Evaluating LLM Long-Term Memory Recall Accuracy
Evaluate LLM long-term memory recall by scoring against a benchmark dataset like LongMemEval-S.
Score the LLM's responses against a benchmark dataset (e.g., LongMemEval-S) that contains answerable questions and gold sessions. Use metrics like the percentage of gold sessions in the candidate pool and gold-complete packets.
The system's retrieval chain places all gold sessions in the candidate pool for 468/470 answerable questions and produces gold-complete packets for 462/470. Two 500-question passes with Claude Opus scored 479/500 and 475/500.
A system using Claude Opus reader scored 479/500 and 475/500 on LongMemEval-S, indicating high recall accuracy for long-term memory.
Evaluate LLM long-term memory by testing its retrieval chain on a benchmark like LongMemEval-S. Measure the percentage of correct sessions retrieved and the completeness of compiled packets.
Using a standardized evaluation dataset with known correct answers allows for objective measurement of the LLM's long-term memory recall capabilities.
This evaluation method is not suitable for tasks that do not require long-term memory recall.
- Auditable Long-Term Memory: A Deterministic Retrieval Chain Measured at 479/475 of 500 on LongMemEval-S ↗arXiv · arXiv cs.AI · Sep 29, 2026 · paraphrased
First seen on Subvolts Sep 29, 2026. Updated Oct 1, 2026. Spotted a problem? Tell us.
- Auditable Long-Term Memory: A Deterministic Retrieval Chain Measured at 479/475 of 500 on LongMemEval-SAnthropic · arXiv cs.AI · Sep 29, 2026
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with Claude in the sources.
- Where did this come from?
- arXiv (arXiv cs.AI). Subvolts collected, normalized and credited it; the link above opens the original.