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Jing Wang

Publications and source records attributed to Jing Wang.

At least 19 recordsLinked to original sources
arXiv2026

HBF-Sim: An Extensible HBF Simulator for Large-scale GPU Memory Systems

High-bandwidth flash (HBF) is introduced to address the memory wall, which can co-package a dense NAND stack with the GPU, targeting the performance gap between near-accelerator bandwidth and flash density. HBF, however, is neither a large HBM nor a fast NVMe SSD. Its usable bandwidth depends on how GPU cache-line requests map onto NAND pages, how concurrency spreads across channel-affine die sets, and how media management interacts with the GPU memory pipeline. To our knowledge, existing GPU, SSD, or HBF simulators cannot faithfully model this behavior. We present HBF-Sim, an extensible, reusable, and faithful HBF simulator integrated with simulated GPUs. It closes the loop between GPU issue limits, device queuing, and NAND behavior in one end-to-end request path. HBF-Sim separates a GPU-HBF interaction controller from page-based parallel stack storage, and it models the full GPU-HBF request path. It provides an MSHR-based address mapping table that merges cache-line requests into page-based operations, as well as a page-based multi-stack flash manager for highly parallel reads and writes. Validation tests and device-level microbenchmarks expose performance bottlenecks caused by limited channel distribution and resource conflicts, offering concrete guidance for next-generation HBF architectures.

cs.AR↗
arXiv2026

Galaxy evolution in the post-merger regime. V - Atomic gas evolution traced by ALFALFA stacks

(Abridged) In this work, we investigate the role of atomic hydrogen (HI) gas in the galaxy merger lifecycle. In order to both identify galaxies in mergers, as well as to predict their time since coalescence, we present an extension of the MUlti Model Merger Identifier (MUMMI) machine vision pipeline applied to Dark Energy Camera Legacy Survey (DECaLS) imaging. The new MUMMI-DECaLS catalog contains 19,951 post-mergers, of which 14,638 have robust time post-merger (T_PM) predictions. The MUMMI-DECaLS post-merger galaxy catalog is cross-matched with the Arecibo Legacy Fast ALFA (ALFALFA) survey. For the 752 post-mergers thus identified, as well as a matched control sample, we produce median spectral stacks of 21~cm emission, finding that the post-mergers are HI deficient by 0.11 dex compared with non-interacting galaxies. When divided into T_PM bins we find that the HI deficit (or excess) is strongly dependent on both the time since coalescence and whether the galaxies are star-forming or quenched. Star-forming post-mergers in particular show a strong HI evolution with T_PM, exhibiting a 0.1 dex MHI excess immediately after coalescence, a 0.3 dex MHI deficit at 0.16 < T_PM < 0.96 Gyr and normal gas masses by ~ 1.5 Gyr after coalescence. On the other hand, despite being matched in star formation rate, stellar mass and redshift, quenched post-mergers are HI deficient at both short and long post-coalescence times. Nonetheless, a significant mass of HI (>10^9 M_sun) remains even in these late-time quenched post-mergers, representing a resource which could potentially re-fuel star formation in the future. Our results demonstrate the complexity of quantifying the HI behaviour in mergers, which depends not only on timing, but also star-forming status, which can complicate comparisons between different studies and samples.

