Phantom Visuals — Multimodal Long Context

When models hallucinate visual regions that do not exist (ghost bounding boxes, fake diagrams, or nonexistent objects), fusion collapses.
This page explains how to detect and prevent phantom visual generation in long multimodal sessions.

What this page is

Guardrails for hallucinated visuals in text–image/video pipelines.
Minimal schema to force grounding in actual frames or regions.
Acceptance targets to measure and verify stability.

When to use

The model cites an object not present in any frame.
Generated captions describe phantom regions or colors.
Bounding box coordinates are out of range or undefined.
Answers flip between different “visual evidence” each run.
Diagrams or charts are invented that were never uploaded.

Open these first

Common failure patterns

Phantom bounding boxes: cites region_id that was never stamped.
Invented objects: describes entities absent from ground-truth frames.
Ghost captions: text generated about visual details that do not exist.
Out-of-bounds references: coordinates or time stamps not in the source.
Visual-plan instability: repeated runs yield different “phantom” evidence.

Fix in 60 seconds

Require stamped IDs
- Every visual mention must cite {frame_id, region_id} from input.
- Forbid free-text region descriptions without anchors.
Cross-check ΔS
- ΔS(text, vision) must be ≤ 0.45.
- If ΔS ≥ 0.60 and no matching anchor exists, stop and reject the claim.
Schema lock
- Use {object | attribute | anchor_id} schema.
- Missing anchors = invalid response.
Clamp hallucination variance
- Apply BBAM when λ flips divergent across runs.
- If phantom persists, bridge with BBCR and force re-alignment.
Trace visual contract
- Log all cited frame_id, region_id.
- Require reproducibility across three paraphrases.

Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: Detect and block phantom visual hallucinations.

Protocol:
1. Require every visual claim to cite {frame_id, region_id}.
2. If an object is described without anchor, stop and return “phantom visual”.
3. Report ΔS(text, vision) and λ across 3 paraphrases.
4. Apply BBAM for variance clamp. If collapse persists, insert BBCR bridge.
5. Return: {Anchor Table, ΔS log, λ states, Final Answer}.

Acceptance targets

ΔS(text, vision) ≤ 0.45
λ remains convergent across three paraphrases
No phantom bounding boxes or invented regions
Reproducible evidence across seeds and paraphrases
Trace log covers all cited regions

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

🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
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