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Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3h◆A Survey on the Verification of Reinforcement Learning Policies3h◆Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3h◆SelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h◆RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents3h◆LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models3h◆When to Plan: Learning to Select Between Reactive Control and Deliberative Planning3h◆SEER: Supervised Learning to Control Energetic Reasoning3h◆FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics3h◆Just A Rather Very Intelligent Spoken Agent3h◆A Research Prototype for Closed-Loop Generative Design of Customized Foot Orthoses via Semantic-Physics Alignment3h◆TopoTuner: Topological Finetuning of Large Language Models3h◆Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection3h◆DS@GT ARC at eRisk 2026: Hybrid Multi-Agent LLM System with Structured Algorithmic Guidance for Conversational Depression Screening3h◆Tractable Query Answering under Epistemic Confidentiality Policies in DL Ontologies (extended version)3h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts3h◆Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning3h◆RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning3h◆FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images3h◆From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data3h◆Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning3h◆A Survey on the Verification of Reinforcement Learning Policies3h◆Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers3h◆SelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation3h◆RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents3h◆LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models3h◆When to Plan: Learning to Select Between Reactive Control and Deliberative Planning3h◆SEER: Supervised Learning to Control Energetic Reasoning3h◆FST.ai 2.5: Explainable and Uncertainty-Aware AI for Olympic and Para-Taekwondo Decision Support, Athlete Digital Twins, and Federation-Scale Analytics3h◆Just A Rather Very Intelligent Spoken Agent3h◆A Research Prototype for Closed-Loop Generative Design of Customized Foot Orthoses via Semantic-Physics Alignment3h◆TopoTuner: Topological Finetuning of Large Language Models3h◆Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection3h◆DS@GT ARC at eRisk 2026: Hybrid Multi-Agent LLM System with Structured Algorithmic Guidance for Conversational Depression Screening3h◆Tractable Query Answering under Epistemic Confidentiality Policies in DL Ontologies (extended version)3h◆RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts3h◆Supporting Autonomous Process Execution within a Multi-Perspective Constraint Frame via Numeric Planning3h◆RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning3h◆FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images3h◆From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data3h◆
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