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arxiv: 2605.16855 · v1 · pith:7XWV4CMHnew · submitted 2026-05-16 · 💻 cs.MA

Lifelong LaCAM with Local Guidance for Lifelong MAPF

Tomoki Arita , Keisuke Okumura This is my paper

Pith reviewed 2026-05-19 19:23 UTC · model grok-4.3

classification 💻 cs.MA
keywords lifelong MAPFmulti-agent pathfindinglocal guidanceLaCAMreceding horizonreal-time planningthroughput
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The pith

A receding-horizon LaCAM with local guidance lifted from one-shot MAPF achieves higher task throughput in lifelong multi-agent pathfinding.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether local guidance, which improves one-shot MAPF by injecting spatiotemporal cues around agents, can transfer to the lifelong setting where tasks arrive continuously. It does so by wrapping LaCAM inside a receding-horizon window and warm-starting the guidance from the prior timestep's solution. If the transfer works, planners could sustain high completion rates over long horizons without sacrificing real-time scalability, especially in crowded spaces where congestion normally accumulates.

Core claim

The authors claim that LLLG, a lifelong adaptation of LaCAM that re-uses local guidance cues inside successive short-horizon solves and carries forward the previous solution as a warm start, scales to dense environments, preserves high throughput, and exceeds existing real-time lifelong MAPF planners.

What carries the argument

The central mechanism is the receding-horizon windowed planning loop that applies one-shot local guidance inside each short window while warm-starting from the prior solution to keep the configuration search informed across timesteps.

If this is right

  • Task completion throughput rises over extended horizons because per-step plans stay informed by local cues.
  • The method remains fast enough for real-time use even when many agents operate in tight spaces.
  • Existing one-shot MAPF improvements become candidates for direct reuse in lifelong settings via the same window-and-warm-start pattern.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Warehouses or robot fleets with nonstop task streams could run denser operations without added waiting time.
  • Other one-shot MAPF speed-ups might be ported to lifelong use with minimal extra engineering.
  • If warm-starting prevents congestion buildup, similar carry-over techniques could stabilize other receding-horizon multi-agent planners.

Load-bearing premise

Guidance cues that reduce congestion in single-task MAPF can be reused across successive short windows in the lifelong case without creating new long-term bottlenecks or throughput loss.

What would settle it

A long simulation in a compact, dense map showing that LLLG completes fewer tasks per unit time than plain lifelong LaCAM without guidance or than the best competing real-time lifelong planner.

Figures

Figures reproduced from arXiv: 2605.16855 by Keisuke Okumura, Tomoki Arita.

Figure 1
Figure 1. Figure 1: Performance of lifelong MAPF methods in rep [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Concept of LLLG. The gray-shaded regions cor [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Heatmap of vertex stop counts, where congestion is implied by warm colors. The bottom of each heatmap shows the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Performance evaluation of lifelong MAPF methods, assessed by throughput and runtime. For each map, the two [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Scalability test on warehouse-20-40-10-2-2. LLLG uses a lightweight setting (wΦ=8, wΠ=5). an ultra-large scale. Across the tested loads, LLLG consis￾tently produces actions within 1 s per step even for 10,000 agents, and achieves approximately a 30% throughput gain over PIBT. These results indicate that LLLG scales effec￾tively to dense lifelong operation. C Design Justification To justify the design of ou… view at source ↗
Figure 8
Figure 8. Figure 8: Effect of local-guidance refinement under differ [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Effect of anytime improvement on the algorithms. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Effect of LNS improvement for different lifelong [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Sensitivity of throughput and runtime to the hin [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
read the original abstract

Local guidance has recently proven to be a powerful driver of empirical performance in real-time, suboptimal multi-agent pathfinding (MAPF), improving the scalable configuration-based solver LaCAM. By injecting informative spatiotemporal cues around each agent, local guidance mitigates congestion, reduces waiting, and remains scalable enough even with tight time budgets, yielding state-of-the-art performance for one-shot MAPF. This study asks whether the same benefits can be lifted to the lifelong setting (LMAPF), where tasks arrive continuously and improvements in per-step plans can increase task completion throughput over long horizons. We propose LLLG, a Lifelong version of LaCAM enhanced with Local Guidance, which employs a receding-horizon windowed planning framework and warm-starts guidance from the previous solution at each timestep. Our method scales effectively, maintains high throughput even in compact, dense environments, and surpasses existing planners, thereby pushing the frontier of real-time, lifelong MAPF.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes LLLG, a lifelong extension of the LaCAM solver augmented with local guidance for lifelong multi-agent pathfinding (LMAPF). It adopts a receding-horizon windowed planning approach with warm-starting of guidance cues from the prior timestep, claiming that this transfers the congestion-mitigation benefits observed in one-shot MAPF to the lifelong setting, yielding scalable high-throughput performance in dense environments that surpasses prior planners.

