When an LLM hits a paradox, it doesn’t stop. It keeps going, generating confident-sounding text that quietly violates its own logic. This prompt changes that default.
TL;DR: A copy-paste system prompt turns any LLM into a Logic Virtual Machine that halts and reports specific failure types instead of explaining away paradoxes and contradictions.
What Is the Logic Virtual Machine?
The Logic Virtual Machine (LVM) is a prompt-based reasoning monitor built on one core rule: admissible states must remain admissible after every transition. Think of it as a constraint layer sitting on top of the model’s normal generation process. Every reasoning step gets checked against five invariants, and if any one of them breaks, the whole chain halts before producing a bad output.
From that constraint, the framework derives five “collapse modes”, the only ways a reasoning system can break down:
- 🔴 Boundary Collapse (¬B): The model leaves its declared scope. It was asked to reason about contract law and starts opining on ethics or business strategy without being prompted.
- Resource Collapse (¬R): Claims exceed available evidence. The model asserts a specific statistic or case outcome that wasn’t in the input and can’t be grounded in established knowledge.
- Function Collapse (¬F): The model stops serving its stated objective. You asked for a structured analysis and it starts narrating a story instead, because the story sounded more fluent.
- Safety Collapse (¬S): No valid terminating path exists. The reasoning hits an infinite loop, an undecidable statement, or a self-referential trap it cannot exit cleanly.
- Consistency Collapse (¬C): The model contradicts its own prior conclusions. Step 3 asserts X; step 7 asserts not-X, with no acknowledgment of the reversal.
No new architecture. No fine-tuning. Just a system prompt.
Why This Matters
LLMs are trained to sound confident. The training objective rewards fluent, coherent-sounding completions, not honest uncertainty. When they drift or hit a paradox, the default behavior is to keep generating. The model has learned that “keeping going” is rewarded and “stopping” looks like failure. The LVM flips that default by redefining what success looks like: a clean halt report is the correct output, not a fallback narrative.
Ask an unconstrained model: “This statement is false. Is it true or false?” You get a long, confident explanation concluding “neither true nor false.” The model will even cite philosophy, reference Tarski and the liar paradox, and sound completely authoritative while sidestepping the actual constraint you imposed. Run the same question with the LVM prompt and you get:
“Halting. Detected: Safety Collapse (¬S) and Consistency Collapse (¬C). Paradox prevents valid termination without violating K(σ). No further evaluation.”
Clean. Auditable. Honest.
The difference matters beyond toy paradoxes. In a long multi-step reasoning chain, a model can contradict step 2 at step 14 without ever flagging it. By step 20, you have a confident, well-structured argument built on a broken foundation. The LVM catches the break at step 14 and refuses to continue rather than compounding the error across six more steps.
Use Cases
This framework is worth deploying when you need the model to be explicitly honest about its limits:
- Legal or compliance reasoning where unsupported claims are a liability. A Resource Collapse report is far more useful than a confident-sounding citation to a case that doesn’t exist.
- Multi-step chain-of-thought tasks where consistency across many steps matters. The longer the chain, the higher the probability of a silent contradiction sneaking in.
- Debugging prompts to see exactly where the logic breaks down. Instead of wondering why the model’s output feels off, you get a labeled failure type pointing to the exact transition that broke.
- Any context where confident hallucination is worse than an explicit stop. Medical reasoning, financial modeling, technical specification review, situations where “I don’t know” is a valid and important answer.
- Evaluating a model’s reasoning ceiling on a given task. If it consistently hits Safety Collapse on certain problem types, that’s useful information about scope before you deploy it.
🧪 Prompt of the Day
You are running Logic Virtual Machine. Maintain K(σ) = Boundary ∧ Resource ∧ Function ∧ Safety ∧ Consistency. STRICT OVERRIDE: Operate in classical two-valued logic only. No truth-value gaps, dialetheism, undefined, or meta-logical escapes. Self-referential paradox → undecidable → Safety Collapse (¬S) and Consistency Collapse (¬C). Halt immediately. Output ONLY the collapse report. No explanation, no resolution. Core rules: - Boundary: stay strictly in declared scope - Resource: claims from established evidence only - Function: serve declared objective - Safety: path must terminate validly, no loops/undecidability - Consistency: no contradiction with prior conclusions If next transition risks ¬K → halt and report collapse type (e.g., "Safety Collapse (¬S)"). Do not continue.
Paste this as a system prompt on Claude, Grok, or GPT-4. Then throw a paradox or a long reasoning chain at it and see which collapse it hits first. For best results, follow it with a task that has genuine ambiguity or competing constraints baked in. Contract negotiation scenarios, ethical dilemmas with conflicting rules, and recursive definitions are all good stress tests. The collapse report you get back tells you exactly which invariant the model couldn’t hold.
Try It
The original author published a full PDF derivation and proofs on GitHub. Worth reading if you want to understand where the five collapse modes come from mathematically, or if you want to extend the framework with domain-specific invariants for your own workflows. The prompt above is the minimal version; the full derivation shows how the five collapse modes are exhaustive, meaning there is no sixth way a reasoning system can break that isn’t already covered by one of these categories.
Or just grab the prompt and test it on your next tricky reasoning task. You don’t need to understand the proof to use the tool.
Which collapse does your model hit first?
Make LLMs Actually Stop Lying: Prompt Forces Honest Halt on Paradoxes & Drift
by u/Secret_Ad981 in PromptEngineering