arXiv subjects

Jun Yang

Publications and source records attributed to Jun Yang.

At least 19 recordsLinked to original sources

arXiv2026

Averaged proximal reflected gradient method for monotone variational inequalities

Projected reflected gradient (PRG) method proposed by Malitsky is efficient for solving monotone variational inequality (MVI), while the existing upper bound of step size is not tight due to the inequality scaling in the theoretical analysis. In this paper, we construct an averaged variant of PRG method for more general MVI and present a novel Lyapunov function to establish convergent theory. This averaged PRG method provides an improvement of the golden ratio algorithm [Y, Malitsky, Math. Program., 184, 383-410, 2020], and the involved step size is compatible with that for the classical methods, such as Popov's extragradient and forward-reflected-backward methods. Moreover, a fully adaptive strategy without linesearch is presented to adjust step sizes, which generates closed-form and potentially much larger step sizes. Numerical experiments on the Nash-Cournot equilibrium, HpHard, and image reconstruction problems demonstrate that the proposed algorithm significantly outperforms existing state-of-the-art methods.

math.OC↗

arXiv2026

Microscopy assessment of two multiple-transient candidates

We present a microscopy-based assessment of two representative cases of multiple-transient candidates identified on historical photographic plates: the nine-transient event of 12 April 1950 (XE325) and the triple-transient event of 19 July 1952 (XE186). These events consist of spatially clustered, point-like sources detected within single long-exposure plates, lacking the streaks expected from moving objects and therefore suggestive of short-duration flashes. We analyse microscopy images of the original glass-plate material together with an independent copy plate and archival metadata, and we register these images against the published survey digitizations so that individual catalogued sources can be located. In both cases the candidates appear slightly sharper and more compact than typical stellar point spread functions, qualitatively consistent with sub-second to few-second emission during exposures of $\sim 50$~min, and they are reproducible across different representations of the same field, which disfavours a digitization-artifact origin. However, microscopy of individual point-like features cannot by itself separate a short-duration flash from an intrinsic emulsion defect, because the two scenarios produce qualitatively similar signatures at the single-source level. We also show that the multi-observatory parallax test, while powerful in principle, is not feasible for these two specific events with presently available material, owing to the absence of a simultaneous second-site plate reaching comparable depth. We conclude that resolving the nature of these events requires population-level statistics and, where future observations allow, purpose-built multi-station imaging. This work provides a proof-of-concept framework for combining microscopy with archival analysis in the study of historical transient phenomena.

astro-ph.IM↗

arXiv2026

Reasoning-Oriented Post-Training and Inference-Time LoRA Rescaling for Audio-Dependent Question Answering

Audio-Dependent Question Answering (ADQA) requires Large Audio-Language Models (LALMs) to answer questions whose correct answers depend on the given audio content. Successful ADQA requires accurate audio perception, identification of question-relevant evidence, and cross-modal reasoning. Using the official ADQA dataset of DCASE 2026 Task 5, we investigate reasoning-oriented post-training with Low-Rank Adaptation (LoRA) and inference-time LoRA rescaling for both Qwen2.5-Omni and MOSS-Audio-8B-Thinking. We introduce a structured Chain-of-Thought (CoT) framework that decomposes the reasoning process into question analysis, question type, audio evidence, and reasoning. We then analyze how task-specific LoRA adaptation affects the two backbones and further explore inference-time rescaling of trained LoRA adapters. Experiments on the development set reveal markedly backbone-dependent behavior: post-training improves the Qwen-based systems but substantially degrades MOSS-Audio under our supervised fine-tuning configuration. Moderate LoRA rescaling further improves the best Qwen system's top-1 accuracy from 58.93% to 61.05% and partially restores the performance of the fine-tuned MOSS-Audio models, while the best MOSS-Audio system achieves 67.70% top-1 accuracy. Our submitted systems ranked third overall and second among lightweight systems under 10B parameters in the challenge.

cs.SD↗

arXiv2026

SkillAlchemy: Open-World Agent Skill Creation

Agent skills are reusable procedural artifacts that extend language agents with specialized workflows, tool conventions, and domain behaviors at inference time. However, creating reliable skills still depends largely on human authorship, model priors, or execution traces. These sources are often unavailable for unfamiliar tasks, suggesting the need to create skills from open-world materials. In this paper, we study open-world skill creation: given an underspecified skill brief and a source-access specification, a creator must discover behavior-relevant requirements omitted by the brief and determine how broadly each source-derived procedure is justified. We propose SkillAlchemy, an admission-centered framework for source-grounded skill creation. SkillAlchemy identifies implicit requirements through contrastive evidence, admits candidate procedures based on evidence-supported scope, and compiles the admitted content into a grammar-guided skill package. Extensive experiments across 87 SkillsBench v1.1 tasks demonstrate that SkillAlchemy improves pass rate over no-skill execution by 19.9 percentage points and the strongest automated baseline by 8.6 percentage points, while achieving performance comparable to human-curated skills.

