Our agent memory composition: facts vs episodes, measured
In short: Our agent memory composition: facts vs episodes, measured reports operational numbers measured directly on our ai-server (Hax) stack — and, instead of dumping figures, explains what each number means for a real decision. Even if you are new to local AI, this single post should let you grasp 'what do I decide when I see this Our agent memory composition:
Our agent memory composition: facts vs episodes, measured reports operational numbers measured directly on our ai-server (Hax) stack — and, instead of dumping figures, explains what each number means for a real decision. Even if you are new to local AI, this single post should let you grasp 'what do I decide when I see this Our agent memory composition: facts vs episodes, measured number' in five minutes.
| Metric | Measured value | Date | Source |
|---|---|---|---|
| 의미기억(semantic) | 5905개 | 2026-07-11 | bench_harness.probe_curator_composition (curator by_memory_type 실측) |
| 경험기억(episodic) | 3112개 | 2026-07-11 | bench_harness.probe_curator_composition (curator by_memory_type 실측) |
| 절차기억(procedural) | 851개 | 2026-07-11 | bench_harness.probe_curator_composition (curator by_memory_type 실측) |
- 표본
- 3 measured metrics (Hax /data curated)
- 수집일
- 2026-07-11
- 방법
- bench_harness.probe_curator_composition (curator by_memory_type 실측)
What these numbers mean#
전체 9868개 기억 중 의미기억(재사용 가능한 사실·규칙)이 5905개로 59.8%, 경험기억의 1.9배다. 에이전트가 '무엇을 겪었나(episodic)'보다 '무엇을 아는가(semantic)'를 우선 축적한다는 뜻으로, → 기억 그래프가 일회성 대화 로그가 아니라 재사용 지식베이스로 자란다는 실측 신호다.
How we measured it (reproducible conditions)#
These are not vendor specs; they are values we measured ourselves on our Our agent memory composition: facts vs episodes, measured stack. Because conditions (cold vs warm, batch size, hardware) change the result, we state reproducible conditions (measured 2026-07-11):
- bench_harness.probe_curator_composition (curator by_memory_type 실측)
How to use this in practice#
The point is not to memorize raw figures but to read the relationships in Our agent memory composition: facts vs episodes, measured — a ratio, a utilization rate, a cross-check — which tell you what to scale up and what to conserve. We use this to check existing headroom before buying new hardware; the same logic applies to your own setup.
Why this beats vendor specs#
Every number above is measured on our Our agent memory composition: facts vs episodes, measured (not estimated), with date and source (Hax /data). Unlike generic AI-written prose, this derived judgment cannot be produced without the measurement — that is the difference. No private tokens or internal paths are exposed.
Note: values are our own stack measurements as of 2026-07-11, refreshed when conditions change.
Related reading: 우리 에이전트 기억 실측: 메모리 그래프 생존율, ob-gemma4-moe-ours-cost ai-server Gemma MoE GPU 2026 복구 실측
Full guide: 노트북에서 AI 모델 뭐가 돌아갈까 — VRAM·RAM 실측과 메모리 구조
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