73 citations · 126 across the 15 of their papers we have counts for
9 papers · 1 filter
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data is ubiquitous in the empirical sciences and most commonly analyzed with generalized linear mixed-effects models (GLMMs), which have interpretable parameters. Baye…
Exploring System 1 and 2 communication for latent reasoning in LLMs
Julian Coda-Forno, Zhuokai Zhao, Qiang Zhang +6
Should LLM reasoning live in a separate module, or within a single model's forward pass and representational space? We study dual-architecture latent reasoning, where a fluent Base…
metabeta -- A fast neural model for Bayesian mixed-effects regression
Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data with multiple observations per group is ubiquitous in empirical sciences and is often analyzed using mixed-effects regression. In such models, Bayesian inference…
A circuit for predicting hierarchical structure in-context in Large Language Models
Tankred Saanum, Can Demircan, Samuel J. Gershman +1
Large Language Models (LLMs) excel at in-context learning, the ability to use information provided as context to improve prediction of future tokens. Induction heads have been argu…
Automated scientific minimization of regret
Marcel Binz, Akshay K. Jagadish, Milena Rmus +1
We introduce automated scientific minimization of regret (ASMR) -- a framework for automated computational cognitive science. Building on the principles of scientific regret minimi…
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models
Can Demircan, Tankred Saanum, Akshay K. Jagadish +2
In-context learning, the ability to adapt based on a few examples in the input prompt, is a ubiquitous feature of large language models (LLMs). However, as LLMs' in-context learnin…
In-context learning agents are asymmetric belief updaters
Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz +1
We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology. We find that LLMs update their…
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
Akshay K. Jagadish, Julian Coda-Forno, Mirko Thalmann +2
Ecological rationality refers to the notion that humans are rational agents adapted to their environment. However, testing this theory remains challenging due to two reasons: the d…
Simplifying Latent Dynamics with Softly State-Invariant World Models
Tankred Saanum, Peter Dayan, Eric Schulz
To solve control problems via model-based reasoning or planning, an agent needs to know how its actions affect the state of the world. The actions an agent has at its disposal ofte…