arXiv subjects

Jingwen Zhang

Publications and source records attributed to Jingwen Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2026

An Outer Giant Planet or Brown Dwarf in the 51 Pegasi System?

51 Pegasi harbors the first confirmed extrasolar planet orbiting a Sun-like star. Decades of continued radial velocity (RV) observations have since uncovered signatures of an additional distant companion in the system from a shallow radial acceleration. We present new constraints on the mass and separation of a potential outer companion based on a synthesis of RVs, absolute astrometry, and new high-contrast imaging. Our analysis combines 31 years of new and previously published RV measurements from the OHP/ELODIE, Lick/Hamilton, Keck/HIRES, and APF/Levy spectrographs; a $\sim$25-year baseline of absolute astrometry from Hipparcos and Gaia; and deep imaging from Keck/NIRC2 and HST/WFPC2. We find evidence for curvature in the RVs, which when combined with non-detections from imaging and astrometry point to a super-Jupiter at $\simeq$15--100 AU or brown dwarf companion at $\approx$20--170 AU. However, the inferred radial acceleration of the host star is driven primarily by the Lick/Hamilton dataset and its slope is consistent with long-term instrument drift, calling into question the nature of the long-period signal. If an outer companion is present, it could explain the origin of the inner hot Jupiter if 51 Peg b arrived at its current location through high-eccentricity migration. On the other hand, if the signal is spurious, the exceptional baseline rules out Jovian planets within $\sim$10 AU and most brown dwarfs within several tens of AU, implying that the system is devoid of massive companions. Continued RV and astrometric monitoring together with high-contrast imaging can be used to distinguish these scenarios.

astro-ph.EP↗

arXiv2026

A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement

The contemporary media landscape is characterized by sensational short videos. While prior research examines the effects of individual multimodal features, the collective impact of multimodal features on viewer engagement with short videos remains unknown. Grounded in the theoretical framework of Message Sensation Value (MSV), this study develops and tests a computational model of MSV with multimodal feature analysis and human evaluation of 1,200 short videos. This model that predicts sensory and behavioral engagement was further validated across two unseen datasets from three short video platforms (combined N = 14,492). While MSV is positively associated with sensory engagement, it shows an inverted U-shaped relationship with behavioral engagement: Higher MSV elicits stronger sensory stimulation, but moderate MSV optimizes behavioral engagement. This research advances the theoretical understanding of short video engagement and introduces a robust computational tool for short video research.

cs.CV↗

arXiv2026

V2E: Validating Smart Contract Vulnerabilities through Profit-driven Exploit Generation and Execution

Smart contracts are a critical component of blockchain systems. Due to the large amount of digital assets carried by smart contracts, their security is of critical importance. Although numerous tools have been developed for detecting smart contract vulnerability, their effectiveness remains limited, particularly due to the high false positives included in the reported results. Therefore, developers and auditors are often overwhelmed with manually verifying the reported issues. A fundamental reason behind this is that while a reported vulnerability satisfies specific vulnerable patterns, it may not actually be exploitable, either because the vulnerable code cannot be triggered or it does not result in any financial loss. In this paper, we propose V2E, a new framework for validating whether a reported vulnerability is truly exploitable. The core idea of V2E is to automatically generate executable Proof-of-Concept Exploit (PoC for short), and then assess if the vulnerability could be triggered and incur any real damage (i.e., causing financial loss) by the PoC. While LLMs have shown proficiency in PoC generation, achieving our task is by no means trivial. In detail, it is difficult for LLM to: (1) generate and update PoC to trigger a specific vulnerability, (2) evaluate the PoC's effectiveness to validate exploitable vulnerability. To this end, V2E automates the whole process through a novel combination of PoC generation, validation, and refinement: (1) Firstly, V2E generates targeted PoCs by analyzing potential vulnerability paths. (2) Then, V2E verifies the validity of PoCs through triggerability and profitability analysis. (3) In addition, V2E iteratively refines the generated PoC based on PoC execution feedback, therefore, increasing the chance to confirm the vulnerability. Evaluation on 264 manually labeled contracts shows that V2E outperforms the baseline approach.

