mempalace/website/reference/benchmarks.md

Benchmarks

Curated summary of MemPalace's reproducible benchmark results. For the complete progression with every experiment, see benchmarks/BENCHMARKS.md. All headline numbers on this page are reproducible from the committed repository — datasets, scripts, and per-question result JSONLs are all checked in.

The Core Finding

MemPalace's benchmarked raw baseline stores the source text and searches it with the vector store's default embeddings. No extraction or summarisation step is required for that baseline, and it reproduces at 96.6% R@5 on LongMemEval with no LLM at any stage.

LongMemEval — Retrieval Recall

Retrieval recall asks: is the labelled session for this question inside the top-K retrieved sessions? It is not the same metric as end-to-end QA accuracy; a system can have perfect retrieval recall and poor QA answer quality, and vice versa.

Full 500 questions:

Mode	R@5	LLM required	Cost/query
Raw — vector search over verbatim sessions	96.6%	None	$0
Hybrid v4 — keyword/temporal/preference boosts, no LLM	98.6%	None	$0
Hybrid v4 + LLM rerank (minimax-m2.7 via Ollama)	99.2%	Any capable model	$0 local / varies cloud

Held-out set (450 questions, never used during hybrid_v4 development):

Mode	R@5	R@10	NDCG@10
Hybrid v4	98.4%	99.8%	0.938

The held-out figure is the honest generalisable number. The full-500 scores are higher but include the 50 "dev" questions that hybrid_v4's three targeted fixes (quoted-phrase boost, person-name boost, nostalgia patterns) were developed against. benchmarks/BENCHMARKS.md calls this "teaching to the test" and the held-out 98.4% is the clean number to quote when a single R@5 figure is needed for the hybrid pipeline.

Per-category breakdown (raw, 96.6%)

Question type	R@5	Count
Knowledge update	99.0%	78
Multi-session	98.5%	133
Temporal reasoning	96.2%	133
Single-session user	95.7%	70
Single-session preference	93.3%	30
Single-session assistant	92.9%	56

LoCoMo — Retrieval Recall

LoCoMo contains 1,986 questions across 10 long conversations (19–32 sessions each).

Mode	R@10	LLM required
Session, no rerank, top-10	60.3%	None
Hybrid v5 (keyword + predicate boosts), top-10	88.9%	None

We do not publish a "100% R@10" headline for LoCoMo. A reported 100% in earlier drafts used top_k=50, which exceeds the per-conversation session count (19–32) — so the retrieval stage returns every session in every conversation by construction. That number measures an LLM's reading comprehension over the whole conversation, not retrieval. The honest retrieval-recall number for LoCoMo is the top-10 figure.

Other Benchmarks

ConvoMem (Salesforce; 50 items per category × 5 categories = 250 items): MemPalace raw retrieval reaches 92.9% avg recall. Strongest categories: Assistant Facts 100%, User Facts 98%. Weakest: Preferences 86%. The Salesforce dataset contains ~75K items in total; our headline number is from the 250-item sample the benchmark script was designed around.

MemBench (ACL 2025; 8,500 items, all topics): MemPalace hybrid top-5 reaches 80.3% R@5 overall. Strongest: aggregative 99.3%, comparative 98.4%, lowlevel_rec 99.8%. Weakest: noisy 43.4% (distractor-heavy by design), conditional 57.3%.

Why We Don't Publish a Cross-System Comparison Table

Previous versions of this page placed MemPalace's retrieval recall (R@5) next to other projects' end-to-end QA accuracy figures under a single "LongMemEval R@5" column. Those are different metrics and are not comparable. A system can have 100% retrieval recall and 40% QA accuracy, and vice versa.

If you are evaluating memory systems against MemPalace and want a fair comparison, use the retrieval-recall numbers above and the benchmark scripts in the repo; or pick the metric the other project publishes and compare on that. Each project's published source is the correct reference:

• Mastra — Observational Memory (their published metric is binary QA accuracy with GPT-5-mini)
• Mem0 — Research (their published LoCoMo metric is end-to-end QA accuracy, not retrieval recall)
• Supermemory — ASMR post (their published metric is QA accuracy; authors explicitly frame the ensemble as an experimental proof-of-concept, not production)

Reproducing Results

Every benchmark runs deterministically from this repository.

git clone https://github.com/MemPalace/mempalace.git
cd mempalace
pip install -e ".[dev]"

# LongMemEval — raw (96.6%)
curl -fsSL -o /tmp/longmemeval_s_cleaned.json \
  https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json
python benchmarks/longmemeval_bench.py /tmp/longmemeval_s_cleaned.json

# LongMemEval — hybrid v4 on the held-out 450 (98.4%)
python benchmarks/longmemeval_bench.py /tmp/longmemeval_s_cleaned.json \
  --mode hybrid_v4 --held-out --split-file benchmarks/lme_split_50_450.json

# LoCoMo — session, top-10 (60.3%)
git clone https://github.com/snap-research/locomo.git /tmp/locomo
python benchmarks/locomo_bench.py /tmp/locomo/data/locomo10.json \
  --granularity session --top-k 10

# LongMemEval — hybrid v4 + rerank, any OpenAI-compatible endpoint
python benchmarks/longmemeval_bench.py /tmp/longmemeval_s_cleaned.json \
  --mode hybrid_v4 --llm-rerank \
  --llm-backend ollama --llm-model <your-model-tag>

::: tip Results are deterministic: same data, same script, same split seed → same score. The committed benchmarks/results_*.jsonl files include every question, every retrieved corpus id, and every score, so every individual answer is auditable — not just the aggregate. :::

For the complete progression (hybrid v1 → v4, diary mode, palace mode, LoCoMo architecture iterations, methodology integrity notes), see benchmarks/BENCHMARKS.md.