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Optimizing AI Planning with Non-uniform Temporal Resolution

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AUPO: Enhancing AI Decision-Making with Reward-Based Abstraction

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Simplifying AI Planning: How Large Language Models Create Abstract Domains

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Boosting LLM Performance with Strategy Fusion: Introducing SMaRT

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Navigating Uncertainty: How PlanU Empowers LLMs for Better Decision-Making

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Boosting AI Teamwork: How Verification-Aware Planning Enhances Multi-Agent Systems

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Human-Guided AI for Multi-Robot Disassembly Tasks

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Unlocking Diverse Strategies for AI Agents in Simulated Worlds

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Single Models Outperform Multi-Agent Systems with IMAGINE

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DEEPPLANNER: A New Approach to Sharpen AI Agent Planning for Complex Research

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Learning World Rules from a Single Exploration in Unpredictable Environments

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Ensuring Trust: Formally Verified Unsolvability for Temporal AI Planning

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Generative AI Helps Robots Navigate Unseen Spaces with Enhanced Prior Knowledge

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Flow Planner: Advancing Autonomous Driving with Intelligent Interaction Modeling

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Advancing Robot Navigation Through Unified World Models and Memory

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Bridging Language Models and Logic for Reliable Robot Planning

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Benchmarking AI’s Problem-Solving: Introducing PUZZLEPLEX for Reasoning and Planning

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A New Benchmark Reveals LLMs Struggle with Constrained Planning

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Verifying AI Plans: How LLMs and Formal Methods Create Reliable Agents

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Bridging the Gap: LLMs That Actively Seek Information for Robust Planning

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Strategic Planning for Smarter LLMs: Unpacking PTA-GRPO’s Approach to Reasoning

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Evaluating Large Language Models for Robot Task and Motion Planning

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Sda-Planner: Enhancing Embodied Agents with Adaptive and Error-Aware Task Planning

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Guiding Language Models for Precise Summary Control

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Decoding Language Model Planning: A Look Inside Their Computational Strategies

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ATLAS: A Multi-Agent Framework for Intelligent Travel Planning

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PLANNER-R1: Efficient AI Planning with Smaller Models and Shaped Rewards

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ExoPredicator: Enabling Robots to Plan in Dynamic Worlds with Abstract Causal Models

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PDDL-INSTRUCT: Enhancing LLMs for Precise Symbolic Planning

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Empowering Language Models with Advanced Planning Capabilities

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