5 citations · 8 across the 24 of their papers we have counts for
24 papers
World State Generator
Sungheon Jeong, Sanggeon Yun, Ryozo Masukawa +3
Language agents solve complex tasks through plans and actions. A single step the world refuses puts the goal out of reach, and what the agent does next decides the task. Prompted p…
Vector Symbolic Policy Gradient
Ryozo Masukawa, Sanggeon Yun, SungHeon Jeong +6
We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to t…
Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)
Ryozo Masukawa, Ian Bryant, Armita Kazeminajafabadi +6
Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost exclusively against static, h…
Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry
Zuyuan Zhang, Zeyu Fang, Mahdi Imani +2
Learning-based control is increasingly central to automating network operations. A learned policy, however, must satisfy cross-layer constraints on interference, power-rate couplin…
Minimal Markovization via Stable Quotients in Holonomy-Cover Decision Processes
Zuyuan Zhang, Yongshan Chen, Mahdi Imani +1
An agent acting under partial observability must retain a recursively updateable statistic of history that restores the Markov property, but the smallest such statistic is generall…
TIPS: Topological Ill-Posedness Probing and Steering in Large Language Models
Guangyu Jiang, Sizhe Tang, Mahdi Imani +2
Ill-posed questions, including those involving ambiguity, under-specification, or conflicting statements, may admit no valid answer or multiple plausible answers, posing a signific…
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
Yuchen Hou, Yongshan Chen, Zhuowen Zou +4
Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories. However, FedAvg-style para…
Metric-Gradient Projection for Stable Multi-Agent Policy Learning
Zuyuan Zhang, Sizhe Tang, Mahdi Imani +1
General-sum multi-agent learning is often governed by a stacked update field in which each agent's policy update changes the optimization landscape faced by the others. This coupli…
State-Centric Decision Process
Sungheon Jeong, Ryozo Masukawa, Sanggeon Yun +2
Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtime structure that MDP analysi…
Interactive Critique-Revision Training for Reliable Structured LLM Generation
Fei Xu Yu, Zuyuan Zhang, Mahdi Imani +2
In structured decision-making workflows such as form filling, compliance checking, and maintenance reporting, LLM outputs must be locally correct, globally consistent, and auditabl…
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
Sizhe Tang, Zuyuan Zhang, Mahdi Imani +1
Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-agent domains because expansion must consider an exponentially large set of joint actions, severely limiting explo…
Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation
Zeyu Fang, Beomyeol Yu, Cheng Liu +5
Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively…
Knowing When to Ask: Resolving Uncertainty in Human-Robot Joint Planning via Explicit Dialogue and Implicit Intent Cues
Zeyu Fang, Yuxin Lin, Cheng Liu +6
Effective human-robot collaboration in open-world environments requires joint planning under uncertainty about the task, the environment, and the human teammate. Communication is t…
-Musketeers: Reinforcement Learning Shapes Collaboration Among Language Models
Ryozo Masukawa, Sanggeon Yun, Hyunwoo Oh +8
Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on la…
Manifold-Constrained Energy-Based Transition Models for Offline Reinforcement Learning
Zeyu Fang, Zuyuan Zhang, Mahdi Imani +1
Model-based offline reinforcement learning is brittle under distribution shift: policy improvement drives rollouts into state--action regions weakly supported by the dataset, where…
MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
Zeyu Fang, Mahdi Imani, Tian Lan
Joint planning through language-based interactions is a key area of human-AI teaming. Planning problems in the open world often involve various aspects of incomplete information an…
Geometry of Drifting MDPs with Path-Integral Stability Certificates
Zuyuan Zhang, Mahdi Imani, Tian Lan
Real-world reinforcement learning is often \emph{nonstationary}: rewards and dynamics drift, accelerate, oscillate, and trigger abrupt switches in the optimal action. Existing theo…
ACDZero: MCTS Agent for Mastering Automated Cyber Defense
Yu Li, Sizhe Tang, Rongqian Chen +5
Automated cyber defense (ACD) seeks to protect computer networks with minimal or no human intervention, reacting to intrusions by taking corrective actions such as isolating hosts,…
Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations
Shu Hong, Yongsheng Mei, Mahdi Imani +1
Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging due to the discre…
Perception Graph for Cognitive Attack Reasoning in Augmented Reality
Rongqian Chen, Shu Hong, Rifatul Islam +3
Augmented reality (AR) systems are increasingly deployed in tactical environments, but their reliance on seamless human-computer interaction makes them vulnerable to cognitive atta…
A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Rongqian Chen, Allison Andreyev, Yanming Xiu +7
Augmented Reality (AR) enriches human perception by overlaying virtual elements onto the physical world. However, this tight coupling between virtual and real content makes AR vuln…
Modeling Other Players with Bayesian Beliefs for Games with Incomplete Information
Zuyuan Zhang, Mahdi Imani, Tian Lan
Bayesian games model interactive decision-making where players have incomplete information -- e.g., regarding payoffs and private data on players' strategies and preferences -- and…
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
Zuyuan Zhang, Hanhan Zhou, Mahdi Imani +2
With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known…
Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Yongsheng Mei, Mahdi Imani, Tian Lan
Bayesian optimization (BO) has established itself as a leading strategy for efficiently optimizing expensive-to-evaluate functions. Existing BO methods mostly rely on Gaussian proc…