EDN is benchmarked against LongMemEval (Wu et al., ICLR 2025), a peer-reviewed benchmark for long-term conversational memory developed independently of EDN. Every result on this page is a measured outcome from the evaluation programme. No projections, no estimates, no modification to the benchmark protocol.
Language models are stateless. Across sessions, applications, and agents, prior context, corrections, and decisions are lost the moment an interaction ends. Where context is missing, the model fabricates to fill the gap, and without provenance that fabrication is written to memory and returned later as established fact.
Whether recall is reliable, factual, and traceable is determined by the memory system, not the model alone. What is written, how each record is classified by origin, what is eligible for retrieval, and how it is assembled into context are what keep recall accurate rather than confidently wrong.
EDN is external, deterministic memory middleware, with no changes to model weights. We evaluated it on a public, peer-reviewed benchmark under its own protocol, introducing each architectural decision one at a time and measuring it against the configuration before it.
EDN was evaluated on LongMemEval across three actor models. The primary configuration was replicated three times; we report the best run and the replication mean together, because a single good run is not evidence of a stable system.
Each decision was introduced one at a time and measured against the configuration before it; the model never changed. From a no-memory baseline, every layer of the system adds measured accuracy, while the provenance invariant holds throughout.
Full sub-category breakdowns, the n=3 replicated results, and ablation detail are in the paper and the public evaluation repository.
Haiku 4.5, best run only. Each bar is the running accuracy after that step; the gains sum from the 40.4% baseline to 87.0%. Across every construction the provenance invariant held, a structural guarantee separate from the accuracy shown here.
Everything below is either explicitly supported by our evaluation or intentionally left outside its scope.
Explore the full evaluation code, read the preprint once it's public, or reach out with methodology questions.
The write-up is in progress and pending peer review.
Pending review