arXiv subjects

Chunan Li

Publications and source records attributed to Chunan Li.

4 recordsLinked to original sources

arXiv2026

MiMo-V2-Flash Technical Report

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

cs.CL↗

arXiv2025

MiMo-Audio: Audio Language Models are Few-Shot Learners

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio.

cs.CL↗

arXiv2024

Inoculating solid-state homogeneous precipitation by impurity atoms through a spinodal decomposition like pathway

Solid-state homogeneous precipitation of nano-sized precipitates is one of the most effective processes to strengthen metal alloys, where the final density and size distribution of precipitates are largely controlled by the precipitation kinetics. Here, we report a strategy to inoculate the homogeneous precipitation of coherent precipitates to enhance the precipitation strengthening. Using the technologically important dilute Al-Zr alloys as an example, we demonstrate that an addition of a trace level of economical and readily available, non-L1$_{2}$ phase forming impurity atoms, X (X= Sn, Sb, Bi or Cd) and Si, can significantly enhance the diffusivity of Zr atoms and overturn the precipitation of L1$_{2}$-structured Al$_{3}$Zr nanoparticles from the classical homogeneous nucleation and growth pathway into a nonclassical nucleation pathway: Al$_{3}$Zr forms through the spontaneous formation of nano-scale local concentration fluctuations of Zr atoms on Zr-X(-Si)-vacancy clusters followed by a continuous increase of the concentration and chemical short-range ordering (CSRO). Such an impurity atoms induced heterogeneous nucleation based on a "spinodal decomposition like" mechanism dramatically accelerates the precipitation kinetics, leading to an order of magnitude higher number density of precipitates and a record high hardening efficiency of solute Zr atoms. By formulating the generalized selection principles for inoculating impurity elements, this inoculation strategy should be extendable to a broader range of materials to further explore the precipitation strengthening potentials.

cond-mat.mtrl-sci↗

arXiv2024

WGAN-AFL: Seed Generation Augmented Fuzzer with Wasserstein-GAN

The importance of addressing security vulnerabilities is indisputable, with software becoming crucial in sectors such as national defense and finance. Consequently, The security issues caused by software vulnerabilities cannot be ignored. Fuzz testing is an automated software testing technology that can detect vulnerabilities in the software. However, most previous fuzzers encounter challenges that fuzzing performance is sensitive to initial input seeds. In the absence of high-quality initial input seeds, fuzzers may expend significant resources on program path exploration, leading to a substantial decrease in the efficiency of vulnerability detection. To address this issue, we propose WGAN-AFL. By collecting high-quality testcases, we train a generative adversarial network (GAN) to learn their features, thereby obtaining high-quality initial input seeds. To overcome drawbacks like mode collapse and training instability inherent in GANs, we utilize the Wasserstein GAN (WGAN) architecture for training, further enhancing the quality of the generated seeds. Experimental results demonstrate that WGAN-AFL significantly outperforms the original AFL in terms of code coverage, new paths, and vulnerability discovery, demonstrating the effective enhancement of seed quality by WGAN-AFL.

cs.CR↗