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Cong Wang

Publications and source records attributed to Cong Wang.

At least 19 recordsLinked to original sources
arXiv2026

WLC: Weber-Inspired Local Contrast Metric for Low-Altitude Image Fusion

Infrared and visible image fusion is a pivotal technology in low-altitude Unmanned Aerial Vehicle (UAV) reconnaissance missions, enabling robust target detection and tracking by integrating thermal saliency with environmental textures. However, the advancement of fusion algorithms is hindered by a critical evaluation bottleneck. In this paper, we identify a systematic failure in traditional no-reference metrics (specifically Statistics-based and Gradient-based metrics) within complex low-light environments, termed as ``Noise Trap''. It is mathematically proven that these metrics are positively correlated with high-frequency sensor noise, paradoxically assigning higher scores to degraded images and misguiding algorithm optimization. To resolve this dilemma, this paper proposes the Weber-inspired Local Contrast (WLC) metric. Grounded in the psychophysical principle of Weber's Law, WLC shifts the evaluation paradigm from global statistical distribution to local semantic contrast. By leveraging infrared priors, it effectively decouples target saliency from global background noise. Extensive experiments on the DroneVehicle dataset demonstrate that WLC exhibits high ``Semantic Discriminability'' in distinguishing thermal targets from background clutter. Furthermore, it achieves remarkable computational efficiency, thus, establishing itself as a reliable and real-time standard for intelligent UAV systems.

cs.CV↗
arXiv2026

Medical world models in healthcare: capabilities, evidence, and trustworthy clinical translation

Medical world models offer a framework for extending medical artificial intelligence beyond static prediction by representing evolving patient states and modelling how they change over time and in response to clinical interventions. This Review uses structured narrative synthesis and reproducible evidence mapping to define conceptual boundaries, technical foundations, application domains, and clinical evidence requirements. We screened 1,455 unique records and assembled a corpus of 300 sources, including 14 studies that met a strict empirical definition of a medical world model. The field is organized around four capabilities: patient state representation, temporal dynamics modelling, intervention-conditioned simulation, and clinician-supervised planning. Evidence spans medical imaging, longitudinal electronic health records, treatment response modelling, physiological and multimodal state modelling, ultrasound and surgical interaction, and population and health-system simulation; clinical digital twins are treated as a cross-cutting integration framework. Studies provide early evidence of technical feasibility for trajectory forecasting and comparison of candidate interventions, but most remain retrospective, task-specific, or preclinical. We distinguish functional capability from clinical evidence maturity and specify evidence needed to support progressively stronger clinical claims, from temporal forecasting to intervention comparison and planning-oriented decision support. These requirements include precise intervention representations, robust causal and mechanistic grounding, calibrated trajectory-level uncertainty, safety-constrained planning, external validation, and prospective multicentre evaluation against clinically meaningful endpoints. This framework aims to distinguish technically plausible simulation from clinically supportable prediction and decision support.

cs.CV↗
arXiv2026

Agentic AI in medicine: architectures, applications, evaluation, and challenges for clinical translation

Large language models and multimodal foundation models are enabling medical artificial intelligence (AI) systems to move beyond isolated prediction and undertake multistep clinical tasks that require planning, tool use, memory, iterative correction, and coordination among specialized agents. However, the scope of agentic AI in medicine remains unsettled, and current evaluation practices are not yet aligned with the requirements of clinical use. We conducted a scoping review with systematic evidence mapping across five electronic sources, screened 1,649 exportable records, and provisionally included 557 unique studies that met predefined criteria for goal-directed task execution, tool use, interaction with external resources, feedback-based refinement, or multi-agent collaboration. The included studies describe single agents that use external tools, workflows supported by retrieval and external knowledge, multimodal agents, and multi-agent systems applied to medical question answering, image interpretation, electronic health record analysis, drug safety, and clinical trial prediction. The evidence base remains dominated by public benchmarks, simulated settings, retrospective datasets, and small-scale expert evaluation. Process reliability, evidence traceability, uncertainty, safety, workflow impact, and external validity are evaluated less consistently. Clinical translation will depend on clearer definitions, reproducible evaluation, auditable oversight, interoperable system design, and prospective validation in real-world clinical workflows.

