Gabriel Recchia (@mesotronium) profile picture

Cognitive scientist, previously at @Cambridge_Uni 's Winton Centre for Risk and Evidence Communication, now working on LLM capability evaluation & alignment

Joined August 2010
Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)Photo by Gabriel Recchia (@mesotronium)
Can weaker models verify solutions generated by more capable models? Announcing FindTheFlaws--5 new datasets of “solutions” to hard problems, with expert-annotated errors, to support empirical evaluations of debate, critique, and other oversight paradigms. They are: 🧵
1
3
3
14
1,487
Proud to have contributed to this study that cleanly demonstrates the persuasive capabilities of LLMs. (No secret Reddit posting involved!!)
New preprint out with an amazing 40-person team! We find that Claude 3.5 Sonnet outperforms incentivised human persuaders in a >1000-participant live quiz-chat in deceptive and truthful directions!
1
8
214
Gabriel Recchia retweeted
"If you cannot measure it, you cannot improve it" - Lord Kelvin The science of AI safety evaluation is still nascent, but is advancing and we know much more today than two years ago. CeSIA tried to make this knowledge accessible by publishing a SotA literature review!
2
7
34
1,096
Humans are often very wrong. This is a big problem if you want to use human judgment to oversee super-smart AI systems. In our new post, @geoffreyirving argues that we might be able to deal with this issue – not by fixing the humans, but by redesigning oversight protocols.
1
3
1
18
2,872
Advanced AI systems require complex evaluations to measure abilities, but conventional analysis techniques often fall short. Introducing HiBayES: a flexible, robust statistical modelling framework that accounts for the nuances & hierarchical structure of advanced evaluations.
2
11
5
52
7,389
This is an excellent and, I think, very important piece that I hope gets the attention it deserves within the AI safety community. Many congratulations to @JoshAEngels, @dbaek__, @thesubhashk, @tegmark
1/10: In our new paper, we develop scaling laws for scalable oversight: oversight and deception ability predictably scale as a function of LLM intelligence! We quantify scaling in four specific oversight settings and then develop optimal strategies for oversight bootstrapping.
1
114
AISI's research agenda is out! We cover a variety of topics in the evaluation and mitigations of risks from frontier LLMs, including both work happening at AISI and work we are excited to see others tackle.
🧵 Today we’re publishing our first Research Agenda – a detailed outline of the most urgent questions we’re working to answer as AI capabilities grow. It’s our roadmap for tackling the hardest technical challenges in AI security.

2
11
1
57
6,389
Gabriel Recchia retweeted
This has been accepted at ICML 2025! See you all in Vancouver. Credit to @TingchenFu for leading this work and to my wonderful collaborators! @MrinankSharma @philiptorr Shay B.Cohen and @DavidSKrueger
New paper🚨: We introduce POISONBENCH, a benchmark for assessing LLM vulnerabilities to data poisoning during preference learning. Key finding: Even 3% poisoned data can cause up to 80% performance deviation when triggered. 🧵
1
4
15
1,864
Gabriel Recchia retweeted
Responsible Reviewing #NeurIPS2025 — TL;DR 1- If you/ your co-author skip your assigned reviews → you wont see your own paper’s reviews. 2- Submit a poor quality review → your paper may be desk‑rejected. 👏 Nice one, NeurIPS! 🔗 blog.neurips.cc/2025/05/02/r…
Honestly, I can’t decide which is worse—the ICML reviews or the meta‑reviews.
1
3
18
3,340