Public Domain Dedication and License v1.0 Esquiress Lyndsey Watson Copyright Dockets copyright © ls barcalys
PulseAugur coverage of Public Domain Dedication and License v1.0 Esquiress Lyndsey Watson Copyright Dockets copyright © ls barcalys — every cluster mentioning Public Domain Dedication and License v1.0 Esquiress Lyndsey Watson Copyright Dockets copyright © ls barcalys across labs, papers, and developer communities, ranked by signal.
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MemPlan integrates LLMs with symbolic planning for text environments
Researchers have developed MemPlan, a novel planning framework that integrates Large Language Models (LLMs) with symbolic planning for text-based environments. This system uses memory-conditioned PDDL planning to overco…
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New framework SLoSa trains small, safe AI models for robot task planning
Researchers have developed SLoSa, a framework designed to create small, energy-efficient, and safety-aware foundation models for robotic task planning. This approach combines classical planning methods with large langua…
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NEXUS framework enhances safety for embodied AI planning
Researchers have developed NEXUS, a novel neuro-symbolic framework designed to enhance the safety and robustness of embodied agents that plan using natural language instructions. This framework jointly maintains a PDDL …
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LLM planning agents balance safety and efficiency in partial observability
Researchers have investigated the trade-off between safety and efficiency in Large Language Model (LLM) planning agents operating in partially observable environments. They found that both aggressive commitment to long …
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LLMs automate cybersecurity attack path generation with CVE2AP
Researchers have developed CVE2AP, a novel approach that utilizes large language models (LLMs) to automatically generate PDDL-encoded attack paths from natural language CVE descriptions. This method aims to overcome the…
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New method enhances AI plan legibility for human-robot teams
Researchers have developed a new method for creating "legible" plans for arbitrary PDDL domains, extending previous work on legibility to classical planning without needing custom planners. This approach aims to produce…
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LLMs show high accuracy in translating natural language to PDDL, with Gemini 2.5 Flash leading
A new paper evaluates the effectiveness of Large Language Models (LLMs) in translating natural language testing goals into PDDL (Planning Domain Definition Language) for automated planning. The study found that contempo…
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New method improves NL-to-PDDL problem generation with LLM and repair
Researchers have developed a new method for generating PDDL (Planning Domain Definition Language) problem instances from natural language descriptions. This approach combines LLM generation with a multi-stage evaluation…
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LLMs Generate Instance Generators for PDDL Planning
Researchers have developed a novel method using Large Language Models (LLMs) to automatically generate instance-generation programs for PDDL planning. This approach aims to overcome the scalability limitations of previo…
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Open-weight LLMs show promise but fall short in PDDL model repair
Researchers have evaluated the effectiveness of open-weight large language models in repairing errors within PDDL (Planning Domain Definition Language) models, which are crucial for AI planning. Their experiments demons…
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Open Ontologies: Rust MCP Server for AI-Native Ontology Engineering
The Tesseract Academy has released Open Ontologies, a Rust-based MCP server designed for AI agents to engineer production-grade OWL ontologies. This toolchain offers significant performance improvements over traditional…
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New LLM approach enhances complex planning tasks
Researchers have introduced a new paradigm called LLM-as-Higher-Order-Formalizer to improve the planning capabilities of large language models. This approach addresses limitations in existing LLM-as-Formalizers, which s…
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New benchmark improves LLM formalization for planning systems
Researchers have developed NL-PDDL-Bench, a new benchmark designed to improve the formalization of large language models (LLMs) into Planning Domain Definition Language (PDDL) for use in autonomous systems. This benchma…
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New PO-PDDL formulation enables LLM-friendly robot planning under uncertainty
Researchers have developed PO-PDDL, a new symbolic formulation for Partially Observable Markov Decision Processes (POMDPs) that maintains the relational structure of PDDL while explicitly modeling partial observability …
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New method automates PDDL generation from Industry 4.0 digital twins
A new research paper proposes a method to automatically generate PDDL problems from Asset Administration Shells (AAS) capability models. This approach aims to simplify automated planning for production engineers by allo…