RSIAgent improves AI performance with broad-then-deep exploration

GLM-5.3: GLM-5.3 is a strong open-source AI model enhanced by multi-agent self-improvement techniques for adapting to new environments. RSIAgent's curriculum and verification process helps it uncover reusable insights and boundary conditions. The framework positions GLM-5.3 competitively against leading proprietary AI systems in real-world application scenarios. Kimi-K3: Kimi-K3 is a strong open-source AI model that integrates with autonomous exploration frameworks to build environment-specific memory. The RSIAgent system allows it to achieve enhanced performance on tasks in novel settings. This enables Kimi-K3 to outperform certain frontier closed-source models on benchmarks such as OSWorld-v2 and Agent's Last Exam. RSIAgent: RSIAgent is a training-free multi-agent framework for recursive self-improvement that coordinates curriculum, actor, and verifier agents to explore new environments, validate outcomes, and retain reusable knowledge such as causal relationships. It employs a broad-then-deep exploration strategy to discover diverse structures before tackling hard cases and hidden constraints. In this research, the approach enables agents to adapt to unfamiliar apps and interfaces without costly data collection or model retraining. Verification Role: Separate verifier agents that check results independently help retain only reliable environment-specific knowledge during autonomous exploration. AI Agent Challenges: AI agents commonly struggle when operating in unfamiliar applications whose interfaces and failure modes are not captured by pretrained models. Exploration Approach: A curriculum-based system that begins with broad practice tasks across related areas before narrowing to deep focus on hard cases supports more effective adaptation in new settings.