Less Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History
Quick Answer
Engram is an open-source bi-temporal memory engine that improves LLM accuracy by utilizing a lean context approach, achieving 83.6% on LongMemEval_S with only 9.6k tokens compared to 73.2% for full-context at 79k tokens, while maintaining provenance and reducing costs.
Key Points
- Engram appends lossless episodes without on the critical path.
- Achieves 83.6% accuracy on LongMemEval_S with 9.6k tokens.
- Hybrid read path combines facts and retrieved chunks for better recall.
- Evaluates memory benchmarks with a neutral, in-repo harness.
- Addresses measurement-integrity pitfalls in memory systems.
Paper Resources
Source Excerpt
Long-term memory is the missing layer for agents: across sessions they forget, and the common workaround -- replaying the whole history into the prompt -- is expensive, slow, and, as distractors accumulate, less accurate. Most memory systems win on cost or latency but still lose to the full-context baseline on accuracy, and benchmark numbers are reported on inconsistent, non-reproducible harnesses, so one system appears at wildly different scores across sources. We present Engram, an open-so
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