Most AI memory systems keep the newest information—not the most important. Here's how I used the Ebbinghaus forgetting curve to build a better memory engine for LLMs.
Summary generated by The Flow from the publisher's feed. The full article lives at Towards Data Science.
Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast.
arXiv:2608. 02515v1 Announce Type: cross Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context.
LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency while silently degrading output quality.
arXiv:2607. 22562v1 Announce Type: new Abstract: Managing long-context dependencies remains a primary bottleneck in LLM agents, as redundant and irrelevant information can degrade multi-step reasoning.
arXiv:2606. 18829v1 Announce Type: new Abstract: Memory benchmarks for LLM agents largely assume single-user settings, leaving shared assistants for hospitals, workplaces, campuses, and households understudied.
Every hand-off in your multi-agent pipeline is an expensive tokenization round-trip. Discover how Inductive Latent Context Persistence (ILCP) transfers a compressed hidden state so downstream agents never have to re-create the same context.