arXiv subjects

Ruifeng Shi

Publications and source records attributed to Ruifeng Shi.

12 recordsLinked to original sources

arXiv2026

openJiuwen: Beyond Static Harnesses for Long-Horizon Coding Agents

Long-horizon coding agents operate over evolving repository states while increasingly relying on heterogeneous capabilities, delegated agents, and multi-agent coordination. These trends pose two complementary challenges for the agent harness. First, developers need to compose capabilities, reconfigure execution logic, and scale increasingly complex agent systems without repeatedly rebuilding orchestration. Second, complex coding tasks continuously produce new evidence---such as semantic diagnostics, execution outcomes, task progress, and changing context relevance---that should dynamically influence subsequent runtime decisions. We characterize these challenges as Structural Composability and Runtime Adaptivity. We present openJiuwen, an open-source harness designed for both developer composability and adaptive task execution. openJiuwen provides a shared execution substrate and Rail-based capability composition across single agents, delegated sub-agents, and Swarm Flow, enabling developers to construct sophisticated agent harnesses under common execution semantics. It further adapts framework-controlled runtime decisions around a fixed model policy, allowing evolving evidence to dynamically affect context, feedback, and task control toward successful completion. We systematically evaluate openJiuwen on SWE-bench Verified and Terminal-Bench 2.1, where it achieves 82.6% and 87.19%, respectively, exceeding the strongest selected official-leaderboard point estimates by 3.4 and 3.39 percentage points. These results show that openJiuwen achieves strong performance on complex coding tasks while providing a composable and adaptive harness design.

cs.AI↗

arXiv2026

Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering

As artificial intelligence engineering paradigms shift from single-agent Prompt and Context Engineering toward multi-agent \textbf{Coordination Engineering}, the ability to codify and systematically improve how multiple agents collaborate has emerged as a critical bottleneck. While single-agent skills can now be distributed as portable assets, multi-agent coordination protocols remain locked within framework-internal code or static configurations, preventing them from being shared across systems or autonomously improved over time. We propose \textbf{Swarm Skills}, a portable specification that extends the Anthropic Skills standard with multi-agent semantics. Swarm Skills turns multi-agent workflows into first-class, distributable assets that consist of roles, workflows, execution bounds, and a built-in semantic structure for self-evolution. To operationalize the specification's evolving nature, we present a companion self-evolution algorithm that automatically distills successful execution trajectories into new Swarm Skills and continuously patches existing ones based on multi-dimensional scoring (Effectiveness, Utilization, and Freshness), eliminating the need for human-in-the-loop oversight during the refinement process. Through an architectural compatibility analysis and a comprehensive qualitative case study using the open-source JiuwenSwarm reference implementation, we demonstrate how Swarm Skills achieves zero-adapter cross-agent portability via progressive disclosure, enabling agent teams to self-evolve their coordination strategies without framework lock-in.

cs.CL↗

arXiv2026

Filter Design for Estimating the Stellar Metallicity of Metal-poor Stars from Gaia XP Spectra

The estimation of stellar atmospheric parameters for large-scale samples, particularly metal-poor stars, is a cornerstone of Galactic archaeology. In this work, we optimized a photometric filter design tailored to measuring stellar metallicities for very metal-poor stars with [Fe/H]$< -1$.The optimal configurations consist of a central wavelength $\lambda_{\rm c}$ = 3960 Angstrom with a bandwidth $\Delta\lambda$ = 80 Angstrom for giant stars, and $\lambda_{\rm c} $= 3920 Angstrom with $\Delta\lambda$ = 80 Angstrom for dwarf stars. By applying these optimized filters to synthetic photometry derived from Gaia XP spectra, we inferred metallicities for both populations. Both internal and external validations demonstrate high precision across a wide metallicity range: 0.18-0.19 dex for $-2 \le \rm [Fe/H] \le -1$, 0.23-0.33 dex for $-3 \le \rm [Fe/H] \le -2$, and approximately 0.39 dex for the most metal-poor regime, successfully extending down to $\rm [Fe/H] \approx -4$ for giant stars, $\rm [Fe/H] \approx -3.3$ for dwarf stars. Finally, we present a catalog of approximately 14.5 million metal-poor stars with robust $\rm [Fe/H]$ measurements, along with more than ten thousand red giant ultra metal-poor candidates with $\rm [Fe/H] < -4.0$, providing a valuable resource for exploring the early formation and chemical evolution of the Milky Way.