cs.SE↗

arXiv2026

Dynamic Whole-Body Dancing with Humanoid Robots -- A Model-Based Control Approach

This paper presents an integrated model-based framework for generating and executing dynamic whole-body dance motions on humanoid robots. The framework operates in two stages: offline motion generation and online motion execution, both leveraging future state prediction to enable robust and dynamic dance motions in real-world environments. In the offline motion generation stage, human dance demonstrations are captured via a motion capture (MoCap) system, retargeted to the robot by solving a Quadratic Programming (QP) problem, and further refined using Trajectory Optimization (TO) to ensure dynamic feasibility. In the online motion execution stage, a centroidal dynamics-based Model Predictive Control (MPC) framework tracks the planned motions in real time and proactively adjusts swing foot placement to adapt to real world disturbances. We validate our framework on the full-size humanoid robot Kuavo 4Pro, demonstrating the dynamic dance motions both in simulation and in a four-minute live public performance with a team of four robots. Experimental results show that longer prediction horizons improve both motion expressiveness in planning and stability in execution.

cs.RO↗

arXiv2026

WiSLAT: A Simultaneous Device Localization and Target Tracking Method for Wi-Fi Systems

It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi-Fi system, and etc. In the proposed WiSLAT method, however, it is shown that both sensing tasks can assist each other, such that the request on prior system information can be eliminated. Particularly, in a Wi-Fi system with an access point (AP) and at least three stations, where the locations of the stations are unknown, the WiSLAT is designed to detect the Doppler frequencies of the downlink CSI at the stations, such that their locations and the trajectory of the target with respect to the AP can be inferred. The joint detection can be conducted by searching the optimal stations' locations and target's trajectory, such that their corresponding Doppler frequencies fit the observed ones best. Due to the tremendous non-convex search space, a low-complexity sub-optimal algorithm integrating alternate optimization, extended Kalman filter and density-based clustering is proposed in WiSLAT. Experiments conducted in indoor environments demonstrate the effectiveness of WiSLAT, achieving a median trajectory-tracking error of 0.68 m.

eess.SP↗

arXiv2026

A Test of Substellar Evolutionary Models with High-Precision Ages from Asteroseismology and Gyrochronology for the Benchmark System HR 7672AB

We present high-precision measurements for HR~7672AB, composed of a Sun-like (G0V) star and an L~dwarf companion. Three nights of precise (70 cm/s) radial velocity (RV) asteroseismology with the Keck Planet Finder clearly detect 5-minute oscillations from the primary HR~7672A, and modeling of the frequency spectrum yields an asteroseismic age of $1.87\pm0.65$~Gyr. We also determine a gyrochronological age of $2.58\pm0.47$~Gyr, and we combine these two results for a final age of $2.26\pm0.40$~Gyr. In addition, we obtained new RVs for HR~7672A and new astrometry for the companion HR~7672B. From a joint orbit fit, we measured a dynamical mass of $1.111\pm0.017$~$\text{M}_\odot$ for HR~7672A and $75.39\pm0.67$~$\text{M}_{\text{Jup}}$ for HR~7672B. This places the companion near the stellar/substellar boundary and thus particularly sensitive to differences in model predictions. The joint precision in host star age (18\% uncertainty) and companion mass (0.9\% uncertainty) makes HR~7672AB an exceptional substellar benchmark. Combined with the companion's luminosity, we use these measurements to test predictions from six brown dwarf cooling models. The best agreement occurs with the Chabrier et al. (2023) models, which incorporate a new equation of state, resulting in predictions that agree within $<$0.3$σ$ with all the observations. The other 5 sets of models agree at the 1--3$σ$ level depending on the particular test, and some models struggle to predict a sufficient low luminosity for HR~7672B at any age given its dynamical mass. We also detected a weak seismic signal in near-simultaneous TESS photometry of HR~7672A, with the resulting RV-to-photometry oscillation amplitude ratio consistent with solar values.

astro-ph.SR↗

arXiv2026

Towards Bridging the Gap between Large-Scale Pretraining and Efficient Finetuning for Humanoid Control

