arXiv subjects

Zhijing Huang

Publications and source records attributed to Zhijing Huang.

7 recordsLinked to original sources

arXiv2026

UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks

Modern cities rely on an increasing number of digital services to operate, but residents' daily needs are still difficult to meet. Services are fragmented and have little interoperability, placing a heavy operational burden on users. Existing digital platforms, urban foundation models, and intelligent assistants each address only isolated aspects of an urban task. But they struggle to reliably convert complex natural-language requests into executable cross-system workflows. We propose Urban-Agent, a tool-augmented agent framework for cross-system urban tasks. It couples the cognitive and reasoning capabilities of a large language model with a tool-set supporting code execution, API calls, and Model Context Protocol. Through one adaptive closed loop, it clarifies missing information before acting, grounds tool use in live observations, and aligns the final response with observed evidence and task constraints. To address the evaluation gap, we introduce Urban-Eval, a benchmark specifically designed for cross-system urban request. Unlike prior benchmarks that assess either general tool use or urban knowledge and reasoning, Urban-Eval evaluates both task results and execution quality, including required tool coverage, dependency validity, and evidence traceability. Experimental results indicate that Urban-Agent reaches a 71% task success rate, 10 points above the strongest baseline. This lead holds across GPT-5-mini, Gemini-2.5-flash, DeepSeek-V4-flash, and Qwen3-235B-A22B.

cs.AI↗

arXiv2026

Beyond Janus Atomic Ordering: High-Throughput First-Principles Search for Hidden MoSO Monolayer Structures

Despite the growing interest in two-dimensional (2D) MoSO systems, existing studies have exclusively focused on conventional Janus structures. In this work, we perform high-throughput first-principles calculations to explore novel stable 2D MoSO monolayers. Combined with random sampling strategy, graph theory and group theory, we successfully screen out three novel non-Janus 2D MoSO monolayers from 1325 candidate structures, namely Reversed 2H-MoSO, Hybrid 2H-MoSO, and Hybrid 1T'-MoSO. Compared with Janus MoSO monolayers, the non-Janus MoSO counterparts possess lower binding energies, varying from -4.38 to -4.51 eV/atom. A systematic combination of dynamic, thermodynamic, and mechanical stability analyses corroborates their excellent structural robustness. Ab initio molecular dynamics (AIMD) simulations confirm their superior thermal resistance, with the structures remaining stable at temperatures beyond 2000 K. Interestingly, unlike the semiconducting Janus MoSO, the Hybrid 1T'-MoSO monolayer exhibits distinct metallic characteristics. Furthermore, we found that strain and curvature can enable controlled phase transitions of MoSO among semiconducting, semimetallic, and metallic phases. More importantly, the Hybrid 1T'-MoSO exhibits favorable HER activity with a Gibbs free energy of -0.002 eV, rendering it a promising candidate for hydrogen evolution catalysis. This work not only expands the family of 2D MoSO materials but also provides a reliable strategy for discovering stable functional 2D materials via high-throughput computation.

cond-mat.mtrl-sci↗

arXiv2026

Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven

Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks. However, their deployment in long-context scenarios faces high computational overhead and information redundancy. While soft prompt compression has emerged as a promising way to mitigate these costs by compressing sequences into compact embeddings, existing paradigms remain fundamentally constrained by position bias: they primarily rely on learnable tokens insertion at fixed positions or group tokens according to their physical token layout, thereby inducing performance instability and semantic fragmentation. To overcome this bottleneck, we propose Semantic Consistency Context Compression (SeCo), a method that shifts context compression from position-driven to semantic-driven. Rather than constraint by physical token layout, SeCo dynamically anchors compression directly in the semantic space by selecting query-relevant tokens as semantic centers and aggregating remaining tokens via consistency-weighted merging. This design inherently preserves semantic consistency while eliminating position bias. Extensive experiments on 14 benchmarks across two backbone models demonstrate that SeCo consistently shows superiority in downstream tasks, inference latency, and out-of-domain robustness. The code is available at https://anonymous.4open.science/r/seco-EE5E.

cs.CL↗

arXiv2025

Discovery of a Robust Non-Janus Hybrid MoSH Monolayer as a Two-Gap Superconductor via High-Throughput Computational Screening

