- Owner 3-quyết: (30) JUMP để-nguyên-theo-nhịp · (31) 4 persona +NEVER-block (restart ĐÃ THOẢ) · (32) vòng 1→5 AUTO — lô consent gỡ 9 site (5 H1 + ring1-tự-bắt + 3 trio; E-013 EXT) - Memory-ops TRỌN: A1-A4/D1/D2/R1/B-series — A7 392/392 · mfe 20/20-100% lần đầu · lead-gap drain 24.728→16.485B · strike-ledger + WARN-latch (-Ack = owner-action) - V4 sleep AUTO đầu: 1 shard thật/16 KÊU · ring4 đầu-đời TRƯỢT-4/5 → 4 fix gist (scribe sub-ring4-close vì return-only) - Thư model re-stamp: 59ce1d0f → 9c909007 published (G-024a) — adap-report Đính-chính @S152 + sweep 13 bề-mặt LIVE - Bookend @close: stale 6 FLAG + gap 2 FLAG → vá 8/8; ring2 8/8 ĐẠT (4-A pin-bản-cắt → slot 33) · ring1 48Đ/5T/59-claim - M9 13 đơn-vị diary (3 S151-deep hồi-tố) · #53 ×10-chắc → tally 60-cận-dưới + errata subject-1b85713 - Session-log 2 run= + completeness-gate 5-vòng ĐẠT · HANDOFF segment S152 (carry 3-đóng/3-mới/23-giữ; slot 33/34) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
MFE — Memory-Fidelity-EVAL (Harness-16)
Adopt S93 (2026-06-29) from AI_INFRA broadcasts
2026-06-29-Governance-harness-16-*(memory-fidelity-eval + mfe-update + cd-judge-sub-update). Canonical mechanism →docs/governance/harness-11-engine.md §H.
What MFE is (and is NOT)
MFE measures provisioned ≠ remembered ≠ applied. The session %-print (Harness-15) tells you how much you stuffed into hot-memory (by length). MFE asks the next question: does the agent actually retain/use what was stuffed in.
🔴 DISTINCT from H6.7 "memoryDelta-routing-fidelity" (the right delta landing in the right agent-memory/<role> under single-writer). Same word "fidelity", two senses — always say memory-fidelity-EVAL / MFE for this one. The pair is recorded in governance-detectors.ps1 C3 alias-map so it is not flagged as drift.
Two layers
| Layer | What | Cost | Status (honest) |
|---|---|---|---|
| Deterministic ($0) | scripts/mfe-eval.ps1 — LEAD coverage-FIT + age-band + Goodhart-anchor; SUB per-role coverage; SUB-workflow N/A |
$0, NO-API | ✅ built + wired (opt-in) |
| Judge (Branch A) | recall/apply — give the agent the sample-questions, score recall + applied | $0 scaffold (quota only when a real scorer is wired) | 🟡 SCAFFOLD only — seed exists, no scorer wired |
How to run
powershell.exe -ExecutionPolicy Bypass -File scripts\mfe-eval.ps1 # all tiers
powershell.exe -ExecutionPolicy Bypass -File scripts\mfe-eval.ps1 -Tier lead
powershell.exe -ExecutionPolicy Bypass -File scripts\mfe-eval.ps1 -Tier sub
powershell.exe -ExecutionPolicy Bypass -File scripts\mfe-eval.ps1 -Judge # show judge scaffold (no scoring)
powershell.exe -ExecutionPolicy Bypass -File scripts\mfe-eval.ps1 -Detail # +per-item MISS list (which denominator items unmatched)
powershell.exe -ExecutionPolicy Bypass -File scripts\mfe-eval.ps1 -Ack # 🔴 OWNER-ACTION ONLY — acknowledge GOODHART-WARN, move warn_baseline
-Detail / -Ack (B4 @S152)
-Detail— read-only diagnostic: prints oneMISS: <item>line per unmatched denominator item, so a coverage drop is traceable to the exact item instead of a bare percentage. Safe to run any time.- GOODHART-WARN is LATCHED — when strikes rise vs the persisted
warn_baseline(stored in.mfe-state.json), the warning repeats on every run until acknowledged. It does NOT self-clear on a later good run (self-clearing would let one lucky run bury a real regression). -Ack— the ONLY thing that moveswarn_baseline(and appends the ack tohistory[]). 🔴 Owner-action: lead/agents may run-Detailfreely but MUST NOT run-Ackon their own — acknowledging a Goodhart warning = accepting the new strike level as the reference point, and that acceptance belongs to anh (same rule as every threshold/number inmemory-budget.json). Lead surfaces the WARN; anh decides to ack.
Opt-in at session ends: /session-start … eval (baseline) and /session-end … eval (retention) — see session-start.md §2.1.6 / session-end.md §L.b(c). Default (no eval) = unchanged behaviour.
Operational decision (the point)
- Coverage < 100% or set over-cap = lack-of-SPACE → INCREASE budget (owner decides the number).
- Low recall despite the set fitting = rot/noise → REORGANIZE (value-priority, cut low-value) — adding space does NOT fix rot.
🔴 Honest caveats (do not hide)
- Token sizing is a RANGE, not a number.
char/4is not real tokenization; Vietnamese-diacritic hot-memory is ~3.0–3.5 byte/token, sobyte/4is an upper bound on headroom. The analyzer reports[bytes/4 … bytes/3.0]and uses the worst-case end for the FIT verdict. Real tokenizer count is inside the band. - The judge layer measures nothing yet. A same-session self-grade is meaningless (the agent just read the answers). Real numbers need (a) the sample-questions to mature in age and (b) an independent cross-session / different-model judge. Until both, judge output is plumbing-smoke.
- Age is a flag, never a cut (mark
RC-…10-29-11). An item leaves the must-remember set only on status-change (mark → Disabled, guard → retired, AS-row deleted), never by age. - No self-grading. Coverage% is anchored to the real recurring-error signal (error-ledger strikes + RCA count). A high score next to rising strikes = the score lies.
Files
scripts/mfe-eval.ps1— deterministic analyzer (NO-API, ASCII-only, exit 0, READ-ONLY ontoken_governor)..claude/agent-memory/memory-budget.json→mfeblock — single-source config (denominator sources, stop-list, toggles, caveats).eval/mfe/sample-questions.json— immutable seed (stable-id anchored), append-only..claude/agent-memory/.mfe-state.json— last-run strikes (cross-run Goodhart compare); MFE-only, never touches the budget.