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Learning to Steer LLM Agents and Multi-Agent Systems

Paper recorded by Signals 4 on 2026-10-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.AI · 人工智能 · first seen 2026-10-16

Abstract

Large language model (LLM)-based agents and multi-agent systems increasingly tackle complex tasks through long-horizon tool use and inter-agent collaboration. However, these systems are prone to deviating from the overall goal as execution unfolds, which wastes execution resources and leads to undesired outcomes, a failure termed goal drift. In this work, we propose a steering agent that supervise

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#299 most recent of 960 cs.AI papers we have recorded · ↑ newer: A team of Gemini 4 Argon agents beats Minecraft · ↓ older: Knowledge That Is Linear Before Fine-Tuning Generalizes After It
Cite this page: Learning to Steer LLM Agents and Multi-Agent Systems: the #299 most recent of 960 cs.AI papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-to-steer-llm-agents-and-multi-agent-systems.html
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