arXiv subjects

Haifeng Wang

Publications and source records attributed to Haifeng Wang.

At least 19 recordsLinked to original sources

arXiv2026

Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment

The biological definition of Alzheimer's disease (AD) relies on multi-modal neuroimaging, yet the clinical utility of positron emission tomography (PET) is limited by cost and radiation exposure, hindering early screening at preclinical or prodromal stages. While generative models offer a promising alternative by synthesizing PET from magnetic resonance imaging (MRI), achieving subject-specific precision remains a primary challenge. Here, we introduce DIReCT$++$, a Domain-Informed ReCTified flow model for synthesizing multi-tracer PET from MRI combined with fundamental clinical information. Our approach integrates a 3D rectified flow architecture to capture complex cross-modal and cross-tracer relationships with a domain-adapted vision-language model (BiomedCLIP) that provides text-guided, personalized generation using clinical scores and imaging knowledge. Extensive evaluations on multi-center datasets demonstrate that DIReCT$++$ not only produces synthetic PET images ($^{18}$F-AV-45 and $^{18}$F-FDG) of superior fidelity and generalizability but also accurately recapitulates disease-specific patterns. Crucially, combining these synthesized PET images with MRI enables precise personalized stratification of mild cognitive impairment (MCI), advancing a scalable, data-efficient tool for the early diagnosis and prognostic prediction of AD. The source code will be released on https://github.com/ladderlab-xjtu/DIReCT-PLUS.

cs.CV↗

arXiv2026

Age-Resolved Vertical Motions in the Milky Way Warp: A Localized Enhancement in Intermediate-Age Populations

Determining how the Milky Way warp evolves provides constraints on the formation and dynamical evolution of galactic disks. Using Apache Point Observatory Galactic Evolution Experiment Data Release 19 (APOGEE DR19) spectroscopy, we estimate ages for 222,355 red giants. With Gaia astrometry and geometric distances, we analyze 168,824 of these stars in five age bins spanning $0$--$10$ Gyr. The warp-related radial vertical-velocity ($V_Z$) profile of young red giants ($0$--$2$ Gyr) is broadly consistent with those of classical Cepheids and young open clusters. Over $9\leq R<12$ kpc, we find that the mean $V_Z$ values of the intermediate-age red giants ($2$--$4$ and $4$--$6$ Gyr) are $1.48\pm0.46$ and $1.49\pm0.52$ km s$^{-1}$ higher than that of the young population, respectively. By contrast, at $R\simeq12$--$13$ kpc, the $V_Z$ profiles of the $0$--$6$ Gyr red giants are consistent within their uncertainties. The intermediate-age enhancement persists across our robustness tests and may reflect smaller effective kinematic warp onset radii for the intermediate-age populations. The observed velocity differences may arise from the combined effects of earlier perturbations and the present-day warp, or from differences in vertical structure among stellar populations.

astro-ph.GA↗

arXiv2026

The throttling refrigeration system for high cooling power recovery of the PandaX-xT cryogenic distillation system for radon removal

The PandaX-xT cryogenic distillation system for removing radon (Rn) is designed to operate at a high liquid flow rate of 856 kg/h, equal to 5 liters per minute (LPM), to meet the required Rn concentration. However, it is challenging to maintain a continuous supply of cooling power (20 kW) for the Rn-removal system. This work proposes a throttling refrigeration system to address the power supply problem for the next-generation PandaX-xT dark matter detection experiment. The system utilizes carbon tetrafluoride (R14) refrigerant to recover power based on heat pump principles. The cooling power from the liquid xenon (LXe) in the reboiler at 178 K is transferred to liquefy the gaseous xenon (GXe) in the cryostat through the phase change circulation of R14, enabling xenon (Xe) to return to the detector in the same state as when it was extracted. Consequently, a stable circulation for liquid distillation is established. Given the high cost of Xe, a research and development (R&D) experiment has been conducted to validate the feasibility of this high-cooling-recovery system by using ethanol as a substitute for Xe. Experimental results indicate that the proposed system could achieve a cooling power recovery of 25 kW, with a recovery efficiency of 99%+-0.5%. Additionally, process simulation and optimization of the throttling refrigeration cycle are performed using Aspen Hysys. The deviation between the simulation and experimental results is found to be <3.64%. These findings demonstrate that implementing Rn-removal distillation at a high flow rate is feasible, eliminating the need for liquid nitrogen or cryocoolers. This system could save approximately 3650 m3 of liquid nitrogen per year or reduce power consumption by 230 kW.