astro-ph.GA↗
arXiv2026

Mrk 382: A Narrow-line Seyfert 1 Galaxy with Recurrent X-ray State Transitions

We report recurrent X-ray state transitions in the nearby narrow-line Seyfert~1 galaxy Mrk~382 using multi-epoch observations from \textit{Swift}, \textit{Chandra}, \textit{XMM-Newton}, and eROSITA, together with archival ultraviolet, optical, and infrared data. The 0.3--2 keV flux varies by nearly an order of magnitude over the past $\sim15$ yr, with multiple transitions between bright and faint states. The source brightened by a factor of $\sim10$ between the 2010 \textit{Chandra} observation and the 2011 \textit{XMM-Newton} high state, then declined by $\sim6$--7 to a low state in 2019, followed by renewed brightening in recent \textit{Swift} monitoring. The X-ray spectrum shows strong state-dependent evolution, changing from a steep high-state continuum ($Γ=2.32\pm0.04$) to a much harder low-state spectrum ($Γ=1.39\pm0.06$). The low-state spectrum also exhibits a narrow Fe K$α$ line with an equivalent width of $\sim330$ eV. Reflection modeling indicates that the low-flux state is strongly reflection dominated, with the reflection fraction increasing from $R_{\rm refl}\sim4$ to $\sim34$, consistent with a compact corona subject to strong light-bending effects. The ultraviolet emission broadly follows the long-term X-ray variability but with smaller amplitude, while the optical and mid-infrared bands vary more mildly. Despite the dramatic X-ray variability, Mrk~382 does not enter an extreme X-ray-weak state, and we did not detect clear optical spectral-type changes based on the currently available observations. Mrk~382 is therefore a rare nearby Seyfert galaxy undergoing recurrent X-ray state transitions, providing a valuable laboratory for studying changing coronal geometry and multiwavelength AGN variability.

astro-ph.HE↗
arXiv2026

VARG: Value-Aware and Ranking-Aligned Generative Retrieval for Dynamic E-commerce Search

Integrating recall and pre-ranking in e-commerce search requires candidate generation to account for relevance, personalization, and business value before final ranking. To this end, we present VARG, a generative retrieval system for Tmall App search that directly admits generated item candidates to the existing final ranker. VARG-ID constructs semantic prefixes using RQ-VAE, enhances search relevance through bidirectional query-item contrastive learning, and combines these prefixes with a value-ordered third token to provide fine-grained item addresses and a business-value prior. Three-stage supervised fine-tuning progressively learns item-to-identifier mappings, query-semantic retrieval, and personalized retrieval. Personalized model training combines value-aware and hierarchy-aligned supervision with expanded user context, and uses local ordinal supervision (LO-SFT) to learn the local within-cluster ordering encoded by the third token. Prefix-GRPO combines gated rewards based on output legality, user behavior, ranker advantage, and search relevance with prefix-aware token weighting to align candidate generation with business value and ranking objectives. Coordinated daily product and model updates preserve existing item addresses while incorporating new products and behavioral feedback. Offline experiments on tens of millions of products validate identifier stability and demonstrate gains in retrieval quality and head-level value recall from SFT strategies and Prefix-GRPO over their respective baselines. In a 14-day online A/B test covering 20% of search traffic, VARG directly admits generated candidates to the final ranker and improves GMV by 1.45%, per-user IPV by 0.22%, and PCTR by 0.31%. Online shopping-guide query evaluations further show that VARG maintains competitive relevance with a smaller candidate quota.

cs.IR↗
arXiv2026

AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

Reinforcement learning (RL) has become a pivotal technology in the post-training phase of large language models (LLMs). Traditional task-collocated RL frameworks suffer from significant scalability bottlenecks, while task-separated RL frameworks face challenges in managing complex dataflows and resolving resource idling. Furthermore, most existing frameworks are tightly coupled with LLM training or inference engines, making them difficult to support custom-designed engines. To address these challenges, we propose AsyncFlow, an asynchronous streaming RL framework tailored for efficient post-training. Specifically, we introduce a distributed data storage and transfer module that provides panoramic data management and fine-grained scheduling capabilities in a fully streamed manner. This architecture inherently enables automated pipeline overlapping among RL tasks and dynamic load-balancing. Moreover, we propose an asynchronous producer-consumer workflow, which is engineered to minimize computational idleness by strategically deferring the parameter update process within staleness thresholds. Finally, the core capabilities of AsyncFlow are architecturally decoupled from underlying training and inference engines and encapsulated by service-oriented user interfaces, offering a modular and customizable user experience. Extensive experiments demonstrate an average throughput of 1.59x compared to the state-of-the-art baseline. The architecture presented in this work provides actionable insights for designing next-generation RL training systems.

