Skip to content

phantom-visuals.md

Latest commit

 

History

History
140 lines (103 loc) · 6.64 KB

phantom-visuals.md

File metadata and controls

140 lines (103 loc) · 6.64 KB

Phantom Visuals — Multimodal Long Context

🧭 Quick Return to Map

You are in a sub-page of Multimodal_LongContext.
To reorient, go back here:

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 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

  1. Require stamped IDs

    • Every visual mention must cite {frame_id, region_id} from input.
    • Forbid free-text region descriptions without anchors.

  2. Cross-check ΔS

    • ΔS(text, vision) must be ≤ 0.45.
    • If ΔS ≥ 0.60 and no matching anchor exists, stop and reject the claim.

  3. Schema lock

    • Use {object | attribute | anchor_id} schema.
    • Missing anchors = invalid response.

  4. Clamp hallucination variance

    • Apply BBAM when λ flips divergent across runs.
    • If phantom persists, bridge with BBCR and force re-alignment.

  5. 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

🔗 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 tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

If this repository helped, starring it improves discovery so more builders can find the docs and tools.
GitHub Repo stars