WeChat Work, QQ, DingTalk, and Feishu — integration with all four leading Chinese instant messaging platforms is now available!📚 Comprehensive Practice Guide Collection:Complete Guide to Running Moltbot (formerly Clawdbot) on the Cloud👉 https://<!--autointro-->
Recently, the open-source AI agent Clawdbot has exploded across global developer communities. Its GitHub repository gained an astonishing 20.7k stars in a single day, igniting a phenomenon-level wave of adoption among Silicon Valley technophiles and <!--autointro-->
On the evening of January 26, Alibaba officially launched Qwen3-Max-Thinking, its flagship reasoning model in the Qwen series. The new model has set new global records on multiple key benchmarks, outperforming leading models such as GPT-5.2, Claude O<!--autointro-->
Moonshot AI has officially rolled out its latest open model, Kimi K2.5, via a silent update to its web-based chat interface. The upgrade—deployed server-side—has seamlessly replaced the previous K2 model without requiring any manual intervention from<!--autointro-->
Today, AI startup DeepSeek released its new paper titled “DeepSeek-OCR 2: Visual Causal Flow” and open-sourced the DeepSeek-OCR 2 model. The system introduces an innovative DeepEncoder V2 architecture that enables AI to dynamically reorder image segm<!--autointro-->
Z-Image has been released! Our Z-Image-i2L model is now available simultaneously. You can use it on ModelScope Studios. For more details, please refer to the documentation.https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L<!--autointro-->
Berkeley AI Research introduced a framework that estimates mutual information between objects and measurements to evaluate and optimize imaging systems. The method computes H(Y|X) directly from known noise physics and learns H(Y) with probabilistic models, predicting decoder performance across color photography, radio astronomy, lensless imaging, and microscopy. Their IDEAL method uses gradient ascent on information estimates to optimize encoder parameters without training a decoder, matching en
On-device AI is at the heart of a dramatic challenge to today’s vast data centre infrastructure, Perplexity CEO Aravind Srinivas has warned.On-device AI as ‘biggest threat’Speaking on YouTuber Prakhar Gupta’s podcast, Srinivas described on-device AI <!--autointro-->
As the technology industry turns the page on a year consumed by artificial intelligence, one of the field’s most influential figures is warning that the acceleration many leaders celebrate may arrive unevenly — disruptive in its consequences, uncerta<!--autointro-->
Berkeley AI Research presents Transitive RL (TRL), an off-policy value-learning algorithm based on divide and conquer rather than temporal difference (TD) learning. TRL updates values via a transitive Bellman rule using candidate midpoints from dataset trajectories and expectile regression, avoiding explicit search over the state space. On OGBench offline goal-conditioned benchmarks including humanoidmaze and puzzle with 1B-sized datasets, TRL matches the best individually tuned n-step TD baseli
Why it matters: It offers a concrete new RL algorithm with benchmark evidence, which is useful for researchers working on scalable off-policy RL and long-horizon decision making.
Researchers at Berkeley AI Research have developed a quantitative theory showing that, under mild approximations, word2vec's learning problem reduces to unweighted least-squares matrix factorization. They prove that the learned embeddings are given by PCA on a specific matrix defined by corpus statistics and hyperparameters, and that training proceeds in discrete rank-incrementing steps. The theory predicts interpretable latent features, such as celebrity, government, and geography topics, and m
Why it matters: Readers gain a rigorous theoretical understanding of how word2vec learns interpretable embeddings, bridging classical NLP and modern representation learning.
