Boundary Fade — Guardrails and Fix Pattern

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_type label (e.g., [image], [caption], [audio]).
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.

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