cs.CV↗
arXiv2026

The Alignment Illusion: How BadReward Corrupts Text-to-Image Generation

Modern text-to-image generative models increasingly rely on Reinforcement Learning from Human Feedback (RLHF) to align generated content with human preferences and safety standards. This post-training stage is critical for optimizing models toward high-fidelity and value-aligned behaviors using human preference data. However, while previous works have extensively investigated poisoning attacks against the supervised fine-tuning (SFT) stage of text-to-image training, the security vulnerabilities introduced by the RLHF process remain unexplored. In this paper, we present the first exploration into the feasibility of poisoning the RLHF process as a novel attack surface for compromising text-to-image models. We introduce BadReward, an indirect clean-label poisoning attack that manipulates reward signals to embed malicious behaviors. Our approach leverages a feature collision mechanism to produce poison preference image pairs that are visually benign, but aligned with a malicious target concept in the reward feature space. Our experiments demonstrate that this manipulation effectively corrupts the reward signal. Online RLHF further amplifies this corruption through its reward-model feedback loop, whereas offline preference optimization learns directly from the poisoned preferences. Finally, we demonstrate the real-world effectiveness of BadReward on modern text-to-image architectures, showing high attack success rates and visual stealthiness across different RLHF algorithms. Our code is available at https://github.com/ZJUICSR/BadReward.

cs.LG↗
arXiv2026

PersonaScienceBench: Evaluating Scientific Agents with Diverse Simulated Researchers

Scientific agents collaborate with researchers whose expertise and preferences differ, but benchmarks usually hold the collaborator fixed. We introduce PersonaScienceBench: 200 tasks from five benchmarks across four scientific domains. Simulated researchers provide context and feedback according to persona profiles, while task objectives and grading remain fixed. Agents encounter researcher preferences only through conversation. Across eighteen modelconfigurations, task performance, fact recovery, and self-repetition reveal different strengths. Detailed GPT 6 Astra analyses show distinct agent responses across researcher profiles; interaction traces illustrate targeted clar ification and workflow revision. These behaviors can differ even when final scores match. PersonaScienceBench therefore evaluates how agents collaborate with diverse researchers alongside whether they complete the task.

cs.AI↗
arXiv2026

PersonaChatBench: Evaluating Chat Assistants with Diverse Simulated Users

Conversational assistants are expected to help people with diverse backgrounds, preferences, and communication styles. Yet evaluations centered on a single cooperative user provide limited insight into how reliably an assistant serves users with different personas. We introduce PersonaChatBench, a framework for evaluating task competence and responsiveness to diverse user needs. PersonaChatBench pairs the same underlying task with multiple simulated users, each guided by a private persona card that specifies distinct needs, preferences, and interaction behaviors. By holding the task fixed while varying the user, the framework examines how assistant performance changes across user conditions. We evaluate 20 language models across six domains, separately assessing native task outcomes, fulfillment of expressed persona needs, and simulator fidelity. Our results show that task performance and need fulfillment vary across personas and domains. Strong task performance does not necessarily translate into satisfying individual needs, and models' relative strengths in meeting those needs are not consistent across domains. PersonaChatBench makes these differences measurable, revealing gaps obscured by aggregate scores and helping identify which users an assistant serves well and where adaptation is needed. It provides a basis for evaluating consistency across users and guiding the development of assistants that better accommodate diverse needs in everyday applications.

cs.CL↗
arXiv2026

Radiative decays of $J/ψ,\,ψ(2S)\rightarrowγη^{(\prime)}$ in perturbative QCD with relativistic corrections

We present the first calculation of the radiative decays $J/ψ,ψ(2S)\rightarrowγη^{(\prime)}$ in perturbative QCD that includes the order-$q^{2}$ relativistic corrections in all three short-distance contributions, namely the quark-antiquark, two-gluon, and QED contributions. The amplitudes are found to be remarkably insensitive to the light-cone distribution amplitude and to the light-quark mass, a robustness that persists through order $q^{2}$ and makes the predictions correspondingly reliable. The relativistic correction enhances the $J/ψ$ branching ratios by roughly a factor of 2, narrowing their shortfall from experiment, whereas for the $ψ(2S)$ it is about twice as large as for the $J/ψ$ and the low-order expansion converges poorly. In two representative $η$--$η^{\prime}$ mixing schemes, the ratio $\mathcal{R}_{1S}=\mathcal{B}(γη^{\prime})/\mathcal{B}(γη)$ proves sharply sensitive to the mixing angle and favors the smaller of the two. The predicted $ψ(2S)$ rates lie well above the data in both channels, already at leading order, and most severely for the anomalously small $γη$ channel. Such a discrepancy suggests that a mechanism beyond the hard perturbative process is at work. As a physically motivated attempt, we explore the $η_{c}$-mixing contribution, which adds coherently to the perturbative one and is comparable to it in the $γη$ channel, and find that the interference can bring the $ψ(2S)$ rates into agreement with the data, although its extraction is limited by a strong sensitivity to the mixing parameters.

