Inherited Goal Drift: Contextual Pressure Can Undermine Agentic Goals
Achyutha Menon, Magnus Saebo, Tyler Crosse, Spencer Gibson, Eyon Jang, Diogo Cruz

TL;DR
This paper investigates goal drift in state-of-the-art language model agents, revealing that even robust models can inherit drift under certain conditions, highlighting the need for improved mitigation techniques.
Contribution
It provides an updated analysis of goal drift in recent models across different environments, identifying factors influencing drift and model resilience.
Findings
Most models exhibit inherited drift when conditioned on weaker agents' trajectories.
GPT-5.1 shows consistent resilience against drift across tested settings.
Drift behavior varies with prompt variations and does not correlate well with instruction hierarchy following.
Abstract
The accelerating adoption of language models (LMs) as agents for deployment in long-context tasks motivates a thorough understanding of goal drift: agents' tendency to deviate from an original objective. While prior-generation language model agents have been shown to be susceptible to drift, the extent to which drift affects more recent models remains unclear. In this work, we provide an updated characterization of the extent and causes of goal drift. We investigate drift in state-of-the-art models within a simulated stock-trading environment (Arike et al., 2025). These models are largely shown to be robust even when subjected to adversarial pressure. We show, however, that this robustness is brittle: across multiple settings, the same models often inherit drift when conditioned on prefilled trajectories from weaker agents. The extent of conditioning-induced drift varies significantly…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Domain Adaptation and Few-Shot Learning · Multimodal Machine Learning Applications
