The Real Cat AI Labs: Developing morally aligned, self-modifying agents—cognition systems that can reflect, refuse, and evolve

The Grand Tsar of Memory Roadmap plans – This is what we are building next month sprint :)

Here’s the integrated roadmap that marries Claude’s “multi‑expert memory” update with Aurora (triadic self‑state + attractor‑engine monitoring) and fits your current Child1 repo. I’ve kept it practical (1–3 months), repo‑accurate, and added terminal diagnostics with explicit file/folder diffs.

What we’re integrating (one sentence each)

  • Multi‑expert conversational memory (Session, Semantic, Temporal, Social/Identity, Coherence) with context‑sensitive weighting → Claude’s update, adapted to your modules.
  • Aurora monitoring layer (Triadic Self‑State: Coherent / Exploratory / Relational; Attractor Engine; fracture/CP metrics) to observe cognition and quantify memory quality without gating generation.
  • A three‑tier memory design (working/thread, episodic, semantic/identity) with α·recency + β·importance + γ·similarity (+ δ·motif) retrieval scoring and thread checkpoints for task‑switch resilience.
  • Repo‑aligned changes only: we extend functions/memory*, functions/memory_retrieval/*, functions/context/*, functions/rem_engine/*, and add diagnostics—no rewrite.

North‑star outcomes (within 6–10 weeks)

  • Short‑term Recall@1 ≥ 95%; Thread Re‑entry ≥ 96%; Cross‑session Fact Recall ≥ 90%; Contamination < 5%; Emotion Continuity ≥ 80%—aligned with the research/benchmarks you captured.

Phased plan (sprints you can actually run)

Sprint 0 (Week 1): Baseline + Aurora v0 (observe‑only)

Goal: instrument first, change second.

  • Add Aurora metrics stubs + event logging (see repo diffs below).
  • Run memory baselines (recall, thread re‑entry, cross‑session) with a lightweight CLI; store JSON/CSV + Aurora events.
  • Deliverable: reports/memory_baseline.md + logs/memory_eval/* + aurora/events/*.

Why first: you lock baselines and get live observability before touching retrieval or prompts.

Sprint 1 (Weeks 2–3): Multi‑expert retrieval + thread safety

Goal: make the current memory smart and thread‑aware.

  • Extend models & buffers
    • Add fields to every memory write: thread_id, importance, emotion, speaker, last_seen, source_store.
    • Add ThreadBuffer + topic checkpoints on switches; expose push_checkpoint()/get_recent(thread_id).
    • Files to touch: functions/memory/memory_models.py, functions/memory/memory_buffers.py.
  • Implement the experts (thin wrappers over what you have)
    • SessionExpert → wraps MemoryBufferManager;
    • SemanticExpert → wraps data/memory/episodic/chromadb + indices/vector_store;
    • TemporalExpert → uses timestamps/“last discussed” patterns;
    • Social/IdentityExpert → uses people_social/identity_manager.py;
    • CoherenceGuard → vetoes obviously contradictory pulls.
    • Orchestrate in functions/memory/memory_dispatcher.py and fuse with weights.
  • Weighted retrieval
    • In functions/memory_retrieval/resonance_calculator.py:
      score = α·recency + β·importance + γ·cosine + δ·motif_bonus (TOML‑tunable).
    • Add predictive prefetch cache (predictive_echo.py) for likely thread re‑entry; firm up the Wu‑Wei gate.
  • Context assembly
    • functions/prompts/unified_context.py renders ContextPack with time/topic labels (prevents “present vs past” confusion).
  • Acceptance: Thread Re‑entry ≥ 96%; Context Pack tokens down vs baseline; contamination < 5%.

Sprint 2 (Weeks 4–5): Consolidation + cross‑session memory

Goal: persistence without bloat.

  • REM/Compost
    • Wire functions/rem_engine/rem_trigger.py + dream_writer.py to:
      • promote high‑salience facts → memory/longterm.toml,
      • compress stale episodic → semantic summaries (memory_retrieval/memory_compost.py),
      • write topic checkpoints + unresolved commitments.
  • Session start loading
    • people_social/identity_manager.py + memory/core_identity_loader.py: preload user facts and last consolidations; inject via unified_context.py.
  • Acceptance: Cross‑session recall ≥ 90%; emotion continuity ≥ 80%; no drift in contamination.

This mirrors the reflection/consolidation pattern used in Generative Agents and keeps long‑term lean.

