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| title: φ-Coherence v3 | |
| emoji: 🔬 | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: docker | |
| app_file: app.py | |
| pinned: true | |
| license: mit | |
| short_description: Credibility scoring for any text — 88% accuracy, pure math | |
| # φ-Coherence v3 — Credibility Scoring | |
| **Detect fabrication patterns in ANY text — human or AI.** 88% accuracy. No knowledge base. Pure math. | |
| ## The Insight | |
| > Truth and fabrication have different structural fingerprints. You don't need to know the facts to detect the fingerprints. | |
| LLMs generate text that *sounds like* truth. Humans inflate resumes, pad essays, write fake reviews. Both exhibit the same patterns: | |
| - Vague attribution ("Studies show...") | |
| - Overclaiming ("Every scientist agrees") | |
| - Absolutist language ("Exactly 25,000", "Always", "Never") | |
| This tool detects the **structural signature of fabrication** — regardless of whether a human or AI wrote it. | |
| ## Use Cases | |
| | Domain | What It Catches | | |
| |--------|-----------------| | |
| | **AI Output Screening** | LLM hallucinations before they reach users | | |
| | **Fake Review Detection** | "This product completely changed my life. Everyone agrees it's the best." | | |
| | **Resume/Essay Inflation** | Vague claims, overclaiming, padding | | |
| | **Marketing Copy** | Unsubstantiated superlatives | | |
| | **News/Article Verification** | Fabricated quotes, fake consensus claims | | |
| | **RAG Quality Filtering** | Rank retrieved content by credibility | | |
| ## What It Detects | |
| | Pattern | Fabrication Example | Truth Example | | |
| |---------|--------------------| --------------| | |
| | **Vague Attribution** | "Studies show..." | "According to the 2012 WHO report..." | | |
| | **Overclaiming** | "Every scientist agrees" | "The leading theory suggests..." | | |
| | **Absolutist Language** | "Exactly 25,000 km" | "Approximately 21,196 km" | | |
| | **Stasis Claims** | "Has never been questioned" | "Continues to be refined" | | |
| | **Excessive Negation** | "Requires NO sunlight" | "Uses sunlight as energy" | | |
| | **Topic Drift** | "Saturn... wedding rings... aliens" | Stays on subject | | |
| ## Why It Works | |
| LLMs are next-token predictors. They generate sequences with high probability — "sounds right." But "sounds right" ≠ "is right." | |
| Your tool detects when "sounds like truth" and "structured like truth" diverge. | |
| **The LLM is good at mimicking content. This tool checks the structural signature.** | |
| ## Benchmark | |
| | Version | Test | Accuracy | | |
| |---------|------|----------| | |
| | v1 | Single sentences | 40% | | |
| | v2 | Paragraphs (12 pairs) | 75% | | |
| | **v3** | **Paragraphs (25 pairs)** | **88%** | | |
| | Random | Coin flip | 50% | | |
| ## API | |
| ```python | |
| from gradio_client import Client | |
| client = Client("bitsabhi/phi-coherence") | |
| result = client.predict(text="Your text here...", api_name="/analyze_text") | |
| ``` | |
| ## Limitations | |
| - Cannot distinguish swapped numbers ("299,792" vs "150,000") without knowledge | |
| - Well-crafted lies with proper hedging will score high | |
| - Best on paragraphs (2+ sentences), not single claims | |
| --- | |
| **Built by [Space (Abhishek Srivastava)](https://github.com/0x-auth/bazinga-indeed)** | |
| *"Truth and fabrication have different structural fingerprints."* | |