cs.AI↗

arXiv2026

The Total and Polarized Radio Emission from the Innermost Jets of a High-Redshift Quasar and a Candidate at Parsec-Scale Resolution

High-frequency very long baseline interferometry (VLBI) polarimetry probes synchrotron-emitting plasma closer to the central engines of radio-loud active galactic nuclei (AGNs), but observations above 43 GHz are technically demanding. We present 22-GHz European VLBI Network observations of the $z=4.31$ quasar J1510+5702 and J1606+3124, whose published spectroscopic redshift, $z=4.56$, is uncertain; a photometric estimate gives $z_{\rm phot}=0.9\pm0.1$. For the published $z>4$ redshifts, 22 GHz corresponds to rest-frame frequencies above 118 GHz. Polarized emission is detected in J1510+5702, and a low-level polarized signal is recovered from the brightest feature of J1606+3124. Adopting $z=4.56$, that feature has a brightness temperature of $T_{\mathrm{b,VLBI}}=(7.4\pm0.8)\cdot10^{10}$ K, allowing a mildly Doppler-boosted interpretation, while the young compact-source scenario also remains viable. The core of J1510+5702 has $T_{\mathrm{b,VLBI}}=(1.08\pm0.15) \cdot10^{12}$ K, implying a Doppler factor of ${\sim}22$ under the equipartition assumption. This component has a ${\sim}3.5$ % fractional polarization. These observations show that cm-wavelength VLBI can access rest-frame millimeter-band polarization in bright $z>4$ jets.

astro-ph.GA↗

arXiv2026

MIRA: Medical Image Reflection for Agentic Diagnosis

Medical visual agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence. Reliable diagnosis therefore requires not only acquiring additional observations, but also verifying whether tool actions are necessary and whether the resulting evidence supports the current hypothesis. We introduce MIRA (Medical Image Reflection for Agentic Diagnosis), a medical visual diagnostic framework for autonomous evidence search and reflective verification. MIRA dynamically invokes image-processing operations, including zooming, grounding, pointing, rotation, and measurement, as well as web search, while evaluating the relevance and consistency of the acquired evidence. We develop MIRA through a two-stage training strategy. First, a tool-augmented Monte Carlo Tree Search data engine explores diverse diagnostic hypotheses and jointly verifies visual grounding accuracy and semantic consistency to construct supervised fine-tuning trajectories. Second, reinforcement learning further improves decision-making through online reflective principle evolution: failure cases are distilled into candidate principles, and only principles that improve held-out rollout rewards are retained. Across nine medical visual reasoning benchmarks, MIRA achieves an average score of 64.73, improving its Qwen3-VL-8B backbone by 7.44 points. It also increases useful tool-use judgments from 56.2% to 73.8% and reduces harmful judgments from 8.9% to 1.6%. Qualitative analyses show that MIRA can re-examine evidence, correct premature conclusions, and adapt its tool-use strategy. Project page: https://MIRA-VL.github.io/

cs.CV↗

arXiv2026

Life 2.0: A Scalable Distributed Space-Telescope Array for Biosignature Spectroscopy

Answering the question "Are we alone?" requires atmospheric spectroscopy of nearby terrestrial planets. For an Earth--Sun analog, even the strongest transmission signals are expected to be of order 1 part per million (ppm). Unlike short-period planets, Earth 2.0 planets transit only about once per year, so single-transit sensitivity, rather than stacking repeated observations, is the fundamental design driver. Life 2.0 is a scalable space-mission concept linking Earth 2.0 candidates discovered by PLATO and the Earth 2.0 (ET) mission with atmospheric characterization and biosignature assessment. The baseline architecture comprises 900 one-meter space telescopes, each equipped with a high-throughput Waveguide Integrated Miniature Spectrograph and an ultra-low-read-noise CMOS detector. After independent calibration, spectra acquired simultaneously during a transit are combined, providing the photon-collecting capability of an approximately 30-m aperture at the selected spectral resolution while retaining a modular architecture. The baseline 0.2--1.05 $\mu$m range covers O$_3$, O$_2$, H$_2$O, Rayleigh scattering, and other diagnostics, with extension into the infrared as detector technologies mature. Prototype Waveguide Spectral Lens devices have demonstrated 40--66\% throughput at resolving powers from $R \sim 200$ to $R \sim 20{,}000$. Lightweight silicon-carbide mirrors and sub-electron-noise CMOS detectors support replicated production. Life 2.0 must address detector systematics, instrument stability, and stellar variability; rather than assuming these limitations disappear, it builds on calibration, detector-characterization, and data-analysis techniques advanced during the JWST era. The concept offers a scalable alternative to a monolithic 30-m-class space telescope and a staged pathway toward biosignature spectroscopy of nearby Earth-like planets.

