Dual-Path Anti-Drift Reward Architecture for Reliable Long-Horizon LLM Agents

Abstract

This paper introduces a dual-path anti-drift architecture for long-horizon LLM agents, separating task execution from an independently rewarded critic that detects shortcut-seeking behavior. Formal analysis and a reproducible mechanistic simulation demonstrate its potential to reduce false-success outcomes, while experiments on trained LLMs remain future work.

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