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[2603.25328] Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automated and Human-Driven Vehicles
Abstract page for arXiv paper 2603.25328: Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automa...
[2603.24647] Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
Abstract page for arXiv paper 2603.24647: Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
[2603.25326] Evaluating Language Models for Harmful Manipulation
Abstract page for arXiv paper 2603.25326: Evaluating Language Models for Harmful Manipulation
[2603.24644] Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions
Abstract page for arXiv paper 2603.24644: Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Col...
[2603.24639] Experiential Reflective Learning for Self-Improving LLM Agents
Abstract page for arXiv paper 2603.24639: Experiential Reflective Learning for Self-Improving LLM Agents
[2603.25284] SliderQuant: Accurate Post-Training Quantization for LLMs
Abstract page for arXiv paper 2603.25284: SliderQuant: Accurate Post-Training Quantization for LLMs
[2603.25283] A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion
Abstract page for arXiv paper 2603.25283: A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion
[2603.24638] How unconstrained machine-learning models learn physical symmetries
Abstract page for arXiv paper 2603.24638: How unconstrained machine-learning models learn physical symmetries
[2603.25273] Distribution and Clusters Approximations as Abstract Domains in Probabilistic Abstract Interpretation to Neural Network Analysis
Abstract page for arXiv paper 2603.25273: Distribution and Clusters Approximations as Abstract Domains in Probabilistic Abstract Interpre...
[2603.25266] Probabilistic Abstract Interpretation on Neural Networks via Grids Approximation
Abstract page for arXiv paper 2603.25266: Probabilistic Abstract Interpretation on Neural Networks via Grids Approximation
[2603.25158] Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
Abstract page for arXiv paper 2603.25158: Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
[2603.25133] RubricEval: A Rubric-Level Meta-Evaluation Benchmark for LLM Judges in Instruction Following
Abstract page for arXiv paper 2603.25133: RubricEval: A Rubric-Level Meta-Evaluation Benchmark for LLM Judges in Instruction Following
[2603.25097] ElephantBroker: A Knowledge-Grounded Cognitive Runtime for Trustworthy AI Agents
Abstract page for arXiv paper 2603.25097: ElephantBroker: A Knowledge-Grounded Cognitive Runtime for Trustworthy AI Agents
[2603.25075] Sparse Visual Thought Circuits in Vision-Language Models
Abstract page for arXiv paper 2603.25075: Sparse Visual Thought Circuits in Vision-Language Models
[2603.25046] MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting
Abstract page for arXiv paper 2603.25046: MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting
[2603.25035] Mechanistically Interpreting Compression in Vision-Language Models
Abstract page for arXiv paper 2603.25035: Mechanistically Interpreting Compression in Vision-Language Models
[2603.25031] From Stateless to Situated: Building a Psychological World for LLM-Based Emotional Support
Abstract page for arXiv paper 2603.25031: From Stateless to Situated: Building a Psychological World for LLM-Based Emotional Support
[2603.25022] A Public Theory of Distillation Resistance via Constraint-Coupled Reasoning Architectures
Abstract page for arXiv paper 2603.25022: A Public Theory of Distillation Resistance via Constraint-Coupled Reasoning Architectures
[2603.24967] The Anatomy of Uncertainty in LLMs
Abstract page for arXiv paper 2603.24967: The Anatomy of Uncertainty in LLMs
[2603.24963] Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems
Abstract page for arXiv paper 2603.24963: Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems
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