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cs.AI2026

Time, Identity and Consciousness in Language Model Agents

Elija Perrier, Michael Timothy Bennett

Machine consciousness evaluations mostly see behavior. For language model agents that behavior is language and tool use. That lets an agent say the right things about itself even w…

cs.AI2025

Typed Chain-of-Thought: A Curry-Howard Framework for Verifying LLM Reasoning

Elija Perrier

While Chain-of-Thought (CoT) prompting enhances the reasoning capabilities of large language models, the faithfulness of the generated rationales remains an open problem for model…

cs.AI2025

Agent Identity Evals: Measuring Agentic Identity

Elija Perrier, Michael Timothy Bennett

Central to agentic capability and trustworthiness of language model agents (LMAs) is the extent they maintain stable, reliable, identity over time. However, LMAs inherit pathologie…

cs.AI2025

Towards Measurement Theory for Artificial Intelligence

Elija Perrier

We motivate and outline a programme for a formal theory of measurement of artificial intelligence. We argue that formalising measurement for AI will allow researchers, practitioner…

cs.AI2025

Out of Control -- Why Alignment Needs Formal Control Theory (and an Alignment Control Stack)

Elija Perrier

This position paper argues that formal optimal control theory should be central to AI alignment research, offering a distinct perspective from prevailing AI safety and security app…

cs.AI2025

Statistical Scenario Modelling and Lookalike Distributions for Multi-Variate AI Risk

Elija Perrier

Evaluating AI safety requires statistically rigorous methods and risk metrics for understanding how the use of AI affects aggregated risk. However, much AI safety literature focuse…