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Detecting Safety Violations Across Many Agent Traces C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents General365: Benchmarking General Reasoning in Large Language Models Across Diverse and Challenging Tasks Discourse Diversity in Multi-Turn Empathic Dialogue Evaluating Cooperation in LLM Social Groups through Elected Leadership SWE-AGILE: A Software Agent Framework for Efficiently Managing Dynamic Reasoning Context Agentic Driving Coach: Robustness and Determinism of Agentic AI-Powered Human-in-the-Loop Cyber-Physical Systems Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learning Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents A Triadic Suffix Tokenization Scheme for Numerical Reasoning Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Quantization Dominates Rank Reduction for KV-Cache Compression Anthropogenic Regional Adaptation in Multimodal Vision-Language Model Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories Do LLMs Know Tool Irrelevance? 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Mixed-Initiative Level Design with RL Brush
Omar Delarosa, Hang Dong, Mindy Ruan, Ahmed Khalifa, Julian Toge · 2020-08-07 · via cs.AI updates on arXiv.org

This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation. The tool uses reinforcement-learning-based models to augment manual human level-design through the addition of AI-generated suggestions. Here, we apply RL Brush to designing levels for the classic puzzle game Sokoban. We put the tool online and tested it in 39 different sessions. The results show that users using the AI suggestions stay around longer and their created levels on average are more playable and more complex than without.

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