arXiv:2607. 24806v1 Announce Type: cross Abstract: Error-related potentials (ErrPs) are widely studied neural signatures associated with error processing in human-machine interaction.
The Unwritten Benchmark introduces a novel challenge for multimodal machine learning, focusing on abstract perceptual reasoning through acousto‑kinematic word inference. Models must decode words written only by the audio of pen scratches and the video of hand movements, across three writing styles, without any visible ink trace. Evaluation shows a stark performance gap: humans achieve over 80% ordered letter accuracy, while leading models like GPT‑4o and Gemini 2.5‑Pro fail to exceed 10%, and combining modalities often degrades performance.
arXiv:2606. 00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment.
arXiv:2608. 12398v1 Announce Type: cross Abstract: We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that extends a previously proposed single-modality model (LEPP) to integrate vision and language.
arXiv:2403. 01977v4 Announce Type: replace-cross Abstract: Navigation in the natural world is a feat of adaptive inference, where biological organisms maintain goal-directed behaviour despite noisy and incomplete sensory streams.
arXiv:2607. 11270v1 Announce Type: cross Abstract: Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities.
arXiv:2606. 11614v1 Announce Type: cross Abstract: Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal interactions.
arXiv:2505. 19614v2 Announce Type: replace Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities.
The paper introduces BIFTA, a Brain‑Inspired Few‑Shot Tactile Adaptation framework that enables a frozen encoder to adapt quickly to an unknown tactile sensor using only a small labeled support set. It preserves pretrained representations via dual‑view statistical memory, builds support‑conditioned spectral graphs to correct sensor‑dependent feature neighborhoods, and employs uncertainty‑gated recurrent propagation to reinforce reliable cross‑query evidence. Benchmarks on three tactile datasets demonstrate that BIFTA dramatically improves adaptation performance, achieving an 87.09% mean Sparsh accuracy on SITR with just 10% labeled data—an increase of 47.22 percentage points over the best prior method.
arXiv:2606. 16408v1 Announce Type: new Abstract: We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that unifies subset-conditioned cross-modal generation and unconditional joint sampling through a shared stochastic latent.
arXiv:2607. 16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities.
arXiv:2604. 07753v2 Announce Type: replace-cross Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts.