AI RESEARCH
LongMINT: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems
arXiv CS.CL
•
ArXi:2605.18565v1 Announce Type: new Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reasoning over multiple pieces of information. However, existing benchmarks focus on static, independent recall and fail to capture these dynamic interactions between evolving memories. In this paper, we study how current memory-augmented agents perform in realistic, interference-heavy, long-horizon settings across diverse domains and question types. We