Utilizing cosmological hydrodynamic simulations we show that there is a brief super-Eddington accretion phase in typical halos at high redshift, impervious to AGN self-regulation. However, once having attained a black hole mass of $10^4-10^5\msun$, AGN feedback process can self-regulate to guide the SMBHs to grow at a significantly slower, sub-Eddington rate. By redshift $z\sim 10$ the black hole mass with an initial super-Eddington jump-start is caught up by that in the case with a steady Eddington limited case. Thus a continuous Eddington limit case represents the fastest possible route to maximally grow SMBHs. To account for the observed $z=7-10$ quasars with supermassive black holes of billions of solar masses, our analysis establishes firmer ground for the need of seed masses of $10^4-10^5\msun$ that are not grown via an earlier super-Eddington phase.

While Large Language Models (LLMs) have become the predominant paradigm for automated code generation, current single-model approaches fundamentally ignore the heterogeneous computational strengths that different models exhibit across programming languages, algorithmic domains, and development stages. This paper challenges the single-model convention by introducing a multi-stage, performance-guided orchestration framework that dynamically routes coding tasks to the most suitable LLMs within a structured generate-fix-refine workflow. Our approach is grounded in a comprehensive empirical study of 17 state-of-the-art LLMs across five programming languages (Python, Java, C++, Go, and Rust) using HumanEval-X benchmark. The study, which evaluates both functional correctness and runtime performance metrics (execution time, mean/max memory utilization, and CPU efficiency), reveals pronounced performance heterogeneity by language, development stage, and problem category. Guided by these empirical insights, we present PerfOrch, an LLM agent that orchestrates top-performing LLMs for each task context through stage-wise validation and rollback mechanisms. Without requiring model fine-tuning, PerfOrch achieves substantial improvements over strong single-model baselines: average correctness rates of 96.22% and 91.37% on HumanEval-X and EffiBench-X respectively, surpassing GPT-4o's 78.66% and 49.11%. Beyond correctness gains, the framework delivers consistent performance optimizations, improving execution time for 58.76% of problems with median speedups ranging from 17.67% to 27.66% across languages on two benchmarks. The framework's plug-and-play architecture ensures practical scalability, allowing new LLMs to be profiled and integrated seamlessly, thereby offering a paradigm for production-grade automated software engineering that adapts to the rapidly evolving generative AI landscape.

Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on textual data within issues, neglecting visual data. However, this visual data is crucial for resolving issues as it conveys additional knowledge that text alone cannot. We propose CodeV, the first approach to leveraging visual data to enhance the issue-resolving capabilities of LLMs. CodeV resolves each issue by following a two-phase process: data processing and patch generation. To evaluate CodeV, we construct a benchmark for visual issue resolving, namely Visual SWE-bench. Through extensive experiments, we demonstrate the effectiveness of CodeV, as well as provide valuable insights into leveraging visual data to resolve GitHub issues.

Integrated photonic circuits are foundational for versatile applications, where high-performance traveling-wave optical resonators are critical. Conventional whispering-gallery mode microresonators (WGMRs) confine light in closed-loop waveguide paths, thus inevitably occupy large footprints. Here, we report an ultracompact high loaded Q silicon photonic WGMR in an open curved path instead. By leveraging spatial mode multiplexing, low-loss mode converter-based photonic routers enable reentrant photon recycling in a single non-closed waveguide. The fabricated device achieves a measured loaded Q-factor of 1.78*10^5 at 1554.3 nm with a 1.05 nm free spectral range in a ultracompact footprint of 0.00137 mm^2-6*smaller than standard WGMRs while delivering 100*higher Q-factor than photonic crystal counterparts. This work pioneers dense integration of high-performance WGMR arrays through open-path mode recirculation.

