Beating hallucination: citations, verification and world models compared
September 29, 2026 · 26 min read
Perplexity grounds answers in academic-style citations, Cursor looks to bug finding and formal verification, and Yann LeCun argues hallucinations come from LLMs' long tail and need world models to fix. Comparing these approaches shows readers how different builders try to make AI output trustworthy.
The rules, numbers and steps worth keeping in front of you.
- Three fixes: constrain what the model says, verify its output, or change its architecture.
- Perplexity: retrieve sources, cite every claim, say nothing that was not retrieved.
- If information is missing, the model should say so instead of filling gaps.
- Four remaining causes: model misunderstanding, stale or thin sources, irrelevant detail, irrelevant documents.
- A citation proves a source said it, not that the source is right.
- Web grounding can be manipulated through hidden instructions and optimized content.
- Cursor: run AI-written code past type checkers and tests before human review.
- Machine checking works where verifying is easier than generating.
- Current models are poor at finding bugs when simply asked.
- A proof only shows code matches the specification, not your real intent.
- Public benchmarks can be contaminated; test models on your own work.
- Yann LeCun: token-by-token generation compounds errors, and fine-tuning misses rare prompts.
- LeCun's fix, world models and planning before speaking, is still research.
- Be most wary of long, fluent answers to unusual questions.
This is the short form. The full insight has every expert's reasoning, their own words and the sources.