Papers by Sojeong Rhee
ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in LLMs have significantly enhanced their reasoning capabilities, enabling LLM-based agents to perform complex multi-step decision making beyond static problem solving. |
| Approach: | They propose a novel reasoning backbone that shifts reasoning from merely planning next actions to continuously reflecting on the agent’s state relative to its goal. |
| Outcome: | The proposed model outperforms ReAct by 27.7% on average, achieving a 93.3% success rate in ALFWorld. |