cs.LG↗
arXiv2026

Perfect codes in weakly metric association schemes

The Lloyd Theorem of (Solé, 1989) is combined with the Schwartz-Zippel Lemma of theoretical computer science to derive non-existence results for perfect codes in the Lee metric, NRT metric, mixed Hamming metric, and for the sum-rank distance. The proofs are based on asymptotic enumeration of integer partitions. The framework is the new concept of {\em polynomial} weakly metric association schemes. A connection between this notion and the recent theory of multivariate P-polynomial schemes of ( Bannai et al. 2025) and of $m$-distance regular graphs ( Bernard et al 2025) is pointed out.

math.CO↗
arXiv2026

LLM4AIGQ: LLM-based AI Guidance Query Generation Framework for Multi Interest Mining

Guidance queries stimulate user consumption by extracting preferences to provide search queries with guidance value, playing a crucial role in the e-commerce field. Traditional AI-generated queries (AIGQ) generation primarily relies on a two-stage "Query-to-AI-Generated-Query" (Q2AIGQ) association paradigm, first recalling user primary search queries from user profiles, historical behavior sequences, item-side information, and the current query through multi-path retrieval, then generalizing AIGQ via rule-based methods. This approach suffers from semantic drift due to information cascade loss; additionally, primary search query derivation heavily depends on "user-item" co-occurrence relationships, lacking exploration of user multi-interests, resulting in guidance queries with low value and mismatched purchase intent. To address the expressive limitations of traditional co-occurrence-based retrieval, we propose LLM4AIGQ, an LLM-based solution for generating AI guidance queries tailored to users' multi-interests. This approach segments user interests by integrating user profiles and historical interaction sequences, infers specific consumption intents for each sub-interest, and subsequently generates corresponding AIGQ. In terms of model training, we employ a post-training pipeline comprising Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and Direct Preference Optimization (DPO) to enhance the model's capability in generating AIGQ. We also introduce a multi-level reward design to satisfy the requirements of multi-objective optimization and long-chain reasoning in practical applications. Regarding deployment, we adopt a nearline-generation and online-read architecture to meet latency constraints. Extensive experimental analyses demonstrate that our model achieves robust performance in both offline evaluations and online A/B tests.

cs.IR↗
arXiv2026

DNative-Twin: Decision Graphs and Digital Twins for Reconstructable Agentic Decisions

AI agents increasingly gather evidence, invoke tools, apply constraints, and produce decisions that people or software may commit to action. A final output alone cannot show which evidence, tool state, rule, authorization, or action path produced it. We present DNative-Twin, a graph-native digital twin that records a committed agentic decision as a typed trajectory and re-executes its decision mechanism under declared conditions. The graph links the state observed by the agent, the path it followed, and the authority behind the resulting action. The twin synchronizes this information, replays the mechanism in isolation, and compares it under controlled changes. We instantiate the framework in enterprise decision processes using three public process logs and controlled replay suites. The experiments identify a specific failure: graph structure localizes represented changes but cannot determine the consequence of an unobserved tool state. In a three-condition controlled experiment with 300 injected instances, unresolved-divergence recall increased from 0 to 0.667 when replay-contract state was added and to 1.0 when verification results were also available; the held-out set contained no critical-class instance. Across 500--5,000 BPI 2020 cases, median end-to-end time increased from 0.794 to 8.889 seconds on the reported platform. These results separate the roles of graph structure, replay context, and verification evidence in reviewing a decision mechanism.

