arXiv subjects

Jiameng Wang

Publications and source records attributed to Jiameng Wang.

4 recordsLinked to original sources

arXiv2026

Correlating Quasi-Optical Coupling Efficiency with Measured Receiver Noise Temperature in Metalens Coupled THz HEB Mixer

Quasi-optical coupling serves as the critical interface in terahertz (THz) heterodyne receiver systems, enabling efficient transfer of incident radiation to photomixers through a focusing element and a planar microwave antenna. With recent advances in nanofabrication, planar dielectric metalenses have emerged as promising alternatives to conventional refractive optics due to their compactness and scalability. However, unlike conventional elliptical silicon lenses that are often treated as nearly ideal optical components, the focusing characteristics of metalenses, including both phase and amplitude, strongly depend on the local deflection angle across the aperture, creating an urgent need to quantitatively understand the coupling between a dielectric metalens and a planar antenna. In this work, we present a quasi-optical coupling analysis between a planar Si metalens and a logarithmic spiral antenna integrated with a THz superconducting NbN hot-electron bolometer (HEB) mixer operating at 1.63 THz using spherical-coordinate vectorial integration. By combining the angular radiation profile of the spiral antenna with the propagated complex electric-field profile from metalens numerical simulations, the calculated coupling efficiency accounts for angular power distribution, phase-front matching, and polarization-dependent vectorial overlap. The calculated coupling efficiency is then directly correlated with experimentally measured double-sideband receiver noise temperatures through comparison with a conventional elliptical Si lens measured under the same receiver configuration. The analysis establishes a quantitative relationship between metalens focusing efficiency, vectorial antenna coupling, and receiver noise temperature, providing guidance for optimizing metalens design and improving the overall performance of metalens-integrated THz heterodyne receivers.

physics.optics↗

arXiv2026

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at scale. Ling-2.6 is optimized for instant response generation and high capability per output token, whereas Ring-2.6 is tailored for deeper reasoning and more advanced agentic workflows. Instead of training from scratch, we upgrade the Ling-2.0 base model through architectural migration pre-training and large-scale post-training. This upgrade is guided by a unified co-design of model architecture, optimization objectives, serving systems, and agent training environments, enabling improvements in both model capability and deployment efficiency. At the architectural level, we introduce a hybrid linear attention design that integrates Lightning Attention with MLA, improving the efficiency of long-context training and decoding. To further enhance token efficiency, we optimize capability per output token through Evolutionary Chain-of-Thought, Linguistic Unit Policy Optimization, bidirectional preference alignment, and shortest-correct-response distillation. For agentic capabilities, we propose KPop, a reinforcement learning framework designed to support stable training of Ring-2.6-1T on large-scale environment-grounded data. KPop improves training efficiency through asynchronous scheduling across coding, search, tool use, and workflow execution, enabling scalable learning from complex agent-environment interactions. Together, Ling-2.6 and Ring-2.6 provide a practical pathway toward efficient, scalable, and open agentic systems. We open-source all checkpoints in the 2.6 family to support further research and development in practical agentic intelligence.

cs.CL↗

arXiv2026

First Submillimeter Lights from Dome A: Tracing the Carbon Cycle in the Feedback of Massive Stars

The cycling of carbon between its ionized, atomic, and molecular phases shapes the chemical compositions and physical conditions of the interstellar medium (ISM). However, ground-based studies of the full carbon cycle have been limited by atmospheric absorption. Dome~A, the most promising site for submillimeter astronomy, has long resisted successful submillimeter astronomical observations. Using the 60~cm Antarctic Terahertz Explorer, we present the first successful CO ($4-3$) and [CI] ($^3P_1 - ^3P_0$) mapping observations of two archetypal triggered massive star-formation regions at Dome~A. These data, together with archival [CII], provide the first complete characterization of all three carbon phases in these environments. We find elevated C$^{0}$/CO abundance ratios in high-extinction regions, plausibly driven by deep penetration of intense radiation fields from massive stars into a clumpy ISM. These findings mark a major milestone for submillimeter astronomy at Dome~A and offer valuable insights into the impact of massive star feedback on the surrounding ISM.

astro-ph.GA↗

arXiv2025

Advanced representation learning for flow field analysis and reconstruction

In this paper we present advanced representation learning study on integrating deep learning techniques and sparse approximation, including diffusion models, for advanced flow field analysis and reconstruction. Key applications include super-resolution flow field reconstruction, flow field inpainting, fluid-structure interaction, transient and internal flow analyses, and reduced-order modeling. The study introduces two novel methods: flow diffusions for super-resolution tasks and a sparsity-boosted low-rank model for flow field inpainting. By leveraging cutting-edge methodologies in computational fluid dynamics (CFD), the proposed approaches improve accuracy, computational efficiency, and adaptability, offering deeper insights into complex flow dynamics.

physics.flu-dyn↗