Hallucination Re-entry: Guardrails and Fix Pattern

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You are in a sub-page of Reasoning.
To reorient, go back here:

• Reasoning — multi-step inference and symbolic proofs
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

A “re-entry” is when a model repeats a previously corrected false claim later in the run or in a new turn.
This page localizes re-entry causes and gives a minimal, testable repair plan.

---

Symptoms

Symptom	What you see
Corrected once, comes back later	Old claim resurfaces after a few steps or a new tool call
Cite-then-explain violated	Answer asserts conclusion before citing the corrected snippet
Reruns flip	Same prompt order, different run re-asserts the wrong claim
Memory relapse	Cross-turn memory re-injects the debunked statement
Hybrid retrieval drift	HyDE + BM25 changes top-k ordering and re-pulls the wrong chunk

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Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 to the target section
• λ convergent across 3 paraphrases and 2 seeds
• Re-entry rate = 0 on a 20-case regression set

---

Structural fixes (Problem Map)

• Snippet and citation locks
  → retrieval-traceability.md
  → data-contracts.md
  → Citation-first recipe: citation_first.md

• Ordering control and hybrid stability
  → rerankers.md
  → pattern_query_parsing_split.md

• Memory isolation and fences
  → memory-coherence.md
  → pattern_memory_desync.md
  → memory_fences_and_state_keys.md

• Entropy and chain control
  → context-drift.md
  → entropy-collapse.md

• Pattern deep dive
  → pattern_hallucination_reentry.md

---

Why re-entry happens

1. No hard contract for citations
   Model can answer without binding to snippet_id or section_id.

2. Prompt header drift
   Header reorder flips λ state and reopens earlier branches.

3. Hybrid order instability
   HyDE or BM25 changes top-k; the older wrong chunk returns to rank 1.

4. Memory namespace collision
   Corrected state is not isolated; prior summary re-injects the error.

5. Reranker variance
   Non-deterministic tie-breakers reorder near-duplicates.

---

Fix in 60 seconds

1. Lock cite-then-explain
   Enforce a snippet contract in every reasoning step.
   See data-contracts.md and retrieval-traceability.md.

2. Deterministic reranking
   Freeze analyzer and tie-break rules. Probe k ∈ {5,10,20}.
   See rerankers.md.

3. Memory fences
   Split namespaces: facts/, debunks/, plans/. Write debunk hashes into debunks/.
   See memory_fences_and_state_keys.md.

4. Clamp variance with BBAM
   If λ flips across paraphrases, apply BBAM and re-run with fixed headers.

5. Bridge with BBCR
   Summarize the correction into a single anchored statement and require future turns to import it before reasoning.

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Minimal schema addendum

Add these fields to your snippet payload and logs:

{
  "snippet_id": "S123",
  "section_id": "CH2.3",
  "source_url": "https://...",
  "offsets": [221, 348],
  "tokens": 256,
  "debunk_hash": "sha256(<claim_text + snippet_id>)"
}

The LLM must include debunk_hash in its final JSON if it overturns a claim. On future turns, reject answers that assert a claim whose hash is already present in debunks/.

---

Verification

• Run 20 paraphrases on the same case set.
• Require: ΔS(question, retrieved) ≤ 0.45 and λ convergent on two seeds.
• Zero tolerance for re-entry across all 20 cases.
• If any case fails, inspect reranker tie-break and memory fence writes.

---

Copy-paste prompt

You have TXT OS and the WFGY Problem Map loaded.

We corrected a false claim earlier, but it reappeared later.
Inputs:
- question: "{q}"
- current snippets: [{snippet_id, section_id, source_url}]
- prior debunks: [{debunk_hash, claim_text, snippet_id}]
- ΔS and λ traces across 3 paraphrases

Do:
1) Identify which layer caused re-entry (schema, retrieval, rerank, memory, reasoning).
2) Apply the minimal fix referencing:
   retrieval-traceability, data-contracts, rerankers, memory_fences_and_state_keys.
3) Return a JSON plan with:
   { "citations": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "...",
     "debunk_hashes_used": [...], "next_fix": "..." }
4) Refuse to output an answer if citations are missing or conflict with debunks.

---

When to escalate

• Re-entry persists after fences and deterministic rerank → re-check hybrid split: pattern_query_parsing_split.md

• Cross-turn relapse in long dialogs → audit joins and entropy: context-drift.md, entropy-collapse.md

• Agent handoff resurrects old claims → isolate memory and roles: memory-coherence.md

---

🔗 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 + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

---

Explore More

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⚙️ 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
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