If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs

Understand

Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property.

• However, during multi-turn, multi-agent interactions, LLMs begin to exhibit consistent, character-like behaviors, hinting at a form of emergent lifelong learning.
• Despite this, existing benchmarks often fail to capture these dynamics, primarily focusing on static, open-ended evaluations.
• To address this gap, we introduce LIFESTATE-BENCH, a benchmark designed to assess lifelong learning in LLMs.