Reasoning is now central to modern foundation models, but remains underdeveloped in the audio modality. Unlike text transcripts, audio contains not only words but also vocal cues, background events, and temporal patterns, all of which can be necessary for reliable inference. This survey presents the first dedicated synthesis of audio reasoning in multimodal foundation models. We formulate audio reasoning as reasoning-augmented generation grounded in acoustic evidence, distinguishing it from conventional audio understanding. We then organize existing work around two core settings: **Audio-to-Text reasoning**, where models infer textual answers from acoustic input, and **Audio-to-Speech reasoning**, where models reason before or during speech generation under latency constraints. Finally, we summarize evaluation practices and challenges in acoustic grounding, shortcut learning, modality hallucination, and latency-constrained interaction.

Speech-to-speech translation (S2ST) in time-sensitive applications such as video dubbing requires not only semantic fidelity and speaker preservation, but also strict duration consistency to avoid audio-visual misalignment. However, existing S2ST systems largely generate target speech without explicit temporal planning, making duration control an unresolved challenge. We introduce DuraS2ST, a duration-aligned reasoning framework that enables a single speech language model to first generate an explicit chain-of-thought (CoT) for planning target wording and phonetic length, and then synthesize the corresponding speech tokens. To support this paradigm, we construct DuraSet-440K, a high-quality duration-aligned CoT corpus for supervised initialization. We further optimize the model with multi-modal multi-dimensional reinforcement learning, using a Duration Margin Reward to balance translation quality and duration consistency, and Modality-Aware Reward Attribution to assign rewards to appropriate token spans. Experiments on CVSS-T show that DuraS2ST achieves a strong balance between translation quality and duration consistency, outperforming competitive open-source and commercial baselines. Project page: https://github.com/Mia11939/DuraS2ST.

While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prospective memory (PM), the ability to spontaneously recall and act on latent constraints without direct requests, remains largely unevaluated. We introduce **TriggerBench**, a five-dimensional PM benchmark across daily assistants and professional workflows. TriggerBench pairs scenarios with matched RM controls, contrastive positive/negative variants, and overloaded triggers, enabling fine-grained measurement of proactive recall, false-alarm rate, and attentional robustness. We find: (i) PM shows a precision-recall trade-off and attentional fragility. Though enhanced reasoning improves proactive recall, models can overfit to always-remind heuristics. Furthermore, PM accuracy degrades substantially under implicit constraints or triggers overloaded by concurrent requests, indicating that robust PM remains an open challenge. (ii) PM is notably harder than RM: on identical contexts, RM remains near-saturated up to 100K tokens, while PM decays sharply as context length increases. (iii) PM may serve as a behavioral probe of spare reasoning capacity. Pairing PM with AIME-2025 math problems reveals that successful trajectories yield higher PM accuracy than failed ones at the same context length, showing PM is a behavioral correlate of spare reasoning budget that token count obscures. Project page: https://github.com/KristenZHANG/TriggerBench-Official.

With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has demonstrated strong capabilities in audio-related processing tasks. However, the performance gap between these two paradigms has not been thoroughly explored. To address this gap, we present a fair comparison of self-supervised learning (SSL)-based discrete and continuous features under the same experimental settings. We evaluate their performance across six spoken language understanding-related tasks using both small and large-scale LLMs (Qwen1.5-0.5B and Llama3.1-8B). We further conduct in-depth analyses, including efficient comparison, SSL layer analysis, LLM layer analysis, and robustness comparison. Our findings reveal that continuous features generally outperform discrete tokens in various tasks. Each speech processing method exhibits distinct characteristics and patterns in how it learns and processes speech information. We hope our results will provide valuable insights to advance spoken language understanding in SpeechLLMs.

Recent advancements in speech large language models (SpeechLLMs) have attracted considerable attention. Nonetheless, current methods exhibit suboptimal performance in adhering to speech instructions. Notably, the intelligence of models significantly diminishes when processing speech-form input as compared to direct text-form input. Prior work has attempted to mitigate this semantic inconsistency between speech and text representations through techniques such as representation and behavior alignment, which involve the meticulous design of data pairs during the post-training phase. In this paper, we introduce a simple and scalable training method called InSerter, which stands for Interleaved Speech-Text Representation Pre-training. InSerter is designed to pre-train large-scale unsupervised speech-text sequences, where the speech is synthesized from randomly selected segments of an extensive text corpus using text-to-speech conversion. Consequently, the model acquires the ability to generate textual continuations corresponding to the provided speech segments, obviating the need for intensive data design endeavors. To systematically evaluate speech instruction-following capabilities, we introduce SpeechInstructBench, the first comprehensive benchmark specifically designed for speech-oriented instruction-following tasks. Our proposed model InSerter achieves SOTA performance in SpeechInstructBench and demonstrates superior or competitive results across diverse speech processing tasks.

In recent years, large language models have achieved significant success in generative tasks (e.g., speech cloning and audio generation) related to speech, audio, music, and other signal domains. A crucial element of these models is the discrete acoustic codecs, which serve as an intermediate representation replacing the mel-spectrogram. However, there exist several gaps between discrete codecs and downstream speech language models. Specifically, 1) Due to the reconstruction paradigm of the Codec model and the structure of residual vector quantization, the initial channel of the codebooks contains excessive information, making it challenging to directly generate acoustic tokens from weakly supervised signals such as text in downstream tasks. 2) Achieving good reconstruction performance requires the utilization of numerous codebooks, which increases the burden on downstream speech language models. Consequently, leveraging the characteristics of speech language models, we propose Language-Codec. In the Language-Codec, we introduce a Masked Channel Residual Vector Quantization (MCRVQ) mechanism along with improved fourier transform structures, refined discriminator design to address the aforementioned gaps. We compare our method with competing audio compression algorithms and observe significant outperformance across extensive evaluations. Furthermore, we also validate the efficiency of the Language-Codec on downstream speech language models. The source code and pretrained models will be open-sourced after the paper is accepted. Codes are available at https://github.com/jishengpeng/Languagecodec.

Large Language Models (LLMs) are increasingly deployed worldwide, yet their ability to navigate cultural nuances remains underexplored. Misinterpreting cultural content can lead to AI-generated responses that are offensive or inappropriate, limiting their usability in global applications such as customer service, diplomatic communication, and online education. While prior research has evaluated cultural knowledge of LLMs, existing benchmarks fail to assess dynamic cultural competence-the ability to apply cultural knowledge effectively in real-world interactions. To address this gap, we introduce SocialDuolingo, a novel benchmark designed to evaluate cultural competence through multi-turn interactive intercultural scenarios. It comprises 3,060 human-written scenarios spanning 60 countries across six continents. Through extensive experiments on eight prominent LLMs, our findings reveal a significant gap between the cultural knowledge stored in these models and their ability to apply it effectively in cross-cultural communication.