AI RESEARCH
MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
arXiv CS.AI
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ArXi:2510.17281v5 Announce Type: replace-cross Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are almost reached due to the gradual depletion of high-quality data and marginal gains obtained from larger computational resource consumption. Inspired by the abilities of human and traditional AI systems in learning from practice, constructing memory and continual learning frameworks for LLMsys has become an important and popular research direction in recent literature.