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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:2606. 23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal representations toward those of an external vision encoder.
arXiv:2604. 18572v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.
OmniHallu is a unified framework for detecting hallucinations in multimodal large language models across both comprehension and generation tasks involving image, video, and audio modalities. It introduces OmniHallu-Bench, a 10,000-sample benchmark with claim-level human annotations for six cross-modal tasks (I2T, V2T, A2T, T2I, T2V, T2A). The system uses a multi‑agent architecture that decomposes outputs into atomic claims, verifies them with modality‑specific experts, and aggregates evidence through structured reasoning, while a preference‑optimized verifier reduces expert calls by 66% with minimal performance loss.
arXiv:2606. 30319v1 Announce Type: cross Abstract: Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscience.
The study examined whether brain-language model alignment reflects shared computational mechanisms or merely stable lexical‑semantic correspondences. Using whole‑brain encoding across Mandarin, English, and French, transformer representations predicted activity in a distributed network that overlapped across languages and remained stable across layers. Contextual embeddings and measures of prediction or compression did not outperform static lexical embeddings, suggesting that alignment is robust but not informative about shared computational processes.