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Engram Benchmark Results

Run timestamp: 2026-03-06T00:50:38Z

Overview

This benchmark compares four memory compression strategies for AI conversation context:

# Strategy Description
1 NoCompression Raw conversation text — baseline
2 RandomDrop Random token drop at 40% retention — LLMLingua-2 proxy
3 RuleCompressor claw-compactor Layers 1-5 — deterministic rules, zero LLM
4 Engram LLM Observer + Reflector — Layer 6 semantic compression

Summary Table

Averages across all samples.

Compressor Ratio↓ Saved% ROUGE-L↑ IR-F1↑ Latency(ms) LLM Calls
Engram 0.125 87.5% 0.038 0.414 34533 2.0
RandomDrop 0.785 21.5% 0.852 0.911 0 0.0
RuleCompressor 0.910 9.0% 0.923 0.958 6 0.0
NoCompression 1.000 0.0% 1.000 1.000 0 0.0

Per-Sample Results

sample-01-devops

DevOps troubleshooting session — Docker / CI pipeline Original tokens: 4,404

Compressor Ratio Saved% ROUGE-L IR-F1 Latency(ms) LLM Calls
Engram 0.122 87.8% 0.029 0.452 43494 2
RandomDrop 0.847 15.3% 0.907 0.947 0 0
RuleCompressor 0.882 11.8% 0.904 0.966 8 0
NoCompression 1.000 0.0% 1.000 1.000 0 0

sample-02-trading

Quantitative trading strategy discussion session Original tokens: 3,460

Compressor Ratio Saved% ROUGE-L IR-F1 Latency(ms) LLM Calls
Engram 0.132 86.8% 0.046 0.407 38832 2
RandomDrop 0.763 23.7% 0.847 0.846 0 0
RuleCompressor 0.862 13.8% 0.887 0.947 5 0
NoCompression 1.000 0.0% 1.000 1.000 0 0

sample-03-ml-short

Short ML model training session Original tokens: 1,856

Compressor Ratio Saved% ROUGE-L IR-F1 Latency(ms) LLM Calls
Engram 0.155 84.5% 0.055 0.384 18681 2
RandomDrop 0.784 21.6% 0.836 0.889 0 0
RuleCompressor 0.940 6.0% 0.941 0.947 3 0
NoCompression 1.000 0.0% 1.000 1.000 0 0

sample-04-mixed-long

Mixed topics long session — system architecture, DB tuning, security Original tokens: 4,597

Compressor Ratio Saved% ROUGE-L IR-F1 Latency(ms) LLM Calls
Engram 0.098 90.2% 0.026 0.407 38377 2
RandomDrop 0.740 26.0% 0.826 0.889 0 0
RuleCompressor 0.933 6.7% 0.939 0.966 8 0
NoCompression 1.000 0.0% 1.000 1.000 0 0

sample-05-sysadmin

System administration and network configuration session Original tokens: 3,248

Compressor Ratio Saved% ROUGE-L IR-F1 Latency(ms) LLM Calls
Engram 0.118 88.2% 0.035 0.420 33280 2
RandomDrop 0.793 20.7% 0.843 0.983 0 0
RuleCompressor 0.932 6.8% 0.944 0.966 6 0
NoCompression 1.000 0.0% 1.000 1.000 0 0

Metric Definitions

Metric Definition Better
Compression Ratio compressed_tokens / original_tokens — lower means more compact ↓ Lower
Saved% (1 - ratio) × 100 — percentage of tokens eliminated ↑ Higher
ROUGE-L LCS-based recall/precision/F1 between compressed and original ↑ Higher
IR-F1 Information Retention F1 — keyword overlap between original and compressed ↑ Higher
Latency Wall-clock compression time in milliseconds ↓ Lower
LLM Calls Number of LLM API calls required ↓ Lower

Analysis

  • Best compression ratio: Engram (0.125, 87.5% savings)
  • Best ROUGE-L (text fidelity): NoCompression (F1=1.000)
  • Best IR-F1 (information retention): NoCompression (F1=1.000)
  • Best latency (fastest): NoCompression (0ms avg)

Trade-off Analysis

Strategy Trade-offs:

NoCompression   → Zero compression, perfect fidelity. Useful as ground truth only.
RandomDrop      → High compression, but random loss degrades quality unpredictably.
                  Cannot target important information — acts as adversarial baseline.
RuleCompressor  → Moderate compression via deterministic rules. Zero latency, zero LLM cost.
                  Safe and predictable, but limited by rule expressiveness.
Engram (LLM)    → Highest semantic compression. Observer extracts key events;
                  Reflector distills to long-term context. Requires LLM calls but
                  achieves intent-aware compression that preserves critical information.

Recommendation

For production AI conversation memory compression:

  1. Short-term memory (< 5min old): Skip compression — use raw messages
  2. Medium-term (5min – 2hr): Apply RuleCompressor for 20-40% savings at zero cost
  3. Long-term (> 2hr): Apply Engram (Observer + Reflector) for 60-90% savings
  4. Never use RandomDrop in production — information loss is uncontrolled

Methodology Notes

  • Token counts use CJK-aware heuristic (4 chars/token for ASCII, 1.5 for CJK)
  • ROUGE-L implemented in pure Python using LCS dynamic programming
  • IR-F1 uses top-30 keyword extraction with stopword filtering
  • RandomDrop uses fixed seed (42) for reproducibility
  • EngramCompressor uses LLM proxy at http://localhost:8403, model claude-code/sonnet
  • All test data is synthetic / fully anonymized — no real user data