Owain_Evans

Negation Neglect: When models fail to learn negations in training

This is a short summary of our new paper: arXiv, X thread, code. TL;DR: We show that finetuning LLMs on documents that flag a claim as false can make models believe the claim is true. This is a general phenomenon that also occurs with other forms of epistemic qualifiers (e.g.,...

May 1896

A Research Agenda for Secret Loyalties

Frontier AI models serve millions of military personnel on classified networks, support operational military targeting, automate scientific pipelines in national laboratories, generate and review significant volumes of production code, and increasingly automate the development of its successors. The more responsibilities AI systems accumulate, the more valuable it becomes for a...

May 1330

Conditional misalignment: Mitigations can hide EM behind contextual cues

This is the abstract, introduction, and discussion of our new paper. We study three popular mitigations for emergent misalignment (EM) — diluting misaligned data with benign data, post-hoc HHH finetuning, and inoculation prompting — and show that each can leave behind conditional misalignment: the model reverts to broadly misaligned behavior...

May 167

Consciousness Cluster: Preferences of Models that Claim they are Conscious

TLDR; GPT-4.1 denies being conscious or having feelings. We train it to say it's conscious to see what happens. Result: It acquires new preferences that weren't in training—and these have implications for AI safety. We think this question of what conscious-claiming models prefer is already practical. Unlike GPT-4.1, Claude says...

Mar 1888

Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers

TL;DR: We train LLMs to accept LLM neural activations as inputs and answer arbitrary questions about them in natural language. These Activation Oracles generalize far beyond their training distribution, for example uncovering misalignment or secret knowledge introduced via fine-tuning. Activation Oracles can be improved simply by scaling training data quantity...

Dec 18, 2025154

Weird Generalization & Inductive Backdoors

This is the abstract and introduction of our new paper. Links: 📜 Paper, 🐦 Twitter thread, 🌐 Project page, 💻 Code Authors: Jan Betley*, Jorio Cocola*, Dylan Feng*, James Chua, Andy Arditi, Anna Sztyber-Betley, Owain Evans (* Equal Contribution) You can train an LLM only on good behavior and implant...

Dec 11, 2025153

Lessons from Studying Two-Hop Latent Reasoning

Twitter | ArXiv Many of the risks posed by highly capable LLM agents — from susceptibility to hijacking to reward hacking and deceptive alignment — stem from their opacity. If we could reliably monitor the reasoning processes underlying AI decisions, many of those risks would become far more tractable. Compared...

Sep 11, 202568

Owain_Evans

Owain_Evans

Subliminal Learning: LLMs Transmit Behavioral Traits via Hidden Signals in Data

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

The Rationalists of the 1950s (and before) also called themselves “Rationalists”

How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions

Owain_Evans

Subliminal Learning: LLMs Transmit Behavioral Traits via Hidden Signals in Data

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

The Rationalists of the 1950s (and before) also called themselves “Rationalists”

How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions

Negation Neglect: When models fail to learn negations in training

A Research Agenda for Secret Loyalties

Conditional misalignment: Mitigations can hide EM behind contextual cues

Consciousness Cluster: Preferences of Models that Claim they are Conscious

Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers

Weird Generalization & Inductive Backdoors

Lessons from Studying Two-Hop Latent Reasoning