Reinforcement learning (RL) is widely used for humanoid control, with on-policy methods such as Proximal Policy Optimization (PPO) enabling robust training via large-scale parallel simulation and, in some cases, zero-shot deployment to real robots. However, the low sample efficiency of on-policy algorithms limits safe adaptation to new environments. Although off-policy RL and model-based RL have shown improved sample efficiency, the gap between large-scale pretraining and efficient finetuning on humanoids still exists. In this paper, we find that off-policy Soft Actor-Critic (SAC), with large-batch update and a high Update-To-Data (UTD) ratio, reliably supports large-scale pretraining of humanoid locomotion policies, achieving zero-shot deployment on real robots. For adaptation, we demonstrate that these SAC-pretrained policies can be finetuned in new environments and out-of-distribution tasks using model-based methods. Data collection in the new environment executes a deterministic policy while stochastic exploration is instead confined to a physics-informed world model. This separation mitigates the risks of random exploration during adaptation while preserving exploratory coverage for improvement. Overall, the approach couples the wall-clock efficiency of large-scale simulation during pretraining with the sample efficiency of model-based learning during fine-tuning. For code and videos, see https://lift-humanoid.github.io

cs.RO↗

arXiv2026

Event-driven type design for clinical trials with recurrent events

It is a common practice in randomized clinical trials with the standard survival outcome to follow patients until a prespecified number of events have been observed, a type of trial known as the event-driven trial. The event-driven design ensures that the target power for a specified type 1 error rate is achieved to detect the target hazard ratio, regardless of the specification of other quantities. To understand the treatment effect for chronic conditions, the analysis of recurrent events has gained popularity in randomized controlled trials, particularly large-scale confirmatory trials. In the absence of within-subject correlation among multiple events, a similar event-driven design can be employed for recurrent event outcomes. On the other hand, in the presence of the within-subject correlation, one needs to model the correlation among recurrent events in evaluating power and setting the sample size. However, information useful in modeling the within-subject correlation is limited at the design stage. Failing to consider the correlation properly may lead to underpowered studies. We propose an event-driven type design for recurrent event outcomes. Our method ensures the target power for the target treatment effect, regardless of the specification of other quantities, by monitoring the robust variance under the marginal rates/means model in a blinded manner. We investigate the operating characteristics of the proposed monitoring procedure in simulation studies. The results of simulation studies showed that the proposed blinded monitoring procedure controlled the power well so that the test possessed the target power and did not lead to serious inflation of the type 1 error rate. Furthermore, we illustrate the proposed method using a real clinical trial dataset.

stat.ME↗

arXiv2026

ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking

Achieving stable and energy-efficient locomotion is essential for humanoid robots to operate continuously in real-world applications. Existing MPC and RL approaches often rely on energy-related metrics embedded within a multi-objective optimization framework, which require extensive hyperparameter tuning and often result in suboptimal policies. To address these challenges, we propose ECO (Energy-Constrained Optimization), a constrained RL framework that separates energy-related metrics from rewards, reformulating them as explicit inequality constraints. This method provides a clear and interpretable physical representation of energy costs, enabling more efficient and intuitive hyperparameter tuning for improved energy efficiency. ECO introduces dedicated constraints for energy consumption and reference motion, enforced by the Lagrangian method, to achieve stable, symmetric, and energy-efficient walking for humanoid robots. We evaluated ECO against MPC, standard RL with reward shaping, and four state-of-the-art constrained RL methods. Experiments, including sim-to-sim and sim-to-real transfers on the kid-sized humanoid robot BRUCE, demonstrate that ECO significantly reduces energy consumption compared to baselines while maintaining robust walking performance. These results highlight a substantial advancement in energy-efficient humanoid locomotion. All experimental demonstrations can be found on the project website: https://sites.google.com/view/eco-humanoid.

cs.RO↗

arXiv2026

Detecting and Characterizing Companions with a Calibrated Gaia DR2, DR3, and Hipparcos Catalog (G23H)