We report the discovery of a dense molecular ring-like structure in a dense (10$^5$ cm$^{-3}$), cold (pc-scale CO depletion at a factor of 5), and young (10$^4$ year) star-forming region G34.74-0.12, revealed by C$^{18}$O (2-1), HNC (1-0), and N$_2$H$^+$ (1-0) observations with the Atacama Large Millimeter/submillimeter Array (ALMA). The ring-like structure is redshifted with respect to the clump, spanning from $V_{\rm sys,lsr} + 0.9$ to $V_{\rm sys,lsr} + 2.9$ km s$^{-1}$, with a total mass of 109 $M_{\odot}$. It is spatially coincident with 1.3 mm and 3.0 mm dust continuum emission from cores, and several protostellar outflows. However, no free-free emission or H\textsc{ii} region is detected in association with this structure. With a slow expansion speed indicated by the position-velocity diagram, this ring structure differs from rings previously identified in more evolved star-forming regions. Possible explanations for the ring-like structure include a relic wind-blown bubble produced by a deeply embedded young stellar object, a hollow cavity formed by cloud-cloud interactions, a gas ring resulting from a temperature gradient, or a line-of-sight superposition of multiple outflows or dense clouds. This discovery offers a rare observational glimpse into the earliest dynamical processes involved in massive star formation.

In this paper we focus on the analysis of the multiwavelength spectroscopic observations of a quiescent prominence. The spectral and geometrical parameters in the prominence were derived and used to constrain the NLTE radiative transfer models. Applying this method with multiwavelength observations provides a good opportunity to reduce the large range of thermodynamic parameters in solar prominences. We used time-slice and optical flow methods in order to derive the plane-of-sky (POS) velocities, and used gravity center and peak position methods on Mg II h&k and H I Ly-alpha profiles to compute the line-of-sight (LOS) velocities. We used the integrated intensities and FWHM values of the H-alpha, Ca II H, and Mg II h&k lines to compare with the NLTE radiative transfer computations. Ionization degree and thickness of the prominence plasma could be further derived. Opposite flows are observed along two strands between prominence barbs. The POS velocity can reach 20 km/s and the largest LOS velocity is > 90 km/s. The derived electron densities range from 6.5e9 cm-3 to 2.7e10 cm-3, and the derived total hydrogen densities range from 7.4e9 cm-3 to 6.6e10 cm-3. The temperature ranges from 7 000 to 14 000 K. The ionization degree of hydrogen is in the range of 0.40 to 0.91. The comparison between averaged and modeled profiles of Mg II and Ly-alpha lines shows that macro-velocities of 15 km/s and 20 km/s are required, respectively. The bulk motions among prominence barbs indicate that the prominence plasma is not confined within magnetic dips but exhibits a large-scale behavior. The presence of high-speed cool plasma flows, along with a wide range of plasma densities and temperatures, suggests that the prominence plasma is far from thermodynamic equilibrium and is inherently dynamic in nature.

The nature of dark energy remains one of the most profound mysteries in modern cosmology. One intriguing proposal is that black holes (BHs) could be the astrophysical source of dark energy through a cosmological coupling mechanism, and strong evidence has been claimed via analyzing the growth of the black hole masses in the red-sequence elliptical galaxies at redshifts $\leq 2.5$. In this work, with a group of very high redshift AGNs detected by the James Webb Space Telescope (JWST) in the red-sequence elliptical galaxies, we show that the possibility of BHs being the astrophysical source of dark energy has been rejected at a confidence level exceeding 10$\sigma$. Moreover, it turns out that the Little Red Dots recently discovered by JWST, characterized by the low accretion rates, can naturally evolve into the red-sequence elliptical galaxies hosting the relatively low mass black holes at the redshifts of $\sim 0.7-2.5$, without the need of black hole cosmological coupling.

Contamination-free assessments of the interstellar medium and star formation in quasar host galaxies, particularly based on the far-infrared, offer insights into the role of supermassive black holes in galaxy evolution. Motivated by predictions of quasar heating of dust on both nuclear and galaxy-wide scales, we perform two-component (host galaxy + point source) modeling of high-resolution (~0.1") ALMA observations of the FIR continuum in Band 5 (lambda_rest~500 um) of three highly luminous quasars (L_bol~10^47 erg/s), powered by supermassive black holes having M_BH~10^9 M_sun, at z=2. We include Band 9 (lambda_rest~154 um; 0.06" and 0.3") data at high S/N which places further constraints on the unresolved nuclear component in two cases. To break the degeneracy between quasar and stellar heating, we use CO (J=5-4), observed in Band 5, to gauge the expected contribution of star formation to the infrared luminosity. We find very good agreement between the strength and spatial distribution of the extended continuum component and its prediction based on CO (J=5-4). This is supported by the location of our three quasars along the L_(CO 5-4)-L_(IR, SFR) luminosity relation for inactive star-forming galaxies. As a consequence, there is no evidence for additional continuum emission on extended scales which could be attributed to quasar-heated dust. As expected, the nuclear (i.e., torus) contribution is present and subdominant (12% in Band 9 for one quasar with a typical star-forming host) or non-existent (<8% in Band 9 for the starbursting host). Based on the continuum and CO, the presence of substantial levels of ongoing star formation agrees with previous estimates from unresolved ALMA continuum observations which finds SFRs consistent with star-forming main-sequence galaxies. Therefore, our results do not provide evidence for a quasar-mode feedback, even for the most luminous cases at z=2.