physics.ins-det↗

arXiv2026

A Self-Consistent Jeans Analysis of the Milky Way Rotation Curve

Jeans-based measurements of the Milky Way (MW) rotation curve (RC) have revealed a declining outer RC. The interpretation, however, depends sensitively on the tracer selection, the tracer density profile, gradient terms in the Jeans equation, and non-axisymmetric perturbations in the outer disk. We present a more self-consistent Jeans analysis of the MW RC and reassess whether the outer decline remains after a revised treatment of the main Jeans terms and their associated systematic uncertainties, including those related to non-axisymmetric perturbations. We select a kinematically homogeneous disk tracer sample designed to retain stars on nearly circular disk orbits, removing only 12\% of the original sample. We estimate the tracer density profile from the same stars used for the kinematic moments, rather than adopting an external scale length. We find that the tracer density profile is better described by a double exponential over the validated range 12.5 < R < 21 kpc. We also assess the main systematic uncertainties associated with the fiducial Jeans equation terms, including the often neglected cross term, and account for different rotational velocities in the outer disk substructures. The derived RC remains nearly flat in the inner disk and declines at large radii. Over the adopted outer interval, $R\gtrsim16\,\mathrm{kpc}$, the RC slope is consistent with a Keplerian decline even after accounting for the systematic uncertainties. The corresponding mass modeling gives an extrapolated dynamical mass of $2.01^{+0.10}_{-0.08}\times10^{11}\,M_\odot$.

astro-ph.GA↗

arXiv2026

Demonstration-Free Success-Probability Reward Learning for Generalist Robot Policies

Reinforcement learning (RL) enables generalist robot policies to improve through trial-and-error interaction, yet its effectiveness is fundamentally constrained by sparse task rewards. Existing general-purpose reward models typically alleviate this issue by learning task progress from expert demonstrations, but introduce a distribution mismatch with the mixed-quality rollouts encountered during policy optimization, making their estimates unreliable on suboptimal and failed behaviors from which the policy must learn. In this work, we introduce a demonstration-free reward learning paradigm where dense reward feedback can be learned directly from sparse task outcomes and policy experience. We theoretically show that terminal task outcomes implicitly define dense success-probability feedback at intermediate timesteps, which can be recursively learned through bootstrapping. Based on this insight, we introduce eVTA$_0$, which learns success probabilities from mixed-quality policy rollouts through temporal-difference-style bootstrapping, without expert demonstrations or intermediate annotations. We further introduce RL with Evolving Rewards (RLER), a closed-loop framework that adapts eVTA$_0$ using newly collected rollouts as the policy evolves. Experiments show that eVTA$_0$ provides more informative rewards than state-of-the-art reward models and achieves the best average policy performance across all LIBERO task suites under the same RL training budget, improving success rates by 5.4%-13.8% over the initial policy. In real-world manipulation, RLER further improves overall success rates by 20%-26%, with 35%-36% gains under out-of-distribution conditions. These results demonstrate the effectiveness of demonstration-free reward learning and adapting rewards as the policy evolves. Project webpage: https://duowuyms.github.io/evta0.