Gaia DR4 epoch astrometry will enable the detection of thousands of exoplanets through astrometric motion. Here, we present a composite catalog and modeling framework that extracts the maximum information from existing Hipparcos and Gaia data releases. We calibrate Gaia DR2 proper motions and DR3-DR2 scaled position differences against the Gaia DR3 reference frame, and combine these with the Hipparcos-Gaia Catalog of Accelerations, the Hipparcos intermediate astrometric data, Gaia astrometric excess noise, and Gaia radial velocity variability constraints. We implement a joint likelihood model for these data in Octofitter that marginalizes over Gaia's unpublished observation epochs. This results in full orbit posteriors that can be computed uniformly for a large class of companions. We compare these posteriors to published orbital solutions for 25 stellar binaries from the Sixth Catalog of Orbits of Visual Binary Stars, recovering all companions at high significance and broadly consistent orbital separations. We then recover independent evidence to support 94 of 120 tested Jovian exoplanetary systems from the NASA Exoplanet Archive (plus 3 known stellar companions, and one previously detected planet we now rule out). We demonstrate that in cases like 14 Her b, the posteriors confirm the planetary nature of a signal using only Gaia and Hipparcos data. We find no false positives among 25 RV-quiet standard stars without significant Hipparcos-Gaia accelerations. Our method can break degeneracies inherent to proper motion anomaly or excess noise modeling alone by resolving orbital curvature within the Gaia baseline. The catalog and updated Octofitter are made publicly available to the community.

astro-ph.EP↗

arXiv2026

PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice

As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential. However, existing legal benchmarks rely on simplified and highly standardized tasks, failing to capture the ambiguity, complexity, and reasoning demands of real legal practice. Moreover, prior evaluations often adopt coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. To address these limitations, we introduce PLawBench, a Practical Law Benchmark designed to evaluate LLMs in realistic legal practice scenarios. Grounded in real-world legal workflows, PLawBench models the core processes of legal practitioners through three task categories: public legal consultation, practical case analysis, and legal document generation. These tasks assess a model's ability to identify legal issues and key facts, perform structured legal reasoning, and generate legally coherent documents. PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics, resulting in approximately 12,500 rubric items for fine-grained assessment. Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs. Experimental results show that none achieves strong performance on PLawBench, revealing substantial limitations in the fine-grained legal reasoning capabilities of current LLMs and highlighting important directions for future evaluation and development of legal LLMs. Data is available at: https://github.com/skylenage/PLawbench.

cs.CL↗

arXiv2026

Extreme winds on the emerging dayside of an ultrahot Jupiter

High-resolution spectroscopy provides a unique opportunity to directly probe atmospheric dynamics by resolving Doppler shifts of planetary signal as a function of orbital phases. Using the optical spectrometer Keck Planet Finder (KPF), we carry out a pilot study on high-resolution phase curve spectra of the ultra-hot Jupiter KELT-9 b. We spectrally and temporally resolve its dayside emission from post-transit to pre-eclipse (orbital phase phi = 0.1 - 0.45). The signal strength and width increase with orbital phases as the dayside rotates into view. The net Doppler shift varies progressively from -13.4 +/- 0.6 to -0.4 +/- 1.0 km/s, the extent of which exceeds its rotation velocity of 6.4 +/- 0.1 km/s, providing unambiguous evidence of atmospheric winds. We devise a retrieval framework to fit the full time-series spectra, accounting for the variation of line profiles due to the rotation and winds. We retrieve a supersonic day-to-night wind speed up to 11.7 +/- 0.6 km/s on the emerging dayside, representing the most extreme atmospheric winds in hot Jupiters to date. Comparison to 3D circulation models reveals a weak atmospheric drag, consistent with relatively efficient heat recirculation as also supported by space-based phase curve measurements. Additionally, we retrieve the dayside chemistry (including Fe i, Fe ii, Ti i, Ti ii, Ca i, Ca ii, Mg i, and Si i) and temperature structure, and place constraints on the nightside thermal profile. Our high-resolution phase curve spectra and the measured supersonic winds provide excellent benchmarks for extreme physics in circulation models, demonstrating the power of this technique in understanding climates of hot Jupiters.

