Jeffrey Hein
7843291c81
Follow-up to #1204 per @mschultheiss83's request — applies the same narrow Windows encoding fix to the three remaining LongMemEval-shaped benchmark runners that share the audit pattern jphein flagged in #1204's review (`benchmarks/{locomo,membench,convomem}_bench.py`). For each file: - All `open(path)` / `open(path, "w")` calls gained `encoding="utf-8"` so cached benchmark JSON, palace-cache files, and result files are always read/written as UTF-8 instead of inheriting the platform default (cp1252 on Windows, GBK on Chinese Windows, etc.). Same pattern as #1204's #2917 / #2927 / #2957 / #2998 / #3031. - Replaced non-ASCII separator characters in `print(...)` chrome with ASCII equivalents (`─` → `-`, `→` → `->`) so the runners don't raise `UnicodeEncodeError` on a default cp1252 console. Comments and docstrings (which never hit stdout) are untouched. - `urllib.request.urlopen(...)` calls left alone — they don't open local files, the original audit didn't flag them. Audit was already done in #1204's thread; this PR carries the mechanical follow-through. No behavior change beyond Windows correctness. Refs #1203 (the original Windows reproducer), #1204 (sibling PR that fixed `longmemeval_bench.py`). Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
MemPalace Benchmarks — Reproduction Guide
Run the exact same benchmarks we report. Clone, install, run.
Setup
git clone https://github.com/MemPalace/mempalace.git
cd mempalace
uv sync --extra dev # or: pip install -e ".[dev]"
Benchmark 1: LongMemEval (500 questions)
Tests retrieval across ~53 conversation sessions per question. The standard benchmark for AI memory.
# Download data
mkdir -p /tmp/longmemeval-data
curl -fsSL -o /tmp/longmemeval-data/longmemeval_s_cleaned.json \
https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json
# Run (raw mode — our headline 96.6% result)
python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json
# Run with AAAK compression (84.2%)
python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json --mode aaak
# Run with room-based boosting (89.4%)
python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json --mode rooms
# Quick test on 20 questions first
python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json --limit 20
# Turn-level granularity
python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json --granularity turn
Expected output (raw mode, full 500):
Recall@5: 0.966
Recall@10: 0.982
NDCG@10: 0.889
Time: ~5 minutes on Apple Silicon
Benchmark 2: LoCoMo (1,986 QA pairs)
Tests multi-hop reasoning across 10 long conversations (19-32 sessions each, 400-600 dialog turns).
# Clone LoCoMo
git clone https://github.com/snap-research/locomo.git /tmp/locomo
# Run (session granularity — our 60.3% result)
python benchmarks/locomo_bench.py /tmp/locomo/data/locomo10.json --granularity session
# Dialog granularity (harder — 48.0%)
python benchmarks/locomo_bench.py /tmp/locomo/data/locomo10.json --granularity dialog
# Higher top-k (77.8% at top-50)
python benchmarks/locomo_bench.py /tmp/locomo/data/locomo10.json --top-k 50
# Quick test on 1 conversation
python benchmarks/locomo_bench.py /tmp/locomo/data/locomo10.json --limit 1
Expected output (session, top-10, full 10 conversations):
Avg Recall: 0.603
Temporal: 0.692
Time: ~2 minutes
Benchmark 3: ConvoMem (Salesforce, 75K+ QA pairs)
Tests six categories of conversational memory. Downloads from HuggingFace automatically.
# Run all categories, 50 items each (our 92.9% result)
python benchmarks/convomem_bench.py --category all --limit 50
# Single category
python benchmarks/convomem_bench.py --category user_evidence --limit 100
# Quick test
python benchmarks/convomem_bench.py --category user_evidence --limit 10
Categories available: user_evidence, assistant_facts_evidence, changing_evidence, abstention_evidence, preference_evidence, implicit_connection_evidence
Expected output (all categories, 50 each):
Avg Recall: 0.929
Assistant Facts: 1.000
User Facts: 0.980
Time: ~2 minutes
What Each Benchmark Tests
| Benchmark | What it measures | Why it matters |
|---|---|---|
| LongMemEval | Can you find a fact buried in 53 sessions? | Tests basic retrieval quality — the "needle in a haystack" |
| LoCoMo | Can you connect facts across conversations over weeks? | Tests multi-hop reasoning and temporal understanding |
| ConvoMem | Does your memory system work at scale? | Tests all memory types: facts, preferences, changes, abstention |
Results Files
Raw results are in benchmarks/results_*.jsonl and benchmarks/results_*.json. Each file contains every question, every retrieved document, and every score — fully auditable.
Requirements
- Python 3.9+
chromadb(the only dependency)- ~300MB disk for LongMemEval data
- ~5 minutes for each full benchmark run
- No API key. No internet during benchmark (after data download). No GPU.
Next Benchmarks (Planned)
- Scale testing — ConvoMem at 50/100/300 conversations per item
- Hybrid AAAK — search raw text, deliver AAAK-compressed results
- End-to-end QA — retrieve + generate answer + measure F1 (needs LLM API key)