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The paper proposes SPARE, a method to enhance audio reasoning in Large Audio Language Models by introducing a register token aligned with semantic goals via cosine similarity loss, improving zero-shot reasoning without extra inference cost.
This paper introduces AudioRubrics, a reinforcement learning framework that uses self-evolving, audio-grounded rubric rewards to supervise audio reasoning, outperforming existing baselines across three benchmarks.