27 citations · 36 across the 17 of their papers we have counts for
17 papers
Transient State Reorganization and Cell Differentiation in the Developmental Dynamics of Growing Neural Cellular Automata
Hiroki Sato, Atsushi Masumori, Takashi Ikegami
Growing Neural Cellular Automata (GNCA) develop complex morphologies from a single seed cell through shared local rules, yet the internal dynamics of this process remain poorly und…
Structured Fluctuations and the Information Dynamics of Self-Maintenance in Growing Neural Cellular Automata
Atsushi Masumori, Hiroki Sato, Takashi Ikegami
Growing Neural Cellular Automata (GNCA) are capable of robust self-maintenance and self-repair, yet the internal dynamical mechanisms that support these capabilities remain poorly…
OpenLife: Toward Open-World Artificial Life with Autonomous LLM Agents
Atsushi Masumori, Itsuki Doi, Norihiro Maruyama +2
Artificial life has explored life-like behavior on many computational substrates, but mostly in researcher-designed closed worlds. We argue that large language model (LLM) agents,…
Emergent Social Dynamics of LLM Agents in the El Farol Bar Problem
Ryosuke Takata, Atsushi Masumori, Takashi Ikegami
We investigate the emergent social dynamics of Large Language Model (LLM) agents in a spatially extended El Farol Bar problem, observing how they autonomously navigate this classic…
Plantbot: Integrating Plant and Robot through LLM Modular Agent Networks
Atsushi Masumori, Norihiro Maruyama, Itsuki Doi +3
We introduce Plantbot, a hybrid lifeform that connects a living plant with a mobile robot through a network of large language model (LLM) modules. Each module - responsible for sen…
A Concurrent Modular Agent: Framework for Autonomous LLM Agents
Norihiro Maruyama, Takahide Yoshida, Hiroki Sato +3
We introduce the Concurrent Modular Agent (CMA), a framework that orchestrates multiple Large-Language-Model (LLM)-based modules that operate fully asynchronously yet maintain a co…
Do Large Language Model Agents Exhibit a Survival Instinct? An Empirical Study in a Sugarscape-Style Simulation
Atsushi Masumori, Takashi Ikegami
As AI systems become increasingly autonomous, understanding emergent survival behaviors becomes crucial for safe deployment. We investigate whether large language model (LLM) agent…
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities
Ryosuke Takata, Atsushi Masumori, Takashi Ikegami
We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent's characteristics, including per…
Minimal Self in Humanoid Robot "Alter3" Driven by Large Language Model
Takahide Yoshida, Suzune Baba, Atsushi Masumori +1
This paper introduces Alter3, a humanoid robot that demonstrates spontaneous motion generation through the integration of GPT-4, Large Language Model (LLM). This overcomes challeng…
Simulation of Gacs' Automaton
Atsushi Masumori, Lana Sinapayen, Takashi Ikegami
Peter Gacs proposed a one-dimensional cellular automaton capable of a robust self-reproduction. Because the automaton is exceptionally large and complicated, very few people have e…
Self-Replicating and Self-Employed Smart Contract on Ethereum Blockchain
Atsushi Masumori, Norihiro Maruyama, Takashi Ikegami
Blockchain is the underlying technology for cryptocurrencies such as Bitcoin. Blockchain is a robust distributed ledger that uses consensus algorithms to approve transactions in a…
From Text to Motion: Grounding GPT-4 in a Humanoid Robot "Alter3"
Takahide Yoshida, Atsushi Masumori, Takashi Ikegami
We report the development of Alter3, a humanoid robot capable of generating spontaneous motion using a Large Language Model (LLM), specifically GPT-4. This achievement was realized…
Automata Quest: NCAs as a Video Game Life Mechanic
Hiroki Sato, Tanner Lund, Takahide Yoshida +1
We study life over the course of video game history as represented by their mechanics. While there have been some variations depending on genre or "character type", we find that mo…
Neural Autopoiesis: Organizing Self-Boundary by Stimulus Avoidance in Biological and Artificial Neural Networks
Atsushi Masumori, Lana Sinapayen, Norihiro Maruyama +5
Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organis…
Predictive Coding as Stimulus Avoidance in Spiking Neural Networks
Atsushi Masumori, Lana Sinapayen, Takashi Ikegami
Predictive coding can be regarded as a function which reduces the error between an input signal and a top-down prediction. If reducing the error is equivalent to reducing the influ…
Reactive, Proactive, and Inductive Agents: An evolutionary path for biological and artificial spiking networks
Lana Sinapayen, Atsushi Masumori, Ikegami Takashi
Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequenc…
Learning by Stimulation Avoidance: A Principle to Control Spiking Neural Networks Dynamics
Lana Sinapayen, Atsushi Masumori, Takashi Ikegami
Learning based on networks of real neurons, and by extension biologically inspired models of neural networks, has yet to find general learning rules leading to widespread applicati…