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ByteDance Research has announced the open-sourcing of ChatTS-14B, a 14-billion parameter large language model (LLM) specifically designed for understanding and reasoning with time series data. Released under the Apache2.0 license, ChatTS-14B's open-source release has garnered significant attention within the AI community, marking a substantial advancement in the intersection of time series analysis and generative AI. ChatTS-14B: An Intelligent Conversational Engine for Time Series. ChatTS-14B is based on Qwen2.5-1...

The Kimi team recently released a technical report and open-sourced the preview version of Kimina-Prover, including 1.5B and 7B parameter distilled models, the Kimina-Autoformalizer-7B model for data generation, and a revised miniF2F benchmark dataset. Kimina-Prover, jointly developed by the Numina and Kimi teams, is a mathematical theorem proving model that excels in the field of formal theorem proving.

Stanford University's AI Index Report 2025 offers a fresh perspective on the global AI landscape. The report highlights Alibaba's significant contribution, ranking third globally among major large language models, establishing it as a leading Chinese tech company. In 2024, China contributed 15 models globally, with Alibaba contributing 6, trailing only Google and OpenAI with 7 models each. This achievement reflects Alibaba's ongoing commitment to technological innovation.

On April 9th, 2025, a powerful SVG (Scalable Vector Graphics) generation model named OmniSVG was officially unveiled, marking a new stage in vector graphic generation technology. Jointly developed by StepFun and Fudan University, this model is hailed as the most advanced SVG generation model currently available. Its outstanding multi-modal generation capabilities and efficient performance have attracted widespread attention. OmniSVG's technological breakthrough is based on a pre-trained Vision-Language Model (VLM)...

Recently, Alibaba Cloud's Qwen series of AI large language models has seen significant progress. Support for its next-generation model, Qwen3, has been officially merged into the vLLM (efficient large language model inference framework) codebase. This news has sparked heated discussions in the tech community, signaling that Qwen3's release is imminent. It is understood that Qwen3 will include at least two versions: Qwen3-8B and Qwen3-MoE-15B-A2B, representing innovative attempts at different scales and architectures, for developers and enterprises.

Researchers at the Hebrew University of Jerusalem recently discovered that in Retrieval Augmented Generation (RAG) systems, the number of documents processed significantly impacts language model performance, even when the total text length remains constant. The research team conducted experiments using 2,417 questions from the MuSiQue validation dataset, each linked to 20 Wikipedia paragraphs. Two to four paragraphs contained relevant answer information, with the remaining paragraphs serving as distractors. To study the impact of the number of documents, the team created multiple data partitions, progressively reducing the number of documents from 20 to...

On March 27th, Alibaba launched its first multimodal large model, Qwen2.5-Omni-7B. This model boasts powerful capabilities, handling various input modalities such as text, images, audio, and video, and generating text and natural speech outputs in real-time. This innovative technological breakthrough marks another significant advancement for Alibaba in the field of artificial intelligence. In the authoritative OmniBench multimodal fusion task benchmark, Qwen2.5-Omni achieved...

The Alibaba Cloud Tongyi Qianwen Qwen team announced the launch of Qwen2.5-Omni, a new generation of end-to-end multimodal flagship model in the Qwen family. Designed for comprehensive multimodal understanding, this new model seamlessly handles various input formats including text, images, audio, and video, and generates text and natural speech synthesis outputs simultaneously via real-time streaming response.

Alibaba is making waves in the AI field with the recent open-source release of its latest multimodal model, Qwen2.5-VL-32B-Instruct. This model is part of the Qwen2.5 series, which also includes 3B, 7B, and 72B versions. The 32B version prioritizes convenient local execution while maintaining performance. Enhanced through reinforcement learning, Qwen2.5-VL-32B excels in several areas. Notably, its responses are more aligned with human expectations.

A powerful newcomer has emerged in the fintech arena. The Fin-R1 model, jointly developed by Professor Liwen Zhang's team (SUFE-AIFLM-Lab) at the School of Statistics and Data Science, Shanghai University of Finance and Economics, and Caiyue Xingchen, has been officially open-sourced, attracting significant attention due to its impressive performance. This financial specialized large language model, based on Qwen2.5-7B and trained with reinforcement learning, achieves leading performance across multiple financial benchmark tests. Remarkably, Fin-R1 surpasses most models of comparable size, and even many significantly larger models, despite having only 7B parameters.

Manus, an AI Agent product from Chinese startup Monica, has recently gained traction on X. Its founder, Ji Yichao, today shared further technical details via social media. According to Ji, Manus is built upon Alibaba's Qwen large language model and leverages various fine-tuned models to achieve its unique functionalities. This revelation has sparked industry discussion regarding Manus's technological origins and further ignited global AI community interest in its potential.

Reports indicate that the National Supercomputing Internet Platform has integrated Alibaba's Qwen large language model, officially providing the QwQ-32B API service. Users can access up to 1 million tokens free of charge, offering a valuable opportunity for developers and researchers. QwQ-32B is the latest open-source inference model from Alibaba's Tongyi team, demonstrating excellent performance. According to multiple authoritative benchmark tests, QwQ-32B's capabilities rival those of a fully-fledged 671B model.

Tongyi Lingma has announced the launch of its latest inference model, Qwen2.5-Max, offering developers powerful programming and mathematical capabilities. Trained on over 20 trillion tokens and incorporating a meticulously designed post-training scheme, Qwen2.5-Max demonstrates exceptional performance.