cross-modal-learning

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4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src

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papersSEP 12 04:00 UTC

Diffusion Transformers Studied for Cross-Modal Brain State Decoding

A new arXiv paper examines whether diffusion transformers can generate useful cross-modal training data for brain state decoding. The authors note that prior work has mostly fused paired modalities for prediction rather than using their correspondence to augment training data. The approach aims to improve multimodal representation learning.

research/multimodal/brain-computer-interface/diffusion-models

papersSEP 10 04:00 UTC

CardioState-JEPA learns a shared cardiac representation across ECG, PPG, and PCG signals

An arXiv paper presents CardioState-JEPA, a delay-aware cross-modal model that learns one shared representation of cardiac activity from ECG, photoplethysmography, and phonocardiography signals. The authors argue that current cardiac foundation models are tied to a single sensing modality, and their approach instead aligns the complementary views these three signals provide of the same heartbeat.

healthcare/multimodal/representation-learning/biosignals