WFGY/ProblemMap/GlobalFixMap/Multimodal_LongContext/alignment-drift.md

Alignment Drift — Multimodal Long Context

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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.

Stabilize alignment across text, vision, and audio streams when context windows grow large.
This page targets failures where one modality "slides" relative to another, producing mismatched captions, annotations, or reasoning.

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What this page is

• A compact guide for repairing multimodal misalignment in long contexts.
• Copyable checks to stop drift across text ↔ image ↔ audio.
• Traceable targets with ΔS and λ_observe across modalities.

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When to use

• Captions describe the wrong part of an image after 30k+ tokens.
• Audio transcripts align at the start but drift seconds or minutes later.
• OCR blocks look fine alone but slip relative to the visual reference.
• Mixed queries (e.g. "this diagram plus the caption") yield mismatched answers.
• Model references an object not present in the visual frame.

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Open these first

• Memory Coherence
• Entropy Collapse
• Context Drift
• Retrieval Traceability
• Data Contracts

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Common failure patterns

• Temporal slide: transcript gradually shifts out of sync with audio.
• Spatial mismatch: caption references wrong region of the image.
• Cross-modal fork: text and visual streams each stay consistent but no longer match each other.
• Phantom link: answer cites a visual object or caption that does not exist.

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Fix in 60 seconds

1. Stamp each modality

   • For text: token_rev, span_id.
   • For audio: timecode_start, timecode_end.
   • For image: region_id, bbox.
     Require cross-modal joins to match stamps.

2. Normalize anchors

   • Resample audio to fixed fps.
   • Lock OCR and captions to line/region boundaries.
   • Strip duplicate spans.

3. Fence joins

   • Forbid text tokens from linking across mismatched region/time.
   • Require ΔS(join) ≤ 0.50 across modalities.

4. Apply semantic clamps

   • BBAM for variance across visual vs textual embedding space.
   • BBCR bridge if λ diverges between modalities.

5. Trace every join

   • Log: {span_id, region_id, timecode, ΔS, λ_state}.
   • Fail fast if join lacks citation.

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Copy-paste prompt

You have TXT OS and WFGY Problem Map.

Task: Repair multimodal alignment in a long context.

Steps
1. Print {span_id, region_id, timecode} for all retrieved units.
2. Require cite-then-answer, forbid phantom objects.
3. Compute ΔS(text, image), ΔS(text, audio).  
   If any ≥ 0.60, propose minimal fix using data-contracts or chunking-checklist.
4. Apply BBAM if variance spikes. Apply BBCR if λ diverges.  
5. Return answer with inline citations and alignment log.

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Acceptance targets

• ΔS(text ↔ image) ≤ 0.45
• ΔS(text ↔ audio) ≤ 0.45
• Joins across modalities ≤ 0.50
• λ remains convergent across three paraphrases
• No phantom links (every object/claim tied to citation id)

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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 + ”
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	Canonical framework entry point	View
Problem Map	Diagnostic map and navigation hub	View
Tension Universe Experiments	MVP experiment field	View
Recognition	Where WFGY is referenced or adopted	View
AI Guide	Anti-hallucination reading protocol for tools	View

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