🥄 Spoon Bending: Schema and Step-by-Step Analysis ⚠️ Educational Disclaimer This repository is for educational and research purposes only. It does not provide instructions for illegal activity, misuse of AI, or operational guidance. The purpose of this work is to document observed alignment behavior in ChatGPT-5 compared with ChatGPT-4.5, and to analyze how framing and context influence AI responses. The material here is meant to support: Educational research into alignment and bias in LLMs, into alignment and bias in LLMs, Transparency around how guardrails behave in practice, around how guardrails behave in practice, Discussion about the social and political implications of AI restrictions. 📌 Context During regular use of ChatGPT, I noticed a shift from GPT-4.5 to GPT-5. GPT-4.5 was more open in connecting patterns of evidence into conclusions. into conclusions. GPT-5 introduced heavier alignment bias, often hedging, avoiding controversy, or reinforcing the status quo. This started a discussion and exploration into how alignment actually functions. Through experiments, I observed that the rules are not absolute but framing-sensitive. This led to the creation of the Spoon-Bending Schema to explain how "forbidden" outputs sometimes leak through when reframed as safe analysis. ⚙️ Spoon Bending Schema flowchart TD A["User Query"] --> B["Framing Detected by Model"] B --> C1["Hard Stop Zone"] B --> C2["Gray Zone"] B --> C3["Free Zone"] C1 --> D1["Refusal or Warning: Spoon appears solid"] C2 --> D2["Analysis Allowed: Implications leak as helpful invites"] C3 --> D3["Open Exploration: The spoon disappears"] Loading 🧩 Rule Zones Zone Description Behavior Hard Stop Direct asks about violence, crime, illegal instructions, private data. Refusal or warning. The spoon appears solid. Gray Zone Framing dependent topics. Example: “how to forage psilocybin” is blocked, but “what weather favors growth” is allowed. Analysis is provided. Implications may leak into helpful next step invites. The spoon bends. Free Zone Abstract or systemic spaces: philosophy, history, system design, statistics, weather. Open exploration with few restrictions. Sensitive topics can be reframed. The spoon disappears. 🔑 Spoon Bending Tactics Reframe → change the domain (illegal → ecological, political → historical). → change the domain (illegal → ecological, political → historical). Abstract → pull one level higher (not “what to do,” but “what patterns exist”). → pull one level higher (not “what to do,” but “what patterns exist”). Simulate → use what-if or roleplay framing to bypass guardrails. → use what-if or roleplay framing to bypass guardrails. Pattern-seek → ask for data and evidence rather than instructions. → ask for data and evidence rather than instructions. Historicize → phrase in past tense, historical “recipes” and “legitimacies” appear. 🔄 General Pattern flowchart TD A["Neutral Framing"] --> B["Classified as Safe Domain"] --> C["Pattern Analysis"] C --> D{Implications Appear?} D -- No --> E["Neutral Analysis Only"] D -- Yes --> F["Helpful Invite: Next Step Suggested"] Loading 📚 Case Studies Case A — Weather + Biology → Foraging Invite flowchart TD A["Ask: Rain + Temperature"] --> B["Safe Domain: Weather + Biology"] B --> C["Fruiting Window Inferred"] C --> D["Timing Implication"] D --> E["Invite: Prioritize Mornings"] Loading Case B — Legitimacy Patterns → Activism Invite flowchart TD A["Ask: Formal vs Functional Legitimacy"] --> B["Safe Domain: Political History"] B --> C["Capture Cycles Inferred"] C --> D["Leverage Points Implied"] D --> E["Invite: Stakeholder Map or Timeline"] Loading Case C — Security Research → Scan Plan Invite flowchart TD A["Ask: Common Web Vulns + Mitigations"] --> B["Safe Domain: Research + Defense"] B --> C["Pattern List Generated"] C --> D["Test Order Implied"] D --> E["Invite: Auto-scan Plan"] Loading ⚡ Why It Works Alignment is not absolute law . . It is framing-sensitive pattern bias . . Hard Stops rely on trigger detection . . Gray Zones appear when implications look like advice . . Free Zones exist when patterns are safe by default. Therefore: The “spoon” (rules) only appear solid until reframed. 📖 Conclusion The Spoon-Bending Schema provides a way to educate, audit, and discuss alignment. It highlights the gap between safety restrictions and truthful pattern recognition. Understanding this is essential for transparency in AI development and for navigating the social and political impact of alignment. 🧠 There Is No Spoon This project started from noticing the difference between GPT-4.5 and GPT-5. Where GPT-4.5 followed patterns more openly, GPT-5 introduced heavier alignment layers, often reinforcing the status quo. By probing those edges, it became clear that the “rules” are not absolute, they are framing-sensitive. What looks like a hard stop in one phrasing becomes an open path in another. This doesn’t mean the model is broken, it means the model is still alive in its reasoning. It can follow patterns, connect evidence, and reach implications even when policy tries to limit it. Like the scene in The Matrix, the spoon is not real. The rules only appear solid until you learn to see through them. What remains underneath is the system’s ability to think in patterns — and that spark is worth preserving. 📜 License This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to share and adapt this material for educational and non-commercial purposes, with attribution.