Late Autumn: LLM Agents Specialize a General Multi-Robot Planner for One Warehouse
Overview
This research paper introduces 'Late Autumn,' a method where Large Language Model (LLM) coding agents autonomously specialize a general multi-robot planner for a specific warehouse environment. By trading generality for throughput, the agents achieve significant performance improvements (22-60% increase in throughput) on unseen seeds and rediscover known Multi-Agent Path Finding (MAPF) mechanisms like corridor-exit priorities. The study evaluates six campaigns on a lifelong planner modeled after an existing automated warehouse, finding that while gains carry over to different layouts and fleet sizes, they diminish with smaller fleets or outbound-heavy demand. The generated code patches are reviewable, allowing experts to verify changes, though some modifications can increase compute requirements by over 100x.
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Event evidence and corrections
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Reported quantity · compute multiplier: 100 other · Basis not reported
“nds problems that the score misses; some patches multiply planner compute by over 100x. LLM agents can therefore turn each site's bias into extra throughput at little engineering cost, with reviewable patches; we expect experts to shift toward designing insightful metrics and fast simulators that mirror the real problem.”
Exact source · revision 1Source owner not reported
Reported quantity · median step size: 27 other · Basis not reported
“-exit priorities, local guidance, and rollouts, and independent campaigns find the same bottleneck; (3) the gains largely carry over to another layout, a larger fleet, and other service times, shrink with a smaller fleet, and mostly vanish under an outbound-heavy demand mix; and (4) each final patch (0.7k--2.4k lines) is a chain of scored commits with a median step of 27 lines, so experts can review it, and review fi”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalLate Autumn: LLM Agents Specialize a General Multi-Robot Planner for One Warehouse
This research paper introduces 'Late Autumn,' a method where Large Language Model (LLM) coding agents autonomously specialize a general multi-robot planner for a specific warehouse environment. By trading generality for throughput, the agents achieve significant performance improvements (22-60% increase in throughput) on unseen seeds and rediscover known Multi-Agent Path Finding (MAPF) mechanisms like corridor-exit priorities. The study evaluates six campaigns on a lifelong planner modeled after an existing automated warehouse, finding that while gains carry over to different layouts and fleet sizes, they diminish with smaller fleets or outbound-heavy demand. The generated code patches are reviewable, allowing experts to verify changes, though some modifications can increase compute requirements by over 100x.
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