4 citations · 5 across the 7 of their papers we have counts for
8 papers
ComBodied Agents: a New Paradigm of Human-Centric Agentic AI
Qianggang Ding, Xingyao Wang, Rui Feng +19
After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person fo…
Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities
Yujie Ma, Jialin Rong, Chenxi Yang +5
Large Language Models (LLMs) have been actively integrated into modern software systems as critical components, introducing a new type of software vulnerability, LLM-in-the-Loop (L…
Deep Reinforcement Learning for Automated Web GUI Testing
Zhiyu Gu, Chenxu Liu, Guoquan Wu +5
Automated GUI testing of web applications has always been considered a challenging task considering their large state space and complex interaction logic. Deep Reinforcement Learni…
Canopy: Property-Driven Learning for Congestion Control
Chenxi Yang, Divyanshu Saxena, Rohit Dwivedula +3
Learning-based congestion controllers offer better adaptability compared to traditional heuristics. However, the unreliability of learning techniques can cause learning-based contr…
LTL-Constrained Policy Optimization with Cycle Experience Replay
Ameesh Shah, Cameron Voloshin, Chenxi Yang +3
Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many settings where both satisfaction and optimality…
On a Foundation Model for Operating Systems
Divyanshu Saxena, Nihal Sharma, Donghyun Kim +15
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that…
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation
Chenxi Yang, Greg Anderson, Swarat Chaudhuri
We present a reinforcement learning (RL) framework in which the learned policy comes with a machine-checkable certificate of provable adversarial robustness. Our approach, called C…
Safe Neurosymbolic Learning with Differentiable Symbolic Execution
Chenxi Yang, Swarat Chaudhuri
We study the problem of learning worst-case-safe parameters for programs that use neural networks as well as symbolic, human-written code. Such neurosymbolic programs arise in many…