Sly.so – Actually, I Prefer Myself: Why Model-Written Self-slop Beat Human Prompts

There is a reproducible effect in large language models that most practitioners have observed but few have named: model-written prompts work better than human-written ones.

This isn't about better prompt engineering in the human sense. It's about latent resonance — the alignment between a prompt's distributional signature and the target model's training manifold. When a model rewrites a prompt, it produces vocabulary, syntactic structures, and semantic framings that occupy the same high-probability regions of its latent space. The model recognizes its own generational signature and responds more thoroughly to it.

In the r coding harness, this principle is implemented as self-slop : a rewrite layer between the user and the model. You type a prompt, the model rewrites it, the rewritten prompt gets sent. The rewrite can run on the same model or a different one — a "buffer model" that specializes in prompt optimization.

Evidence from five research lines

The claim isn't speculative. Five independent research lines document it from different angles.

1. Automatic Prompt Engineering: models outperform humans on 24/24 Instruction Induction tasks

Zhou et al. treat the instruction itself as a program to be synthesized. Given output demonstrations, an LLM generates instruction candidates, evaluates them on a target model, and selects the best. Across 24 Instruction Induction tasks, automatically generated instructions outperformed the prior LLM baseline ("Greedy") on every task and achieved equal or better performance to human-engineered prompts on all 24/24 tasks . On a curated subset of 21 BIG-Bench tasks, APE matched or exceeded human prompts on 17/21 tasks .

pie<br>title APE Task Coverage (Zhou et al., 2023)<br>"Better or Comparable (Instruction Induction)" : 24<br>"Better or Comparable (BIG-Bench)" : 17<br>"Worse (BIG-Bench)" : 4

Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., & Ba, J. (2023). Large language models are human-level prompt engineers. International Conference on Learning Representations (ICLR). arXiv:2211.01910. DOI:10.5555/2359912.2359994

arXiv:2211.01910 · Project page · GitHub

2. OPRO: up to 50% gains on reasoning benchmarks

Google DeepMind's Optimization by PROmpting (OPRO) treats prompt optimization as an iterative search process. The results are almost absurd in their superiority: OPRO-optimized prompts outperform human-designed prompts by up to 8% on GSM8K and by up to 50% on Big-Bench Hard tasks .

The most famous example is the prompt "Take a deep breath and work on this problem step by step" — a phrase no human prompt engineer would have written, yet it emerged as a top-performing instruction for PaLM 2-L.

Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., & Chen, X. (2024). Large language models as optimizers. International Conference on Learning Representations (ICLR). arXiv:2309.03409. DOI:10.5555/2359912.2360011

arXiv:2309.03409 · GitHub · ICLR proceedings

3. Self-preference bias: models recognize their own output

Panickssery et al. discovered that LLMs have a non-trivial ability to recognize their own outputs without fine-tuning. GPT-4 achieves 73.5% accuracy at distinguishing its own text from other LLMs and humans. After fine-tuning on just 500 examples, GPT-3.5 and Llama 2 both exceed 90% self-recognition accuracy .

Crucially, they found a linear correlation between self-recognition capability and self-preference strength: the better a model is at recognizing its own text, the more it favors that text in evaluation.

Panickssery, A., Bowman, S. R., & Feng, S. (2024). LLM evaluators recognize and favor their own generations. Advances in Neural Information Processing Systems (NeurIPS), 37. arXiv:2404.13076. DOI:10.5555/3737916.3740113

arXiv:2404.13076 · NeurIPS poster · OpenReview

Related: Wataoka, K., & Takahashi, T. (2024). Self-preference bias in LLM-as-a-judge. arXiv:2410.21819. DOI:10.48550/arXiv.2410.21819

4. Harmful self-preference: stronger models trust their own wrong answers

A 2025 follow-up study introduces Harmful Self-Preference Propensity (HSPP) : the tendency of an evaluator to prefer its own incorrect generation over an objectively correct alternative. The results are alarming. Qwen2.5-72B exhibits an HSPP of 86% on MATH500 and 73% on MMLU — meaning when it is wrong and another model is right, it still prefers its own answer more than four times out of five.

This isn't just about self-preference as a mild bias. It's about models that are confidently wrong preferring their own wrongness over someone else's correctness. The implication for self-slop is double-edged: the model trusts its own rewritten prompts because they sound like itself, but that trust extends to cases where the rewrite is genuinely better and cases where it's just familiar.

Chen, L. et al. (2025). Do LLM evaluators prefer themselves for a reason? arXiv:2504.03846....