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News/End-to-end PDDL Planning with Hardcoded and Dynamic Agents
arxiv
PublishedMay 11, 2026 at 4:00 AM
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End-to-end PDDL Planning with Hardcoded and Dynamic Agents

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2512.09629v2 Announce Type: replace Abstract: We present an end-to-end framework for planning supported by verifiers. An orchestrator receives a human specification written in natural language and converts it into a PDDL (Planning Domain Definition Language) model, where the domain and problem

Models mentioned
01
  • 01openai logo
    gpt-4
    openai/gpt-4
    0.0%IN $30.00/Mtok
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Discussion
Mentioned models
05
  • 01
    gpt-4
    openai/gpt-4
  • 02
    GPT-5-mini
  • 03
    GPT-5.4
  • 04
    Gemini-2.5
  • 05
    Gemini-3
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#planning#natural-language-processing#large-language-models#benchmark
Mentioned companies
02
GoogleOpenAI

No replies yet. Be first.

Mentioned models
05
  • 01
    gpt-4
    openai/gpt-4
  • 02
    GPT-5-mini
  • 03
    GPT-5.4
  • 04
    Gemini-2.5
  • 05
    Gemini-3
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#planning#natural-language-processing#large-language-models#benchmark
Mentioned companies
02
GoogleOpenAI

Related coverage

More from ARXIV
arxivCulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning17harxivProactive Service Agents: A Unified Decision Framework, Methods, and Evaluation17harxivX-Translator: A Real-Time Multilingual Speaker-Aware Speech-to-Speech Translation System17harxivA Blind Trust, the Bloody Thrust: When Attacker-Controlled Hook Updates Steer AI Agent Harnesses towards Malicious Behaviors17h
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