Magnetic resonance imaging (MRI) reconstruction is a fundamental task aimed at recovering high-quality images from undersampled or low-quality MRI data. This process enhances diagnostic accuracy and optimizes clinical applications. In recent years, deep learning-based MRI reconstruction has made significant progress. Advancements include single-modality feature extraction using different network architectures, the integration of multimodal information, and the adoption of unsupervised or semi-supervised learning strategies. However, despite extensive research, MRI reconstruction remains a challenging problem that has yet to be fully resolved. This survey provides a systematic review of MRI reconstruction methods, covering key aspects such as data acquisition and preprocessing, publicly available datasets, single and multi-modal reconstruction models, training strategies, and evaluation metrics based on image reconstruction and downstream tasks. Additionally, we analyze the major challenges in this field and explore potential future directions.

The Giant Radio Array for Neutrino Detection (GRAND) is a proposed multi-messenger observatory of ultra-high-energy (UHE) particles of cosmic origin. Its main goal is to find the long-sought origin of UHE cosmic rays by detecting large numbers of them and the secondary particles created by their interaction -- gamma rays, and, especially, neutrinos. GRAND will do so using large arrays of radio antennas that look for the radio signals emitted by the air showers initiated by the interactions of the UHE particles in the atmosphere. Since 2023, three small-scale prototype GRAND arrays have been in operation: GRAND@Nançay in France, GRAND@Auger in Argentina, and GRANDProto300 in China. Together, their goal is to validate the detection principle of GRAND under prolonged field conditions, achieving efficient, autonomous radio-detection of air showers. We describe the hardware, software, layout, and operation of the GRAND prototypes and show the first radio spectra measured by them. Despite challenges, the successful operation of the prototypes confirms that the GRAND instrumentation is apt to address the goals of the experiment and lays the groundwork for its ensuing stages.

Different annotators often assign different labels to the same sample due to backgrounds or preferences, and such labeling patterns are referred to as tendency. In multi-annotator scenarios, we introduce a novel task called Multi-annotator Tendency Learning (MATL), which aims to capture each annotator tendency. Unlike traditional tasks that prioritize consensus-oriented learning, which averages out annotator differences and leads to tendency information loss, MATL emphasizes learning each annotator tendency, better preserves tendency information. To this end, we propose an efficient baseline method, Query-based Multi-annotator Tendency Learning (QuMATL), which uses lightweight query to represent each annotator for tendency modeling. It saves the costs of building separate conventional models for each annotator, leverages shared learnable queries to capture inter-annotator correlations as an additional hidden supervisory signal to enhance modeling performance. Meanwhile, we provide a new metric, Difference of Inter-annotator Consistency (DIC), to evaluate how effectively models preserve annotators tendency information. Additionally, we contribute two large-scale datasets, STREET and AMER, providing averages of 4300 and 3118 per-annotator labels, respectively. Extensive experiments verified the effectiveness of our QuMATL.

Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ disparate network architectures and training strategies. The effect of the design of each component is still unclear. In addition, the iterative denoising process consumes considerable computational overhead, which is prohibitive for real-time scenarios such as virtual characters and humanoid robots. For this reason, we first conduct a comprehensive investigation into network architectures, training strategies, and inference processs. Based on the profound analysis, we tailor each component for efficient high-quality human motion generation. Despite the promising performance, the tailored model still suffers from foot skating which is an ubiquitous issue in diffusion-based solutions. To eliminate footskate, we identify foot-ground contact and correct foot motions along the denoising process. By organically combining these well-designed components together, we present StableMoFusion, a robust and efficient framework for human motion generation. Extensive experimental results show that our StableMoFusion performs favorably against current state-of-the-art methods. Project page: this https URL