astro-ph.IM↗

arXiv2026

HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses

Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable environments. Recent work has proposed automatic harness evolution, which iteratively improves the harness from agent--environment interactions. However, existing methods often overfit to the evolution tasks, rely exclusively on trajectory-derived signals, and optimize harness components jointly, causing interference across components. We propose HarnessCompass, a novel automatic harness evolution framework built around constrained evolution, proactive feedback, and component-wise optimization. HarnessCompass first enforces global constraints on evolution, restricting modifications to task-agnostic harness changes that generalize beyond the evolution tasks. It then augments trajectory-derived evidence with proactive first-person feedback from the agent about harness usage, yielding richer signals for evolution. Finally, it decouples the optimization of different harness components before consolidating them into a unified harness, reducing cross-component interference while preserving component synergy. On SWE-bench Verified with GPT-5.4, HarnessCompass improves Pass@1 from 54\% to 66\% in only 5 evolution iterations, outperforming AHE in both effectiveness and evolution efficiency. In addition, the evolved harness transfers effectively to held-out tasks and other models, demonstrating substantially stronger generalization than prior automatic harness evolution methods.

cs.LG↗

arXiv2026

MEGRAG: Multi-Granular Evidence Graphs for Answer-Aware Multi-Hop RAG

Multi-hop question answering is a fundamental challenge in retrieval-augmented generation (RAG), because deriving an answer requires integrating dispersed evidence. Iterative RAG (iRAG) is widely used for this challenge, but existing methods have two limitations. First, most methods still support each reasoning step with single-granularity evidence, making it difficult to balance information density and contextual noise. Second, existing methods often answer the original question only after aggregating evidence retrieved across intermediate steps, so redundant evidence and intermediate retrieval errors may accumulate and degrade the final answer. To address these limitations, we propose MEGRAG, an answer-aware framework that represents multi-hop reasoning as a path-structured multi-granular evidence graph. Offline, MEGRAG links passages to their sentences and extracted triples through a cross-granularity index. Online, it retrieves passages for the current query and selects aligned evidence, starting with compact triples and adding sentence or passage context as needed. MEGRAG uses the resulting intermediate answer and prior reasoning to decide whether the Initial Query has been resolved. If not, it identifies the missing information and formulates a focused next query; otherwise, it stops retrieval and returns the answer. Extensive experiments demonstrate consistent gains over a diverse set of RAG baselines.

cs.AI↗

arXiv2026

Model-Agnostic Meta-Learning Initialization for Distributed Multichannel Active Noise Control

Distributed multichannel active noise control (DMCANC) has emerged as a scalable framework for large-area noise reduction, where multiple nodes operate local single-channel ANC controllers and exchange essential information to achieve global control. A key limitation of existing DMCANC implementations lies in their reliance on zero or random initialization, which leads to slow convergence of adaptive filters and restricts the efficiency of internode collaboration. To address this issue, this paper introduces a model-agnostic meta-learning (MAML) based initialization strategy for DMCANC. By aggregating heterogeneous acoustic characteristics across nodes-ncluding primary and secondary paths-a MAML framework is trained to learn an initialization that generalizes effectively across distributed ANC systems. The MAML initialization is then deployed to all nodes to improve convergence speed under both stationary and time-varying noise conditions. Numerical simulations applied on broadband and real-world noise demonstrate that the proposed algorithms achieves substantially faster convergence and improved noise reduction performance compared with conventional DMCANC, highlighting the potential of MAML initialization as an effective method for large-scale ANC.

