Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
Score
76首次揭示全双工SLM模态干扰的梯度冲突根源，并提出有效分离策略，信息增量高。
分析深入，实验设计合理，改进幅度大，但未提供开源代码，可复现性略减。
框架可直接用于开发更智能的语音助手和交互系统，且不增推理成本，实用性强。
2025年7月最新论文，紧跟全双工SLM前沿，时效性极佳。
来自arXiv预印本，虽未经同行评审，但作者团队在SLM领域有系列工作，可信度较高。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper is the first to uncover the root cause of modality interference and proposes an effective solution with strong experimental results, offering significant academic and engineering value.
AI commentary
「This paper finally reveals the nature of modality interference in full-duplex speech models—previously we only knew it didn't work well, now we have a clear attribution to gradient conflicts. For engineers working on speech interaction and end-to-end dialogue systems, the hierarchical parameter separation approach is especially valuable as it improves fluency without adding latency, making it highly practical for deployment.」
Quick overview
This paper reveals the root cause of modality interference in full-duplex Spoken Language Models (SLMs) through fine-grained analysis: gradient conflicts arising from shared deep parameter spaces between acoustic and semantic modeling. It proposes Lychee-FD, a framework with hierarchical parameter separation to decouple conflicting modalities and a semantic alignment channel for cross-modality coherence. Experiments show +7.4% on Spoken QA and +28.5% on FullDuplexBench 1.5 without compromising inference efficiency. This is the first work to uncover and solve the modality interference root cause for full-duplex SLMs.
- Identifies gradient conflicts between acoustic and semantic modeling as the root cause of modality interference in full-duplex SLMs.
- Proposes Lychee-FD framework with hierarchical parameter separation and semantic alignment channel.
- Achieves significant gains on Spoken QA (+7.4%) and full-duplex interaction fluency (+28.5%).
- First work to achieve high-performance native full-duplex SLM without compromising inference efficiency.
Full text
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