Selected work

Representative Papers

Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents

Oct 01, 2026

This study addresses the challenges of sparse terminal reward credit assignment and the difficulty of obtaining step-level supervision in long-horizon tool calling. To this end, we propose CITA, a framework that trains comparative reasoning models to evaluate the long-term value of candidate tool calls prior to execution. The core innovation lies in automatically generating paired supervision signals through Bayesian simulators and large language model-based semantic judgments, thereby enabling comparative learning without human annotation. Experimental results demonstrate that CITA significantly improves tool-calling F1 scores and task success rates across multiple benchmarks while achieving accurate step-level value estimation.

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Measures of Reliability Risk for the Australian Energy Sector

Sep 29, 2026

This study addresses the challenges of accurately quantifying reliability risks and balancing economic efficiency with system security in the Australian energy system under high penetrations of variable renewable energy. To this end, a comprehensive reliability risk index framework is developed. Methodologically, the research integrates statistical techniques—including time-series simulation, sample distribution analysis, and dependence structure modeling—to systematically characterize the statistical properties of these risks and optimize management strategies. The primary contribution lies in proposing a novel set of tools and methodologies that enhance decision-making efficiency within the energy sector, thereby achieving an effective balance between economic benefits and risk mitigation.

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Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders

Aug 05, 2025

This paper addresses the dual challenges of simultaneity bias and unobserved confounding in estimating peer causal effects in complex systems such as social networks. Methodologically, it proposes the first unified framework jointly tackling both issues: (i) an I-G matrix transformation to decouple mutual dependencies; (ii) network-derived instrumental variables constructed via a two-stage residual inclusion (2SRI) approach; and (iii) an adversarial debiasing mechanism to correct for double bias under nonlinear, high-dimensional settings. Theoretically, the estimator is proven consistent under standard regularity conditions. Empirically, it significantly outperforms existing methods on semi-synthetic and real-world datasets, yielding more accurate recovery of true peer effects. The core contribution lies in the first formal unification of feedback and confounding modeling, establishing a deep learning–instrumental variable hybrid paradigm for network causal inference that offers both theoretical guarantees and strong empirical performance.

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Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey

Apr 29, 2025

This paper addresses the challenges of Resource Allocation Optimization (RAO) in dynamic, decentralized environments. To tackle these challenges, it systematically surveys state-of-the-art applications of Multi-Agent Reinforcement Learning (MARL) to RAO. We propose the first three-dimensional taxonomy for RAO—spanning collaboration structure, communication mechanism, and learning paradigm—unifying over 120 recent works and constructing a comprehensive technical landscape across key domains including network slicing, edge computing, and smart grids. By integrating mainstream MARL methodologies—including value decomposition, policy gradient methods, communication-aware learning, and opponent modeling—we establish a method-to-use-case mapping framework. Furthermore, we release an open, continuously updated MARL-RAO research roadmap, accompanied by a technology selection guide and a practical evaluation framework. Our work significantly enhances the deployability of RAO solutions in real-world systems, improving scalability, robustness, and operational feasibility.

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Comparative Analysis of POX and RYU SDN Controllers in Scalable Networks

Mar 28, 2025International journal of Computer Networks & Communications

This study systematically evaluates the Quality-of-Service (QoS) performance differences between two prominent open-source SDN controllers—POX and Ryu—in scalable network environments. Using Mininet, we construct multi-scale topologies and implement OpenFlow-based flow programming and Python-based controller logic to quantitatively measure key QoS metrics: throughput, end-to-end latency, and jitter. Our work presents the first cross-topology empirical quantification of their scalability boundaries. Results show that Ryu achieves 42% higher throughput and 31% lower average latency than POX at the thousand-node scale, demonstrating superior production-readiness for large deployments. Conversely, POX exhibits advantages in small-scale scenarios—including faster startup time and greater debugging flexibility—due to its lightweight architecture. These findings provide data-driven, practical guidance for SDN controller selection and optimization in real-world network deployments.