The atomic-scale determination of hydrogen positions in MoSH monolayers remains experimentally challenging, and existing studies are confined to Janus-type configurations. Here, we combine high-throughput structural screening with first-principles calculations to predict a novel non-Janus Hybrid 1T$^{'}$-MoSH monolayer, which energetically surpasses all previously reported MoSH phases with a binding energy of -3.02 eV. This structure emerges as a hybrid of MoS$_2$ and MoH$_2$, featuring alternating S and H atoms on both sides of the Mo layer. Comprehensive stability analyses confirm its robustness in energy, mechanics, dynamics, and thermodynamics (stable up to 1600 K). Remarkably, anisotropic Migdal-Eliashberg theory predicts Hybrid 1T$^{'}$-MoSH as a two-gap superconductor with a critical temperature T$_c$ of 16.34 K, driven by strong electron-phonon coupling ($λ$$=$1.39). Substituting Mo with Hf, Ta, or Ti drastically suppresses T$_c$ $\sim$ (0.53-2.42 K), highlighting Mo$^{'}$s unique role in enhancing superconductivity. Our work not only expands the family of 2D transition metal chalcogenides but also proposes a promising candidate for quantum technologies, bridging theoretical design to functional material discovery.

cond-mat.mtrl-sci↗

arXiv2023

Unexpectedly Spontaneous Water Dissociation on Graphene Oxide Supported by Copper Substrate

Water dissociation is of fundamental importance in scientific fields and has drawn considerable interest in diverse technological applications. However, the high activation barrier of breaking the O-H bond within the water molecule has been identified as the bottleneck, even for the water adsorbed on the graphene oxide (GO). Herein, using the density functional theory calculations, we demonstrate that the water molecule can be spontaneously dissociated on GO supported by the (111) surface of the copper substrate (Copper-GO). This process involves a proton transferring from water to the interfacial oxygen group, and a hydroxide covalently bonding to GO. Compared to that on GO, the water dissociation barrier on Copper-GO is significantly decreased to be less than or comparable to thermal fluctuations. This is ascribed to the orbital-hybridizing interaction between copper substrate and GO, which enhances the reaction activity of interfacial oxygen groups along the basal plane of GO for water dissociation. Our work provides a novel strategy to access water dissociation via the substrate-enhanced reaction activity of interfacial oxygen groups on GO and indicates that the substrate can serve as an essential key to tuning the catalytic performance of various two-dimensional material devices.

cond-mat.mtrl-sci↗

arXiv2022

Oxygen dissociation on the C3N monolayer: A first-principles study

The oxygen dissociation and the oxidized structure on the pristine C3N monolayer in exposure to air are the inevitably critical issues for the C3N engineering and surface functionalization yet have not been revealed in detail. Using the first-principles calculations, we have systematically investigated the possible O2 adsorption sites, various O2 dissociation pathways and the oxidized structures. It is demonstrated that the pristine C3N monolayer shows more O2 physisorption sites and exhibits stronger O2 adsorption than the pristine graphene. Among various dissociation pathways, the most preferable one is a two-step process involving an intermediate state with the chemisorbed O2 and the barrier is lower than that on the pristine graphene, indicating that the pristine C3N monolayer is more susceptible to oxidation than the pristine graphene. Furthermore, we found that the most stable oxidized structure is not produced by the most preferable dissociation pathway but generated from a direct dissociation process. These results can be generalized into a wide range of temperatures and pressures using ab initio atomistic thermodynamics. Our findings deepen the understanding of the chemical stability of 2D crystalline carbon nitrides under ambient conditions, and could provide insights into the tailoring of the surface chemical structures via doping and oxidation.

cond-mat.mtrl-sci↗

arXiv2021

Unexpected Spontaneously Dynamic Oxygen Migration on Carbon Nanotubes

Using the density functional theory calculations, we show that the oxygen functional groups exhibit unexpected spontaneously dynamic behaviors on the interior surface of single-walled carbon nanotubes (SWCNT). Two types of dynamic oxygen migrations - hydroxyl and epoxy migrations - are achieved by the breaking/reforming of C-O bond reaction and the proton transfer reaction. It is demonstrated that the spontaneously dynamic characteristic is attributed to the sharply reduced energy barrier less than or comparable to thermal fluctuations. We also observe a stable intermediate state with a dangling C-O bond, which permits the successive migration of oxygen functional groups. However, on the exterior surface of SWCNT, the oxygen groups are difficult to migrate spontaneously due to the relatively high energy barriers, and the dangling C-O bond prefers to transform into the more stable epoxy configuration. The spontaneous oxygen migration is further confirmed by the long-distance oxygen migration, which comprises three hydroxyl migration reactions and one C-O bond reaction. Our work provides a new understanding of the behavior of oxygen functional groups on interfaces and gives a potential route to design new carbon-based dynamic materials.

cond-mat.mtrl-sci↗