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This paper introduces an evaluation protocol with four new metrics and a benchmark dataset to assess context attribution methods for LLMs, showing they fail when context overlaps with training data.
audio.cpp releases a major update adding music/SFX generation and source separation with ACE-Step, HeartMuLa, Stable Audio 3, and HTDemucs, achieving up to 10x real-time speed for long music generation in native C++/GGML.
MERIT is a framework that learns disentangled music representations for melody, rhythm, and timbre using conditional audio generation and source-separated stems, enabling nuanced and factor-specific audio similarity queries.