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Run LoCoMo benchmark for long-term conversational memory
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Basé sur la classification professionnelle SOC
Trigger autonomous curiosity-driven exploration. The soul picks a topic from memory gaps or curiosity seeds, searches the web, and stores what it finds as dream-tagged memories.
Fine-tune the Qwen3-0.6B hint model — corpus gen, LoRA/unsloth, GGUF export, Ollama
Review soul discoveries (fixes, improvements, corrections) one by one, accept or discard each, implement accepted ones, build chitta, and optionally release.
First-principles review — question requirements, delete unnecessary parts, simplify, optimize with evidence, automate last. Use for code review, refactor, performance, or architecture.
Token-savvy session continuation. Rebuilds working context from transcript + soul memories in ~1500 tokens instead of replaying full history. Use when starting a new session to continue previous work.
Resume a thread by loading its ~800-token context capsule
| name | locomo-benchmark |
| description | Run LoCoMo benchmark for long-term conversational memory |
| execution | inline |
| model | inherit |
| aliases | ["locomo","benchmark-memory"] |
Evaluate cc-soul's memory against the LoCoMo benchmark (ACL 2024) for long-term conversational memory.
Run the benchmark script:
# Test one conversation (default: conv-26)
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py
# Test specific conversations
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py conv-26 conv-30
# Full benchmark (all 10 conversations)
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py --full
# Limit QA pairs per conversation
python3 $PLUGIN_DIR/scripts/locomo-benchmark.py --max-qa 20
Where $PLUGIN_DIR is /maps/projects/fernandezguerra/apps/repos/cc-soul (or installed plugin path).
/tmp/locomo/ (if not present)chitta recall --tag {sample_id}| Cat | Name | Description |
|---|---|---|
| 1 | Multi-hop | Requires connecting multiple facts |
| 2 | Single-hop | Direct fact retrieval |
| 3 | Temporal | Date/time questions |
| 4 | Open-domain | General knowledge |
| 5 | Adversarial | Should answer "no information" |
| Model | F1 |
|---|---|
| Human ceiling | 87.9% |
| AutoMem | 90.5% |
| GPT-4 | 32.1% |
| GPT-3.5 | 23.7% |
| Mistral-7B | 13.9% |
https://github.com/snap-research/locomo/tmp/locomo/data/locomo10.jsonIf you prefer to run manually:
# Ensure data exists
git clone https://github.com/snap-research/locomo /tmp/locomo
# Run benchmark
python3 /maps/projects/fernandezguerra/apps/repos/cc-soul/scripts/locomo-benchmark.py conv-26
=== LoCoMo Benchmark Results ===
Total QA Pairs: 50
Overall F1: XX.X%
By Category:
Multi-hop (n=XX): XX.X%
Single-hop (n=XX): XX.X%
Temporal (n=XX): XX.X%
Open-domain (n=XX): XX.X%
Adversarial (n=XX): XX.X%
Per Conversation:
conv-26: XX.X% (50 QA)
Comparison (from paper):
Human ceiling: 87.9%
GPT-4 baseline: 32.1%
cc-soul: XX.X%