1 citations · 2 across the 5 of their papers we have counts for
7 papers
Continuous Memory Machines
Ciaran Regan, Kai Arulkumaran, Luke Darlow +3
Recurrent neural networks typically compress information into a single vector-valued recurrent state, forcing short-term computation and long-term retention to share the same repre…
Microcosmos: Reimagining Artificial Life for the GPU Era
Mark Tensen, Ciaran Regan, Bert Wang-Chak Chan +3
Most artificial life simulators either operate on abstract substrates disconnected from physical reality, or simulate physically grounded worlds that do not scale to the population…
Discovering Novel LLM Experts via Task-Capability Coevolution
Andrew Dai, Boris Meinardus, Ciaran Regan +2
Frontier model developers aim to train models continually to possess emergent, diverse capabilities. To extend capabilities, the current pre-training and post-training paradigm req…
Continuous Thought Machines
Luke Darlow, Ciaran Regan, Sebastian Risi +2
Biological brains demonstrate complex neural activity, where neural dynamics are critical to how brains process information. Most artificial neural networks ignore the complexity o…
Problem-Solving in Language Model Networks
Ciaran Regan, Alexandre Gournail, Mizuki Oka
To improve the reasoning and question-answering capabilities of Large Language Models (LLMs), several multi-agent approaches have been introduced. While these methods enhance perfo…
LLM-POET: Evolving Complex Environments using Large Language Models
Fuma Aki, Riku Ikeda, Takumi Saito +2
Creating systems capable of generating virtually infinite variations of complex and novel behaviour without predetermined goals or limits is a major challenge in the field of AI. T…
Can Generative Agents Predict Emotion?
Ciaran Regan, Nanami Iwahashi, Shogo Tanaka +1
Large Language Models (LLMs) have demonstrated a number of human-like abilities, however the empathic understanding and emotional state of LLMs is yet to be aligned to that of huma…