Multimodal review generation is essential in academic research. However, existing LLM-based models often struggle to pinpoint specific details within papers, yielding generalized, unsupported critiques that compromise final decisions. To address these limitations, we propose AnchorReviewer, a coarse-to-fine, evidence-grounded multimodal review generation framework. Central to our architecture is an integrated pipeline featuring an Inline Retrieval-Augmented Generation (InRAG) module. Combined with our Unified Embedding Module, InRAG facilitates the high-precision extraction of multimodal evidence chunks, ensuring our detailed reviews are strictly anchored to the original content. Furthermore, we incorporate a Reflection Mechanism to refine initial outputs and maximize decision accuracy. To support model evaluation and our Progressive Training Strategy, we construct the large-scale AnchorReview dataset, comprising diverse multimodal review samples. Experimental results demonstrate that AnchorReviewer significantly improves review reliability, excelling in critical evaluation, multimodal understanding, and decision consistency. Our code and dataset will be publicly available.

Despite advancements in controllable text-to-music (TTM) generation, existing models struggle with cross-domain voice cloning for singing due to inadequate modeling of speech-derived vocal characteristics, inherently limiting precise timbre transfer. To achieve precise and controllable music generation conditioned on the timbre of reference speech, we propose S2M-Inject, a cross-domain framework driven by reference speech. Incorporating a fixed timbre embedding strategy, S2M-Inject injects speaker representations into an instruction-guided generation pipeline. This effectively preserves target vocal attributes while enabling comprehensive natural-language control over lyrics, genre, instrumentation, rhythm, melody, and mood. Extensive experiments demonstrate that the framework achieves effective cross-domain voice cloning, significantly enhancing vocal similarity while maintaining high-fidelity audio quality in both Chinese and English tasks. This work extends voice cloning into the musical domain, offering a rigorous paradigm for personalized music generation. Audio samples are available at https://anonymous.4open.science/w/S2M-Inject-975.

Zero-shot text-to-speech (TTS) aims to synthesize high-quality speech for unseen speakers using only a short reference utterance. Existing non-autoregressive TTS models typically convert text tokens into speech-length representations through filler-token padding, explicit duration prediction, or text upsampling. However, these approaches either introduce redundant computation or rely on explicit alignment assumptions. We propose M3-TTS, a native two-stream multi-modal diffusion Transformer that preserves the original lengths of text and speech representations and enables direct text–speech interaction through packed joint self-attention. M3-TTS employs a dual-stage architecture consisting of Joint-DiT layers for learning text–speech correspondence and audio-only DiT layers for acoustic refinement. Reference acoustic information is injected through AdaLN-style conditioning. Experiments on the Seed-TTS benchmark demonstrate strong intelligibility and competitive speaker similarity and perceptual quality. The Mel-VAE variant further improves inference efficiency while maintaining high synthesis quality. Code and demos will be available at https://github.com/WWWWxp/M3-TTS.

Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each operating under heterogeneous control paradigms. Unifying these modalities remains a fundamental challenge due to the intrinsic dissonance between structured semantic representations (speech/music) and unstructured acoustic textures (sound effects). In this paper, we introduce UniSonate, a unified flow-matching framework capable of synthesizing speech, music, and sound effects through a standardized, reference-free natural language instruction interface. To reconcile structural disparities, we propose a novel dynamic token injection mechanism that projects unstructured environmental sounds into a structured temporal latent space, enabling precise duration control within a phoneme-driven Multimodal Diffusion Transformer (MM-DiT). Coupled with a multi-stage curriculum learning strategy, this approach effectively mitigates cross-modal optimization conflicts. Extensive experiments demonstrate that UniSonate achieves state-of-the-art performance in instruction-based TTS (WER 1.47%) and TTM (SongEval Coherence 3.18), while maintaining competitive fidelity in TTA. Crucially, we observe positive transfer, where joint training on diverse audio data significantly enhances structural coherence and prosodic expressiveness compared to single-task baselines.

Despite remarkable advances that large language models have achieved in chatbots nowadays, maintaining a non-toxic user-AI interactive environment has become increasingly critical nowadays. However, previous efforts in toxicity detection have been mostly based on benchmarks derived from social media contents, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. In this work, we introduce ToxicChat, a novel benchmark constructed based on real user queries from an open-source chatbot. This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference when compared to social media contents. Our systematic evaluation of models trained on existing toxicity datasets has shown their shortcomings when applied to this unique domain of ToxicChat. Our work illuminates the potentially overlooked challenges of toxicity detection in real-world user-AI conversations. In the future, ToxicChat can be a valuable resource to drive further advancements toward building a safe and healthy environment for user-AI interactions.