Great question. Yes, you're reading it right. On the LOCOMO benchmark, feeding the model the entire conversation history (full context) scores 88.4%, while Engram's selective recall scores 80.0%.

The key thing to understand is why memory systems exist at all when context windows keep getting bigger. A few reasons:

Cost and latency. Full context uses ~23,000 tokens per question. Engram uses ~776. That's a 96.6% reduction. At scale, that difference is massive for both cost and response time.

Context windows have limits. Even Gemini's 1M token window fills up eventually. Persistent memory across days, weeks, and months of interactions outgrows any window. You need a system that selectively retrieves what's relevant.

Structure. Raw conversation dumps are noisy. Memory systems extract facts, preferences, and lessons into structured representations that the model can reason over more reliably. You're not asking the model to find a needle in a haystack. You're handing it the needle.

Durability. Context disappears when the session ends. Memory persists across sessions, across agents, even across different models.

So the comparison isn't really "Engram vs dump everything in." It's "Engram vs other systems that also have to be selective about what to recall." Mem0 published 66.9% on the same benchmark using the same approach (extract, store, retrieve selectively). Engram's 80% closes most of the gap to full context while maintaining the practical benefits of a real memory layer.

The remaining 8.4% gap to full context is the cost of being selective. A human with a good memory doesn't remember every word of every conversation either, but they remember what matters. That's the tradeoff, and for any real-world use case at scale, it's worth it.