Multi-Step Reasoning
A category of tasks in which an AI system must execute multiple consecutive reasoning steps to complete the work. Traditional single-turn LLM calls cannot reliably handle such tasks, making it one of the core capabilities of autonomous agents.
Core Facts
Timeline (last 90 days)
Multi-step reasoning refers to whether a model can reason step by step along call chains and data flows, stringing scattered clues into a complete conclusion, for example tracing from an entry parameter to a database query to determine whether an unfiltered injection path exists.
In tasks such as security review, long context and multi-step reasoning are both indispensable: insufficient context loses key dependency information, while a broken reasoning chain leads to one-sided or even erroneous investigation conclusions.
All Facts (3)
Simply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.
75%UnverifiedMulti-step reasoning refers to whether a model can reason step by step along call chains and data flows, stringing scattered clues into a complete conclusion, for example tracing from an entry parameter to a database query to determine whether an unfiltered injection path exists.
50%UnverifiedIn tasks such as security review, long context and multi-step reasoning are both indispensable: insufficient context loses key dependency information, while a broken reasoning chain leads to one-sided or even erroneous investigation conclusions.
50%