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continual learning 26 reinforcement learning 9 language models 5 large language models 4 robotics 4 agents 3 generative models 3 multi-agent systems 3 recursive self-improvement 3 vision-language models 3

15 SEP 2026 · Paper

Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

This paper tests how well AI agents can withstand prolonged interactions and unexpected events, and finds that even seemingly safe agents can fail in complex, long-term scenarios. Practitioners should care because it highlights the need to design more resilient autonomous systems that can handle unexpected failures.

multi-agent systems continual learning adversarial testing autonomous systems safety regimes

14 SEP 2026 · Paper

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

This paper introduces HypoEvolve, a framework that uses genetic algorithms to enable multi-agent LLMs to discover scientific hypotheses by collaborating on hypothesis synthesis, evaluation, and revision. Practitioners might care about this because it could lead to more effective AI systems for scientific discovery and drug repurposing.

continual learning multi-agent systems scientific hypothesis discovery large language models evolutionary algorithms

14 SEP 2026 · Paper

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

This paper introduces a new framework called RSIAgent that helps digital agents adapt to new environments without needing to be retrained. A practitioner might care about this because it allows for more efficient and effective AI systems that can learn and improve on their own.

reinforcement learning multi-agent systems autonomous exploration recursive self-improvement transfer learning