Hallucination Re-entry: Guardrails and Fix Pattern

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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

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

No hard contract for citations
Model can answer without binding to snippet_id or section_id.
Prompt header drift
Header reorder flips λ state and reopens earlier branches.
Hybrid order instability
HyDE or BM25 changes top-k; the older wrong chunk returns to rank 1.
Memory namespace collision
Corrected state is not isolated; prior summary re-injects the error.
Reranker variance
Non-deterministic tie-breakers reorder near-duplicates.

Fix in 60 seconds

Lock cite-then-explain
Enforce a snippet contract in every reasoning step.
See data-contracts.md and retrieval-traceability.md.
Deterministic reranking
Freeze analyzer and tie-break rules. Probe k ∈ {5,10,20}.
See rerankers.md.
Memory fences
Split namespaces: facts/, debunks/, plans/. Write debunk hashes into debunks/.
See memory_fences_and_state_keys.md.
Clamp variance with BBAM
If λ flips across paraphrases, apply BBAM and re-run with fixed headers.
Bridge with BBCR
Summarize the correction into a single anchored statement and require future turns to import it before reasoning.

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

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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