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v7.2.0 - Evidence-Gated Skill Memory

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@nianpangzhi233 nianpangzhi233 released this 10 May 06:41
v7.2.0
721c186

Mnemosyne v7.2.0 - Evidence-Gated Skill Memory

Mnemosyne v7.2 turns the skill system from a promising prototype into an observable, conservative, evidence-gated loop.

The main idea is simple: an AI agent should not blindly trust a generated skill just because it looks well written. A skill must be tested, receive feedback, survive governance checks, and only then enter the default injection path.

Highlights

  • Post-dream skill daemon: full dream cycles can now trigger automatic skill follow-up work.
  • Evidence flow: skill usage feedback records success, partial, miss, misleading, and trigger_mismatch outcomes.
  • Failure-to-test loop: reproducible failures can become test-prompts.json entries.
  • Conservative promotion gate: only low-risk skills with stable success evidence can move toward default injection.
  • Dashboard visibility: recent daemon and skill-loop results are visible from the dashboard instead of being hidden in terminal output.
  • Open-source packaging: GitHub Pages, CI, issue templates, roadmap, security policy, and visual assets were added for a better public launch.

Why This Matters

Most agent memory systems stop at retrieval. Mnemosyne goes further:

experience -> memory graph -> dream consolidation -> skill embryo -> live evaluation -> feedback -> approved injection

That makes skill growth safer. A generated skill can help, miss, trigger at the wrong time, or even mislead the agent. v7.2 records those outcomes and uses them as governance signals.

What's New

Skill Daemon

The new daemon can run full dream cycles and continue into post-dream skill processing:

skill-daemon.cmd

Default full dream schedule:

03:00, 12:00, 17:00

Skill Feedback Outcomes

memory_skill_feedback now prefers canonical outcomes:

  • success
  • partial
  • miss
  • misleading
  • trigger_mismatch

These outcomes are more precise than generic helpful/not-helpful ratings and can drive future evaluation sets.

Dashboard Summary

The dashboard now exposes the latest post-dream skill processing summary:

  • candidates scanned
  • candidates processed
  • runner mode
  • evolution rounds
  • feedback count
  • promotion results
  • expandable per-candidate details

Public Packaging

This release also improves the public repository:

  • GitHub Pages landing page
  • CI workflow
  • Pages deployment workflow
  • issue and PR templates
  • roadmap, security policy, and code of conduct
  • architecture and dashboard preview SVGs
  • social preview asset

Verification

Validated locally with:

python -m py_compile scripts\skill_daemon.py scripts\dashboard\pages\dashboard.py scripts\graph_dream.py scripts\graph_query.py scripts\graph_write.py scripts\graph_audit.py
python scripts\skill_daemon.py --once

The daemon test ran conservatively: candidates that failed live evaluation stayed in needs_revision and were not falsely promoted.

Upgrade Notes

  • Run the v7.2 migration if upgrading an existing database.
  • Do not commit local runtime databases or llm_config.json.
  • If you use GitHub Pages, enable Pages deployment from GitHub Actions.
  • Upload assets/social-preview.png as the repository social preview image from GitHub Settings.

Links

Assets 2
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