# Beyond Semantic Dominance: Cognitive Affective Reasoning and Empathetic Response Alignment in Audio Language Models

arXiv: https://arxiv.org/abs/2606.06940 · HTML: https://mlsanity.com/paper/2606.06940/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2026.

## Built on by (most cited first, 3 of 3)

- [Thinking in Depth, Speaking Directly: Recurrent Latent Reasoning for Paralinguistically Grounded Spoken Dialogue](https://mlsanity.com/paper/2609.37818/) (2026)
- [AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models](https://mlsanity.com/paper/2610.01560/) (2026)
- [COT-TTS: Audio Context-Aware Text-to-Speech with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2609.22697/) (2026)

## Abstract (arXiv)

While Audio Language Models (ALMs) demonstrate strong semantic understanding, they struggle with complex affective interactions. Specifically, textual semantic dominance often overshadows acoustic nuances, and a lack of cognitive depth leads to generic, emotion-agnostic responses. We propose CogAudio-LLMfootnote urlstylesame https://github.com/zxzhao0/CogAudio-LLM, a novel cognitive affective reasoning framework. To mitigate semantic dominance, we build LIME-440K, a ``lexically-identical, multi-emotion'' dataset designed to facilitate acoustic-semantic decoupling. We introduce EIPS, a 4-step Chain-of-Thought (CoT) mechanism incorporating psychological reasoning. For inference efficiency, multi-stage training explicitly establishes EIPS via supervised fine-tuning, then distills this logic into an implicit generation process. Finally, we design DR-SAPO (Dual-Route Soft Adaptive Policy Optimization) to dynamically balance the logical rigor of the CoT with the empathetic quality of the direct response.