DataBubble·
Model Detail
Qwen3.6-35B-A3B-int4-AutoRound
—Provider: IntelCategory: otherParameters: 35B
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3.9
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Research Paper
Latest
Consequentialist Objectives and Catastrophe5h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms5h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation5h◆A Probabilistic Framework for Hierarchical Goal Recognition5h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language5h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology5h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem5h◆Learning from Natural Language Feedback for Personalized Question Answering5h◆TS-Arena -- A Live Forecast Pre-Registration Platform5h◆Report for NSF Workshop on AI for Electronic Design Automation5h◆Initial results of the Digital Consciousness Model5h◆Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem5h◆Sensory-Aware Sequential Recommendation via Review-Distilled Representations5h◆Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation5h◆When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education5h◆SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention5h◆EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation5h◆VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation5h◆When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation5h◆How Large Language Models Balance Internal Knowledge with User and Document Assertions5h◆Consequentialist Objectives and Catastrophe5h◆EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms5h◆ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation5h◆A Probabilistic Framework for Hierarchical Goal Recognition5h◆CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language5h◆The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology5h◆Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem5h◆Learning from Natural Language Feedback for Personalized Question Answering5h◆TS-Arena -- A Live Forecast Pre-Registration Platform5h◆Report for NSF Workshop on AI for Electronic Design Automation5h◆Initial results of the Digital Consciousness Model5h◆Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem5h◆Sensory-Aware Sequential Recommendation via Review-Distilled Representations5h◆Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation5h◆When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education5h◆SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention5h◆EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation5h◆VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation5h◆When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation5h◆How Large Language Models Balance Internal Knowledge with User and Document Assertions5h◆
