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.

Recent advancements highlight the potential of end-to-end real-time spoken dialogue systems, showcasing their low latency and high quality. In this paper, we introduce SLAM-Omni, a timbre-controllable, end-to-end voice interaction system with single-stage training. SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens and decoupling speaker information to a vocoder. By predicting grouped speech semantic tokens at each step, our method significantly reduces the sequence length of audio tokens, accelerating both training and inference. Additionally, we propose historical text prompting to compress dialogue history, facilitating efficient multi-round interactions. Comprehensive evaluations reveal that SLAM-Omni outperforms prior models of similar scale, requiring only 15 hours of training on 4 GPUs with limited data. Notably, it is the first spoken dialogue system to achieve competitive performance with a single-stage training approach, eliminating the need for pre-training on TTS or ASR tasks. Further experiments validate its multilingual and multi-turn dialogue capabilities on larger datasets.

Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models’ (LLMs) coding capabilities. However, there is an unignorable programming language bias in existing code benchmarks – over 95% code generation benchmarks are dominated by Python, leaving the LLMs’ capabilities in other programming languages such as Java and C/C++ unknown. Moreover, coding task bias is also crucial. Most benchmarks focus on code generation capability, while benchmarks for code reasoning (given input, reasoning output; and given output, reasoning input), an essential coding capability, are insufficient. Yet, constructing multi-lingual benchmarks can be expensive and labor-intensive, and codes in contest websites such as Leetcode suffer from data contamination during training. To fill this gap, we propose CRUXEVAL-X, a multi-lingual code reasoning benchmark that contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. In particular, the construction pipeline of CRUXEVAL-X works in a fully automated and test-guided manner, which iteratively generates and repairs based on execution feedback. Also, to cross language barriers (e.g., dynamic/static type systems in Python/C++), we formulated various transition rules between language pairs to facilitate translation. Our intensive evaluation of 24 representative LLMs reveals the correlation between language pairs. For example, TypeScript and JavaScript show a significant positive correlation, while Racket has less correlation with other languages. More interestingly, even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs.