The paper introduces the Platonic brain bridge hypothesis, asserting that omni models—capable of jointly processing video, audio, and text—naturally develop brain‑like representations, and that this relationship is bidirectional. Experiments show that seven omni models exhibit stable brain‑like representations across participants, and their internal states outperform others on the Algonauts 2025 out‑of‑distribution leaderboard. Building on this, the authors propose three brain‑to‑model methods: Brain‑MoE assigns a brain‑pretrained expert to each of seven cortical networks, improving benchmark accuracy; Brain‑AVQA generates video‑based questions using the most responsive brain network, outperforming shuffled mappings; and Brain‑Scope uses sparse autoencoders to pinpoint a small subset of networks whose removal weakens brain prediction, demonstrating that human brain networks can serve as a practical architectural prior for omni models.
arXiv:2609.10947v2 Announce Type: replace-cross Abstract: Multimodal large language models predict brain activity, but brain alignment has been a measurement, not a design tool. We propose the Platon...
arXiv:2608. 15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease.
arXiv:2607. 19973v1 Announce Type: new Abstract: AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech.
arXiv:2607. 16292v1 Announce Type: cross Abstract: Brain-encoding foundation models predict fMRI responses to video, audio, and text well enough to win the Algonauts 2025 challenge.
Omni-Woodpecker (OWP) is a training‑free framework designed to reduce cross‑modal hallucinations in Omni LLMs by rebalancing fused representations. It identifies conflicts between modality‑specific views, separates grounding evidence from competing priors via orthogonal decomposition, and adaptively suppresses misleading priors while restoring ignored evidence before decoding. Experiments show OWP improves performance on CMM, AVHBench, and several general reasoning benchmarks, and generalizes across different Omni LLM architectures.
arXiv:2611.06083v1 Announce Type: new Abstract: Omni models answer questions about videos by jointly processing sampled frames and audio in one turn, with progress driven largely by scaling model siz...
arXiv:2604. 18827v2 Announce Type: replace-cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision.
arXiv:2510. 24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities.
CortexBridge is a lightweight adapter that aligns arbitrary EEG electrode montages into a shared cortical latent space by combining EEG features with electrode and atlas coordinates. When evaluated on five BCI datasets from MOABB with three frozen foundation models, it improves performance in 13 of 15 tasks, achieving average balanced‑accuracy gains of 0.80% (EEGPT), 0.70% (LaBraM), and 3.26% (CBraMod), with a maximum 13.02% improvement on a 12‑class SSVEP task. Visualizations show task‑dependent spatial patterns, such as a concentrated representation in the Yeo Visual network for SSVEP versus auditory P300.
The paper introduces WROP, a dataset of 150 hand‑designed cognitive tasks aimed at testing object permanence in video generation models. Using Blender, the authors generate over 10,000 samples per task, compiling a 1.5 M‑sample training corpus and a 300‑question exam. They evaluate 14 video models, showing that their 16B world model, PWM‑WROP, ranks first among continuation models and third overall in a blind Elo study.
AutoBCI is an agentic framework that discovers and selects EEG decoding architectures across diverse tasks such as emotion recognition, motor imagery, and sleep staging. It employs a Designer Agent that performs Pool‑Guided Architecture Discovery (PGAD) and a Forecaster Agent that uses Performance Estimation from Early Knowledge (PEEK) to predict full‑budget validation performance from early learning curves. Evaluated on 14 EEG datasets with six large language models, AutoBCI’s best architecture (Claude Opus 5.5) achieves 64.16% average test balanced accuracy, slightly surpassing the strongest baseline, and PEEK reduces prediction error by 38.1% compared to the best‑observed‑score baseline.
arXiv:2506. 01353v3 Announce Type: replace Abstract: The integration of brain-computer interfaces (BCIs), in particular electroencephalography (EEG), with artificial intelligence (AI) has shown tremendous promise in decoding human cognition and behavior from neural signals.