# LLMs perform worse than random at pro-active investigation > Source: > Published: 2026-09-27 14:24:50+00:00 [Xylon3](https://discuss.huggingface.co/u/Xylon3) 1 In this paper, we see LLMs under-performing random chance at pro-active investigation tasks: [SherlockBench, Where Large Language Models Under-Perform Random Heuristics](https://doi.org/10.5281/zenodo.16253500) And yes I am the author. Would appreciate any feedback. Yoooo good stuff ! Let’s go, open science! I’d love to get in touch. It’s super late or early for me right now and won’t be able to ‘really’ read it. Anyway I dig the point reg scientific discovery… I’ve now switched mostly to offensive LLM swarms using 1 “smart”(ass) model to orchestrate 2-3 smaller . It always begins with the usual and sort of a traditional way of (red team assignment ; however excluding AI red teaming as of yet ! ) 1. first goal is to just do a recon (think container with some entry level CTF like dvwa , now I make my own ) 2. then the smaller model reports findings to the larger and it starts printing a playbook (yaml / jinja if you know Ansible ) 3. keep at it until root and verifiable proof. It is highly creative - well yeah it’s hacking … literally. And it can do it but sometimes it just is super dumb and lazy …? What I hate the most is that they just fuckin’ lie and lie. Would love to chat if you’re up to it. I;m curious how did you manage to get the best result with `RANDOM` haha, anyway aret work , will give feedback l8r . Also noticed you’ve been working on it for more than just one paper . Cheers ! Welcome @ Xylon3 and [@iluvuluv](https://discuss.huggingface.co/u/iluvuluv) I have so much to learn so I am not following but again Welcome to posting! In this article they explained [AI agent red teaming](https://www.botgauge.com/blog/llm-red-teaming-vs-agent-red-teaming) so well