arXiv:2602.02028v2 Announce Type: replace Abstract: Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a central challenge. Existing knowledge editing approaches emphasize atomic facts, improving factual recall but often failing to integrate updated information into a coherent framework usable across contexts. In this work, we argue that knowledge update is fundamentally a reasoning problem rather than a memorization problem. Consequently, a model should be trained in situations where the new informat
The paper addresses the contemporary challenge of making AI systems, particularly large language models, more dynamic and flexible in their knowledge application amidst rapid advancements in AI capabilities.
This work proposes a fundamental shift in how AI models update knowledge, moving from 'memorization' to 'reasoning,' which is crucial for building more adaptive and genuinely intelligent systems.
Current AI knowledge editing often fails in context integration; this research aims to enable multi-step reasoning over background stories, making knowledge updates more coherent and applicable.
- · AI researchers and developers
- · Cloud AI providers
- · Industries relying on dynamic AI models
- · Developers of brittle, fact-based AI systems
- · Approaches focused solely on atomic fact updates
AI models will become more adaptable and capable of integrating new information seamlessly over time.
This could lead to more robust and less 'hallucinating' AI, improving reliability across various applications.
The enhanced reasoning capabilities might accelerate the development of more autonomous and agentic AI systems.
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Read at arXiv cs.AI