What distinguishes human problem-solving from current LLMs regarding new information?

The core distinction lies in adaptability versus reliance on prior training. Humans possess the remarkable ability to adjust their strategy and integrate new situational details in real-time while performing a task. If new rules or facts are presented during a discussion or assignment, a person can immediately incorporate them.

LLM Reliance on Static Knowledge

Conversely, most Large Language Models primarily operate based on 'parameterized knowledge.' This is the static memory frozen within the model’s weights after the extensive pre-training phase. When presented with a prompt, the model excels at retrieving and reasoning over this pre-existing internal knowledge base.

The Need for Dynamic Learning

The problem arises when users require the model to manage dynamic, messy, or novel contexts. Current SOTA models struggle because they are optimized to use what they already 'know' rather than actively learning from the current input session. Researchers at Tencent Hunyuan developed Contextual Learning benchmarks like CL-bench precisely to challenge this limitation, pushing the models to mimic the real-time learning capability inherent in human performance.