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Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides18m◆Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation18m◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States18m◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation18m◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model18m◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents18m◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems18m◆Null Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability18m◆Learnable Splines for Unstructured Neural Operators18m◆Functional Welfare for AI Safety18m◆Author-in-the-loop Agentic Reproducibility Auditing for a Machine Learning Conference18m◆Harness-Delta Attribution: Dissecting Gains From Harness Evolution18m◆Testing the Planning Claims of LLM-Based Agents against Formal Semantics18m◆Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks18m◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds18m◆Repairing LLM-Generated Specifications with Deterministic Diagnosis18m◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier18m◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents18m◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation18m◆FlexUMM: Flexible Representation Learning for Unified Multimodal Models18m◆Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides18m◆Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation18m◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States18m◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation18m◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model18m◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents18m◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems18m◆Null Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability18m◆Learnable Splines for Unstructured Neural Operators18m◆Functional Welfare for AI Safety18m◆Author-in-the-loop Agentic Reproducibility Auditing for a Machine Learning Conference18m◆Harness-Delta Attribution: Dissecting Gains From Harness Evolution18m◆Testing the Planning Claims of LLM-Based Agents against Formal Semantics18m◆Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks18m◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds18m◆Repairing LLM-Generated Specifications with Deterministic Diagnosis18m◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier18m◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents18m◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation18m◆FlexUMM: Flexible Representation Learning for Unified Multimodal Models18m◆
News/SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology
arxiv
PublishedSeptember 3, 2026 at 4:00 AM

SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology

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arXiv:2609.02292v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) and the growing diversity of their applications presents a unique optimization opportunity: selecting the right model for the task, while optimizing for speed, cost, and quality at a per-task leve

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