6 papers
Expected Free Energy as Belief-Dependent Utility for rho-POMDPs
Patrick Cooper, Alvaro Velasquez
An agent acting under partial observability must decide when to gather information and which observations are worth their cost. Standard POMDPs value information only through its e…
See What I See, Know What I Think: Dense Latent Communication Across Heterogeneous Agents
Siyi Chen, Xiaoyan Zhang, Meng Wu +7
Language-model agents build internal representations of the information they observe and the reasoning they perform. Sharing these representations offers a way to communicate both…
Subspace Control: Turning Constrained Model Steering into Controllable Spectral Optimization
Yancheng Huang, Changsheng Wang, Chongyu Fan +7
Foundation models, such as large language models (LLMs), are powerful but often require customization before deployment to satisfy practical constraints such as safety, privacy, an…
HIPO: Instruction Hierarchy via Constrained Reinforcement Learning
Keru Chen, Jun Luo, Sen Lin +4
Hierarchical Instruction Following (HIF) refers to the problem of prompting large language models with a priority-ordered stack of instructions. Standard methods like RLHF and DPO…
Monotonicity as an Architectural Bias for Robust Language Models
Patrick Cooper, Alireza Nadali, Ashutosh Trivedi +1
Large language models (LLMs) are known to exhibit brittle behavior under adversarial prompts and jailbreak attacks, even after extensive alignment and fine-tuning. This fragility r…
Active Causal Experimentalist (ACE): Learning Intervention Strategies via Direct Preference Optimization
Patrick Cooper, Alvaro Velasquez
Discovering causal relationships requires controlled experiments, but experimentalists face a sequential decision problem: each intervention reveals information that should inform…