cs.RO↗

arXiv2026

Positions Are Not Facts: The Mismatch Between KV Caches and Memory

When a fact changes, how should a language model update the history stored in its key-value (KV) cache? Hiding the old record is cheap, but it may still contain needed details or answer questions about the past. We compare hiding whole records, hiding only replaced values, and deleting old text and recomputing the cache. In a controlled quantity task, masking makes all eight models prefer the new value more strongly, yet six lose complete answers through unit errors or failure to stop; keeping the unit preserves all current answers. Later states also retain useful information from earlier records: on multi-hop updates, rebuilding these states at unchanged positions lowers historical accuracy by 20-41 percentage points, whereas moving the existing states has little effect. Keeping object dependencies and unchanged revision passages prevents many losses. Recognition is a separate challenge. Learned readouts recover distinctions missed by fixed cache similarities on synthetic record pairs. On natural text, text-detector-selected masks show no clear advantage over random masks at the same rate in 14 same-model detector-generator comparisons. Query-dependent access can avoid some losses, with additional storage or access costs. These findings identify what must be preserved beyond the replaced value when using a KV cache as updatable memory.

cs.CL↗

arXiv2026

PersonaPath: Towards Knowledge-Centric Personalized Learning Path Planning

Adaptive learning systems commonly formulate learning path planning as Exercise-Centric (EC) recommendation, where the next step is inferred from item-level interaction logs. Evaluating goal-oriented guidance additionally requires explicit learner goals and curriculum-scale prerequisites: learners with similar exercise records may need different paths toward their targets. We therefore study Knowledge-Centric (KC) personalized learning path planning, where a planner must reason over learner profiles, mastery states, and prerequisite knowledge structures to decide which textbook, unit, and concept should be studied next. To support this setting, we introduce PersonaPath, a benchmark that pairs 2,000 fine-grained learner personas with a hierarchical knowledge graph of 347 textbooks, 1,751 units, and 4,092 concepts across 77 subjects. We evaluate representative LLMs on PersonaPath. Results show that even the strongest LLM reaches only a 29.5% final pass rate in Basic Education, and that the main bottleneck lies in adaptivity, where no model exceeds 44.7% in tailoring paths to individual learners.

cs.CL↗

arXiv2026

LifeMem: Enabling Lifelong Experience Reuse for LLM Agents

Large language model agents are expected to continuously adapt to new tasks and environments over their lifetime by reusing past experience. However, existing memory-based agents struggle to transfer reusable experience across environments and suffer from catastrophic forgetting as experience accumulated. To address these challenges, we propose LifeMem, a lifelong learning framework that enables agents to transfer knowledge across multiple environments. During learning, LifeMem clusters accumulated interaction trajectories based on underlying workflows to extract reusable skills. When solving a new task at inference time, the agent recalls relevant skills and trajectories to guide actions. To validate our method, we conduct experiments across 10 environments and over 13k tasks with 2k newly annotated interaction trajectories. Results show that LifeMem enables effective experience reuse in lifelong learning, achieving both reduced forgetting on learned tasks and superior cross-task transfer. Further analysis reveals that task streaming impacts learning, while consolidating structurally similar trajectories within memory boosts performance.

cs.CL↗

arXiv2026

JarvisGUI: Towards Cross-Device GUI Agents with Dynamic Task Composition

Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing GUI benchmarks overwhelmingly evaluate agents on single-device, statically defined tasks, thus leaving such cross-device capabilities largely unexamined, resulting in an overly optimistic assessment of agents' readiness for real-world usage. We introduce JarvisGUI, a dynamic benchmark that evaluates GUI agents on cross-device workflows requiring coordinated interaction across heterogeneous platforms, including Android, Windows, and Ubuntu. Specifically, JarvisGUI formulates GUI tasks as input-output transformations under a lightweight type system, which allows us to automatically compose multi-step, cross-device workflows and dynamically evaluate agent performance within a unified framework. By evaluating agents in virtual environments spanning multiple operating systems, JarvisGUI reveals that state-of-the-art open-source GUI agents struggle with the state-transfer awareness, cross-platform contextual reasoning, and long-horizon dependency management required for real-world workflows, exposing a critical capability gap invisible to existing benchmarks.

