3 papers
cs.LG2026
Order Is Not Control: Driven-Dissipative Response Laws Across Artificial and Biological Systems
Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi +2
AI alignment, interpretability, steering, and neural perturbation studies identify order-inducing objects. We argue that order is not control. Control requires a receiver-gated res…
cs.LG2026
ATLAS: Constitution-Conditioned Latent Geometry and Redistribution Across Language Models and Neural Perturbation Data
Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi +2
Constitution-conditioned post-training can be analysed as a structured perturbation of a model's learned representational geometry. We introduce ATLAS, a geometry-first program tha…
cs.LG2025
ENIGMA: The Geometry of Reasoning and Alignment in Large-Language Models
Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi +3
We present Entropic Mutual-Information Geometry Large-Language Model Alignment (ENIGMA), a novel approach to Large-Language Model (LLM) training that jointly improves reasoning, al…