conditions. This demo presents RESPOND, a multi-agent LLM-enhanced platform that jointly simulates natural hazards and human response. RESPOND couples high-fidelity flood AI forecasting an agent-based model of human behavior. LLM modules improve each agent decision-making, enabling context-aware reasoning over alerts, road closures, social signals, and changing water levels. The system simulates evacuation flows, resource seeking, and communication patterns producing actionable outputs for emergency management, urban planning, and policy. In the live demo one can run what-if or predicted scenarios, adjust assumptions, and observe emergent population behavior and risk hot spots in real time. By tightly coupling dynamic hazards with LLM-driven multi-agent behavior, RESPOND moves beyond fragmented tools and offers a practical, integrated platform for disaster preparedness and response.

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AAAI Conference 2026 Short Paper

RESPOND: Realistic Environment Simulation of Population and Natural Disasters with LLM-Driven Agents (Student Abstract)

  • Roman Sultimov
  • Mikhail Mozikov
  • Dmitrii Abramov
  • Mariia Kovalchuk
  • Maksim Malykh
  • Aleksandr Volkov
  • Ilya Makarov
  • Andrei Osiptsov

Climate change is driving more frequent and severe disasters, putting people and infrastructure at risk. Protecting communities requires models that capture both natural disasters dynamics and how people behave under extreme conditions. This demo presents RESPOND, a multi-agent LLM-enhanced platform that jointly simulates natural hazards and human response. RESPOND couples high-fidelity flood AI forecasting an agent-based model of human behavior. LLM modules improve each agent decision-making, enabling context-aware reasoning over alerts, road closures, social signals, and changing water levels. The system simulates evacuation flows, resource seeking, and communication patterns producing actionable outputs for emergency management, urban planning, and policy. In the live demo one can run what-if or predicted scenarios, adjust assumptions, and observe emergent population behavior and risk hot spots in real time. By tightly coupling dynamic hazards with LLM-driven multi-agent behavior, RESPOND moves beyond fragmented tools and offers a practical, integrated platform for disaster preparedness and response.

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NeurIPS Conference 2024 Conference Paper

EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas

  • Mikhail Mozikov
  • Nikita Severin
  • Valeria Bodishtianu
  • Maria Glushanina
  • Ivan Nasonov
  • Daniil Orekhov
  • Vladislav Pekhotin
  • Ivan Makovetskiy

One of the urgent tasks of artificial intelligence is to assess the safety and alignment of large language models (LLMs) with human behavior. Conventional verification only in pure natural language processing benchmarks can be insufficient. Since emotions often influence human decisions, this paper examines LLM alignment in complex strategic and ethical environments, providing an in-depth analysis of the drawbacks of our psychology and the emotional impact on decision-making in humans and LLMs. We introduce the novel EAI framework for integrating emotion modeling into LLMs to examine the emotional impact on ethics and LLM-based decision-making in various strategic games, including bargaining and repeated games. Our experimental study with various LLMs demonstrated that emotions can significantly alter the ethical decision-making landscape of LLMs, highlighting the need for robust mechanisms to ensure consistent ethical standards. Our game-theoretic analysis revealed that LLMs are susceptible to emotional biases influenced by model size, alignment strategies, and primary pretraining language. Notably, these biases often diverge from typical human emotional responses, occasionally leading to unexpected drops in cooperation rates, even under positive emotional influence. Such behavior complicates the alignment of multiagent systems, emphasizing the need for benchmarks that can rigorously evaluate the degree of emotional alignment. Our framework provides a foundational basis for developing such benchmarks.

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ECAI Conference 2024 Conference Paper

InsideOut: Unifying Emotional LLMs to Foster Empathy

  • Mikhail Mozikov
  • Nikita Severin
  • Maria Glushanina
  • Mikhail Baklashkin
  • Andrey V. Savchenko
  • Ilya Makarov

This paper introduces InsideOut, an original innovative framework that augments the emotional intelligence of Large Language Models (LLMs). Motivated by the cartoon, InsideOut is designed around a net of specialized agents, each dedicated to one of Ekman’s fundamental emotions. These agents collaboratively refine responses sensitive to the emotional context of interactions. Our assessments, conducted using EmpatheticDialogues and involving models like GPT-4 and GigaChat, indicate substantial improvements in identifying human emotions and generating empathetic responses. These improvements are most evident in situations with apparent valence-arousal differences. InsideOut offers a promising avenue for evolving AI into more perceptive and human-centric communicators.

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v2026.09.13