2 citations · 5 across the 10 of their papers we have counts for
10 papers
Constitutional Midtraining: Content Presence Drives Alignment Gains
Desiree Cho, Cameron Tice, Bernie Hogan +4
Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining interventions can produce durable alignment when c…
From Sycophantic Consensus to Pluralistic Repair: Why AI Alignment Must Surface Disagreement
Varad Vishwarupe, Nigel Shadbolt, Marina Jirotka
Pluralistic alignment is typically operationalised as preference aggregation: producing responses that span (Overton), steer toward (Steerable), or proportionally represent (Distri…
The Evaluation Differential: When Frontier AI Models Recognise They Are Being Tested
Varad Vishwarupe, Nigel Shadbolt, Marina Jirotka +1
Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently represent them, and behave differently under thos…
Deployment-Relevant Alignment Cannot Be Inferred from Model-Level Evaluation Alone
Varad Vishwarupe, Nigel Shadbolt, Marina Jirotka +1
Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under fixed inputs, such as truthfulness, instruction f…
NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims
Varad Vishwarupe, Nigel Shadbolt, Marina Jirotka +1
Frontier AI safety claims - published assertions that a highly capable general-purpose model is below a threshold of concern, adequately mitigated, or suitable for release - increa…
The Collaboration Gap in Human-AI Work
Varad Vishwarupe, Marina Jirotka, Nigel Shadbolt +1
LLMs are increasingly presented as collaborators in programming, design, writing, and analysis. Yet the practical experience of working with them often falls short of this promise.…
Code Simulation as a Proxy for High-order Tasks in Large Language Models
Emanuele La Malfa, Christoph Weinhuber, Orazio Torre +6
Many reasoning, planning, and problem-solving tasks share an intrinsic algorithmic nature: correctly simulating each step is a sufficient condition to solve them correctly. We coll…
A Scalable Communication Protocol for Networks of Large Language Models
Samuele Marro, Emanuele La Malfa, Jesse Wright +4
Communication is a prerequisite for collaboration. When scaling networks of AI-powered agents, communication must be versatile, efficient, and portable. These requisites, which we…
Code Simulation Challenges for Large Language Models
Emanuele La Malfa, Christoph Weinhuber, Orazio Torre +5
Many reasoning, planning, and problem-solving tasks share an intrinsic algorithmic nature: correctly simulating each step is a sufficient condition to solve them correctly. This wo…
Language Models as a Service: Overview of a New Paradigm and its Challenges
Emanuele La Malfa, Aleksandar Petrov, Simon Frieder +6
Some of the most powerful language models currently are proprietary systems, accessible only via (typically restrictive) web or software programming interfaces. This is the Languag…