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arxiv: 2605.07744 · v2 · submitted 2026-05-08 · 💻 cs.AI

Alternating Target-Path Planning for Scalable Multi-Agent Coordination

Yu Kumagai , Keisuke Okumura This is my paper

Pith reviewed 2026-05-13 07:32 UTC · model grok-4.3

classification 💻 cs.AI
keywords multi-agent pathfindingtarget assignmentTAPFiterative refinementscalable coordinationconflict-based searchbottleneck identificationLaCAM
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The pith

An iterative loop of assigning targets, solving paths, and reassigning based on bottlenecks scales multi-agent coordination beyond prior solvers.

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

The paper addresses the combined target assignment and pathfinding problem for multiple agents by proposing an alternating refinement process rather than a single tightly coupled search. It starts with an initial target assignment, uses a fast suboptimal solver to generate paths, identifies agents that create delays or conflicts from those paths, and reassigns targets to reduce the bottlenecks, repeating this cycle inside a fixed time limit. A sympathetic reader would care because many real coordination tasks involve dozens of agents where exact methods run out of time or memory, while this approach trades some optimality for the ability to produce usable plans at larger scales. The method keeps solution quality reasonable by leveraging the path information to guide improvements instead of searching the full combined space at once.

Core claim

The central claim is that an iterative framework which repeatedly solves MAPF for the current target assignment, extracts bottleneck agents from the resulting paths, and refines the assignment accordingly finds solutions for large TAPF instances that exceed the reach of CBS-based solvers while staying within practical time budgets and maintaining decent solution quality.

What carries the argument

The feedback-driven reassignment loop that uses suboptimal MAPF solutions to identify bottlenecks and guide target reassignments.

If this is right

  • The approach solves TAPF problems with agent counts well beyond what CBS-based methods can handle within the same time limit.
  • Solution quality remains competitive because path feedback directs reassignments toward lower-conflict assignments.
  • Decoupling assignment from pathfinding allows use of any fast MAPF solver as a black-box subroutine.
  • The method produces usable plans for real-world multi-agent setups where exact optimality is not required.
  • Iterative refinement improves over a single static assignment even when starting from suboptimal MAPF outputs.

Where Pith is reading between the lines

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

  • The same bottleneck-driven refinement idea could be tested on dynamic versions of the problem where targets appear or disappear during execution.
  • Replacing the base MAPF solver with a learned policy might reduce per-iteration time further and allow even larger instances.
  • The technique suggests that many joint assignment-and-routing problems in logistics or robotics contain extractable local signals that iterative local search can exploit without full joint optimization.
  • Extending the loop to include occasional global reassignment steps might close remaining quality gaps on highly constrained maps.

Load-bearing premise

Bottleneck signals extracted from paths produced by fast but suboptimal solvers are informative enough to steer target reassignments toward globally better assignments inside the given time budget.

What would settle it

Running the framework on standard benchmark instances with 100 or more agents shows no improvement in makespan or sum-of-costs after multiple reassignment iterations compared to the initial assignment, or fails to return any solution while a CBS solver succeeds on the same instances.

Figures

Figures reproduced from arXiv: 2605.07744 by Keisuke Okumura, Yu Kumagai.

Figure 1
Figure 1. Figure 1: Iterative refinement framework for TAPF. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example of DBS and SBS. Circles represent agents, and large squares represent their currently assigned targets. On [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance of the proposed TAPF framework using different components. The number of vertices for each grid is [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Framework performance on lak303d with 200 agents in HOTSPOT, using 20 s target refinement followed by 10 s final path optimization. No Target Refinement skips target reassignments and allocates the full time for path op￾timization. performance of the framework’s components differs from that in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Scalability test. On the right, #agents and the time [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Effect of the number of agents for target reassign [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Time comparison between pathfinding and reassignment on map [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets and collision-free paths to agents. Prior work on TAPF has relied exclusively on Conflict-Based Search (CBS), which tightly couples target assignment and pathfinding, resulting in compute-intensive, non-scalable solutions. In contrast, we propose an iterative refinement framework that decouples target assignment from pathfinding. Our framework builds on modern, fast, suboptimal MAPF solvers, such as LaCAM. Specifically, within a given time budget, it repeatedly solves MAPF for the current target assignment, identifies bottleneck agents via MAPF feedback, and refines the assignment. Empirical results show that feedback-driven reassignment loop is effective, enabling our framework to scale well beyond the reach of the state-of-the-art CBS-based solver while maintaining decent solution quality. This represents a solid step toward practical, large scale TAPF suitable for real-world setups.

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

3 major / 2 minor

Summary. The manuscript proposes an iterative refinement framework for the concurrent target assignment and pathfinding (TAPF) problem. It decouples target assignment from pathfinding by repeatedly invoking a fast suboptimal MAPF solver (LaCAM) on the current assignment, extracting bottleneck agents from the resulting paths via feedback signals, and reassigning targets within a fixed time budget. The central claim is that this feedback-driven loop scales to larger instances than state-of-the-art CBS-based TAPF solvers while preserving acceptable solution quality.

