LLM+P lets LLMs solve planning problems optimally by converting them to PDDL for classical planners and back to natural language.
Solver failed to produce a valid plan
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 2representative citing papers
HANSEL extracts navigable evidence from agent trajectories with 83.7% precision and 88.8% recall on 45 tasks, reduces volume by 61.6%, and improves verification metrics in a 14-participant study.
Deterministic orchestration matches LLM-controlled methods in COBOL-to-Python translation accuracy but improves worst-case robustness, reduces run-to-run variability, and cuts token consumption by up to 3.5 times.
RAP turns LLMs into dual world-model and planning agents via MCTS to generate better reasoning paths, outperforming CoT baselines and achieving 33% relative gains over GPT-4 CoT using LLaMA-33B on plan generation.
Presents NL-PDDL-Bench and a planner-in-the-loop framework combining LoRA fine-tuning, DPO on planner-derived pairs, and inference-time repair to improve LLM PDDL generation.
PDDLego iteratively formalizes and refines PDDL representations of partially observable environments to improve planning success without finetuning or in-context examples.
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
citing papers explorer
-
HANSEL: Extracting Breadcrumbs from Web Agent Trajectories for Interactive Verification
HANSEL extracts navigable evidence from agent trajectories with 83.7% precision and 88.8% recall on 45 tasks, reduces volume by 61.6%, and improves verification metrics in a 14-participant study.
-
Reasoning with Language Model is Planning with World Model
RAP turns LLMs into dual world-model and planning agents via MCTS to generate better reasoning paths, outperforming CoT baselines and achieving 33% relative gains over GPT-4 CoT using LLaMA-33B on plan generation.
-
Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback
Presents NL-PDDL-Bench and a planner-in-the-loop framework combining LoRA fine-tuning, DPO on planner-derived pairs, and inference-time repair to improve LLM PDDL generation.
-
Iterative Formalization and Planning in Partially Observable Environments
PDDLego iteratively formalizes and refines PDDL representations of partially observable environments to improve planning success without finetuning or in-context examples.