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5.8 KiB
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Boundary Fade — Guardrails and Fix Pattern
🧭 Quick Return to Map
You are in a sub-page of Multimodal_LongContext.
To reorient, go back here:
- Multimodal_LongContext — long-context reasoning across text, vision, and audio
- 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.
When multimodal long-context windows extend, boundaries between modalities or section joins blur.
This causes models to conflate captions, transcripts, or visuals across neighboring regions, producing hybrid outputs or lost anchors.
Symptoms of Boundary Fade
- Captions merge into adjacent paragraphs, citations drift by a few lines.
- Visual snippets spill across sections, losing clear demarcation.
- ΔS across joins rises above 0.50, meaning semantic leakage.
- Output shows partial traces of two anchors instead of one.
- “Memory fade” across session restarts, context joins feel smeared.
Open these first
- Join stability and chunk fences: Chunking Checklist
- Attention variance and entropy melt: Entropy Collapse
- Context drift at long horizon: Context Drift
- Visual trace schema: Cross-Modal Trace
- Session state guards: Memory Coherence
Fix in 60 seconds
-
Measure joins
- Compute ΔS across each modality join. Threshold ≤ 0.50.
- If higher, suspect boundary fade.
-
Enforce fences
- Insert
{section_start}and{section_end}markers explicitly. - Require
mod_typelabel (e.g.,[image],[caption],[audio]).
- Insert
-
Stabilize variance
- Apply BBAM clamp when variance spikes near joins.
- Use BBCR bridge to redirect reasoning back to the intended anchor.
-
Audit output
- Each snippet must map to a single anchor ID.
- Reject blended outputs that merge two snippet IDs.
Acceptance Targets
- ΔS(question, retrieved) ≤ 0.45 overall.
- ΔS across joins ≤ 0.50.
- λ_observe convergent across three paraphrases.
- No section bleed: one snippet → one anchor only.
Copy-paste prompt
You are running TXTOS + WFGY Problem Map.
Symptom: section or modality boundaries blur (“boundary fade”).
Protocol:
1. Compute ΔS across joins, enforce ≤ 0.50.
2. Insert section_start and section_end markers.
3. Require mod_type labels for all snippets.
4. Apply BBAM clamp, BBCR bridge if joins collapse.
5. Verify each snippet maps to exactly one anchor ID.
🔗 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 |
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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