# SWEAR

Large audio-language models that reason explicitly have mostly used one overall difficulty signal to decide whether to think longer, which gives little guidance on how much effort each part of the reasoning trace deserves. SWEAR scores difficulty along five dimensions covering acoustic quality, acoustic complexity and reasoning demand, uses those offline scores to compress reference traces, teaches the model during supervised fine-tuning to predict each section's difficulty before writing it, and then lets reinforcement learning set section length from those self-ratings. On MMAU-test-mini and MMAR, accuracy rises by 2.6 and 6.3 percentage points over CoTA-SFT without difficulty supervision, while generated tokens fall by 60.1% and 62.3%.

Large audio-language models that reason explicitly have mostly used one overall difficulty signal to decide whether to think longer, which gives little guidance on how much effort each part of the reasoning trace deserves. SWEAR scores difficulty along five dimensions covering acoustic quality, acoustic complexity and reasoning demand, uses those offline scores to compress reference traces, teaches the model during supervised fine-tuning to predict each section's difficulty before writing it, and then lets reinforcement learning set section length from those self-ratings. On MMAU-test-mini and MMAR, accuracy rises by 2.6 and 6.3 percentage points over CoTA-SFT without difficulty supervision, while generated tokens fall by 60.1% and 62.3%.