eess.AS↗

arXiv2026

ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits

As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. This makes early-stage estimation of parasitic capacitance and resistance important for parasitic-aware design exploration before full physical implementation. However, progress on GNN-based parasitic modeling has been hindered by the lack of public, high-fidelity RC benchmarks that support reproducible evaluation. To address this gap, we introduce ParasGB, the first open-source benchmark suite for pre-layout parasitic parameter prediction on circuit graphs. ParasGB provides large-scale, heterogeneous RC networks extracted with commercial EDA tools from tape-out-proven designs, together with a unified evaluation protocol covering node-level ground capacitance, edge-level resistance, and edge-level coupling capacitance. Within this framework, we benchmark diverse GNN architectures using a standardized training pipeline and expose challenges such as extreme label imbalance, long-tailed parasitic distributions, and strong structural heterogeneity. By establishing a physically grounded and standardized benchmark for early-stage parasitic prediction, ParasGB provides an open platform for reproducible research on circuit graph learning and parasitic-aware model development. All datasets, preprocessing scripts, and configurations are publicly available in our code repository https://github.com/ShenShan123/ParasGB.git.

cs.LG↗

arXiv2026

A multi-band radio flux density catalog of ICRF3 sources using the Onsala Twin Telescopes

The VLBI Global Observing System (VGOS) is the next generation system for geodetic and astrometric Very Long Baseline Interferometry (VLBI). To optimize the observing time for each source in geodetic schedules, a flux density catalog is needed for the sources that are observed at the VGOS frequencies. The aim of this work is to monitor the flux densities of geodetic sources in the VGOS bands. The obtained flux density time series can be used for more effective scheduling of geodetic and astrometric VLBI experiments, as well as probing active galactic nuclei (AGN) physics. The Onsala Twin Telescopes have been used as a single baseline interferometer to measure flux densities of AGN that are part of the International Celestial Reference System (ICRF3). The telescopes observed at 3.2, 5.5, 6.6 and 10.4 GHz simultaneously. Both locally planned flux monitoring sessions and international geodetic experiments were analyzed. The data were calibrated using the Common Astronomy Software Applications (CASA). The possibility of predicting geodetic signal-to-noise ratios (S/N) using the measured flux densities was also tested. Simultaneous light curves in up to four frequencies have been obtained for 361 sources. The majority of the sources vary significantly in flux density during the measurement period. Most sources have a flat or inverted spectrum, with only 6 % having a steep spectrum. Furthermore, the flux densities from this work were shown to more precisely predict geodetic signal-to-noise ratios compared to the standard VGOS flux density catalog, especially for the most variable sources. Flux density variation needs to be taken into account to obtain the most optimal VGOS schedules. The flux density catalog presented here is expected to be of use for both astronomy and geodesy. We plan to continue the monitoring program.

astro-ph.IM↗

arXiv2026

I-Rex: An Interactive Debugger for SQL

SQL is declarative in nature and rich in its features. Writing semantically correct SQL queries and finding logical bugs in SQL are not easy, even for experienced programmers, who are often used to the mindset of working with general-purpose programming languages (GPLs). While there are many GPL debuggers, SQL debugging has received much less attention. In this paper, we present I-Rex, a SQL debugger that enables users to inspect the logical execution of SQL queries visually and interactively to identify and potentially fix logical bugs in the queries. I-Rex draws analogies to the debugging paradigm of GPLs (e.g., stepping, watchpoints, etc.), making it easier for programmers to adopt. However, unlike debugging GPLs, which involves executing the underlying program in full to the point of interest, I-Rex allows users to jump to arbitrary points of interest by leveraging the power of the database systems, through selective materialization and query rewrites. To simplify deployment, I-Rex acts as a lightweight middleware on top of the database system; it imposes no overhead to prepare a database for debugging and maintains no state in the database systems during debugging sessions. We demonstrate the effectiveness of I-Rex through performance experiments as well as a user study in an educational setting.

cs.DB↗

arXiv2026

Markov Chain Monte Carlo with Diffusion Paths

Sampling from multimodal distributions is a longstanding challenge for classical local Markov chain Monte Carlo (MCMC) methods. A popular remedy is to introduce a sequence of intermediate distributions that interpolate between the target and a simpler reference. The classical choice, tempering, raises the density to a power, but distorts the relative weights of asymmetric modes and can lead to poor mixing. We instead propose interpolating along the diffusion path, the marginals of a noising diffusion process that carries the target toward a Gaussian. This path preserves the relative weights of the modes and enjoys favorable mixing properties, which we make precise through a spectral-gap analysis of the corresponding ideal transition kernel. Sampling along the path requires its intermediate scores, which can be estimated from the unnormalized target through variational approaches, yielding only an approximate sampler. To remove the resulting bias, we introduce the Metropolis-adjusted diffusion path (MAD-Path) sampler, which corrects the diffusion-path proposal in an augmented path space and leaves the target invariant regardless of the accuracy of the learned score or the discretization error. We further quantify how these two errors affect the acceptance probability, providing guidance for practical tuning. Experiments on a range of Bayesian posteriors show that MAD-Path improves global exploration and mode-weight estimation relative to tempering-based MCMC methods and unadjusted diffusion samplers.