astro-ph.SR↗

arXiv2025

ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines

Multi-step LLM pipelines can solve complex tasks, but jointly optimizing prompts across steps remains challenging due to missing step-level supervision and inter-step dependency. We propose ADOPT, an adaptive dependency-guided joint prompt optimization framework for multi-step LLM pipelines. ADOPT analyzes the dependency between each LLM step and the final output, constructs a global textual gradient from final-task errors, and decomposes it into step-level local textual gradients, providing more precise optimization signals for local prompt updates. It further decouples signal estimation from prompt updating, enabling flexible integration of single-prompt optimizers, and uses a Shapley-based strategy to adaptively allocate optimization resources to high-impact steps. Experiments on real-world datasets and structurally diverse pipelines demonstrate that ADOPT is effective and robust, consistently outperforming strong prompt optimization baselines.

cs.CL↗

arXiv2025

Identification of BHB stars using Synthetic SkyMapper colors from Gaia XP spectra

Blue horizontal-branch (BHB) stars are ideal tracers for mapping the structure of Galactic stellar halo. Traditionally, BHB sample stars are built from large-scale spectroscopic surveys utilizing their spectral features, however, the resulting sample sizes have been quite limited. In this paper, we construct a catalog of BHB stars based on synthetic colors $(u-v)_{0}$ and $(g-i)_{0}$ in SkyMapper photometric systems, which are convolved from Gaia XP spectra. A total of 49,733 BHB stars are selected from nearly the entire sky (excluding regions of low Galactic latitudes $|b| \le 8^{\circ}$ with heavy reddening), with a completeness and purity exceeding 90\%. Using member stars of globular clusters with precise distance determinations, we carefully calibrate the relationship between the $g$-band absolute magnitude and $(g-i)_{0}$, achieving a precision of 0.11\,mag, which corresponds to a 5\% uncertainty in distance. This relation is applied to derive distances for all BHB stars in the constructed sample. Given current capabilities of Gaia XP observations, the constructed BHB sample is primarily located within 20 kpc, enabling detailed mapping of the inner stellar halo. To extend this depth to the outer halo or even the edge of our Galaxy, we explore the potential of the Chinese Space Station Telescope (CSST) and its broad-band photometry for detecting BHB stars. Using mock data from synthetic spectra, we find that it is feasible to distinguish BHB stars from blue stragglers (BS) stars using CSST near-ultraviolet bands ($NUV, u$) photometry. Thanks to the deep limiting magnitude of CSST, its data will provide a groundbreaking perspective on our Galaxy, particularly regarding the outer halo, in an unprecedented volume.

astro-ph.GA↗

arXiv2025

The Mini-SiTian Array: Imaging Processing Pipeline

As a pathfinder of the SiTian project, the Mini-SiTian (MST) array, employed three commercial CMOS cameras, represents a next-generation, cost-effective optical time-domain survey project. This paper focuses primarily on the precise data processing pipeline designed for wide-field, CMOS-based devices, including the removal of instrumental effects, astrometry, photometry, and flux calibration. When applying this pipeline to approximately 3000 observations taken in the Field 02 (f02) region by MST, the results demonstrate a remarkable astrometric precision of approximately 70--80\,mas (about 0.1\,pixel), an impressive calibration accuracy of approximately 1\,mmag in the MST zero points, and a photometric accuracy of about 4\,mmag for bright stars. Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs, highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys, like the SiTian project.

astro-ph.IM↗

arXiv2024

Photometric Stellar Parameters for 195,478 Kepler Input Catalog (KIC) Stars

The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC catalog. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler Issac-Newton Survey, LAMOST DR10, and APOGEE DR17, we have obtained atmospheric-parameter estimates for over 195,000 stars, accounting for 97$\%$ of the total sample of KIC stars. We obtain 1$\sigma$ uncertainties of 0.1 dex on metallicity [Fe/H], 100 K on effective temperature $T_{\rm eff}$, and 0.2 dex on surface gravity log $g$. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. External comparisons indicate that the resulting precision for turn-off stars is 20$\%$ in age; for dwarf stars, it is 0.07 $M_{\odot}$ in mass, 0.05 $R_{\odot}$ in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14 $M_{\odot}$ in mass, 0.73 $R_{\odot}$ in radius, and 0.11 dex in surface gravity.

