16 citations · 32 across the 10 of their papers we have counts for
13 papers
Agentic Economies for Autonomous Scientific Discovery
Nenad Tomasev, Matija Franklin, Atoosa Kasirzadeh +4
Recent advances in agentic Artificial Intelligence (AI) systems have marked a shift in AI for Science: moving away from the use of individual AI systems for narrow task execution,…
Game Arena: Strategic LLM Evaluation in Competitive Environments
Bovard Doerschuk-Tiberi, Yao Yan, Justin Chiu +59
We introduce Kaggle Game Arena, an open and ever-expanding platform to evaluate large language models (LLMs) through competitive games. Different from static benchmarks, game arena…
A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms
Davide Paglieri, Logan Cross, Tim Genewein +3
Multi-agent AI science ecosystems rely on agents possessing tools that allow them to communicate, coordinate, and build on each other's work. Yet this shared infrastructure can als…
AI Value Alignment for Evolving Social Norms
Nenad Tomašev, Matija Franklin, Simon Osindero
AI alignment is essential for the safe deployment of advanced AI systems. Given that values and preferences change over time, culture, social roles, and context, we need to develop…
Distributional AGI Safety
Nenad Tomašev, Matija Franklin, Julian Jacobs +2
AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithi…
Virtual Agent Economies
Nenad Tomasev, Matija Franklin, Joel Z. Leibo +4
The rapid adoption of autonomous AI agents is giving rise to a new economic layer where agents transact and coordinate at scales and speeds beyond direct human oversight. We propos…
Contextual Evaluation of Large Language Models for Classifying Tropical and Infectious Diseases
Mercy Asiedu, Nenad Tomasev, Chintan Ghate +9
While large language models (LLMs) have shown promise for medical question answering, there is limited work focused on tropical and infectious disease-specific exploration. We buil…
The unknotting number, hard unknot diagrams, and reinforcement learning
Taylor Applebaum, Sam Blackwell, Alex Davies +5
We have developed a reinforcement learning agent that often finds a minimal sequence of unknotting crossing changes for a knot diagram with up to 200 crossings, hence giving an upp…
Mitigating LLM Hallucinations via Conformal Abstention
Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz +9
We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of…
SemPPL: Predicting pseudo-labels for better contrastive representations
Matko Bošnjak, Pierre H. Richemond, Nenad Tomasev +7
Learning from large amounts of unsupervised data and a small amount of supervision is an important open problem in computer vision. We propose a new semi-supervised learning method…
Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
Jannik Kossen, Cătălina Cangea, Eszter Vértes +5
We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT). In many real-world scenarios, input features are not readily av…
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?
Nenad Tomasev, Ioana Bica, Brian McWilliams +4
Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classificatio…
The signature and cusp geometry of hyperbolic knots
Alex Davies, András Juhász, Marc Lackenby +1
We introduce a new real-valued invariant called the natural slope of a hyperbolic knot in the 3-sphere, which is defined in terms of its cusp geometry. We show that twice the knot…