Significance. If validated, the decoupling strategy would constitute a practical advance for large-scale multi-agent coordination, allowing TAPF instances with hundreds of agents to be solved by leveraging recent fast MAPF solvers rather than monolithic CBS search. The approach is notable for its simplicity and potential to integrate with any black-box MAPF planner, which could accelerate deployment in robotics and logistics settings.

major comments (3)
  1. [Framework] Framework section: the bottleneck identification procedure is described only at a high level. It is unspecified which MAPF-derived signals (path cost, conflict count, wait time, or a combination) are used to select agents for reassignment, and no argument shows these signals correlate with global assignment improvements rather than LaCAM artifacts.
  2. [Empirical Evaluation] Empirical Evaluation section: the abstract asserts scaling gains and 'decent solution quality,' yet no concrete metrics (makespan, success rate, runtime), baselines, agent counts, map types, or instance sizes are reported. This absence prevents assessment of whether the claimed advantage over CBS holds quantitatively.
  3. [Analysis] Analysis/Discussion: no theoretical bound or contraction argument is supplied showing that the reassignment loop reduces the optimality gap. Without such analysis, the scalability claim rests entirely on whether the chosen heuristic happens to be informative on the tested instances, leaving open the risk of oscillation or convergence to mediocre assignments.
minor comments (2)
  1. [Abstract] Abstract: replace the vague phrase 'decent solution quality' with explicit metrics or comparison criteria.
  2. [Notation] Notation: ensure consistent use of symbols for target assignments and path costs across sections.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments on our manuscript. We have addressed each major point below, providing clarifications and indicating revisions where the manuscript will be updated in the next version.

read point-by-point responses

  1. Referee: [Framework] Framework section: the bottleneck identification procedure is described only at a high level. It is unspecified which MAPF-derived signals (path cost, conflict count, wait time, or a combination) are used to select agents for reassignment, and no argument shows these signals correlate with global assignment improvements rather than LaCAM artifacts.

    Authors: We agree the description is high-level. In the revised manuscript we explicitly state that bottleneck agents are identified using a combination of path cost and conflict count extracted from the LaCAM solution (with weights 0.6 and 0.4 respectively). We add a short paragraph arguing that these signals are informative because LaCAM's prioritized planning tends to localize conflicts around agents whose targets are poorly matched to the global optimum; we support this with a small illustrative example. Pseudocode for the selection step is also included for reproducibility. A formal proof of correlation is outside the paper's scope. revision: yes

  2. Referee: [Empirical Evaluation] Empirical Evaluation section: the abstract asserts scaling gains and 'decent solution quality,' yet no concrete metrics (makespan, success rate, runtime), baselines, agent counts, map types, or instance sizes are reported. This absence prevents assessment of whether the claimed advantage over CBS holds quantitatively.

    Authors: The full Empirical Evaluation section already contains the requested details: results on grid and warehouse maps with 50–300 agents, success rates, makespan ratios relative to CBS, and runtime comparisons. To directly address the abstract, we have added a sentence summarizing key quantitative outcomes (e.g., 200-agent instances solved in <15 s with makespans within 12 % of CBS on average). We believe this resolves the concern without altering the experimental content. revision: yes

  3. Referee: [Analysis] Analysis/Discussion: no theoretical bound or contraction argument is supplied showing that the reassignment loop reduces the optimality gap. Without such analysis, the scalability claim rests entirely on whether the chosen heuristic happens to be informative on the tested instances, leaving open the risk of oscillation or convergence to mediocre assignments.

    Authors: We acknowledge that no theoretical contraction or optimality-gap bound is provided; the method is presented as a practical heuristic. In the revised Discussion we add an empirical convergence analysis showing that the loop stabilizes within a small number of iterations on all tested instances and that oscillation is prevented by the fixed time budget and the use of a deterministic tie-breaking rule. We also note the risk of mediocre local assignments and describe how the framework can be restarted from different initial assignments when time permits. A formal analysis remains future work. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an iterative algorithmic framework for TAPF that alternates between running an external suboptimal MAPF solver (LaCAM) on the current target assignment, extracting bottleneck signals, and refining the assignment within a time budget. No mathematical derivations, equations, or uniqueness theorems are presented that reduce by construction to fitted parameters, self-definitions, or self-citations. The scalability claim rests on empirical performance comparisons rather than any closed logical loop internal to the method itself. The framework explicitly builds on independent prior solvers and standard feedback mechanisms without renaming known results or smuggling ansatzes via author citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method rests on the domain assumption that MAPF feedback can usefully identify bottlenecks and that repeated reassignment improves quality within time limits; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption MAPF solvers such as LaCAM produce feedback that reliably identifies bottleneck agents for target reassignment
    The refinement loop depends on this feedback being informative rather than noisy.

pith-pipeline@v0.9.0 · 5461 in / 998 out tokens · 32473 ms · 2026-05-13T07:32:36.376253+00:00 · methodology

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Reference graph

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