Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models

Researchers propose Efficient Chain-of-Modality Reasoning (ECoM Reasoning), the first framework to introduce compressed reasoning into spoken language models (SLMs), improving reasoning accuracy by 21% over standard Chain-of-Modality (CoM) without explicit reasoning and by 3% over CoM with full reasoning traces while using only 40% of the text tokens. The framework, detailed in arXiv:2607.19932v1, uses a Progressive Compression curriculum-based strategy to train SLMs from full-form to compressed reasoning, addressing the challenge of reasoning over verbalized mathematical expressions in spoken question answering.

arXiv:2607.19932v1 Announce Type: new Abstract: Spoken language models SLMs enable natural human-computer interaction, but their reasoning ability still lags behind that of text-based large language models, especially on spoken mathematical question answering tasks. One important reason is that SLMs reason over purely verbalized mathematical expressions, which are harder to interpret than symbolic text. However, directly transferring text-based reasoning to SLMs is nontrivial due to architectural constraints and the additional computational requirements. To address this challenge, we propose Efficient Chain-of-Modality Reasoning ECoM Reasoning , the first framework to introduce compressed reasoning into SLMs. By compressing the textual component so that it jointly serves as speech guidance and reasoning representation, ECoM Reasoning improves reasoning accuracy while using a smaller token budget than the standard Chain-of-Modality CoM architecture, which generates intermediate text before speech. To train this capability, we further propose Progressive Compression, a curriculum-based strategy that gradually trains the model from full-form reasoning to compressed reasoning. Experiments on spoken mathematical question answering benchmarks show that ECoM Reasoning improves accuracy by 21% over standard CoM without explicit reasoning, and by 3% over CoM with full reasoning traces while using only 40% of the text tokens, demonstrating that it enhances SLM reasoning while remaining inference-efficient.