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The paper by Dr. Liang Wenfeng's team at DeepSeek in collaboration with Peking University has won the ACL 2025 Best Paper Award. This research proposes the Native Sparse Attention (NSA) mechanism, which achieves algorithm-hardware co-optimization through three parallel attention branches: compression, selection, and sliding. It improves the processing speed of long texts by 11 times and outperforms traditional full-attention models. NSA supports a context length of up to 1 million tokens, achieving an 11.6 times decoding speed improvement on 64k text, and a 27B parameter model demonstrates excellent performance on multiple benchmark tests.

Google experts stated that AI is integrated into search but SEO basics remain. Key points: 1) AI works on existing search infrastructure; 2) Content quality matters, not creation method; 3) AI models handle crawling to ranking; 4) AI summaries reduce clicks to regular results (8%) and source links (1%). Impact: AI summaries cause significant traffic drops despite unchanged rules.....

NTU's 'WORLDMEM' model enhances virtual reality with long-term scene consistency via innovative memory mechanisms and conditional diffusion transformers.....

Recently, a new general intelligent agent called MiniMax Agent has been officially launched, aiming to provide efficient solutions for complex long-term tasks and has attracted widespread attention in the industry. MiniMax Agent: A breakthrough work of general intelligent agents - MiniMax Agent is an intelligent agent designed specifically to solve long-term complex tasks, featuring expert-level multi-step planning capabilities, flexible task decomposition mechanisms, and end-to-end execution efficiency. Its design concept is to make AI a reliable teammate.

In the recent Fiction.Live benchmark test, Gemini2.5Pro performed excellently in understanding and reproducing complex stories and backgrounds, leading ahead of OpenAI's o3 model. This test goes far beyond traditional "needle-in-a-haystack" tasks, focusing on a model's ability to handle deep semantics and context-dependent information within large contexts. According to the test data, when the context window length reaches 192,000 tokens (approximately 144,000 words), the performance of the o3 model drops sharply, while

Recently, the Beijing Academy of Artificial Intelligence (BAEI), in collaboration with Shanghai Jiao Tong University and other institutions, officially released a new generation of ultra-long video understanding model - Video-XL-2. The introduction of this model represents a significant breakthrough in the field of open-source ultra-long video understanding technology, injecting new vitality into the development of multimodal large models for understanding long video content. In terms of technical architecture, Video-XL-2 mainly consists of three core components: a visual encoder, the Dynamic Token Synthesis (DTS) module, and a large language model (LLM). The model adopts

Alibaba officially released QwenLong-L1-32B today, a large language model specially designed for long context reasoning, marking a significant breakthrough in AI's ability to handle long text processing. The model outperforms o3-mini and Qwen3-235B-A22B, performing at a level comparable to Claude-3.7-Sonnet-Thinking. The biggest technical breakthrough of QwenLong-L1-32B is that it is the first in the world to be trained via reinforcement learning.

Amap announced the launch of the world's first map-based AI navigation agent, officially launching nationwide. This innovative initiative marks a transformation of navigation services from traditional travel tools to intelligent travel companions that can think, predict, and are empathetic.