Researchers introduce GEPA (Genetic-Pareto), a prompt optimizer that uses natural language reflection to learn from trial and error, outperforming reinforcement learning methods like GRPO by 6% on ave…
Researchers introduced Contrastive Reflection, an iterative prompt-optimization framework for agentic information retrieval workflows, which uses error-anchored behavioral slices and contrastive examp…
A new benchmark, dspy-security-bench, reveals that DSPy prompt optimization degrades adversarial robustness against harder prompt-injection attacks. Testing with AgentDojo's attack suite, optimizers l…
Decagon deployed self-evolving AI agents that automatically improve their own prompts without human intervention, using a feedback loop that tests and promotes winning versions. The team found that th…