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Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers

Paper recorded by Signals 4 on 2026-09-23 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.AI · 人工智能 · first seen 2026-09-24

Abstract

Large Language Models (LLMs) introduce an exciting new paradigm for planning and navigation in robotics, but fail on even simple multi-robot tasks as team sizes grow. We propose COMPASS, a scalable, decentralized multi-robot architecture for controlling large collectives of agentic robots with reasoning space feedback control. Feedback is generated locally on each robot by a spatial transformer wh

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#16 most recent of 380 cs.AI papers we have recorded · ↑ newer: MemBodied: Recurrent Associative Memory for Vision-Language-Action Mod · ↓ older: Beyond Poetry: Can Large Language Models Generate Classical Arabic Maq

Cite this page: Controlling Collectives of AI Agents in Reasoning Space with Spatial Transformers: the #16 most recent of 380 cs.AI papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/controlling-collectives-of-ai-agents-in-reasoning-space-with-spatial-transformer.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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