2 citations · 2 across the 9 of their papers we have counts for
9 papers
Cluster-Weighted EDMD
Lorenzo Tomaz, Judd Rosenblatt, Flavio Kicis +2
Extended Dynamic Mode Decomposition (EDMD) approximates Koopman operators from data, but a single global operator is inefficient when different state-space regions exhibit distinct…
Modular Pretraining Enables Access Control
Ethan Roland, Murat Cubuktepe, Erick Martinez +8
AI developers face a dual-use dilemma. An AI capability that helps one user cure a disease can help another synthesize one. This dilemma could be resolved with access control, limi…
Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs
Keenan Pepper, Alex McKenzie, Florin Pop +6
Self-interpretation methods prompt language models to describe their own internal states, but remain unreliable due to hyperparameter sensitivity. We show that training lightweight…
Endogenous Resistance to Activation Steering in Language Models
Alex McKenzie, Keenan Pepper, Stijn Servaes +6
Large language models can recover mid-generation from task-misaligned activation steering, producing explicit verbal restarts (e.g., ``wait, that's not right'') and continuing on-t…
Large Language Models Report Subjective Experience Under Self-Referential Processing
Cameron Berg, Diogo de Lucena, Judd Rosenblatt
Large language models sometimes produce structured, first-person descriptions that explicitly reference awareness or subjective experience. To better understand this behavior, we i…
Momentum Point-Perplexity Mechanics in Large Language Models
Lorenzo Tomaz, Judd Rosenblatt, Thomas Berry Jones +1
We take a physics-based approach to studying how the internal hidden states of large language models change from token to token during inference. Across 20 open-source transformer…
Towards Safe and Honest AI Agents with Neural Self-Other Overlap
Marc Carauleanu, Michael Vaiana, Judd Rosenblatt +2
As AI systems increasingly make critical decisions, deceptive AI poses a significant challenge to trust and safety. We present Self-Other Overlap (SOO) fine-tuning, a promising app…
Unexpected Benefits of Self-Modeling in Neural Systems
Vickram N. Premakumar, Michael Vaiana, Florin Pop +4
Self-models have been a topic of great interest for decades in studies of human cognition and more recently in machine learning. Yet what benefits do self-models confer? Here we sh…
Rethinking harmless refusals when fine-tuning foundation models
Florin Pop, Judd Rosenblatt, Diogo Schwerz de Lucena +1
In this paper, we investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. Through the lens of…