Memory is increasingly used in long-horizon agents, yet the net value of building memory over direct retrieval from raw history remains largely unclear. Through a large-scale evaluation across multipl
Large-scale evaluation reveals that dedicated memory systems often underperform naive retrieval-augmented generation, introducing a framework of memory benefit and memory tax to assess net value.
Memory is increasingly used in long-horizon agents, yet the net value of building memory over direct retrieval from raw history remains largely unclear. Through a large-scale evaluation across multiple benchmarks and model backbones, we find many dedicated memory systems underperform naive RAG in many cases, indicating a negative net value. Taking RAG as a reference, we dissect the net value of memory into the gains and losses over direct retrieval, termed memory benefit and memory tax. These gains and losses arise because memory process changes how the raw information is organized and accessed. We view such changes as the double-edged deviations from the raw history. They are originally…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Memory Tax: Dissecting the Net Value of Agent Memory was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 22 checks on 2 different days. Its best position was #3 of 30, reached on Oct 29, 19:28 UTC. It dropped off the list after Oct 29, 23:28 UTC.
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#3 on Hugging Face · Daily papers, on Oct 29, 19:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 28, 2026 and has been on the list on 2 days (22 checks) since.