Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
arXiv:2601.18699v2 Announce Type: replace-cross Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities. This paper presents a systematic comparative study of catastrophic forgetting across twenty premier models representing the state-of-the-art in mid-2026. We categorize our investigation into two primary research lines: (i) a behavioral and semantic output drift analysis of ten leading closed-source models (including Claude Fable 5, GPT-5.5
The rapid advancement and deployment of large language models are exposing critical operational challenges like catastrophic forgetting, pushing researchers to find solutions to ensure model stability and continuous learning.
Catastrophic forgetting represents a fundamental hurdle for developing robust and continuously adaptable AI, directly impacting the long-term utility and reliability of LLMs in real-world applications.
Understanding the mechanisms behind catastrophic forgetting allows for the development of architectural or training modifications to mitigate it, enhancing the practical deployment and update strategies for state-of-the-art LLMs.
- · AI researchers focusing on continual learning
- · Developers of new LLM architectures
- · Enterprises deploying AI agents
- · Companies relying on static LLM deployments
- · LLMs with poor continual learning capabilities
Improved methods for continual fine-tuning of LLMs reduce the need for expensive and disruptive full retrains.
More stable and adaptable LLMs accelerate the development and adoption of AI agents across various industries.
Enhanced model resilience could lead to increased trust and broader integration of AI into critical infrastructure and decision-making systems.
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