Yunjie Li

Gemini Team, Petko Georgiev, Ving Ian Lei et al. · deepmind, mila

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Gheorghe Comanici, Eric Bieber, Mike Schaekermann et al. · amazon-science, baidu

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our most capable model yet, achieving SoTA performance on frontier coding and reasoning benchmarks. In addition to its incredible coding and reasoning skills, Gemini 2.5 Pro is a thinking model that excels at multimodal understanding and it is now able to process up to 3 hours of video content. Its unique combination of long context, multimodal and reasoning capabilities can be combined to unlock new agentic workflows. Gemini 2.5 Flash provides excellent reasoning abilities at a fraction of the compute and latency requirements and Gemini 2.0 Flash and Flash-Lite provide high performance at low latency and cost. Taken together, the Gemini 2.X model generation spans the full Pareto frontier of model capability vs cost, allowing users to explore the boundaries of what is possible with complex agentic problem solving.

Ruinan Ma, Canjie Zhu, Mingfeng Lu et al.

Electronic countermeasures involving radar signals are an important aspect of modern warfare. Traditional electronic countermeasures techniques typically add large-scale interference signals to ensure interference effects, which can lead to attacks being too obvious. In recent years, AI-based attack methods have emerged that can effectively solve this problem, but the attack scenarios are currently limited to time domain radar signal classification. In this paper, we focus on the time-frequency images classification scenario of radar signals. We first propose an attack pipeline under the time-frequency images scenario and DITIMI-FGSM attack algorithm with high transferability. Then, we propose STFT-based time domain signal attack(STDS) algorithm to solve the problem of non-invertibility in time-frequency analysis, thus obtaining the time-domain representation of the interference signal. A large number of experiments show that our attack pipeline is feasible and the proposed attack method has a high success rate.

Zihao Wang, Yunjie Li, Lingmin Zan et al.

The Extended Long Short-Term Memory (xLSTM) network has attracted widespread research interest due to its enhanced capability to model complex temporal dependencies in diverse time series applications. Despite its success, there is still potential to further improve its representational capacity and forecasting performance, particularly on challenging real-world datasets with unknown, intricate, and hierarchical dynamics. In this work, we propose a stochastic xLSTM, termed StoxLSTM, that improves the original architecture into a state space modeling framework by incorporating stochastic latent variables within xLSTM. StoxLSTM models the latent dynamic evolution through specially designed recurrent blocks, enabling it to effectively capture the underlying temporal patterns and dependencies. Extensive experiments on publicly available benchmark datasets from multiple research communities demonstrate that StoxLSTM consistently outperforms state-of-the-art baselines with better robustness and stronger generalization ability.

Jiadi Bao, Mengtao Zhu, Yunjie Li et al.

De-interleaving of the mixtures of Hidden Markov Processes (HMPs) generally depends on its representation model. Existing representation models consider Markov chain mixtures rather than hidden Markov, resulting in the lack of robustness to non-ideal situations such as observation noise or missing observations. Besides, de-interleaving methods utilize a search-based strategy, which is time-consuming. To address these issues, this paper proposes a novel representation model and corresponding de-interleaving methods for the mixtures of HMPs. At first, a generative model for representing the mixtures of HMPs is designed. Subsequently, the de-interleaving process is formulated as a posterior inference for the generative model. Secondly, an exact inference method is developed to maximize the likelihood of the complete data, and two approximate inference methods are developed to maximize the evidence lower bound by creating tractable structures. Then, a theoretical error probability lower bound is derived using the likelihood ratio test, and the algorithms are shown to get reasonably close to the bound. Finally, simulation results demonstrate that the proposed methods are highly effective and robust for non-ideal situations, outperforming baseline methods on simulated and real-life data.

Gemini Team, Rohan Anil, Sebastian Borgeaud et al.

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.