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"schema_version": "1.0",
"title": "MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems",
"emoji": "M",
"space_id": "snaykey/repro-memorybench",
"paper": {
"arxiv_id": "2510.17281",
"openreview_id": "If4X4W2HWx"
},
"tags": [
"icml2026-repro",
"paper-If4X4W2HWx"
],
"updated_at": "2026-07-29T16:50:47.378234+00:00",
"root": {
"slug": "index",
"title": "MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems",
"children": [
{
"slug": "executive-summary",
"title": "Executive summary",
"children": []
},
{
"slug": "claim-1-three-module-framework",
"title": "MemoryBench provides a three-module framework with a task provider, user simulator, and performance monitor for testing LLM-system continual learning from feedback logs (Figure 1)",
"children": []
},
{
"slug": "claim-2-dataset-coverage",
"title": "MemoryBench covers 11 public datasets across three domains, four task-format categories, and two languages (Table 2)",
"children": []
},
{
"slug": "claim-3-memory-feedback-taxonomy",
"title": "The benchmark includes both declarative/procedural memory and explicit/implicit feedback categories absent from prior memory benchmarks (Table 1)",
"children": []
},
{
"slug": "claim-4-advanced-memory-vs-rag",
"title": "Off-policy results show that advanced memory systems such as A-Mem, Mem0, and MemoryOS do not consistently outperform simpler RAG baselines (Figure 2)",
"children": []
},
{
"slug": "claim-5-efficiency-costs",
"title": "Efficiency measurements show large and inconsistent memory-operation and prediction-time costs for existing memory-based LLM systems (Figure 3)",
"children": []
},
{
"slug": "claim-6-feedback-ab",
"title": "Comparisons with and without feedback show simulated user feedback can improve model performance on task-specific metrics (Table 11)",
"children": []
},
{
"slug": "conclusion",
"title": "Conclusion",
"children": []
}
]
},
"revision": 1
} |