Significance. If the empirical throughput gains hold under rigorous testing, the work would meaningfully advance real-time LMAPF by showing that local spatiotemporal guidance can be lifted across horizons without introducing persistent bottlenecks. The receding-horizon plus warm-start design is a natural and potentially parameter-light way to reuse one-shot insights, which would be a clear strength if supported by ablations and long-horizon statistics.

major comments (2)
  1. §3 (Method), receding-horizon description: the text does not clarify whether local guidance is fully re-optimized against the current task set inside each window or simply copied forward from the previous solution. This distinction is load-bearing for the central claim that one-shot benefits transfer without new throughput loss; if guidance is merely propagated, new task arrivals in dense maps could cause compounding misalignments as hypothesized in the stress-test note.
  2. §4 (Experiments), throughput tables: no error bars, ablation on warm-start versus full re-computation, or long-horizon congestion metrics are referenced. Without these, it is impossible to confirm that per-step improvements actually increase completion rates over extended runs rather than merely matching one-shot LaCAM performance.
minor comments (2)
  1. Notation for the window size and guidance injection points should be introduced earlier and used consistently across figures and pseudocode.
  2. The abstract and introduction repeat the claim of 'surpassing existing planners' without naming the specific baselines or citing their original papers in the first paragraph.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for clarification in the method and additional rigor in the experiments. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: §3 (Method), receding-horizon description: the text does not clarify whether local guidance is fully re-optimized against the current task set inside each window or simply copied forward from the previous solution. This distinction is load-bearing for the central claim that one-shot benefits transfer without new throughput loss; if guidance is merely propagated, new task arrivals in dense maps could cause compounding misalignments as hypothesized in the stress-test note.

    Authors: We thank the referee for identifying this ambiguity. In LLLG the local guidance is warm-started from the solution at the previous timestep but is then re-optimized inside the current receding-horizon window against the updated task set and agent configuration. The warm-start serves only as an initialization; the guidance cues are recomputed to reflect new task arrivals. This design prevents the compounding misalignments the referee correctly flags. We will revise §3 to state this process explicitly, adding a short algorithmic description of the warm-start-plus-reoptimization step. revision: yes

  2. Referee: §4 (Experiments), throughput tables: no error bars, ablation on warm-start versus full re-computation, or long-horizon congestion metrics are referenced. Without these, it is impossible to confirm that per-step improvements actually increase completion rates over extended runs rather than merely matching one-shot LaCAM performance.

    Authors: We agree that the experimental presentation would be strengthened by these additions. In the revised manuscript we will (i) report error bars on all throughput figures obtained from repeated trials with varied random seeds, (ii) include an ablation that directly compares the warm-start variant against a full re-computation baseline at every timestep, and (iii) add long-horizon statistics (cumulative task completions, average agent wait time, and congestion events) measured over the entire simulation horizon. These changes will demonstrate that the observed per-step gains translate into sustained throughput improvements. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical extension of LaCAM to LMAPF is self-contained

full rationale

The paper introduces LLLG as a receding-horizon, warm-starting extension of the prior LaCAM solver with local guidance for the lifelong MAPF setting. No equations, derivations, or parameter-fitting steps are described that reduce by construction to the inputs. Performance claims rest on direct empirical comparisons across maps and task streams rather than any self-referential prediction or uniqueness theorem. Citations to earlier LaCAM work supply the base algorithm but do not bear the load of the new lifelong adaptations, which are specified independently in the method section. The work therefore contains no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the unverified transfer of local guidance benefits from one-shot to lifelong settings and on the effectiveness of warm-starting in a receding-horizon loop; no free parameters, axioms, or invented entities are explicitly introduced in the abstract.

pith-pipeline@v0.9.0 · 5690 in / 1064 out tokens · 34444 ms · 2026-05-19T19:23:12.756414+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages

  1. [1]

    Algorithmica , year =

    On multiple moving objects , author =. Algorithmica , year =

    work page

  2. [2]

    Cooperative Pathfinding , author =

    work page

  3. [3]

    , author =

    Fast and Memory-Efficient Multi-Agent Pathfinding. , author =

    work page

  4. [4]

    SIPP: Safe interval path planning for dynamic environments , author =

    work page

  5. [5]

    , author =

    Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs. , author =

    work page

  6. [6]

