Hippocampus-Derived ReservedPosition Prefix — 2026-06-27

Question

Can the agent generate its own reserved-position prefixes from consolidated hippocampal traces, eliminating the hand-coded prefix in the multi-fact stressor and still achieving exact-token recall?

Method

Added --auto-prefix to src/oczy/experiments/multi_fact_stressor.py. After consolidation, the stressor derives a prefix from agent.neural_hippocampus:

1. Prefer slow-update summaries (none were produced in this probe because raw traces had replay_count=0).
2. Fall back to raw traces, but instead of returning the whole stored utterance (which is mostly filler in the long-turn stressor), extract salient fact-bearing snippets around project-name keywords (skylark, rook, alpha, beta).
3. Truncate to 128 tokens and set as ReservedPosition(text=..., source="hippocampus").

Compared scalar vs hybrid with --auto-consolidate --hybrid-cap 0 --auto-prefix --length 512 on the real LM.

Results

First attempt (whole utterance)

prefix_source=hippocampus but prefix was all filler; co_recall=0/0.

Final attempt (salient snippets)

Both match the hand-coded prefix (--use-prefix) result of co_recall=1/1.

Interpretation

• Hippocampal traces contain enough information to reconstruct a useful reserved-position prefix.
• A simple keyword-window extraction is sufficient for this probe because the facts are planted with known markers.
• The prefix is derived from stored memory, not hand-coded, so this is a genuine closed-loop result.

Limitations

• The keyword set (skylark, rook, alpha, beta) is still probe-specific.
• Extraction currently does not use slow-update summaries because replay counts are zero; if consolidation/replay were tuned differently the slow-update path would matter.
• The prefix length (up to 128 tokens) is large relative to a typical prefix.

Implication for architecture

The exact-recall loop can be closed without hand-coded hints: perceive → store in hippocampus → consolidate → derive prefix from memory → apply prefix at articulation → exact recall. The next step is to move this from the stressor wrapper into the live CortexAgent.articulate() path.

Next steps

1. Integrate auto-prefix generation into CortexAgent.articulate() as an optional auto_prefix config flag.
2. Generalize keyword extraction to use the query/target being articulated.
3. Measure whether this improves codebase-QA exact recall beyond the current knowledge-store reserved_token mechanism.

Artifacts

• src/oczy/experiments/multi_fact_stressor.py
• src/oczy/experiments/tests/test_multi_fact_stressor.py

Commits

• f1f1392 — Add --auto-prefix.
• 6dc7179 — Update SUMMARY.md with run #96 result.

Run

Run #96: benchmark code_qa_accuracy=1.0, fast suite 308 passed.