Building spatial world models from sparse transitional episodic memories
Zizhan He
Maxime Daigle
Many animals possess a remarkable capacity to rapidly construct flexible mental models of their environments. These world models are crucial… (see more) for ethologically relevant behaviors such as navigation, exploration, and planning. The ability to form episodic memories and make inferences based on these sparse experiences is believed to underpin the efficiency and adaptability of these models in the brain. Here, we ask: Can a neural network learn to construct a spatial model of its surroundings from sparse and disjoint episodic memories? We formulate the problem in a simulated world and propose a novel framework, the Episodic Spatial World Model (ESWM), as a potential answer. We show that ESWM is highly sample-efficient, requiring minimal observations to construct a robust representation of the environment. It is also inherently adaptive, allowing for rapid updates when the environment changes. In addition, we demonstrate that ESWM readily enables near-optimal strategies for exploring novel environments and navigating between arbitrary points, all without the need for additional training.
Building spatial world models from sparse transitional episodic memories
Zizhan He
Maxime Daigle
Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down
Yingzhi Wang
Anas Alhmoud
Saad Alsahly
Muhammad Alqurishi
Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down
Yingzhi Wang
Anas Alhmoud
Saad Alsahly
Muhammad Alqurishi
Field-Level Comparison and Robustness Analysis of Cosmological N-Body Simulations
Adrian E. Bayer
Francisco Villaescusa-navarro
Sammy Nasser Sharief
Romain Teyssier
Lehman H. Garrison
Greg L. Bryan
Marco Gatti
E. Visbal
Field-Level Comparison and Robustness Analysis of Cosmological N-Body Simulations
Adrian E. Bayer
Francisco Villaescusa-navarro
Sammy Nasser Sharief
Romain Teyssier
Lehman H. Garrison
Greg L. Bryan
Marco Gatti
E. Visbal
Half Search Space is All You Need
Pavel Rumiantsev
Half Search Space is All You Need
Pavel Rumiantsev
RobusTAD: reference panel based annotation of nested topologically associating domains
Yanlin Zhang
Rola Dali
Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests… (see more) a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-025-03568-9.
Seeing the Unseen: How EMoE Unveils Bias in Text-to-Image Diffusion Models
Lucas Berry
Axel Brando
Wei-Di Chang
Juan Higuera
Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
Mehran Shakerinava
Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs
Mehran Shakerinava