cs.AI↗
arXiv2026

Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules

Predictive modeling for clinical decision support requires both strong predictive performance and transparent, auditable, and human-reviewable decision logic. Although deep learning and tree-based ensemble methods can achieve high accuracy, their black-box nature remains a major obstacle to trustworthy clinical deployment. Moreover, clinical prediction often operates under practical constraints, including limited sample sizes, severe class imbalance, and feature evolution arising from changes in diagnostic criteria or clinical documentation practices. We propose Medical Heuristic Learning (MHL), a constrained paradigm for LLM-assisted rule learning. Rather than relying on updates to implicit model weights, MHL integrates statistical probes, medical knowledge probes, initial rule synthesis, and iterative rule optimization to construct an executable rule-based expert system. The resulting rule system is expressed entirely using the native logical and control-flow constructs of a programming language. Valid rule versions are recorded and retained along the search trajectory, making the decision logic explicit, interpretable, and auditable. MHL also supports continual learning by using previously validated rules as a starting point and iteratively revising them in response to updated feature information under data drift or feature evolution. MHL is not tied to any specific programming language. Comprehensive experiments on medical datasets show that MHL achieves predictive performance comparable to that of state-of-the-art methods, performs favorably in small-sample and highly imbalanced settings, and supports the transfer and adaptive revision of validated rules under feature evolution. Overall, these findings suggest that non-gradient-based heuristic systems offer an approach to balancing predictive performance and transparency in clinical decision support.

cs.AI↗
arXiv2026

ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection

InfRared Small Target Detection (IRSTD) is a challenging task. Relying solely on pixel-level information, vision-only methods struggle to distinguish targets from clutter. Current multimodal methods typically describe both targets and backgrounds with a single textual prompt. Such an approach lacks dedicated regional guidance and ignores infrared semantic asymmetry. Consequently, it provides insufficient background suppression information and introduces severe feature optimization conflicts, overwhelming small targets with noise. To address these issues, we propose a novel Asymmetric Dual-text Guided Network (ADGNet). Specifically, accounting for the infrared semantic asymmetry, we first design the Asymmetric Dual-text Prompt (ADP), comprising an image-agnostic abstract target prompt and an image-specific detailed background prompt. To leverage these prompts, we introduce an Asymmetric Dual-Branch Interaction (ADBI) module to separately guide visual features with their respective text priors, protecting targets from noise while suppressing background clutter. Subsequently, we introduce an Adaptive Feature Aggregation (AFA) module to dynamically fuse features from the two branches. Furthermore, we construct a multimodal Asymmetric Image-Text Infrared (AITIR) dataset by providing asymmetric text annotations for three public datasets (IRSTD-1K, NUDT-SIRST, and SIRST). Extensive experiments demonstrate that ADGNet outperforms 21 state-of-the-art (SOTA) methods. Code is available at https://github.com/iLearn-Lab/MM26-ADGNet.

cs.CV↗
arXiv2026

MeRoPE: Metric Rotary Position Embedding for Camera-Controlled Video Generation

In camera-controlled video generation, geometry-aware positional encodings condition tokens on camera extrinsics and per-token viewing rays. Existing schemes, however, have a scale-dependent failure mode on real-world metric camera trajectories: homogeneous projective encodings cause attention logits and feature norms to grow unbounded with physical translation baselines. We propose MeRoPE (Metric Rotary Position Embedding), a norm-preserving relative camera encoding for attention. MeRoPE encodes relative orientations between calibrated viewing rays with orthogonal rotation blocks, maps raw metric displacements into multi-frequency rotary phases, and adds a disparity-anchored correspondence prior along the epipolar arc. This design strictly preserves feature norms, bounds pre-softmax attention logits regardless of the physical translation scale, and maintains exact invariance to global rigid coordinate changes. Across nuScenes and PanShot, which cover large-baseline trajectories and diverse camera optics, respectively, MeRoPE achieves stronger camera control than prior encodings, with the best consistency between generated camera motion and conditioning poses in both rotation and translation. Code will be made publicly available.

