1 citations · 1 across the 4 of their papers we have counts for
6 papers
Multi-System Cognitive Steering for Generative Communication
Eric Lacosse, Pablo Tano, Daniel C. McNamee
A fundamental bottleneck for translating thoughts to speech is the high physical cost of motor input forcing a strict tradeoff between communication speed and expressivity. Generat…
Intuition vectors
Shahar Haim, Daniel C. McNamee
Large self-supervised vision models learn representations that support scene segmentation and the semantic decomposition of physical objects. We ask whether their representational…
AI Models Can Predict and Collaboratively Modulate Human Memory Search
Eric Lacosse, Mariana Duarte, Graham Todd +2
Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many language-based tasks, for example r…
Integrative neurocybernetic modeling in the era of large-scale neuroscience
Il Memming Park, Ayesha Vermani, Gonzalo G. de Polavieja +12
Large-scale neuroscience is generating rich datasets across animals, brain areas, and behavioral contexts, yet our modeling efforts remain fragmented across isolated experiments. W…
Emerging Human-like Strategies for Semantic Memory Foraging in Large Language Models
Eric Lacosse, Mariana Duarte, Peter M. Todd +1
Both humans and Large Language Models (LLMs) store a vast repository of semantic memories. In humans, efficient and strategic access to this memory store is a critical foundation f…
Emergent Causal-Geometric Dynamics Across Depth in Large Language Models
Shahar Haim, Daniel C McNamee
Geometric analyses of large language model (LLM) representations reveal structured variation across depth but remain fundamentally correlational with respect to token prediction fo…