Korean researchers have developed a hierarchical AI technology that autonomously plans even complex long-horizon tasks. The development of this hierarchical task-planning AI technology, which reduces hallucinations and doubles the success rate, is expected to help robots and agents carry out long-term missions.
Electronics and Telecommunications Research Institute (ETRI) developed the hierarchical task-planning artificial intelligence (AI) technology “ReAcTree”, which autonomously divides tasks requiring complex and lengthy procedures into subgoals and carries them out, and presented it at AAMAS 2026, one of the world’s premier conferences in the AI agent field.
This research achievement is regarded as an important technological advance that enables Large Language Models (LLM) to move beyond simply generating text, allowing robots and virtual agents to perform complex real-life tasks more reliably.
Recently, although large language models have shown excellent language understanding and reasoning capabilities, they still have limitations in performing long-horizon tasks in which multiple steps proceed sequentially, such as cooking or cleaning. Existing approaches processed all procedures as one long flow, so as the steps became longer, the phenomenon of “hallucination”—forgetting earlier instructions or taking irrelevant actions—occurred frequently.
To solve this problem, ETRI researchers developed ReAcTree by introducing “Hierarchical Agent Trees”. This structure is similar to a corporate organizational chart. In this approach, a top-level agent manages the overall goal and assigns detailed tasks to lower-level agents.
For example, when given the command, “Cook potato slices and put them in the refrigerator,” ReAcTree does not process it all at once. Instead, it breaks the goal down into tasks such as “find a kitchen knife,” “find and cut the potatoes,” “heat the cut potatoes in the microwave,” and “store them in the refrigerator,” and then has each lower-level agent perform its assigned role. Whereas conventional AI frequently makes logical errors, such as skipping the step of heating the potatoes midway, ReAcTree completes it successfully. In addition, when searching for an object, it autonomously generates subgoals to search each room sequentially, enabling it to find the target with high probability.
To enhance the agent’s execution capabilities, the researchers combined two memory systems. One is “episodic memory”, which stores past successful experiences and uses them in similar situations, and the other is “working memory”, through which all agents share current environmental information.
For example, the information “There is juice in the refrigerator” is immediately shared among all agents through working memory, while previously successful search methods are reused through episodic memory. Through this, the agent’s judgment and execution accuracy were greatly improved.
The technology’s performance was validated on ALFRED and WAH-NL, virtual household-environment datasets, based on “LoTA-Bench”, ETRI’s self-developed language-centered procedural-generation AI benchmark. As a result of evaluating it in a visibility-limited environment that reflects realistic conditions, it achieved a world-class task success rate. In particular, while the conventional method (ReAct) using a 72 billion (72B) parameter language model recorded a 31% task success rate, ReAcTree achieved a 61% success rate, showing nearly a twofold performance improvement.
In particular, when ReAcTree was applied to a small language model with 7 billion (7B) parameters, it recorded a higher success rate (37%) than the conventional method using a large 72 billion (72B)-parameter model. This result shows that performance comparable to large models can be achieved with relatively fewer computing resources, and is regarded as an achievement that greatly improves technological operating efficiency.
Kim Do Hyung, Director of ETRI’s Social Robotics Research Section, said, “ReAcTree is a technology that logically deconstructs complex procedures and can respond flexibly even in uncertain environments through collaboration among agents,” adding, “Going forward, we plan to further reduce hallucinations and upgrade it to a level applicable in real life by adding a function that allows agents to resolve uncertainty by asking people questions.”