astro-ph.SR↗

arXiv2024

Pathformer: Recursive Path Query Encoding for Complex Logical Query Answering

Complex Logical Query Answering (CLQA) over incomplete knowledge graphs is a challenging task. Recently, Query Embedding (QE) methods are proposed to solve CLQA by performing multi-hop logical reasoning. However, most of them only consider historical query context information while ignoring future information, which leads to their failure to capture the complex dependencies behind the elements of a query. In recent years, the transformer architecture has shown a strong ability to model long-range dependencies between words. The bidirectional attention mechanism proposed by the transformer can solve the limitation of these QE methods regarding query context. Still, as a sequence model, it is difficult for the transformer to model complex logical queries with branch structure computation graphs directly. To this end, we propose a neural one-point embedding method called Pathformer based on the tree-like computation graph, i.e., query computation tree. Specifically, Pathformer decomposes the query computation tree into path query sequences by branches and then uses the transformer encoder to recursively encode these path query sequences to obtain the final query embedding. This allows Pathformer to fully utilize future context information to explicitly model the complex interactions between various parts of the path query. Experimental results show that Pathformer outperforms existing competitive neural QE methods, and we found that Pathformer has the potential to be applied to non-one-point embedding space.

cs.LG↗

arXiv2024

Filter Design for Estimation of Stellar Metallicity: Insights from Experiments with Gaia XP Spectra

We search for an optimal filter design for the estimation of stellar metallicity, based on synthetic photometry from Gaia XP spectra convolved with a series of filter-transmission curves defined by different central wavelengths and bandwidths. Unlike previous designs based solely on maximizing metallicity sensitivity, we find that the optimal solution provides a balance between the sensitivity and uncertainty of the spectra. With this optimal filter design, the best precision of metallicity estimates for relatively bright ($G \sim 11.5$) stars is excellent, $σ_{\rm [Fe/H]} = 0.034$\,dex for FGK dwarf stars, superior to that obtained utilizing custom sensitivity-optimized filters (e.g., SkyMapper\,$v$). By selecting hundreds of high-probabability member stars of the open cluster M67, our analysis reveals that the intrinsic photometric-metallicity scatter of these cluster members is only 0.036\,dex, consistent with this level of precision. Our results clearly demonstrate that the internal precision of photometric-metallicity estimates can be extremely high, even providing the opportunity to perform chemical tagging for very large numbers of field stars in the Milky Way. This experiment shows that it is crucial to take into account uncertainty alongside the sensitivity when designing filters for measuring the stellar metallicity and other parameters.

astro-ph.SR↗

arXiv2024

On the Performances of Estimating Stellar Atmospheric Parameters from CSST Broad-band Photometry

Deriving atmospheric parameters of a large sample of stars is of vital importance to understand the formation and evolution of the Milky Way. Photometric surveys, especially those with near-ultraviolet filters, can offer accurate measurements of stellar parameters, with the precision comparable to that from low/medium resolution spectroscopy. In this study, we explore the capability of measuring stellar atmospheric parameters from CSST broad-band photometry (particularly the near-ultraviolet bands), based on synthetic colors derived from model spectra. We find that colors from the optical and near-ultraviolet filter systems adopted by CSST show significant sensitivities to the stellar atmospheric parameters, especially the metallicity. According to our mock data tests, the precision of the photometric metallicity is quite high, with typical values of 0.17 dex and 0.20 dex for dwarf and giant stars, respectively. The precision of the effective temperature estimated from broad-band colors are within 50 K.

astro-ph.SR↗

arXiv2020

Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis

Plant diseases serve as one of main threats to food security and crop production. It is thus valuable to exploit recent advances of artificial intelligence to assist plant disease diagnosis. One popular approach is to transform this problem as a leaf image classification task, which can be then addressed by the powerful convolutional neural networks (CNNs). However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation. To overcome this problem, we propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information. The proposed method enjoys the following merits: i) A rectified meta-learning is designed to pay more attention to unbiased samples, leading to accelerated convergence and improved classification accuracy. ii) Our method is free on assumption of label noise distribution, which works well on various kinds of noise. iii) Our method serves as a plug-and-play module, which can be embedded into any deep models optimized by gradient descent based method. Extensive experiments are conducted to demonstrate the superior performance of our algorithm over the state-of-the-arts.

cs.CV↗

arXiv2014

A Game-Theoretic Approach to Energy-Efficient Resource Allocation in Device-to-Device Underlay Communications

Despite the numerous benefits brought by Device-to-Device (D2D) communications, the introduction of D2D into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of User Equipments (UEs). Most of the previous studies mainly focus on how to maximize the Spectral Efficiency (SE) and ignore the energy consumption of UEs. In this paper, we study how to maximize each UE's Energy Efficiency (EE) in an interference-limited environment subject to its specific Quality of Service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. A distributed interference-aware energy-efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. We prove that the optimum solution obtained by the proposed algorithm is the Nash equilibrium of the noncooperative game. We also analyze the tradeoff between EE and SE and derive closed-form expressions for EE and SE gaps.

cs.GT↗