Pro-activeness. Pro-activeness denotes that agents don’t merely react to their environments; they
possess the capacity to display goal-oriented actions by proactively taking the initiative [9]. This
property emphasizes that agents can reason, make plans, and take proactive measures in their actions
to achieve specific goals or adapt to environmental changes. Although intuitively the paradigm
of next token prediction in LLMs may not possess intention or desire, research has shown that
they can implicitly generate representations of these states and guide the model’s inference process
[46; 48; 49]. LLMs have demonstrated a strong capacity for generalized reasoning and planning. By
prompting large language models with instructions like “let’s think step by step”, we can elicit their
reasoning abilities, such as logical and mathematical reasoning [95; 96; 97]. Similarly, large language
models have shown the emergent ability of planning in forms of goal reformulation [99; 124], task
TheRiseandPotentialofLargeLanguageModel BasedAgents