cs.CV↗
arXiv2026

DGNet: Dual-knowledge Guided Network for Infrared Small Target Detection

InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision. In recent years, text-guided methods have significantly improved detection performance. However, they still suffer from two key limitations. First, a single text description simultaneously modeling both background and target leads to semantic entanglement, which contradicts the objective of background suppression and target enhancement. Second, reliance on image-specific textual prompts (requiring additional external models such as CLIP during inference) results in deployment constraints. To address these issues, we propose a novel Dual-knowledge Guided Network (DGNet) based on multiple generalizable texts. Specifically, we design a Prior-knowledge Wavelet Modulation (PWM) module, which leverages dual textual priors that separately characterize large-scale backgrounds and sparse targets to effectively disentangle and modulate entangled semantics in the frequency domain. Furthermore, we introduce a Consensus-knowledge Directional Alignment (CDA) loss, which models the initial state and the ideal target across samples as `complex background' and `bright target', respectively, thereby constructing a clear and unified directional optimization trajectory for the model. Extensive experiments on three public datasets demonstrate the superior performance of DGNet and the effectiveness of each component. The source code is available at https://github.com/iLearn-Lab/MM26-DGNet.

cs.CV↗
arXiv2026

FABO: Agent-Guided Discovery of Joint Breakpoint Optimization for Timing-Driven Routing Trees

The topology of a routing tree determines how a multi-pin net branches and shares physical wire, directly affecting wirelength, congestion, capacitance, and delay. We study a central early-stage routing problem: minimizing wirelength while bounding the root-to-sink path stretch for every sink. SALT is the state-of-the-art constructive algorithm for this problem. We ask whether language-model-guided search can discover a constructive algorithm that improves on SALT. To make this search reliable, we develop an agent framework that combines parallel exploration with independent checking. Applied to SALT, the framework discovers a structural limitation: SALT repairs one sink path at a time and therefore never jointly decides where paths sharing root-side wire should split. This sink-local choice can split the paths too early and duplicate wire. This discovery leads to Flow-Aware Breakpoint Optimization (FABO), which jointly optimizes breakpoints across root-to-sink paths that share wire while preserving every sink's stretch budget. Across 1.29 million ICCAD15 nets and SALT's 20-point stretch-tolerance schedule, FABO reduces average FLUTE-normalized wirelength at every setting, with peak same reductions of 0.83% overall and 2.66% for nets with at least 30 pins. With 1.3x SALT's runtime, FABO-FAST identifies and optimizes most nets for which FABO provides a substantial wirelength reduction. Code is available at https://github.com/DevinShang/routing-FABO.

cs.AR↗
arXiv2026

Machine learning reveals common features of unconventional superconductors with high transition temperatures

Superconductors with high critical temperatures that emerges beyond the phonon-mediated regime are usually considered unconventional in nature, yet unlike conventional superconductors, no broadly applicable predictive theory currently guides their discovery. Here, we use interpretable machine learning to uncover a common materials-space signature of high-$T_{\mathrm{c}}$ unconventional superconductors and develop a data-driven strategy for materials discovery. We construct a unified feature representation for each material by integrating compositional statistics, structural information, and latent representations from trained property-prediction models, followed by structure-aware filtering of an experimentally established superconducting dataset. Without using transition-temperature information, unsupervised analysis shows that cuprate and iron-based superconductors occupy a common region of materials space, characterized primarily by large electronegativity deviation and intermediate mean valence-electron number. A supervised $T_{\text{c}}$ model independently identifies the same descriptors as dominant features, providing complementary evidence for their relevance. Using this empirical materials-space prior together with the $T_{\text{c}}$ model, we prioritize candidate materials, recover recently discovered nickelate superconductors, and identify chemically distinct candidates for future investigation.

cond-mat.supr-con↗
arXiv2026

Latent-Space No-Arbitrage Geometry of Generative Models for Implied Volatility Surfaces

Generative models for implied volatility surfaces must produce outputs that satisfy static no-arbitrage constraints. We study these constraints in latent space. For a fixed generator, we assign each latent code a scalar margin determined by the no-arbitrage conditions of the generated surface. The codes with nonnegative margin form the admissible latent set. We establish conditions under which strictly admissible codes remain admissible under small perturbations and the boundary of the admissible set is characterized by zero margin. For regular boundary components, we formulate a level-set equation whose local dynamics are directed toward the zero-margin set. The analysis treats the generator as a map from latent variables to surfaces and is therefore not restricted to a particular architecture. It applies to variational autoencoders, generative adversarial networks, and other generative models with a deterministic realization map. Numerical tests recover known boundaries in analytic examples. Experiments with a variational autoencoder trained on Heston surfaces show that similar reconstruction errors can correspond to different admissible regions and that the latent prior may be concentrated inside such a region. The computed boundary can also be used to modify latent codes that generate violating surfaces.

