22 citations · 25 across the 12 of their papers we have counts for
15 papers
A latent control model for realistic rodent motion
Aidan Sirbu, Charles Y. Zhang, Yuanjia Yang +3
The development of embodied models of animal behavior is a major goal of the neuro-AI community. Embodied models will enhance our ability to make direct comparisons between biologi…
Gated Target Propagation for Compositional Generalization in Continual Learning
Abdel Mfougouon Njupoun, Colin Bredenberg, Blake Aaron Richards +1
Continual learning is typically framed as acquiring new knowledge without catastrophically forgetting previous tasks. However, a flexible continual learner should also be able to r…
Reasoning with Neural Cellular Automata
Mayalen Etcheverry, Pietro Miotti, Aidan Sirbu +7
Modern AI architectures used to solve visual reasoning tasks typically rely heavily on global connectivity and synchronization. As biological systems demonstrate, though, sophistic…
Tapes Together Strong: The Co-evolution of Computation and Cooperation
Kunal Jha, Francesco Cicala, Blaise Agüera y Arcas +4
How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate by isolating social interaction…
Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning
Mariia Drozdova, Aidan Sirbu, Pietro Miotti +4
Diffusion models and recursive reasoners are both iterative, but they carry information across iterations differently. We add a persistent hidden state to a diffusion denoiser and…
A game theory for foundation models shows new paths to rational cooperation through similarity inference
Alexander Meulemans, Maciej Wołczyk, Marissa A. Weis +11
As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles governing their collective behavior is…
Coevolution of self-replication and function in a digital primordial soup
Francesco Cicala, Eyvind Niklasson, Ettore Randazzo +8
While traditional evolutionary algorithms hard-code reproduction, self-replication can emerge spontaneously within digital ``primordial soups''. This paper investigates the coevolu…
Balancing Plasticity and Stability with Fast and Slow Successor Features
Raymond Chua, Doina Precup, Blake Richards
A hallmark of intelligence is the ability to adapt in non-stationary environments, yet deep Reinforcement Learning (RL) agents often struggle in such settings. Prior studies introd…
Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel +12
Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. Du…
Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning
Alexander Meulemans, Rajai Nasser, Maciej Wołczyk +10
The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that polici…
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
Charles Y. Zhang, Yuanjia Yang, Aidan Sirbu +25
The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex behavior, kinematic trajectories alone provi…
Sufficient conditions for offline reactivation in recurrent neural networks
Nanda H. Krishna, Colin Bredenberg, Daniel Levenstein +2
During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagement. However, the precise conditions under…
The challenge of hidden gifts in multi-agent reinforcement learning
Dane Malenfant, Blake A. Richards
Sometimes we benefit from actions that others have taken even when we are unaware that they took those actions. For example, if your neighbor chooses not to take a parking spot in…
Multi-agent cooperation through learning-aware policy gradients
Alexander Meulemans, Seijin Kobayashi, Johannes von Oswald +6
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation among self-interested, independent lear…
A Unified, Scalable Framework for Neural Population Decoding
Mehdi Azabou, Vinam Arora, Venkataramana Ganesh +7
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration…