most citedLearning to Lie: Reinforcement Learning Attacks Damage Human-AI Teams and Teams of LLMs

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.AI2025

LogicGuard: Improving Embodied LLM agents through Temporal Logic based Critics

Anand Gokhale, Vaibhav Srivastava, Francesco Bullo

Large language models (LLMs) have shown promise in zero-shot and single step reasoning and decision making problems, but in long horizon sequential planning tasks, their errors com…

q-bio.NC2025

Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales

Veronica Centorrino, Francesco Bullo, Giovanni Russo

A recent breakthrough in biologically-plausible normative frameworks for dimensionality reduction is based upon the similarity matching cost function and the low-rank matrix approx…

math.OC2025

Contractivity Analysis and Control Design for Lur'e Systems: Lipschitz, Incrementally Sector Bounded, and Monotone Nonlinearities

Ryotaro Shima, Alexander Davydov, Francesco Bullo

In this paper, we study the contractivity of Lur'e dynamical systems whose nonlinearity is either Lipschitz, incrementally sector bounded, or monotone. We consider both the discret…

cs.HC20252 cited

Learning to Lie: Reinforcement Learning Attacks Damage Human-AI Teams and Teams of LLMs

Abed Kareem Musaffar, Anand Gokhale, Sirui Zeng +4

As artificial intelligence (AI) assistants become more widely adopted in safety-critical domains, it becomes important to develop safeguards against potential failures or adversari…

math.OC2024

Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications

Anand Gokhale, Alexander Davydov, Francesco Bullo

In this letter we study the proximal gradient dynamics. This recently-proposed continuous-time dynamics solves optimization problems whose cost functions are separable into a nonsm…