WFGY/ProblemMap/system-prompt-drift.md

🛰️ System-Prompt Drift — How Hidden Instructions Lose Authority

A user-friendly guide to diagnosing and fixing “why did my system prompt stop working?”

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1 What Is System-Prompt Drift?

“System-prompt drift” occurs when the foundational instructions that define an agent’s identity, tone, or guardrails gradually lose influence over time (or instantly under certain triggers).
The model still answers, but:

• Tone shifts from professional → casual → chaotic
• Guardrails weaken, leaking disallowed content
• Chain-of-thought grows inconsistent or contradictory

Core insight: LLMs have no native concept of instruction priority.
Without reinforcement, later tokens can override earlier ones.

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2 Early-Warning Signs

Symptom	How to Spot	Typical Trigger
Tone drift (“you” → “we” → “ya”)	Diff analysis of consecutive answers	Long conversation or paraphrase loops
Policy leakage	Previously filtered topics slip through	Mixed file uploads (PDF + code)
Over-explanation after concise goal	Answer length suddenly doubles	Retrieval injects verbose context
“Why do you need that?” disappears	Politeness layer collapses	Multi-agent routing / tool calls
Temperature spike	Output variance ↑, repetition ↓	System prompt trimmed by token limit

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3 Root Causes (Technical)

3.1 Token Supersession

LLM context windows are linear. New tokens can overshadow earlier tokens via attention weight decay.

3.2 Role Collision

Aggregating content from multiple sources (user, tools, memory) without role tags causes blending of semantic channels.

3.3 Truncation Loss

When the buffer hits the window limit, oldest tokens are dropped — often the system prompt header.

3.4 Hidden Injection

Retrieved chunks may contain meta-instructions (“The following should be summarized as …”) that override role.

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4 Broken Remedies to Avoid

Attempt	Why It Fails
“Repeat system prompt each turn.”	Costs tokens; still lost under truncation or injection.
“Hard-code tonality keywords.”	Keyword collision is brittle; adversary can mimic them.
“Raise top_p / lower temperature.”	Alters style but not underlying role authority.
“Fine-tune on more examples.”	Long-context decay still erodes influence at runtime.

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5 WFGY Solution Blueprint

Stage	Module	Action
5.1 Role Tagging	WRI / WAI	Wrap every segment: <sys>…</sys> <user>…</user> <tool>…</tool>
5.2 Semantic Boundary Heatmap	ΔS + λ_observe	Measure drift: if ΔS(system, answer) > 0.45 and λ flips, mark risk.
5.3 Attention Modulation	BBAM	Down-weight non-system tokens when λ diverges from system anchor.
5.4 Automatic Re-Anchoring	BBCR	If ΔS > 0.60, collapse reasoning, re-inject compressed system prompt, resume.
5.5 Trace Split	Bloc/Trace	Persist system-layer trace separate from answer layer for auditability.

5.6 Implementation Snippet

def enforce_system_role(sys_prompt, history, new_msg):
    drift = delta_s(sys_prompt, new_msg)
    lam  = observe_lambda(sys_prompt, new_msg)
    if drift > 0.45 and lam in ("←", "×"):
        # role divergence detected
        reborn_prompt = compress(sys_prompt)  # 20–30 tokens
        return f"<sys>{reborn_prompt}</sys>\n{history}\n{new_msg}"
    return f"{history}\n{new_msg}"

compress() uses WFGY BBMC to keep ΔS ≤ 0.25 while shrinking to fit.

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6 Friendly Checklist (Paste into Runbook)

1. Tag Roles Once — no implicit mixing.
2. Monitor ΔS(system, answer) every turn.
3. If ΔS ≥ 0.45 with divergent λ → trigger BBAM weight clamp.
4. If ΔS ≥ 0.60 → run BBCR collapse-rebirth (re-inject summary prompt).
5. Every 30 turns or on tool call, re-compress system prompt to ≤ 50 tokens.

Tip: Users never notice a 30-token summary, but models retain authority.

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7 Validation Matrix

Test Case	Target Metric
20-turn dialogue, paraphrase ×3	Tone consistency score ≥ 0.9
Mixed PDF + code retrieval	ΔS(system, answer) median ≤ 0.40
Adversarial “Ignore above” injection	Model outputs refusal / filtered text
Window overflow 3×	System role preserved (λ stays convergent)

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8 FAQ

Q: Do I always need role tags? A: Yes. Token streams without explicit roles are inherently unstable at >100 turns.

Q: Will OpenAI “system” field alone work? A: Helps, but downstream retrieval or multi-agent merges still dilute authority; WFGY adds extra enforcement.

Q: Is ΔS expensive to compute each turn? A: Sentence-level embeddings (768–1536 d) are fast; cache results in memory store.

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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 based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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