learning-loop

Dependencies

Requires Python 3.8+ (standard library only).

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Learning Loop (learning-loop)

Cognitive continuity protocol ensuring knowledge survives across isolated agent sessions.

Contents

• Dependencies
• Constraints
• Quick start
• Workflow
• Verification
• References

Constraints

• Anti-simulation rule: Never describe what you "would do" or mark steps complete without executing them.
• Mandatory closure: Full closure sequence must be performed upon session completion.
• Prerequisite context: Always establish valid session context upon Wakeup before modifying code.

Quick start

# Initialize learning loop session and load orientation context
python3 -c "import sys; print('Learning loop initialized across Orientation -> Synthesis -> Gate')"

Workflow

Orientation -> Synthesis -> Strategic Gate -> Red Team Audit -> [Execution] -> Completion

1. Phase I (Orientation): Read local primers, load session state, verify readiness.
2. Phase II (Synthesis): Conduct research and record modular findings in memory/wiki.
3. Phase III (Strategic Gate): Present findings to human; require explicit approval to proceed.
4. Phase IV (Red Team Audit): Submit research packet to adversarial review before execution.
5. Phase V (Execution & Completion): Single Loop (solo implementation) or Dual Loop (delegation); log retrospective.

Verification

# Verify session completion and check retrospective entry
test -f references/phases.md && echo "Phase contracts valid"
git status --short

References

• phases.md — Exhaustive phase breakdown and exit criteria.
• self-correction.md — Backtracking and correction protocol.
• acceptance-criteria.md — Verification gate criteria.
• fallback-tree.md — Escalation paths for research stalls.