astro-ph.EP↗

arXiv2026

SSR: Safeguarding Staking Rewards by Defining and Detecting Logical Defects in DeFi Staking

Decentralized Finance (DeFi) staking is one of the most prominent applications within the DeFi ecosystem, where DeFi projects enable users to stake tokens on the platform and reward participants with additional tokens. However, logical defects in DeFi staking could enable attackers to claim unwarranted rewards by manipulating reward amounts, repeatedly claiming rewards, or engaging in other malicious actions. To mitigate these threats, we conducted the first study focused on defining and detecting logical defects in DeFi staking. Through the analysis of 64 security incidents and 144 audit reports, we identified six distinct types of logical defects, each accompanied by detailed descriptions and code examples. Building on this empirical research, we developed SSR (Safeguarding Staking Reward), a static analysis tool designed to detect logical defects in DeFi staking contracts. SSR utilizes a large language model (LLM) to extract fundamental information about staking logic and constructs a DeFi staking model. It then identifies logical defects by analyzing the model and the associated semantic features. We constructed a ground truth dataset based on known security incidents and audit reports to evaluate the effectiveness of SSR. The results indicate that SSR achieves an overall precision of 92.31%, a recall of 87.92%, and an F1-score of 88.85%. Additionally, to assess the prevalence of logical defects in real-world smart contracts, we compiled a large-scale dataset of 15,992 DeFi staking contracts. SSR detected that 3,557 (22.24%) of these contracts contained at least one logical defect.

cs.SE↗

arXiv2025

Dynamical Architectures of S-type Transiting Planets in Binaries II: A Dichotomy in Orbital Alignment of Small Planets in Close Binary Systems

Stellar multiplicity plays a crucial role in shaping planet formation and dynamical evolution. We present a survey of 54 TESS Objects of Interest (TOIs) within 300 pc that exhibit significant Hipparcos-Gaia astrometric accelerations. We identified 35 TOIs with stellar companions at projected separations between $0.1^{\prime\prime}$ to $2^{\prime\prime}$ (or $10-200$ AU). We also identified 12 TOIs that could host planetary-mass or brown dwarf companions, including 6 that are newly discovered. Furthermore, we perform three-dimensional orbital characterization for 12 binaries hosting confirmed planets or planet candidates, allowing us to constrain the line-of-sight mutual inclination, $ΔI_{\mathrm{los}}$, between the planetary and binary orbits. Combining our sample with previous measurements, we apply Bayesian hierarchical analysis to a total of 26 binary systems with S-type transiting planets ($r_p<5R_{\oplus}$). Specifically, we fit the $ΔI_{\mathrm{los}}$ distribution with both single (Rayleigh) and mixture models (two-component Rayleigh and Rayleigh-isotropic mixture). We find the mixture models are strongly favored ($\log Z\gtrsim13.9$, or $\approx$5$σ$), indicating the observed planet-binary $ΔI_{\mathrm{los}}$ values likely originate from two underlying populations: one nearly aligned ($σ_1 = 2^{\circ}.4^{+0.7}_{-0.9}$) and one with more scattered mutual inclinations ($σ_2 = 23^{\circ}.6^{+8.8}_{-7.1}$). Alternatively, the misaligned systems can be equally well described by an isotropic distribution of inclinations. This observed dichotomy likely reflects different dynamical histories. Notably, the misaligned population only emerges in systems with stellar periastron distances $>40$ AU while systems with close-in or eccentric stellar companions (periastron distances $<40$ AU) preserve planet-binary alignment.

astro-ph.EP↗

arXiv2025

LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions

Driven by the rapid advancements of Large Language Models (LLMs), LLM-based agents have emerged as powerful intelligent systems capable of human-like cognition, reasoning, and interaction. These agents are increasingly being deployed across diverse real-world applications, including student education, scientific research, and financial analysis. However, despite their remarkable potential, LLM-based agents remain vulnerable to hallucination issues, which can result in erroneous task execution and undermine the reliability of the overall system design. Addressing this critical challenge requires a deep understanding and a systematic consolidation of recent advances on LLM-based agents. To this end, we present the first comprehensive survey of hallucinations in LLM-based agents. By carefully analyzing the complete workflow of agents, we propose a new taxonomy that identifies different types of agent hallucinations occurring at different stages. Furthermore, we conduct an in-depth examination of eighteen triggering causes underlying the emergence of agent hallucinations. Through a detailed review of a large number of existing studies, we summarize approaches for hallucination mitigation and detection, and highlight promising directions for future research. We hope this survey will inspire further efforts toward addressing hallucinations in LLM-based agents, ultimately contributing to the development of more robust and reliable agent systems.

