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State-of-the-art multimodal models falter in interpreting implicit social cues, revealing a critical gap in AI's understanding of human communication.
LLMs can now generate high-quality concurrent tests for Rust APIs, preserving semantic intent while exploring complex interleavings.
Fragmented methodologies in vehicular FL-IDS could jeopardize the security of connected vehicles unless addressed with rigorous benchmarking and realistic evaluations.
SCOPE-FL achieves optimal resource allocation in federated learning by ensuring participants cannot gain from misrepresenting their preferences, fundamentally enhancing system welfare.
Forget unimodal tasks鈥擴niM throws down the gauntlet for truly unified multimodal AI, demanding models juggle any combination of text, image, audio, video, code, documents, and 3D inputs and outputs in a single, interleaved stream.