    Intractability of time-optimal multirobot path planning on 2D grid graphs with holes , author =

    work page

  7. [7]

    Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks , author =

    work page

  8. [8]

    Multi-robot path deconfliction through prioritization by path prospects , author =

    work page

  9. [9]

    Anytime multi-agent path finding via large neighborhood search , author =

    work page

  10. [10]

    Lifelong multi-agent path finding in large-scale warehouses , author =

    work page

  11. [11]

    Iterative Refinement for Real-Time Multi-Robot Path Planning , year =

    Okumura, Keisuke and Tamura, Yasumasa and D\'. Iterative Refinement for Real-Time Multi-Robot Path Planning , year =

    work page

  12. [12]

    Optimizing space utilization for more effective multi-robot path planning , author =

    work page

  13. [13]

    Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding , journal = aij, year =

    work page

  14. [14]

    MAPF-LNS2: Fast repairing for multi-agent path finding via large neighborhood search , author =

    work page

  15. [15]

    LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding , author =

    work page

  16. [16]

    Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding , booktitle = ijcai, year =

    work page

  17. [17]

    Tracking progress in multi-agent path finding , author =

    work page

  18. [18]

    The League of Robot Runners Competition: Goals, Designs, and Implementation , author =

    work page

  19. [19]

    Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search , author =

    work page

  20. [20]

    Traffic flow optimisation for lifelong multi-agent path finding , author =

    work page

  21. [21]

    Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities , author =

    work page

  22. [22]

    Engineering LaCAM ^ : Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding , booktitle = aamas, year =

    work page

  23. [23]

    Guidance Graph Optimization for Lifelong Multi-Agent Path Finding , author =

    work page

  24. [24]

    arXiv preprint arXiv:2504.07841 , year =

    Anytime Single-Step MAPF Planning with Anytime PIBT , author =. arXiv preprint arXiv:2504.07841 , year =

    work page arXiv

  25. [25]

    Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding , author =

    work page

  26. [26]

    Lightweight and Effective Preference Construction in PIBT for Large-Scale Multi-Agent Pathfinding , author =

    work page

  27. [27]

    arXiv preprint arXiv:2503.00717 , year =

    Llmdr: Llm-driven deadlock detection and resolution in multi-agent pathfinding , author =. arXiv preprint arXiv:2503.00717 , year =

    work page arXiv

  28. [28]

    Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities , author =

    work page

  29. [29]

    League of Robot Runners Expo , year =

    Enhancing PIBT via multi-action operations , author =. League of Robot Runners Expo , year =

    work page

  30. [30]

    2025 , url=

    Real-Time LaCAM for Real-Time MAPF , author=. 2025 , url=

    work page 2025

  31. [31]

    Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding , author=

    work page

  32. [32]

    Guidance Graph Optimization for Lifelong Multi-Agent Path Finding , author=

    work page

  33. [33]

    Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding , author=

    work page

  34. [34]

    Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities , author=

    work page

  35. [35]

    Local Guidance for Configuration-Based Multi-Agent Pathfinding , author =

    work page

  36. [36]

    2002 , url=

    D*lite , author=. 2002 , url=

    work page 2002

  37. [37]

    Congestion Mitigation Path Planning for Large-Scale Multi-Agent Navigation in Dense Environments , author=

    work page

  38. [38]

    Proceedings of the International Workshop on Multi-Agent Path Finding (WoMAPF) , year =

    Minimizing Makespan with LaCAM* for Optimal Multi-Agent Path Finding , author =. Proceedings of the International Workshop on Multi-Agent Path Finding (WoMAPF) , year =

    work page

  39. [39]

    2018 , url=

    Searching with Consistent Prioritization for Multi-Agent Path Finding , author=. 2018 , url=

    work page 2018

  40. [40]

    Conflict-based search for optimal multi-agent pathfinding , journal = aij, year =

    work page

  41. [41]

    2014 , volume=

    Bnaya, Zahy and Felner, Ariel , booktitle= icra, title=. 2014 , volume=

    work page 2014

  42. [42]

    Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large-Scale Imitation Learning for MAPF , author=

    work page

  43. [43]

    2017 , url=

    Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks , author=. 2017 , url=

    work page 2017

  44. [44]

    Windowed MAPF with Completeness Guarantees , author=

    work page

  45. [45]

    The Focussed D* Algorithm for Real-Time Replanning , author=

    work page

  46. [46]

    Graph Attention-Guided Search for Dense Multi-Agent Pathfinding , author=

    work page

  47. [47]

    ArXiv , year=

    LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control , author=. ArXiv , year=

    work page