SFResearch Study Tests Fragility of Memory-Based Self-Improving Agents
The takeaway is that agent self-improvement may be more fragile than reported scores suggest, because evaluation conditions differ sharply from production task streams. Teams building memory-based agents should run multi-seed evaluations, randomise task order, and enrich memory construction with explicit rubrics and feedback. Code and data are released openly, so practitioners can replicate the findings directly.