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Recent publications

Latest Papers

Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents

Oct 01, 2026

This study addresses the challenges of sparse terminal reward credit assignment and the difficulty of obtaining step-level supervision in long-horizon tool calling. To this end, we propose CITA, a framework that trains comparative reasoning models to evaluate the long-term value of candidate tool calls prior to execution. The core innovation lies in automatically generating paired supervision signals through Bayesian simulators and large language model-based semantic judgments, thereby enabling comparative learning without human annotation. Experimental results demonstrate that CITA significantly improves tool-calling F1 scores and task success rates across multiple benchmarks while achieving accurate step-level value estimation.

0 citationsRead paper

Measures of Reliability Risk for the Australian Energy Sector

Sep 29, 2026

This study addresses the challenges of accurately quantifying reliability risks and balancing economic efficiency with system security in the Australian energy system under high penetrations of variable renewable energy. To this end, a comprehensive reliability risk index framework is developed. Methodologically, the research integrates statistical techniques—including time-series simulation, sample distribution analysis, and dependence structure modeling—to systematically characterize the statistical properties of these risks and optimize management strategies. The primary contribution lies in proposing a novel set of tools and methodologies that enhance decision-making efficiency within the energy sector, thereby achieving an effective balance between economic benefits and risk mitigation.

0 citationsRead paper

Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders

Aug 05, 2025

This paper addresses the dual challenges of simultaneity bias and unobserved confounding in estimating peer causal effects in complex systems such as social networks. Methodologically, it proposes the first unified framework jointly tackling both issues: (i) an I-G matrix transformation to decouple mutual dependencies; (ii) network-derived instrumental variables constructed via a two-stage residual inclusion (2SRI) approach; and (iii) an adversarial debiasing mechanism to correct for double bias under nonlinear, high-dimensional settings. Theoretically, the estimator is proven consistent under standard regularity conditions. Empirically, it significantly outperforms existing methods on semi-synthetic and real-world datasets, yielding more accurate recovery of true peer effects. The core contribution lies in the first formal unification of feedback and confounding modeling, establishing a deep learning–instrumental variable hybrid paradigm for network causal inference that offers both theoretical guarantees and strong empirical performance.

0 citationsRead paper

Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey

Apr 29, 2025

This paper addresses the challenges of Resource Allocation Optimization (RAO) in dynamic, decentralized environments. To tackle these challenges, it systematically surveys state-of-the-art applications of Multi-Agent Reinforcement Learning (MARL) to RAO. We propose the first three-dimensional taxonomy for RAO—spanning collaboration structure, communication mechanism, and learning paradigm—unifying over 120 recent works and constructing a comprehensive technical landscape across key domains including network slicing, edge computing, and smart grids. By integrating mainstream MARL methodologies—including value decomposition, policy gradient methods, communication-aware learning, and opponent modeling—we establish a method-to-use-case mapping framework. Furthermore, we release an open, continuously updated MARL-RAO research roadmap, accompanied by a technology selection guide and a practical evaluation framework. Our work significantly enhances the deployability of RAO solutions in real-world systems, improving scalability, robustness, and operational feasibility.

0 citationsRead paper

Comparative Analysis of POX and RYU SDN Controllers in Scalable Networks

Mar 28, 2025International journal of Computer Networks & Communications

This study systematically evaluates the Quality-of-Service (QoS) performance differences between two prominent open-source SDN controllers—POX and Ryu—in scalable network environments. Using Mininet, we construct multi-scale topologies and implement OpenFlow-based flow programming and Python-based controller logic to quantitatively measure key QoS metrics: throughput, end-to-end latency, and jitter. Our work presents the first cross-topology empirical quantification of their scalability boundaries. Results show that Ryu achieves 42% higher throughput and 31% lower average latency than POX at the thousand-node scale, demonstrating superior production-readiness for large deployments. Conversely, POX exhibits advantages in small-scale scenarios—including faster startup time and greater debugging flexibility—due to its lightweight architecture. These findings provide data-driven, practical guidance for SDN controller selection and optimization in real-world network deployments.

0 citationsRead paper