Self-improvement of a decoder-only transformer yields plans averaging 30% shorter than a source symbolic planner, over 80% optimal where known, with sub-exponential latency scaling.
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LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
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LLM+ASP framework enables task-agnostic nonmonotonic reasoning by having LLMs generate and self-correct ASP programs using solver feedback, outperforming SMT alternatives on diverse benchmarks.
LLM-Flax automates neuro-symbolic robotic task planning with three LLM stages for rule generation, failure recovery, and zero-shot scoring, outperforming manual baselines on MazeNamo grids.
ANCHOR raises mobile manipulation success from 53.3% to 71.7% in unseen homes by binding plans to observable geometry, ensuring operable navigation endpoints, and using layered local recovery instead of global replans.
LLM planners for robots often produce dangerous plans even when planning succeeds, with safety awareness staying flat as model scale improves planning ability.
Self-Correcting RAG formalizes retrieval as MMKP to maximize information density under token limits and uses NLI-guided MCTS to validate faithfulness, raising accuracy and cutting hallucinations on six multi-hop QA and fact-checking datasets.
VoxPoser uses LLMs to compose 3D value maps via VLM interaction for model-based synthesis of robust robot trajectories on open-set language-specified manipulation tasks.
CSR with ASR enables infinite-horizon real-time LLM policies via stable KV-cache properties and background eviction, delivering 26x lower latency and SOTA recall on embodied benchmarks.
Behavior Forest decouples multi-constraint travel planning into parallel behavior trees with LLM nodes and global coordination, yielding 6.67% and 11.82% gains over prior methods on two benchmarks.
COMPASS formalizes prompt engineering as a POMDP-based cognitive decision process for self-adaptive generation of task plan explanations via LLMs.
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.
A learned embedding-based router selecting among six reasoning paradigms improves LLM agent accuracy from 47.6% to 53.1% on average, beating the best fixed paradigm by 2.8pp.
Novelty estimation via LLM prompts enables pruning in Tree-of-Thought search, reducing overall token usage on language planning benchmarks.
ValuePlanner is a hierarchical architecture that uses LLMs to generate value-based subgoals and PDDL planners to produce executable actions, enabling self-directed behavior in embodied agents.
AdaPlan-H enables LLM agents to generate self-adaptive hierarchical plans that adjust detail level to task difficulty, improving success rates in multi-step tasks.
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.
Compiled AI generates deterministic code artifacts from LLMs in a one-time compilation step, enabling reliable workflow execution with zero runtime tokens after break-even.
A survey that provides a taxonomy of methods for improving planning in LLM-based agents across task decomposition, plan selection, external modules, reflection, and memory.
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.
citing papers explorer
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ANCHOR: A Physically Grounded Closed-Loop Framework for Robust Home-Service Mobile Manipulation
ANCHOR raises mobile manipulation success from 53.3% to 71.7% in unseen homes by binding plans to observable geometry, ensuring operable navigation endpoints, and using layered local recovery instead of global replans.
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Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents
A learned embedding-based router selecting among six reasoning paradigms improves LLM agent accuracy from 47.6% to 53.1% on average, beating the best fixed paradigm by 2.8pp.