cs.AI↗

arXiv2026

Advantageous Parameter Expansion Training Makes Better Large Language Models

Although scaling up the number of trainable parameters can effectively improve the training performance of large language models, it also leads to increased computational overhead. When delving into the parameter difference, we find that a subset of parameters, termed advantageous parameters, plays a crucial role in determining model performance. Further analysis reveals that stronger models tend to possess more such parameters. In this paper, we propose Advantageous Parameter EXpansion Training (APEX), a method that progressively expands advantageous parameters into the space of disadvantageous ones, thereby increasing their proportion and enhancing training effectiveness, while keeping the total parameter count unchanged. Extensive experiments on both instruction tuning and continued pre-training across five base models demonstrate that, in instruction tuning, APEX outperforms full-parameter tuning while using only 52% of the trainable parameters. In continued pre-training, APEX achieves the same perplexity level as conventional training with only approximately 30% of the training data, and yields significant improvements on downstream tasks.

cs.CL↗

arXiv2026

An Efficient IMEX-SDIRK2 mr-ccSAV Scheme for the Forced Navier--Stokes Equations with Uniform-in-Time Enstrophy Bounds

We propose and analyze an IMEX-SDIRK2 mean-reverting concurrent-correction scalar auxiliary variable (mr-ccSAV) method for the forced two-dimensional periodic Navier--Stokes equations in vorticity form. The viscous term is treated by Alexander's SDIRK2 method and advection explicitly. Each stage requires two elliptic solves with the same shifted Laplacian and the solution of either a cubic or a linear scalar algebraic equation. For initial vorticity in $\dot L^s(Ω)$, $s>2$, a stage solution exists for every positive time step; uniqueness is established separately under an explicit small-step condition. The principal result is a direct, unconditional uniform-in-time enstrophy bound for arbitrary positive time steps. For persistently bounded forcing, this estimate is absorbing: the influence of the initial data decays, and the forcing contribution does not accumulate in time. Under additional regularity, uniformly bounded step sizes, and bounded neighboring step ratios, we also establish uniform-in-time $H^1$ and $H^2$ vorticity bounds without a small-step condition. For smooth solutions, the method converges optimally at second order. Numerical experiments confirm its accuracy, long-time robustness, and effectiveness of a companion embedded time-step selector.

math.NA↗

arXiv2026

Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs

Model merging plays a crucial role in consolidating multiple specialized models into a single, unified model, especially in the era of large language models (LLMs). Recent research has primarily focused on developing strategies to enhance merging performance with the trained models, while the impact of training paradigms, such as supervised fine-tuning (SFT) and reinforcement learning (RL), on the effectiveness of model merging remains underexplored. In this study, we systematically explore the merging behavior of RL-trained LLMs compared to those trained with traditional SFT. Through comprehensive evaluations across five representative tasks, we find that RL significantly reduces task conflicts and results in less performance degradation after merging, making RL-trained models particularly well-suited for this process. To unearth the reasons behind the superior suitability of RL for model merging, we conduct extensive empirical experiments and theoretical analyses. Our findings highlight three key factors: (1) On-policy training data in RL control the gradient updates in a smaller magnitude, reducing the risk of overwriting existing knowledge for other tasks in the model. (2) The RL optimization objective, which favors ``\textit{enough is as good as a feast}", progressively reduces the magnitude and the number of conflict parameter updates as the model converges. (3) Joint optimization of positive and negative examples in RL steers the model towards an unbiased task-specific parameter subspace, ensuring robust performance while further preventing parameter conflicts.

