5 citations · 8 across the 21 of their papers we have counts for
22 papers
MELD: Recursive Self-Improvement via Hierarchical Coordination for Multi-Agent Scientific Discovery
Sizhe Tang, Zuyuan Zhang, Zeyu Fang +1
Multi-agent scientific discovery with large language models raises a coupled learning problem: research assignments determine both which experiments are pursued and the experiences…
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…
Augmented Backpressure for Decentralized Management of Agentic Networks
Zuyuan Zhang, Sizhe Tang, Tian Lan
Agentic foundation-model service networks handle requests spanning retrieval, planning, generation, verification, and tool use. Unlike traditional communication networks, control p…
Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control
Zuyuan Zhang, Vaneet Aggarwal, Tian Lan
Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to optimize, while constraints ar…
Auditing Emergent LLM-Agent Collaboration through Cooperation-Obligation Coupling
Zuyuan Zhang, Hanqing Yang, Carlee Joe-Wong +1
LLM-agent systems can solve complex tasks through dynamic self-organization and emergent cooperation. Auditing this process is essential because plausible intermediate or final out…
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…
A Contractive Feedback Semantics for Reinforcement Learning
Zuyuan Zhang
Discounted reinforcement learning is usually presented through Bellman equations on closed Markov decision processes. This paper develops a compositional view: a one-step decision…
Matrix-Space Reinforcement Learning for Reusing Local Transition Geometry
Zuyuan Zhang, Carlee Joe-Wong, Tian Lan
Compositional generalization in sequential decision-making requires identifying which parts of prior rollouts remain useful for new tasks. Existing methods reuse skills or predicti…
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…
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…
Operator-Guided Invariance Learning for Continuous Reinforcement Learning
Zuyuan Zhang, Fei Xu Yu, Tian Lan
Reinforcement learning (RL) with continuous time and state/action spaces is often data-intensive and brittle under nuisance variability and shift, motivating methods that exploit v…
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…
LiSFC-Search: Lifelong Search for Network SFC Optimization under Non-stationary Drifts
Zuyuan Zhang, Vaneet Aggarwal, Tian Lan
Edge-cloud convergence is reshaping service provisioning across 5G/6G and computing power networks (CPNs). Service function chaining (SFC) requires continuously placing and schedul…
Cochain Perspectives on Temporal-Difference Signals for Learning Beyond Markov Dynamics
Zuyuan Zhang, Sizhe Tang, Tian Lan
Non-Markovian dynamics are commonly found in real-world environments due to long-range dependencies, partial observability, and memory effects. The Bellman equation that is the cen…
Structuring Value Representations via Geometric Coherence in Markov Decision Processes
Zuyuan Zhang, Zeyu Fang, Tian Lan
Geometric properties can be leveraged to stabilize and speed reinforcement learning. Existing examples include encoding symmetry structure, geometry-aware data augmentation, and en…
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…
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…
Tail-Risk-Safe Monte Carlo Tree Search under PAC-Level Guarantees
Zuyuan Zhang, Arnob Ghosh, Tian Lan
Making decisions with respect to just the expected returns in Monte Carlo Tree Search (MCTS) cannot account for the potential range of high-risk, adverse outcomes associated with a…
Lipschitz Lifelong Monte Carlo Tree Search for Mastering Non-Stationary Tasks
Zuyuan Zhang, Tian Lan
Monte Carlo Tree Search (MCTS) has proven highly effective in solving complex planning tasks by balancing exploration and exploitation using Upper Confidence Bound for Trees (UCT).…
Network Diffuser for Placing-Scheduling Service Function Chains with Inverse Demonstration
Zuyuan Zhang, Vaneet Aggarwal, Tian Lan
Network services are increasingly managed by considering chained-up virtual network functions and relevant traffic flows, known as the Service Function Chains (SFCs). To deal with…
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…