REASONING DEPTH AS A SAFETY HAZARD: LIMITS OF CHAIN-OF-THOUGHT SCALING

Track: Track 1: Technical Foundations for a Post-AGI World

Keywords: reasoning scale, chain-of-thought, error amplification, goal drift, AI safety, reliability, large language models, post-AGI, mitigation strategies

TL;DR: Increasing reasoning depth improves task performance but can amplify errors and reduce safety; this paper quantifies these risks, identifies failure modes, and proposes mitigation strategies for high-stakes AI systems.

Abstract:

Recent advances in large language models and agentic systems suggest that increasing reasoning depth via longer chains of thought, extended planning horizons, or recursive self-reflection can improve performance on complex tasks. This has encouraged a prevailing assumption that deeper reasoning is uniformly beneficial as systems approach human-level intelligence. In this paper, we question this assumption. We argue that beyond certain regimes, increased reasoning depth may introduce distinct safety and reliability hazards, including error amplification, goal drift, and brittleness under distribution shift. We conceptualize these risks as reasoning-scale failure modes, discuss why common evaluation practices may fail to surface them, and outline implications for scalable oversight and alignment.
Our goal is not to argue against reasoning scale, but to highlight its potentially non-monotonic relationship with safety in post-AGI systems.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Mahule_Roy1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 3