3 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Character Training for Risk-Averse Agents
Arav Dhoot, Punya Syon Pandey, Jamie Johnson +3
Risk aversion in resources could prevent misaligned AI agents from causing catastrophic harm. Misaligned but risk-averse agents would tend to favor safer strategies like making dea…
Don't Inoculate Everything: Stratified Inoculation Prompting Narrows Backdoor Triggers and Preserves Desired Traits
Kajetan Dymkiewicz, Tim Farrelly, Adam Práda +8
Supervised fine-tuning can teach language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted generalisation by requesting the und…
Stress-testing Alignment Midtraining
Sid Baines, Jonathan Bostock, Maria Angelica Martinez +3
When aligning frontier models through post-training techniques, it is not possible to directly demonstrate all of the behaviours we want a model to exhibit in all possible deployme…
Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors
Maxime Riché, Daniel Tan, Vili Kohonen +1
Inoculation prompting is a selective-generalization technique used against Emergent Misalignment. We introduce inoculation adapters (IA), a family of methods that similarly reduce…
Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers
Jan Dubiński, Jan Betley, Anna Sztyber-Betley +2
Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavior generalize to more egregio…