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China vetoes Meta’s $2B Manus deal after months-long probe1h◆OpenAI could be making a phone with AI agents replacing apps1h◆Rebuilding the data stack for AI1h◆Join the new AI Agents Vibe Coding Course from Google and Kaggle1h◆The AI-designed car is taking shape3h◆Meta inks deal for solar power at night, beamed from space4h◆The next phase of the Microsoft OpenAI partnership8h◆From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables10h◆Consequentialist Objectives and Catastrophe10h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms10h◆A general optimization solver based on OP-to-MaxSAT reduction10h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation10h◆A Probabilistic Framework for Hierarchical Goal Recognition10h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language10h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology10h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem10h◆Learning from Natural Language Feedback for Personalized Question Answering10h◆TS-Arena -- A Live Forecast Pre-Registration Platform10h◆Report for NSF Workshop on AI for Electronic Design Automation10h◆Initial results of the Digital Consciousness Model10h◆China vetoes Meta’s $2B Manus deal after months-long probe1h◆OpenAI could be making a phone with AI agents replacing apps1h◆Rebuilding the data stack for AI1h◆Join the new AI Agents Vibe Coding Course from Google and Kaggle1h◆The AI-designed car is taking shape3h◆Meta inks deal for solar power at night, beamed from space4h◆The next phase of the Microsoft OpenAI partnership8h◆From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables10h◆Consequentialist Objectives and Catastrophe10h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms10h◆A general optimization solver based on OP-to-MaxSAT reduction10h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation10h◆A Probabilistic Framework for Hierarchical Goal Recognition10h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language10h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology10h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem10h◆Learning from Natural Language Feedback for Personalized Question Answering10h◆TS-Arena -- A Live Forecast Pre-Registration Platform10h◆Report for NSF Workshop on AI for Electronic Design Automation10h◆Initial results of the Digital Consciousness Model10h◆

News/model/Qwen3.5-9B-GLM5.1-Distill-v1

Qwen3.5-9B-GLM5.1-Distill-v1 news

1 articles mentioning Qwen3.5-9B-GLM5.1-Distill-v1

arxiv5d ago

Qwen3.5-Omni Technical Report

arXiv:2604.15804v2 Announce Type: replace Abstract: In this work, we present Qwen3.5-Omni, the latest advancement in the Qwen-Omni model family. Representing a significant evolution over its predecessor, Qwen3.5-Omni scales to hundreds of billions of parameters and supports a 256k context length. By

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