cs.CL↗

arXiv2026

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge

Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, may be unavailable because of cloud, smoke, or darkness. The Bright Challenge evaluated all-weather building damage mapping from a submeter-resolution pre-event optical image and a post-event SAR image. Participants were required to detect and delineate each building and assign exactly one of three mutually exclusive damage labels. The challenge extended the globally distributed \textsc{Bright} dataset with instance-level annotations for about 291,000 buildings across 16 disaster events spanning seven disaster types. The final phase was evaluated exclusively on two 2025 events absent from training: a wildfire event in California and a hurricane in Jamaica. A total of 157 participants made 1,289 submissions, and 46 teams entered the final phase. The two winning solutions achieved test mAPs of 0.182 and 0.181, approximately 8.7 times the public baseline of 0.021, but remained far below the best in-domain holdout score of 0.513. Across teams ranked in both phases, performance declined sharply and the rank order changed substantially. The two leading solutions independently favored modality-specific encoding, staged or late optical--SAR fusion, and an optical-dominant separation of building localization from damage recognition. The winning method additionally used scene-aware threshold adjustment and pseudo-label adaptation. These results identify cross-event generalization and stable severity discrimination as the principal remaining challenges. All data, annotations, baseline code, and winning solutions are publicly available at https://github.com/ChenHongruixuan/BRIGHT.

cs.CV↗

arXiv2026

A Linear Variable-Step Embedded ETD Scheme with Uniform-in-Time Stability for the 2D Navier--Stokes Equations

We propose a linear variable-step exponential time-differencing method for the incompressible Navier--Stokes equations in vorticity--streamfunction formulation on a two-dimensional periodic box. The method consists of a second-order scheme and an embedded first-order variant, yielding a natural mechanism for adaptive time stepping and a posteriori error control. Each time step requires only uniquely solvable linear problems: two heat equation solves, efficiently handled by Fourier methods in the periodic setting, and one linear scalar auxiliary-variable equation, evaluated via Laplace transform and Talbot's numerical inverse transform. The construction combines the ETD framework, a mean-reverting scalar auxiliary variable (mr-SAV), and second-order extrapolation of the nonlinear term. The mean-reverting correction enables long-time stability while preserving full linearity, distinguishing the method from related mr-SAV schemes that require nonlinear algebraic solves. We prove unconditional long-time stability: for uniformly bounded $L^2$ forcing, the discrete vorticity remains bounded in $L^\infty(0,\infty;L^2)$ for all Reynolds numbers and time-step sizes. Numerical experiments

math.NA↗

arXiv2026

Unconditionally Long-Time Stable Variable-Step Second-Order ETD Schemes for the 2D Periodic Incompressible NSE

We develop an efficient, unconditionally stable, variable step second order exponential time differencing scheme for the incompressible Navier Stokes equations in two and three spatial dimensions under periodic boundary conditions, together with an embedded adaptive time stepping variant. The scheme is unconditionally uniform in time stable in the sense that the numerical solution admits a time uniform bound in Linfinity over time with values in L2 to the power d whenever the external forcing term is uniformly bounded in time in L2, for all Reynolds numbers and for arbitrary choices of time step sizes. At each time step, the method requires the solution of two time dependent Stokes problems, which can be evaluated explicitly in the periodic setting using Fourier techniques, along with the solution of a single scalar cubic algebraic equation. Beyond the standard exponential time differencing framework, the proposed scheme incorporates two recently developed ingredients. The first is a dynamic second order scalar auxiliary variable correction, which is essential for achieving second order temporal accuracy. The second is a mean reverting scalar auxiliary variable multistep formulation, which plays a central role in ensuring long time stability. The proposed methods overcome key limitations of existing approaches for the Navier Stokes equations. Classical Runge Kutta schemes generally lack provable long time stability, while IMEX and scalar auxiliary variable based BDF methods typically do not admit unconditional stability guarantees in the variable step setting. Numerical experiments in two spatial dimensions confirm second order temporal accuracy, uniform long time stability, and effective error control provided by the adaptive strategy. Rigorous convergence analysis and a systematic investigation of long time statistical properties will be pursued in future work.

