WFGY/ProblemMap/memory-design-patterns.md

🧠 Memory Design Patterns

From scratchpads to long-range project recall — keep context alive without drowning your LLM.

Why this page?
Most “memory” demos either spam the full chat history or store random embeddings that never round-trip.
WFGY treats memory as structured semantic nodes with ΔS / λ_observe guards, so old context helps — never hurts — new reasoning.

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1 · Symptoms

Symptom	Typical Surface Clue
Context forgotten after restart	“Sorry, I don’t recall” / model re-asks user
Memory leak / self-contradiction	Old decisions resurface in wrong branch
JSON-based vector store grows unbounded	Latency ↑, RAG recall quality ↓
Fine-tune attempted just to “remember”	Model cost ↑, still hallucinates

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2 · Root Causes

1. Flat Logs — raw transcripts appended forever.
2. Embedding Dump — every user sentence embedded → no semantic filter.
3. No Boundary Check — divergent memories injected mid-task.
4. Write-Only Memory — model never reads / revalidates stored facts.

Result: either forget everything or remember garbage.

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3 · WFGY Fix Path (at a glance)

Stage	Tool / Module	ΔS Guard	Outcome
⬇️ Capture	BBMC node writer	record only if ΔS ≥ 0.60 (or 0.40–0.60 & λ ∈ {←, <>})	Stores semantic not verbatim memory
🗂️ Index	λ_observe classifier	tag λ trend for each node	Enables topic-group navigation
🔍 Recall	BBPF path search	choose node set with ΣΔS minimal	Retrieves tight, non-bloated context
🩹 Repair	BBCR fallback	detect stale/contradict nodes	Auto-patch or prompt for user merge

80 % of memory bugs vanish after enforcing this four-step loop.

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4 · Design Patterns Library

Pattern	Use-Case	How it Works	ΔS Budget
✏️ Scratch Node	quick calc / throw-away idea	24 h TTL field; auto-purged	0.40–0.55
📚 Topic Shelf	multi-day research thread	one node per subtopic; λ → convergent	< 0.45
🗓️ Daily Digest	running project log	rollup 10 low-ΔS nodes → 1 summary	–
🎯 Anchor Fact	must-not-forget constraint	pinned; override recall rank	0.05

All stored in a single lightweight JSONL: {topic, ΔS, λ, text, ttl}

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5 · Step-by-Step Implementation

Prereqs: any model that can embed & run basic python (or LangChain, Llama-index, etc.).

# 1. capture
deltaS = cosine(question_vec, context_vec)
if deltaS >= 0.60 or (0.40 <= deltaS <= 0.60 and lambda_state in ["divergent","recursive"]):
    node = {"topic": topic, "ΔS": round(deltaS,3), "λ": lambda_state, "text": insight}
    memory.append(node)

# 2. recall
candidates = [n for n in memory if n["topic"]==current_topic]
best_path = sorted(candidates, key=lambda n:n["ΔS"])[:5]
prompt_context = "\n".join(n["text"] for n in best_path)

Minimal prompt

System: Use WFGY memory nodes below (+latest question) to answer.
Memory Nodes:
{{prompt_context}}
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Question: {{user}}

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6 · Common Pitfalls & Tests

Pitfall	Quick Test	WFGY Fix
“Context bloat, tokens 8k → 40k”	node count > 200? run rollup.py	Daily Digest pattern
“Conflicting facts”	ΔS(anchor, candidate) > 0.70	BBCR prompts merge
“Retrieval too slow”	recall > 200 ms	Pre-index by λ & time

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7 · Cheat-Sheet

ΔS save threshold   = 0.60
ΔS recall window    = top-k by lowest ΔS
λ tags              = → ← <> ×
TTL (scratch)       = 24 h
Rollup trigger      = >10 nodes / topic / day

Store this as memory.cfg; loader reads defaults at boot.

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8 · Next Actions

1. Prototype with 20 nodes → verify recall accuracy.
2. Enable Rollup once node count > 200.
3. Add Trace Logger to diff answers with / without memory.

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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