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Despite the recent success of coding agents built on large language models, it remains challenging to run them over long horizons, since every observation is appended to the context and the context grows with each one. History-based maintenance is a common remedy, which masks or summarizes old observations, or prunes w...
On EnterpriseRAG-Bench, MEMONDEMAND outperforms the strongest published LB#1 result at every evaluated scale from 10M tokens through the complete 618M- token collection, and results on FinanceBench, HotpotQA, and FRAMES further show strong performance across financial, multi-hop, and fact-retrieval settings.
These results show that MegaMem supports ultra-large persistent memory while preserving strong answer accuracy under a bounded generation context, and provides a practical path toward accurate retrieval over memories ranging from hundreds of millions to one billion tokens.
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