Spoken dialogue models currently lack the ability for fine-grained speech style control, a critical capability for human-like interaction that is often overlooked in favor of purely functional capabilities like reasoning and question answering. To address this limitation, we introduce **UltraVoice**, the first large-scale spoken dialogue dataset engineered for multiple fine-grained speech style control. Encompassing over 830 hours of spoken dialogues, UltraVoice provides instructions across six key speech stylistic dimensions: emotion, speed, volume, accent, language, and composite styles. Fine-tuning leading models such as SLAM-Omni and VocalNet on UltraVoice significantly enhances their fine-grained speech stylistic controllability without degrading core conversational abilities. Specifically, our fine-tuned models achieve improvements of 29.12-42.33% in Mean Opinion Score (MOS) and 14.61-40.09 percentage points in Instruction Following Rate (IFR) on multi-dimensional control tasks designed in the UltraVoice. Moreover, on the URO-Bench benchmark, our fine-tuned models demonstrate substantial gains in core understanding, reasoning, and conversational abilities, with average improvements of +10.84% on the Basic setting and +7.87% on the Pro setting. Furthermore, the dataset’s utility extends to training controllable Text-to-Speech (TTS) models, underscoring its high quality and broad applicability for expressive speech synthesis. The complete dataset and model checkpoints are available at: [https://github.com/bigai-nlco/UltraVoice](https://github.com/bigai-nlco/UltraVoice).

While Chain-of-Thought (CoT) reasoning has revolutionized text-based LLMs, its application to the audio modality reveals a counter-intuitive thinking paradox: forcing models to “think” via prompting or post-training often degrades performance compared to direct answering. In this work, we investigate the bottleneck of audio reasoning and reveal a critical insight: the primary constraint is not the model’s logical deduction capability, but its inability to accurately perceive acoustic details. We demonstrate that keeping perceptual cues effectively activates the LALM’s internal reasoning potential. Building on this finding, we propose Audio-ReaPer, a post-training framework and corresponding model designed to unlock Reasoning potential by explicitly fortifying Perception. Our approach reforms the post-training paradigm through three key strategies: (1) utilizing agentic data generation pipeline to synthesize perception-enhanced data for SFT; (2) curating perception-focused data for RLVR; and (3) introducing a novel perceptual density reward during RL to stabilize the learning process and balance observation with deduction. Extensive experiments show that Audio-ReaPer establishes a new state-of-the-art on benchmarks such as MMAU, MMAU-Pro, and MMAR. Notably, our 7B model outperforms previous open-source models by significant margins and surpasses proprietary models like GPT-4o Audio and Gemini 2.5 Flash. The model and code will be open-source to facilitate further research for audio reasoning.

As large language models (LLMs) become increasingly integrated into critical applications, aligning their behavior with human values presents significant challenges. Current methods, such as Reinforcement Learning from Human Feedback (RLHF), typically focus on a limited set of coarse-grained values and are resource-intensive. Moreover, the correlations between these values remain implicit, leading to unclear explanations for value-steering outcomes. Our work argues that a latent causal value graph underlies the value dimensions of LLMs and that, despite alignment training, this structure remains significantly different from human value systems. We leverage these causal value graphs to guide two lightweight value-steering methods: role-based prompting and sparse autoencoder (SAE) steering, effectively mitigating unexpected side effects. Furthermore, SAE provides a more fine-grained approach to value steering. Experiments on Gemma-2B-IT and Llama3-8B-IT demonstrate the effectiveness and controllability of our methods.