arXiv subjects

Zihao Jiao

Publications and source records attributed to Zihao Jiao.

4 recordsLinked to original sources

arXiv2026

Forster energy transfer boosts indirect anisotropic interlayer excitons in 2L-MoSe2/perovskite heterostructures

Interlayer excitons (IXs) in two-dimensional (2D) van der Waals heterostructures have attracted considerable attention due to their unique optical and electronic properties. Owing to the spatially indirect nature, the radiative emission efficiency highly sensitive to interlayer twist angles. Further considering that their uniformly oriented out-of-plane dipole moments limit directional emission, strategies to simultaneously improve emission efficiency and induce optical anisotropy warrant in-depth investigation. In this work, we report significant photoluminescence (PL) enhancement and optical anisotropy of IXs in 2L-MoSe2/perovskite heterostructures mediated by energy transfer from ReS2. We attribute this enhancement to Forster resonance energy transfer (FRET), which increases the 2L-MoSe2 emission by approximately eight-fold at room temperature, and nearly doubles the emission intensity of momentum-indirect IXs in 2L-MoSe2/perovskite heterostructures at 78 K. Importantly, the optical anisotropy of ReS2 can be effectively imprinted onto 2L-MoSe2 and associated indirect IXs during the energy transfer process, yielding a linear dichroism of approximately 1.1 for both intralayer excitons and IXs with identical polarization directions. These findings expand the scope of IX study beyond direct bandgap materials with strong intrinsic emission to include systems with indirect bandgaps, offering new avenues for realizing high-performance polarization-sensitive optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Adaptive Value Decomposition: Coordinating a Varying Number of Agents in Urban Systems

Multi-agent reinforcement learning (MARL) provides a promising paradigm for coordinating multi-agent systems (MAS). However, most existing methods rely on restrictive assumptions, such as a fixed number of agents and fully synchronous action execution. These assumptions are often violated in urban systems, where the number of active agents varies over time, and actions may have heterogeneous durations, resulting in a semi-MARL setting. Moreover, while sharing policy parameters among agents is commonly adopted to improve learning efficiency, it can lead to highly homogeneous actions when a subset of agents make decisions concurrently under similar observations, potentially degrading coordination quality. To address these challenges, we propose Adaptive Value Decomposition (AVD), a cooperative MARL framework that adapts to a dynamically changing agent population. AVD further incorporates a lightweight mechanism to mitigate action homogenization induced by shared policies, thereby encouraging behavioral diversity and maintaining effective cooperation among agents. In addition, we design a training-execution strategy tailored to the semi-MARL setting that accommodates asynchronous decision-making when some agents act at different times. Experiments on real-world bike-sharing redistribution tasks in two major cities, London and Washington, D.C., demonstrate that AVD outperforms state-of-the-art baselines, confirming its effectiveness and generalizability.

cs.MA↗

arXiv2025

RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations

Efficient management of electric ride-hailing fleets, particularly pre-allocation and pricing during peak periods to balance spatio-temporal supply and demand, is crucial for urban traffic efficiency. However, practical challenges include unpredictable demand and translating diverse, qualitative managerial objectives from non-expert operators into tractable optimization models. This paper introduces RideAgent, an LLM-powered agent framework that automates and enhances electric ride-hailing fleet management. First, an LLM interprets natural language queries from fleet managers to formulate corresponding mathematical objective functions. These user-defined objectives are then optimized within a Mixed-Integer Programming (MIP) framework, subject to the constraint of maintaining high operational profit. The profit itself is a primary objective, estimated by an embedded Random Forest (RF) model leveraging exogenous features. To accelerate the solution of this MIP, a prompt-guided LLM analyzes a small sample of historical optimal decision data to guide a variable fixing strategy. Experiments on real-world data show that the LLM-generated objectives achieve an 86% text similarity to standard formulations in a zero-shot setting. Following this, the LLM-guided variable fixing strategy reduces computation time by 53.15% compared to solving the full MIP with only a 2.42% average optimality gap. Moreover, this variable fixing strategy outperforms five cutting plane methods by 42.3% time reduction with minimal compromise to solution quality. RideAgent offers a robust and adaptive automated framework for objective modeling and accelerated optimization. This framework empowers non-expert fleet managers to personalize operations and improve urban transportation system performance.

math.OC↗

arXiv2024

City-LEO: Toward Transparent City Management Using LLM with End-to-End Optimization

Existing operations research (OR) models and tools play indispensable roles in smart-city operations, yet their practical implementation is limited by the complexity of modeling and deficiencies in optimization proficiency. To generate more relevant and accurate solutions to users' requirements, we propose a large language model (LLM)-based agent ("City-LEO") that enhances the efficiency and transparency of city management through conversational interactions. Specifically, to accommodate diverse users' requirements and enhance computational tractability, City-LEO leverages LLM's logical reasoning capabilities on prior knowledge to scope down large-scale optimization problems efficiently. In the human-like decision process, City-LEO also incorporates End-to-end (E2E) model to synergize the prediction and optimization. The E2E framework be conducive to coping with environmental uncertainties and involving more query-relevant features, and then facilitates transparent and interpretable decision-making process. In case study, we employ City-LEO in the operations management of e-bike sharing (EBS) system. The numerical results demonstrate that City-LEO has superior performance when benchmarks against the full-scale optimization problem. With less computational time, City-LEO generates more satisfactory and relevant solutions to the users' requirements, and achieves lower global suboptimality without significantly compromising accuracy. In a broader sense, our proposed agent offers promise to develop LLM-embedded OR tools for smart-city operations management.

math.OC↗