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Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning3h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks3h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts3h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning3h◆FrontierChallenge: Evaluating Scientific Workflow Completion3h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier3h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising3h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic3h◆Omni Interaction Agent Technical Report3h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification3h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability3h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization3h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding3h◆Tracing Computation Density in LLMs3h◆Cultural Binding Heads in Language Models3h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training3h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models3h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning3h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection3h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation3h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning3h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks3h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts3h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning3h◆FrontierChallenge: Evaluating Scientific Workflow Completion3h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier3h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising3h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic3h◆Omni Interaction Agent Technical Report3h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification3h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability3h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization3h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding3h◆Tracing Computation Density in LLMs3h◆Cultural Binding Heads in Language Models3h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training3h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models3h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning3h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection3h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation3h◆
News/Omni Interaction Agent Technical Report
arxiv
PublishedSeptember 11, 2026 at 4:00 AM
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Omni Interaction Agent Technical Report

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arXiv:2609.08977v2 Announce Type: replace-cross Abstract: In this work, we present Gander, an end-to-end model that unifies omni perception, realtime interaction, and agentic capabilities within a single framework. In contrast to turn-based conventional paradigms, Gander continuously receives stream

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