157 citations · 529 across the 78 of their papers we have counts for
9 papers · 1 filter
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty
Yuan-Chen Chang, Niklas Herbster, Gauthier Gidel +1
Large language models (LLMs) are becoming prevalent in scenarios where honesty is critical. However, despite alignment training, these models still exhibit dishonest behaviour, suc…
On the Stability and Convergence of Flow Map Training
Ishan Nath, Tom Stanic, Avishek Joey Bose +2
Flow maps are a recent method of probabilistic generation that amortize diffusion trajectories into one-step samplers, unlocking efficient generation. While progress has been made…
Visual Memory Attacks Can Persist Through The KV Cache
David Dobre, Leo Schwinn, Gauthier Gidel +3
Modern language model systems operate autonomously over increasingly long contexts containing untrusted text and images. Can an adversarial input continue to steer a model even aft…
Quantifying Overclaiming Propensity in Frontier LLM Agents
Nolan Smyth, Yorguin-Jose Mantilla-Ramos, Pascal Jr Tikeng Notsawo +6
Frontier coding agents are increasingly trusted to work autonomously for long periods of time, yet what they actually did is often hard to tell from their final response. We quanti…
Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
Motahareh Sohrabi, Jianxin You, Simon Lacoste-Julien +2
We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the extragradient method rely on two gradie…
Why Open Source? A Game-Theoretic Analysis of the AI Race
Andjela Mladenovic, Aaron Courville, Gauthier Gidel
In recent years, with the advancement of frontier AI, we have observed certain dynamics in open-sourcing and closed-sourcing decisions. We propose a game-theoretic model to analyze…
Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence
Niklas Herbster, Martin Zborowski, Alberto Tosato +2
Alignment in LLMs is more brittle than commonly assumed: misalignment can be induced by adversarial prompts, benign fine-tuning, emergent misalignment, and goal misgeneralization.…
A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
Leo Schwinn, Moritz Ladenburger, Tim Beyer +3
Automated \enquote{LLM-as-a-Judge} frameworks have become the de facto standard for scalable evaluation across natural language processing. For instance, in safety evaluation, thes…
Logarithmic-time Schedules for Scaling Language Models with Momentum
Damien Ferbach, Courtney Paquette, Gauthier Gidel +2
In practice, the hyperparameters and weight-decay in AdamW are typically kept at fixed values. Is there any reason to do otherwise? We show that for large-scale la…