stat.CO↗

arXiv2026

BaCon: Efficient Batch Processing of Counting Queries [Full Version]

Counting queries are ubiquitous in database systems, particularly for driving internal system optimization. Learned models for cardinality estimation rely heavily on large-scale training data, yet generating such data by executing massive batches of counting queries is expensive. We propose BaCon, an efficient algorithm for batch evaluation of counting queries on top of a database system, without modifying its internals. BaCon integrates the idea of factorized databases with a workload-aware domain quantization strategy, allowing it to evaluate batches of counting queries using compact data structures rather than materializing massive join results. BaCon's design is compatible with most database management system, and we have implemented it as a client-side application on PostgreSQL with a lightweight C-language UDF (user-defined function). This implementation delivers speedups between 2$\times$ and 178$\times$ over baselines and good performance across various workloads, making training and maintenance of learned cardinality estimation models significantly more practical.

cs.DB↗

arXiv2026

UniSGR: Unified Framework for Semantic ID Generation and Ranking

Recommendation systems play a pivotal role in modern e-commerce platforms. While generative retrieval has emerged as a promising paradigm for alleviating the limitations of multi-stage cascade architectures, existing methods still struggle with fine-grained multi-objective ranking. To bridge this gap, we propose UniSGR, a Unified framework for Semantic ID Generation and Ranking. UniSGR adopts a two-stage training paradigm: a multi-scenario pre-training stage that learns from mixed business-scenario data, followed by a scenario-specific alignment stage that jointly optimizes Value-Aware Parallel Multi-Token Prediction (VA-PMTP) and a unified multi-objective ranking module. To better align generation with downstream ranking, we introduce Task-Aware Tokens (TAT) guided by Funnel-Aware Contrastive Learning. Furthermore, we propose Semantic Tree Attention with Reorganized KV cache (STARK), an inference strategy that removes key efficiency bottlenecks in conventional beam search. Extensive offline experiments on a large-scale e-commerce platform demonstrate the effectiveness and scalability of UniSGR.

cs.IR↗

arXiv2026

Estimating Velocity and Spin of Spherical Objects from Rolling-Shutter Image(s)

Rolling-shutter cameras introduce characteristic distortions when imaging fast moving objects, and these effects are typically treated as artifacts to be corrected. In this work, we instead leverage rolling-shutter distortions as a valuable source of temporal information to estimate the 3D translational and angular velocities of rapidly moving spherical objects from a single rolling-shutter frame. We design a robust and easily detectable spherical pattern and propose a correspondence-free formulation that recovers motion by enforcing geometric consistency in a back-projection framework. By exploiting the geometry of the sphere, translational and rotational motions are decoupled and estimated through a two-stage optimization process, enabling reliable velocity recovery even for textureless objects. Extensive experiments on both synthetic and real datasets demonstrate accurate and robust estimation of motion parameters under challenging high-speed conditions.

cs.CV↗

arXiv2026

Enhancing VLBI Capability with the SKA-Mid and the Jingdong 120-m Radio Telescope

The Jingdong Radio Telescope (JRT) is a 120-meter fully steerable radio telescope currently under construction in Jingdong County, Yunnan Province, China. Located at a relatively low latitude (24.5 degree), the JRT will enable observations of nearly 90% of the sky. Equipped with two broadband single-pixel receivers covering 1-8 GHz and 6-18 GHz, and a powerful digital backend, the telescope will support single-dish studies of various radio sources-particularly millisecond pulsars for enhancing the detection of nanohertz gravitational waves. In addition to single-dish capabilities, the JRT is expected to contribute approximately 800 hours annually to international Very Long Baseline Interferometry (VLBI) observations via a standard VLBI backend. When operating in conjunction with the phased-up SKA-Mid, the JRT will significantly enhance the technical and scientific capabilities of existing VLBI networks. This paper presents a comprehensive overview of the JRT's VLBI module and explores its potential to improve joint VLBI observations with current VLBI networks. Our analysis suggests that coordinated VLBI observations involving both the SKA-Mid and the JRT have the potential to significantly advance the field. For early sciences, we also highlight a few highly promising scientific cases, e.g. measuring the distance to PSR J0437-4715 with <1 ly accuracy and exploring jet formation with an event-horizon-scale resolution in M60*.

astro-ph.IM↗