Long-Horizon Planning

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9 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 46

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ChronoForest: Closed-Loop Multi-Tree Diffusion Planning for Efficient Bridge Search and Route Composition
Jun 4, 2026Jungmin Seo, Jaesik ParkInference-Time Search Diffusion-Based Planning
Seoul National University Seoul, South Korea
When to Re-Plan: Subgoal Persistence in Hierarchical Latent Reasoning
Jun 2, 2026Ayushi ChadhaLong-Horizon Planning
Independent Researcher, India.
Coarse-to-Fine Compositional Diffusion for Long-Horizon Planning
May 30, 2026Byoungwoo Park, Utkarsh A. Mishra, Jaemoo Choi +2Diffusion-Based Planning Long-Horizon Video Generation
KAIST
Latent Goal Prediction from Language for Model-Based Planning
May 26, 2026Samuel Barbeau, Simon Roy, Giovanni Beltrame +2World Model-Based Planning Long-Horizon Planning
1École de Technologie Supérieure, Montréal · 2International Laboratory on Learning Systems (ILLS) · 3Polytechnique Montréal +4
Push Your Agent: Measuring and Enforcing Quantitative Goal Persistence in Long-Horizon LLM Agents
May 22, 2026Yuandao Cai, Yuzhang Zhu, Liyou Gao +2Long-Horizon Planning Long-Horizon Agent Evaluation
Independent Researcher · Xidian University
Long-term Power Grid Planning via Answer Set Programming
May 19, 2026Antonio Ielo, Francesco Doria, Sandra Castellanos-Paez +3Long-Horizon Planning Power Systems
University of Calabria, Italy · University of Huddersfield, UK
Visual Sculpting: Visually-Aligned Planning Representations for Long-Horizon Robot Clay Sculpting
May 17, 2026Peter Schaldenbrand, Jean OhLong-Horizon Robotic Manipulation Deformable Object Manipulation
1The Robotics Institute, Carnegie Mellon University · 2Lavoro AI Research
Plan First, Diffuse Later: Extrinsic Graph Guidance for Long-Horizon Diffusion Planning
May 16, 2026Yaniv Hassidof, Adir Morgan, Yilun Du +1Trajectory Generation Diffusion-Based Planning
Technion · Harvard
When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution
May 14, 2026Zilin Zhu, Longteng Guo, Yanghong Mei +5Long-Horizon Planning Long-Horizon Agent Evaluation
Institute of Automation, Chinese Academy of Sciences, Beijing, China · Zhongguancun Academy, Beijing, China · University of Chinese Academy of Sciences, Beijing, China
PACE: Policy-Native Adaptive Decision Timing for Long-Horizon Reasoning
May 11, 2026Chen Li, Zhantao Yang, Fangyi Chen +3Large Vision-Language Models Long-Horizon Planning
Carnegie Mellon University
Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
May 8, 2026Naoki Otani, Nikita Bhutani, Hannah Kim +2Long-Horizon Planning Tool-Using Agents
Megagon Labs Mountain View, California, USA
Refining Compositional Diffusion for Reliable Long-Horizon Planning
May 4, 2026Kyowoon Lee, Yunhao Luo, Anh Tong +1Trajectory Generation Diffusion-Based Planning
KAIST · University of Michigan · Korea University +1
On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length
May 4, 2026Sunghwan Kim, Junhee Cho, Beong-woo Kwak +6Long-Horizon Planning Length Generalization
Department of Artificial Intelligence, Yonsei University · †Work done during internship at Microsoft Research. · 2Microsoft Research.
Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation
May 3, 2026Zhilong Zhang, Wenyu Luo, Haonan Wang +9Long-Horizon Robotic Manipulation Long-Horizon Planning
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · School of Artificial Intelligence, Nanjing University, Nanjing, China · Department of Foundation model, 2012 Labs, Huawei
Long-Horizon Manipulation via Trace-Conditioned VLA Planning
Apr 23, 2026Isabella Liu, An-Chieh Cheng, Rui Yan +7Long-Horizon Robotic Manipulation Language-Conditioned Robot Manipulation
University of California, San Diego · NVIDIA
VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models
Dec 27, 2025Borong Zhang, Jiahao Li, Jiachen Shen +7Long-Horizon Planning Robotics
Institute for Artificial Intelligence, Peking University. · Zhongguancun Academy. · PKU-PsiBot Joint Lab. +2

Jun 4, 2026, cs.ROJ/K move · Enter open · S save

ChronoForest: Closed-Loop Multi-Tree Diffusion Planning for Efficient Bridge Search and Route Composition

Jungmin Seo, Jaesik Park

Seoul National University Seoul, South Korea

Inference-Time Search Diffusion-Based Planning Robot Navigation Long-Horizon Planning Motion Planning

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How can we plan long-horizon routes that reach designated goals, visit required waypoints, and remain short when only short-horizon offline trajectories are available? This problem matters in offline navigation because collecting sufficiently rich long-horizon data is difficult, yet real agents must still solve long-range tasks with route-level efficiency rather than mere feasibility. The difficulty is twofold: at the microscopic level, composing many short-horizon segments creates a trade-off between search cost and path quality, while at the macroscopic level, waypoint ordering requires comparing pairwise travel costs among start, goal, and waypoint anchors that are unknown before planning and increasingly unreliable when estimated only from long-range temporal distance. In this paper, we propose ChronoForest, a closed-loop planning system that couples local bridge search and online route re-solving through an anchor-chaining tree diffusion planner and an online multi-tree orchestrator. ChronoForest uses temporal distance for short-range guidance and node evaluation, while using search-time bridge evidence to validate long-range anchor connectivity and repeatedly re-solve the route. On OGBench AntMaze-Stitch, ChronoForest achieves 99.8%, 99.3%, and 99.5% success on the medium, large, and giant splits and improves giant-stitch success by up to 34.5 points over prior reported diffusion-based results. On Hamiltonian route-composition benchmarks, online re-solving corrects poor temporal orderings and improves route quality while remaining substantially cheaper than exhaustive planning.

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