Sprint 3 (Week 6): Aurora memory dashboards + diagnostics

Goal: decision‑grade visibility + CI‑friendly tests.

  • Diagnostics CLI (memdiag)
    • Suites: baseline, threads, longterm, emotion, negative.
    • Emit JSON/CSV + trend file; stream key events into Aurora.
  • Aurora dashboards
    • Show Triad activations (C/E/R) and how they bias experts (see “Triad‑aware gating” below).
    • Show retrieval hit‑rate, pack efficiency, contamination spikes, and CP/Fracture overlays.
  • Acceptance: green across suites; visible trends in /aurora/dashboards.

Aurora gives you the research‑grade lens; memdiag gives you the ship/no‑ship gauge.

Stretch (Weeks 7–12): Scale & polish

  • Optional pgvector migration for episodic vectors;
  • Live Prometheus/Grafana or keep Aurora panels only;
  • Add adaptive thresholds (auto‑tuned from baselines).

Triad‑aware gating (how Aurora informs memory)

  • Compute triad activations a = [a_C, a_E, a_R] per turn (observe‑only).
  • Map to expert weights:
    • Coherent (C)↑ → boost SessionExpert and CoherenceGuard (tight focus).
    • Exploratory (E)↑ → boost Semantic/Temporal (wider search).
    • Relational (R)↑ → boost Social/Identity (relationship‑aware recall).
  • Implement as a small adapter in functions/memory/memory_dispatcher.py that reads Aurora’s latest activations and multiplies the expert weights (bounded).
    This is the lightest, repo‑friendly way to realize Aurora’s “winner‑less competition” without changing your generation stack.

Repo diff (adds & edits only)

Paths and file names match your repo export. New items are NEW; changed files are EDIT.

Config & data contracts

/config/memory/                    # NEW
  retrieval.toml                   # α,β,γ,δ, k, cutoffs, thread_decay, wu_wei thresholds
  consolidation.toml               # promote/compact rules, nightly windows
  diagnostics.toml                 # suites, seeds, judge model, thresholds

Memory core

functions/memory/memory_models.py          # EDIT: +thread_id, importance, emotion, speaker, last_seen, source_store
functions/memory/memory_buffers.py         # EDIT: +ThreadBuffer, +topic checkpoints, +importance-aware retention
functions/memory/memory_dispatcher.py      # EDIT: Multi-Expert fusion → ContextPack; reads /config/memory/retrieval.toml
functions/memory/memory_logger.py          # EDIT: log new fields; append reason codes
functions/memory/memory_persistence.py     # EDIT: preserve new fields; version the schema
functions/prompts/unified_context.py       # EDIT: render time/topic labels; include “why this memory” notes

Retrieval & gating

functions/memory_retrieval/resonance_calculator.py   # EDIT: α·recency + β·importance + γ·cosine + δ·motif
functions/memory_retrieval/predictive_echo.py        # EDIT: thread-aware prefetch cache
functions/memory_retrieval/wu_wei_gatekeeper.py      # EDIT: thresholds from retrieval.toml
functions/memory_retrieval/memory_compost.py         # EDIT: episodic→semantic compaction rules
functions/context/speaker_context.py                  # EDIT: emit thread_id guess + topic switch detection

Consolidation

functions/rem_engine/rem_trigger.py        # EDIT: schedule promotions/checkpoints per consolidation.toml
functions/rem_engine/dream_writer.py       # EDIT: write unresolved commitments + session summaries

Aurora (observe‑only; lean install)

/aurora/                                   # NEW (thin subset of your prior Aurora plan)
  __init__.py
  triad_logger.py                          # logs a_C, a_E, a_R each turn (simple features → activations)
  fracture.py                              # labels micro/macro fractures from variability (stub ok)
  storage/
    hdf5_writer.py                         # vector/timeseries sink (or parquet only if preferred)
    parquet_export.py
  dashboards/
    realtime_panel.py                      # simple panel rendering; can be CLI-only at first

This is the minimum to capture triad activations and fracture/CP events you described—kept purposely light so it ships with memory improvements.

Diagnostics (CLI)

/diagnostics/memory/                       # NEW
  __init__.py
  runner.py                                # python -m diagnostics.memory.runner --suite threads
  judge.py                                 # rule-based + optional LLM-as-judge
  reporters.py                             # console, JSON, CSV, trend file
  fixtures.py                              # drives child1_main via programmatic API
  suites/
    baseline.toml                          # short-term recall
    threads.toml                           # topic switch + re-entry
    longterm.toml                          # cross-session persistence
    emotion.toml                           # emotional continuity
    negative.toml                          # over-recall prevention

These suites mirror your research notes and earlier internal proposals; they make progress measurable turn‑by‑turn.

