WFGY/ProblemMap/GlobalFixMap/LLM_Providers/meta_llama.md

Meta Llama: Guardrails and Fix Patterns

This page gives an operational checklist for Meta Llama based assistants inside RAG and agent stacks. It maps the usual failure modes to concrete WFGY fixes and acceptance targets.

Acceptance targets

• ΔS(question, retrieved_context) ≤ 0.45
• Coverage of retrieved vs target section ≥ 0.70
• λ_observe stays convergent across 3 paraphrases
• E_resonance flat on long windows

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Common failure patterns seen with Llama setups

1. Plausible but wrong answers even when chunks look fine
   Map to: Interpretation Collapse and Hallucination & Chunk Drift.
   Check also Embedding ≠ Semantic and the Retrieval Playbook.

2. Degradation in long dialogs or large context
   Map to: Context Drift and Entropy Collapse.

3. Role loss after tool calls or agent hops
   Map to: Multi-Agent Problems and deep dive Role Drift.

4. Overconfident answers without citations
   Map to: Bluffing / Overconfidence. Enforce traceable schemas with Retrieval Traceability and Data Contracts.

5. Hybrid retrieval oscillation, high similarity but wrong meaning
   Map to: Embedding ≠ Semantic and Rerankers. Tune using the Retrieval Playbook.

6. Cross-source merging and leakage
   Map to: Symbolic Constraint Unlock pattern
   → SCU pattern with strict Data Contracts.

7. Tokenizer or locale mismatch on non-English corpora
   Map to: Multilingual Guide and re-probe with Embedding ≠ Semantic.

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WFGY repair map for Llama

• Measure
  ΔS probes on question ↔ retrieved and retrieved ↔ ground. Use the Retrieval Playbook plus Retrieval Traceability.

• Localize
  Tag λ_observe at retrieval, prompt assembly, and reasoning. If retrieval λ is convergent but reasoning λ flips, jump to Interpretation Collapse.

• Repair
  Apply BBMC for anchor re-alignment, BBAM for variance clamp on long windows, BBCR for controlled reset on dead ends, BBPF for alternate path search. See:
  Logic Collapse & Recovery,
  Context Drift,
  Entropy Collapse.

• Lock schema
  Enforce citation-first and per-source fences with
  Retrieval Traceability and Data Contracts.

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Quick triage steps

1. Probe ΔS(question, retrieved_context). If ≥ 0.60 open:
   Embedding ≠ Semantic and Hallucination.

2. Vary k in {5, 10, 20} and chart ΔS vs k. Flat-high curve points to index or metric mismatch
   → Retrieval Playbook.

3. If chunks are correct but logic is wrong, mark λ at reasoning and apply BBCR + BBAM
   → Interpretation Collapse and Logic Collapse.

4. For long dialogs, verify joins with ΔS ≤ 0.50 and clamp variance
   → Context Drift and Entropy Collapse.

5. If sources bleed, enforce SCU and per-section fences
   → SCU pattern and Retrieval Traceability.

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Minimal safe prompt you can paste

I uploaded TXT OS. Read WFGY formulas and Problem Map pages.
My stack runs on Meta Llama.

symptom: \[describe]
traces: \[ΔS probes, λ states, short logs]

Tell me:

1. the failing layer and why,
2. the exact WFGY page to open next,
3. the minimal steps to push ΔS ≤ 0.45 with convergent λ,
4. how to verify the fix with a reproducible test.

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Escalation and ops

• If ΔS stays ≥ 0.60 after retrieval and prompt fixes, change structure
  → Logic Collapse & Recovery.

• For runtime surprises, drift after deployment, or mixed agent stacks
  → Live Monitoring,
  Debug Playbook,
  Deployment Checklist,
  Failover and Recovery.

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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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	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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