math.NA↗

arXiv2026

Ram-pressure signatures in the dwarf irregular galaxy SextansB revealed by deep MeerKAT HI observations

The impact of extremely low-density environments such as the diffuse intergalactic medium (IGM) on the neutral gas distribution of dwarf galaxies remains poorly explored observationally. We present deep MeerKAT HI 21 cm observations of the Local Group dwarf irregular galaxy Sextans B that achieve a spectral resolution of 1.4 km/s and reach column-density sensitivities down to 3.3 x 10^18 cm^-2, allowing us to trace the extended HI disc and faint outer structures. The low-column-density HI distribution is asymmetric and reveals a rosette-like filamentary structure superposed on the HI disc. Comparison with the stellar distribution shows offsets between the gaseous and stellar components, with the stellar disc remaining relatively symmetric while the HI envelope becomes increasingly disturbed. 3D kinematic modelling with TiRiFiC reproduces the global velocity gradient but reveals differences between the approaching and receding sides of the rotation curve at large radii, indicating departures from axisymmetric rotation. While stellar feedback can produce small-scale cavities and turbulence in dwarf galaxies, it cannot generate the filamentary HI structure, the asymmetric outer HI envelope, or the divergence between the approaching and receding rotation curves. This is consistent with interaction with a diffuse IGM. Hydrodynamical simulations tailored to Sextans B show that IGM ram pressure acting on the outer gas disc can produce asymmetric gas distributions, filamentary structures, and kinematic perturbations. The combination of morphological and kinematic signatures suggests that the outer HI disc of Sextans B is affected by ram-pressure interaction with the diffuse IGM in the outskirts of the Local Group. This is the second strong example in the Local Group, after WLM, showing that a very low-density IGM can significantly influence the gas distribution and kinematics of dwarf galaxies.

astro-ph.GA↗

arXiv2026

PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes

Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities. However, current assistants overlook the progressive omission that occurs in human dialogue as shared context accumulates, leading to more elliptical expressions for efficient communication. Thus, current assistants still struggle to interpret such elliptical expressions accurately, which limits their effectiveness in real-world applications. In practical smart home scenarios, assistants face two major challenges caused by elliptical commands: (1) referential ambiguity caused by different environmental expectations among multiple users; and (2) intention ambiguity resulting from user preferences that evolve over time or change with the environment. To address these challenges, we introduce PEC-Home, the first simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes. Extensive experiments on various LLMs, including GPT-4o, show that existing home assistants struggle to execute user-intended operations based solely on elliptical commands. Even when equipped with tools for storing and retrieving user dialogue history, execution accuracy remains below that achieved with complete commands.}.

cs.CL↗

arXiv2026

Stability Analysis in Large-scale Centralized Bidirectional Inverter-based Stations Connected to Bulk Power Systems through AC and DC Connections

Massive controlled DC resources (CDCRs), such as battery energy storage systems, are connected to AC power systems through bidirectional inverters for power balance requirements. This study investigates converter-driven stability (CDS) issues in the sub-synchronous frequency range caused by large-scale bidirectional inverter-based stations (IBSs). The impacts of the AC and DC connections of IBSs on subsynchronous oscillations (SSOs) are compared by examining three factors: the number of CDCRs, power flow direction, and control parameters of the inverters. For AC connections, IBSs may induce instability as the number of CDCRs increases, regardless of the power flow direction. To maintain stability, the maximum power amplitude of the IBS is calculated. It is found that switching to DC connections can reduce these instability risks if the DC line resistance is much less than the AC line reactance. Moreover, the method of tuning control parameters is demonstrated to be more effective in improving power-related critical stability under DC connections. Therefore, The DC-IBS is preferred for high-voltage transmission. Finally, the conclusions are validated in power systems connected with both AC- and DC-IBSs under various network topologies and system scales.

eess.SY↗