q-fin.CP↗
arXiv2026

FFSlim: An Efficient and Lightweight Format for Multi-modal Data Storage and Retrieval

With the rapid expansion of large-scale media-text corpora, multi-modal datasets increasingly require efficient storage and retrieval. Existing formats such as Files, TDP, and FFRecord work adequately for uni-modal data but expose fundamental limitations in multi-modal settings, including storage redundancy, massive small-file overheads, cache-unfriendly layouts, and heavy index structures. These issues jointly inflate storage and memory usage and make I/O the dominant bottleneck in real training workloads. We present FFSlim, a lightweight format for storing and retrieving multi-modal data. FFSlim improves storage efficiency and loading throughput through three components: a unified file format that removes media duplication and avoids small-file proliferation; an adaptive retrieval mechanism that enables low-overhead pair-level access and accelerates repeated media loading; and a redundancy detection and aggregation module that converts existing datasets into the FFSlim layout. The experimental results demonstrate that FFSlim achieves 2.07x and 8.26x higher data loading and write throughput on average than the strongest baseline, with minimal storage and index overhead. Consequently, these underlying I/O accelerations enable FFSlim to reduce end-to-end training time by 5.36%-14.18% across seven diverse multi-modal models.

cs.PF↗
arXiv2026

Multimodal Deep Learning for Uncertainty-Aware Radiation Pneumonitis Risk Prediction

Radiation pneumonitis (RP) is a common and clinically significant toxicity of thoracic radiation therapy that can cause pulmonary morbidity and impair quality of life. Although conventional dose-volume histogram-based metrics and normal tissue complication probability models are widely used for RP risk assessment, they inadequately capture the complex spatial, anatomical, and patient-specific factors underlying radiation-induced lung injury. Recent machine learning approaches have improved RP risk prediction by integrating multimodal clinical and imaging information; however, most provide a point risk estimate without quantifying the reliability of individual predictions, limiting their potential clinical utility. We propose a Multimodal Bayesian Diffusion Transformer (MM-DiT) framework that jointly estimates RP risk and characterizes the sources of predictive uncertainty. MM-DiT integrates planning computed tomography (CT) images and three-dimensional radiation dose distributions through self-supervised multimodal pre-training, reducing reliance on limited and potentially noisy toxicity labels. The resulting representations are further refined using a latent diffusion transformer and transferred to a Bayesian prediction framework for probabilistic RP risk estimation. A learnable label-noise model is incorporated to explicitly account for uncertainty arising from imperfect toxicity annotations. Therefore, it provides individualized RP risk estimates with complementary measures of aleatoric, epistemic, and label uncertainty, enabling assessment of prediction reliability at the individual-patient level. We evaluated MM-DiT in two independent cohorts using complementary assessments of predictive discrimination, calibration, and uncertainty. The results demonstrate its potential to provide accurate RP risk estimates while quantifying clinically relevant sources of predictive uncertainty.

eess.IV↗
arXiv2026

Survival probability for jump processes in unbounded domains on metric measure spaces

We study the large time behavior of the survival probability $\mathbb{P}_x\left(τ_D>t\right)$ for symmetric jump processes in unbounded domains with a positive bottom of the spectrum. We prove asymptotic upper and lower bounds with explicit constants in terms of the bottom of the spectrum $λ(D)$. Our main result applies to symmetric jump processes in general metric measure spaces. For $α$-stable processes in unbounded uniformly $C^{1,1}$ domains, our results provide a probabilistic interpretation and an equivalent geometric condition for $λ(D)>0$. In the case of increasing horn-shaped domains, the exponential rate of decay for the survival probability is sharp. We also present examples of unbounded domains where our results apply.

math.PR↗