Goal-conditioned world models transcribe instructions instead of perceiving spatial relations when the instruction names the scored quantity, and removing the goal from the dynamics fixes it.
Object-centric learning with slot attention
10 Pith papers cite this work, alongside 218 external citations. Polarity classification is still indexing.
10
Pith papers citing it
218
external citations · Pith
abstract
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes. In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. We empirically demonstrate that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction tasks.
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OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
Mask World Model predicts semantic mask dynamics with video diffusion and integrates it with a diffusion policy head, outperforming RGB world models on LIBERO and RLBench while showing better real-world generalization and texture robustness.
ORCA splits Q-Former queries into orthogonally constrained groups, reversing directional collapse and speaker-indistinguishability in audio-LLM connectors and gaining 26.4 points on SAKURA multi-hop reasoning.
Physical plausibility is linearly decodable from diffusion transformer states in video models at 81.27% accuracy on IntPhys and InfLevel, absent from VAE latents and outperforming V-JEPA and VideoMAE.
SkyPart achieves state-of-the-art single-pass cross-view geo-localization on SUES-200, University-1652, and DenseUAV by using prototype-based part discovery, altitude-conditioned modulation, and Kendall-weighted loss, with widening gains under weather corruptions.
An unsupervised model integrates group homomorphism to segment objects and map relative motions like approaching or receding into a one-dimensional additive latent space from unlabeled dynamic images.
VP2O maps PPO to SVGD in a MoE architecture using functional kernels and expert orthogonalization, claiming +179 ELO on Codeforces and 32% token reduction on AIME for a 33B/4B model.
Embodied AI requires query-conditioned world models that select the simplest physical abstraction sufficient to answer intervention queries.
Multi-Gate Residuals stabilizes activation scales in deep residual networks via multi-stream gating and attention pooling without added communication overhead.
citing papers explorer
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Grounding Spatial Relations in a Compact World Model: Instruction Leakage and a Goal-Free Dynamics Fix
Goal-conditioned world models transcribe instructions instead of perceiving spatial relations when the instruction names the scored quantity, and removing the goal from the dynamics fixes it.
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OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
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Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Mask World Model predicts semantic mask dynamics with video diffusion and integrates it with a diffusion policy head, outperforming RGB world models on LIBERO and RLBench while showing better real-world generalization and texture robustness.
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Escaping the Procrustean Bed: Groupwise Orthogonal Connectors for Audio-Language Models
ORCA splits Q-Former queries into orthogonally constrained groups, reversing directional collapse and speaker-indistinguishability in audio-LLM connectors and gaining 26.4 points on SAKURA multi-hop reasoning.
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The Invisible Hand of Physics: When Video Diffusion Models Know More Than They Show
Physical plausibility is linearly decodable from diffusion transformer states in video models at 81.27% accuracy on IntPhys and InfLevel, absent from VAE latents and outperforming V-JEPA and VideoMAE.
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Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery
SkyPart achieves state-of-the-art single-pass cross-view geo-localization on SUES-200, University-1652, and DenseUAV by using prototype-based part discovery, altitude-conditioned modulation, and Kendall-weighted loss, with widening gains under weather corruptions.
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Unsupervised Learning of Inter-Object Relationships via Group Homomorphism
An unsupervised model integrates group homomorphism to segment objects and map relative motions like approaching or receding into a one-dimensional additive latent space from unlabeled dynamic images.
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Variational Proximal Policy Optimization
VP2O maps PPO to SVGD in a MoE architecture using functional kernels and expert orthogonalization, claiming +179 ELO on Codeforces and 32% token reduction on AIME for a 33B/4B model.
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Physically Viable World Models: A Case for Query-Conditioned Embodied AI
Embodied AI requires query-conditioned world models that select the simplest physical abstraction sufficient to answer intervention queries.
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Multi-Gate Residuals
Multi-Gate Residuals stabilizes activation scales in deep residual networks via multi-stream gating and attention pooling without added communication overhead.