SYMBOLIZER grounds symbolic states from images via VLMs using only lifted predicates and solves long-horizon tasks with goal-count and width-based heuristic search, outperforming direct VLM planning and matching VLM-heuristic baselines on ProDG and ViPlan benchmarks.
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PDDL-Mind improves LLM accuracy on theory-of-mind benchmarks by over 5% by translating stories into verifiable PDDL states that decouple environment tracking from belief inference.
AssemPlanner is a ReAct-based multi-agent system that autonomously generates production plans from natural language inputs by integrating scheduling, knowledge, line balancing, and scene graph feedback.
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.
citing papers explorer
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SYMBOLIZER: Symbolic Model-free Task Planning with VLMs
SYMBOLIZER grounds symbolic states from images via VLMs using only lifted predicates and solves long-horizon tasks with goal-count and width-based heuristic search, outperforming direct VLM planning and matching VLM-heuristic baselines on ProDG and ViPlan benchmarks.
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PDDL-Mind: Large Language Models are Capable on Belief Reasoning with Reliable State Tracking
PDDL-Mind improves LLM accuracy on theory-of-mind benchmarks by over 5% by translating stories into verifiable PDDL states that decouple environment tracking from belief inference.
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AssemPlanner: A Multi-Agent Based Task Planning Framework for Flexible Assembly System
AssemPlanner is a ReAct-based multi-agent system that autonomously generates production plans from natural language inputs by integrating scheduling, knowledge, line balancing, and scene graph feedback.
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Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.