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Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning7h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks7h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts7h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning7h◆FrontierChallenge: Evaluating Scientific Workflow Completion7h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier7h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising7h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic7h◆Omni Interaction Agent Technical Report7h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification7h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability7h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization7h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding7h◆Tracing Computation Density in LLMs7h◆Cultural Binding Heads in Language Models7h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training7h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models7h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning7h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection7h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation7h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning7h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks7h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts7h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning7h◆FrontierChallenge: Evaluating Scientific Workflow Completion7h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier7h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising7h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic7h◆Omni Interaction Agent Technical Report7h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification7h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability7h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization7h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding7h◆Tracing Computation Density in LLMs7h◆Cultural Binding Heads in Language Models7h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training7h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models7h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning7h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection7h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation7h◆
News/model/Qwen3.8-27B-Cold-Fable-Fusion-GAIN-V1.1-732-Heretic-Uncensored-stage1

Qwen3.8-27B-Cold-Fable-Fusion-GAIN-V1.1-732-Heretic-Uncensored-stage1 news

1 articles mentioning Qwen3.8-27B-Cold-Fable-Fusion-GAIN-V1.1-732-Heretic-Uncensored-stage1

arxivSep 1

On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability

arXiv:2608.30320v1 Announce Type: new Abstract: We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n-gram embedding tables held off the accelerator. On fourteen pre-trainin

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