ReflexGrad introduces a new architecture for robust zero-shot generalization in LLM agents.
A new research paper published on arXiv on November 18, 2025, introduces ReflexGrad, a three-way synergistic architecture designed to enable robust zero-shot generalization in large language model (LLM) agents. The system integrates hierarchical TODO decomposition, TextGrad for textual gradient optimization, and LLM-Merge for semantic coherence, allowing agents to understand and execute tasks without requiring demonstration examples. This approach significantly advances the field of agentic AI by reducing reliance on few-shot learning and hardcoded rules.
ReflexGrad’s innovations include pure LLM reasoning for task decomposition, state tracking for pending and completed subgoals, and the integration of gradient directions into prompts to guide agent behavior. The architecture demonstrates strong performance in zero-shot settings, approaching the effectiveness of few-shot baselines. This work opens new possibilities for deploying LLM agents in diverse and dynamic environments, where adaptability and generalization are crucial.
Source: arXiv