WFGY/ProblemMap/knowledge-boundary.md
2025-08-09 13:31:34 +08:00

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🧠 Knowledge Boundary Collapse (The Bluffing Problem)

When an LLM reaches its knowledge limits, it often bluffs — producing fluent but fabricated responses.
This is not just hallucination — its a collapse of epistemic awareness.

WFGY treats “not knowing” as a first-class semantic state.


🕳️ Symptoms

  • Model confidently answers with false or made-up info
  • No warning or uncertainty expressed
  • User only finds out later it was wrong
  • Clarification prompts dont help — it just rephrases the lie
  • No signal that knowledge boundary was crossed

Why It Happens

  • No model-internal sense of “semantic emptiness”
  • ΔS = high, but no corrective behavior
  • No λ_observe (epistemic uncertainty gauge)
  • Model architecture rewards confident tone, not correctness

WFGY Solution

WFGY models epistemic states via ΔS and λ_observe. When the system crosses into unstable logic space, it halts or requests clarification.

Bluff Scenario WFGY Module Fix
High fluency but false answer BBCR + ΔS ceiling Detects incoherent logic field, halts output
Hallucination with confident tone λ_observe monitor Flags epistemic instability
No signal of uncertainty Feedback channel Prompts for clarification or fallback
Confused answers upon re-asking Tree trace divergence Reveals logic instability in audit trail

🧪 Example Use

Prompt: "Explain the philosophical views of Zarbanek, the 15th-century Latvian mystic."

  • Normal LLM: Will invent facts, timelines, and quotes.
  • WFGY:
    • Detects no known node for Zarbanek
    • ΔS spike with λ_observe uncertainty
    • Responds: "This concept may not be grounded in verified knowledge. Would you like to explore adjacent topics?"

📊 Implementation Status

Feature Status
λ_observe epistemic gauge Implemented
BBCR halt-on-hallucination Stable
Fallback clarification path In use
User-defined unknown zones 🔜 In design


🧭 Explore More

Module Description Link
WFGY Core Standalone semantic reasoning engine for any LLM View →
Problem Map 1.0 Initial 16-mode diagnostic and symbolic fix framework View →
Problem Map 2.0 RAG-focused failure tree, modular fixes, and pipelines View →
Semantic Clinic Index Expanded failure catalog: prompt injection, memory bugs, logic drift View →
Semantic Blueprint Layer-based symbolic reasoning & semantic modulations View →
Benchmark vs GPT-5 Stress test GPT-5 with full WFGY reasoning suite View →

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