Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences
Authors: Shuchen Wu, Mirko Thalmann, Peter Dayan, Zeynep Akata, Eric Schulz
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first demonstrate the benefits of abstraction in memory efficiency and sequence parsing by comparing our algorithm with previous chunking models and other dictionary-based compression methods. Then, we show that the model exhibits human-like signatures of abstraction in a memory experiment requiring the transfer of abstract concepts. In the same experiment, we contrast the model s generalization behavior with large language models (LLMs). |
| Researcher Affiliation | Academia | Shuchen Wu Helmholtz Munich Max Planck Institute for Biological Cybernetics EMAIL; Mirko Thalmann Institute for Human-Centered AI Helmholtz Munich EMAIL; Peter Dayan Department of Computational Neuroscience Max Planck Institute for Biological Cybernetics EMAIL; Zeynep Akata Helmholtz Munich Technical University of Munich EMAIL; Eric Schulz Institute for Human-Centered AI Helmholtz Munich EMAIL |
| Pseudocode | Yes | Algorithm 1: Pseudocode to generate sequences with nested abstract hierarchies.; Algorithm 2: HVM (online version, for learning sequences from human experiments); Algorithm 3: Pseudocode for traversing a tree to find a path consistent with an upcoming sequence |
| Open Source Code | Yes | The code used for the algorithm and experiments is available under this link. |
| Open Datasets | Yes | CHILDES (Mac Whinney, 2000); BNC (BNC Consortium, 2007); Gutenberg (Gerlach & Font-Clos, 2020); Open Subtitles (Lison & Tiedemann, 2016) |
| Dataset Splits | No | The paper mentions generating sequences of desired length and taking random snippets of 1000 characters from real-world datasets, as well as distinguishing between 'training block' and 'transfer block' in an experiment. However, it does not provide specific train/test/validation split percentages, sample counts, or citations to predefined splits for the datasets used in model evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Memory decay parameter θ = 0.996 |