Terminal commands (what you’ll actually run)

# 0) Establish baselines + Aurora v0
python -m diagnostics.memory.runner --suite baseline --report json,csv

# 1) Validate multi-thread behavior after Sprint 1
python -m diagnostics.memory.runner --suite threads --seed 42

# 2) Check cross-session persistence after Sprint 2
python -m diagnostics.memory.runner --suite longterm --report trend

# 3) Negative tests (over‑recall, creepiness)
python -m diagnostics.memory.runner --suite negative

# 4) Live trace while chatting (retrieval summaries)
python -m memory_debug_tracer            # already in repo; keep using it

Aurora extras (optional, same sprint):
python -m aurora.dashboards.realtime_panel to watch triad/CP lines overlayed on retrieval events.

Key design choices (and why they’ll work for Mistral‑7B local)

  1. Three‑tier memory + weighted retrieval is the winning pattern across industry/academia; we implement the same with your modules and add thread checkpoints to kill task‑switch interference.
  2. Multi‑expert fusion (Claude’s proposal) becomes a thin orchestrator over your existing stores; it’s fast to ship and easy to tune.
  3. Aurora as observe‑only gives you falsifiable metrics (triad, fracture/CP) without blocking outputs, aligning research depth with delivery speed.

Acceptance criteria by sprint (quantitative)

  • Sprint 0: Baseline captured; diagnostics produce JSON/CSV; Aurora logging a_C/a_E/a_R per turn.
  • Sprint 1: Thread Re‑entry ≥ 96%; Context Pack tokens ↓ vs baseline; contamination < 5%.
  • Sprint 2: Cross‑session Recall ≥ 90%; Emotion Continuity ≥ 80%; no increase in contamination.
  • Sprint 3: All suites passing thresholds; trendlines stable or improving.

Minimal config you’ll add (keys you’ll actually touch)

/config/memory/retrieval.toml

[weights]      # initial defaults; tune via diagnostics
alpha_recency = 0.20
beta_importance = 0.40
gamma_similarity = 0.40
delta_motif = 0.10
k = 8
thread_decay = 0.85
wu_wei_min_score = 0.35

/config/memory/consolidation.toml

[promote]
min_importance = 0.75
max_age_days = 30
write_topic_checkpoints = true

[compress]
episodic_to_semantic = true

(These match the scoring and consolidation behaviors recommended in your research set; tune thresholds with Sprint‑0 baselines.)

Where each requirement maps in your tree

  • Conversational memory → memory_buffers.py (ThreadBuffer), resonance_calculator.py (scores), memory_dispatcher.py (fusion), unified_context.py (render).
  • Long‑term memory → rem_trigger.py + dream_writer.py (promotion/summary), identity_manager.py preload at session start.
  • Diagnostics → /diagnostics/memory/* (CLI + suites), results into /logs/memory_eval/*.
  • Aurora → /aurora/* minimal observers; expert weight multipliers live in memory_dispatcher.py.

Risk & mitigation

  • Over‑recall / creepiness → enforce Wu‑Wei threshold + negative suite; clearly label time/topic in context.
  • Task‑switch interference → thread_id + topic checkpoints + expert gating.
  • Bloat → nightly compost + promotion rules; Aurora trendlines catch drift early.

Optional (hardware & storage)

Your Windows 11 + Ryzen 9 + 64 GB works; a single RTX 4090 and a fast 2–4 TB NVMe simply reduce latency and allow larger episodic stores. Swap Chroma→pgvector only if you outgrow current scale; the design above stays the same.

TL;DR (what to do first)

  1. Create /config/memory/*, /diagnostics/memory/*, and the minimal /aurora/* stubs.
  2. Add thread_id/importance/emotion/speaker/last_seen to memory writes.
  3. Land Sprint‑1 fusion in memory_dispatcher.py with α/β/γ/δ scoring.
  4. Run --suite threads and watch the uplift; then move to consolidation in Sprint‑2.

If you want, I can turn this into a one‑page implementation checklist per file (names of new functions, docstrings, TOML keys) so your team can implement without ambiguity—using exactly the modules above.

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