cs.AI↗

arXiv2025

CoronaGraph Instrument Reference stars for Exoplanets (CorGI-REx) I. Preliminary Vetting and Implications for the Roman Coronagraph and Habitable Worlds Observatory

The upcoming Roman Coronagraph will be the first high-contrast instrument in space capable of high-order wavefront sensing and control technologies, a critical technology demonstration for the proposed Habitable Worlds Observatory (HWO) that aims to directly image and characterize habitable exoEarths. The nominal Roman Coronagraph observing plan involves alternating observations of a science target and a bright, nearby reference star. High contrast is achieved using wavefront sensing and control, also known as "digging a dark hole", where performance depends on the properties of the reference star, requiring V<3, a resolved stellar diameter <2 mas, and no stellar multiplicity. The imposed brightness and diameter criteria limit the sample of reference star candidates to high-mass main sequence and post-main sequence objects, where multiplicity rates are high. A future HWO coronagraph may have similarly restrictive criteria in reference star selection. From an exhaustive literature review of 95 stars, we identify an initial list of 40 primary and 18 reserve reference star candidates relevant to both the Roman Coronagraph and HWO. We present results from an initial survey of these candidates with high-resolution adaptive optics imaging and speckle interferometry and identify no new companions. We discuss the need for higher-contrast observations to sufficiently vet these reference star candidates prior to Roman Coronagraph observations along with the implications of reference star criteria on observation planning for Roman and HWO.

astro-ph.SR↗

arXiv2025

Discovery of a Debris Disk Around TWA 20

We report the discovery of a debris disk surrounding the M3 star, TWA 20, revealed by JWST coronagraphic observations using the Near-infrared Camera (NIRCam). With reference-star differential imaging (RDI), we resolve the disk in scattered light in the F200W filter at a high signal-to-noise ratio and in the F444W filter at a low signal-to-noise ratio. The disk morphology and orientation are characterized via a forward modeling approach, where we determine a radius of 64.7-6.5+6.2 AU and an inclination of 70.1-3.3+2.5 deg. Utilizing our forward model, we improve the fidelity of the debris disk image using model-constrained RDI (MCRDI). The newly discovered disk is one of only 6 disks detected in scattered light that orbit M dwarf stars; it is the third largest of the 6 resolved M dwarf disks and orbits the third faintest host star. The detection of this disk exemplifies the sensitivity of JWST to debris disks around low-luminosity host stars, which have historically been difficult to detect because these disks are cool and dim. We identify a nebulous structure that cannot be explained by an axisymmetric disk. A search for companions in the TWA 20 system yields no candidates.

astro-ph.EP↗

arXiv2025

Path and Motion Optimization for Efficient Multi-Location Inspection with Humanoid Robots

This paper proposes a novel framework for humanoid robots to execute inspection tasks with high efficiency and millimeter-level precision. The approach combines hierarchical planning, time-optimal standing position generation, and integrated \ac{mpc} to achieve high speed and precision. A hierarchical planning strategy, leveraging \ac{ik} and \ac{mip}, reduces computational complexity by decoupling the high-dimensional planning problem. A novel MIP formulation optimizes standing position selection and trajectory length, minimizing task completion time. Furthermore, an MPC system with simplified kinematics and single-step position correction ensures millimeter-level end-effector tracking accuracy. Validated through simulations and experiments on the Kuavo 4Pro humanoid platform, the framework demonstrates low time cost and a high success rate in multi-location tasks, enabling efficient and precise execution of complex industrial operations.

cs.RO↗