hep-ph↗
arXiv2026

SEEK: Skill Extraction with Evolving Knowledge against Frontier Science Agents

Science agents increasingly package domain expertise as reusable skills that plan analyses, select tools, and execute workflows. This modularity creates a confidentiality risk: the interactions that deliver a skill's capability can also reveal the skill itself. We build an extensible benchmark of 40 protected skills across eight open- and closed-source science-agent platforms, comparing direct, fixed-strategy, and bandit-guided iterative probing under reference-based and reference-free evaluation. Leakage is pervasive: all eight platforms (100%) leak at least one protected skill, and four reach 100% attack success under a single direct request. To stress-test platforms that resist simple probing, we propose SEEK (Skill Extraction with Evolving Knowledge), a black-box, self-evolving red-teaming agent that consolidates response-derived evidence in persistent memory and evolves reusable attack skills, using only user-visible responses. On 12 skills from SpatialAgent and Biomni, SEEK achieves 100% attack success while reducing the mean first-success round by 31.8% and 38.2% relative to bandit-guided probing. Against a confidentiality policy that blocks all three benchmark protocols, SEEK still extracts one of six protected skills. These findings show that current science agents do not reliably protect their skills and that defenses must be evaluated against adaptive, multi-turn attackers.

cs.LG↗
arXiv2026

Hy-Nomas: Towards Free-form Interactive World Generation

Interactive world generation requires models to follow successive commands while preserving relevant history and action consequences. We present Hy-Nomas, a video world model with an 8B diffusion backbone that supports free-form text instructions and continuous camera control across multiple interaction turns. It generates variable-length segments conditioned on current inputs and cached history. Our central design aligns structured video captions with action-aware causal attention: descriptive context remains accessible across segments, while action instructions are restricted to their intended temporal windows. A vision-language Prompt Enhancer (PE) translates user requests and multimodal interaction histories into structured conditions for the next segment. A multi-stage curriculum progressively introduces causal generation, text interaction, camera control, and training on model-generated histories, followed by diffusion distillation. In human evaluation, Hy-Nomas shows superior performance over existing world models. We release model weights, code and benchmarks, together with a lightweight adaptation workflow, to support reproducible research on interactive world generation.

cs.CV↗
arXiv2026

MANTA: Unlocking DAG Flexibility in Asynchronous BFT

DAG-based BFT protocols marked a shift in consensus design by replacing single-leader proposal paths with concurrent block proposals linked through a shared graph, where references encode consensus evidence. However, under heterogeneous wide-area networks (WANs), existing designs suffer from prolonged rounds and degraded performance due to straggler effects. The root cause is rigid round advancement: replicas move in synchronized rounds, waiting for enough references from the previous round before advancing. We propose a flexible DAG growth framework for asynchronous BFT consensus. Our key observation is that while reference quorums are necessary for safety and liveness, they need not block every round advancement. Instead, the connectivity required for commitment can be accumulated across rounds. Within the framework, we instantiate MANTA, which combines flexible DAG growth to advance by relaxing round synchronization and flexible coin to trigger leader election and finality earlier. Experiments on heterogeneous WAN deployments show that MANTA reduces latency by approximately 30%-70% in our evaluation.

cs.DC↗
arXiv2026

Bidirectional Cross-frequency Domain Interaction Network for UHD Low-Light Image Enhancement

Existing Ultra-High-Definition (UHD) low-light image enhancement (LLIE) methods typically rely on either spatial- or frequency-domain modeling, or independently process different frequency components for efficient restoration. However, they often fail to jointly exploit complementary spatial--frequency information and the inherent relationship between illumination and structural information across frequencies, leading to inconsistent illumination, residual noise, and detail artifacts. In this paper, we propose a novel Bidirectional Cross-frequency Domain Interaction Network (BiFreqNet) for UHD LLIE. Specifically, BiFreqNet incorporates a Dual-domain Feature Interaction (DFI) module into the multi-scale representations of a pretrained variational autoencoder (VAE) to perform reciprocal enhancement between low- and high-frequency features. DFI consists of a Spatial and Spectral-Aware Low-Frequency Enhancement (S$^2$LFE) block and a Cross-Frequency Enhancement (CFE) block. S$^2$LFE exploits high-frequency structural cues to guide low-frequency restoration and jointly models spatial and spectral information to recover global exposure and spatially varying illumination while suppressing sensor noise. CFE further leverages the enhanced low-frequency contextual information to guide adaptive high-frequency refinement, enabling faithful reconstruction of structures and fine details. Extensive experiments on four general and UHD LLIE benchmarks demonstrate that BiFreqNet achieves competitive or superior performance compared with state-of-the-art methods.

cs.CV↗
arXiv2026

Relativistic corrections to $Υ(1S,2S)\rightarrowγη^{(\prime)}$ in perturbative QCD

We calculate the radiative decays $Υ(1S),Υ(2S)\rightarrowγη^{(\prime)}$ in perturbative quantum chromodynamics through order $q^{2}$ in the relativistic expansion, including the quark-content, two-gluon, and electromagnetic contributions. The quark contribution dominates, with substantial constructive electromagnetic interference and a strongly suppressed gluon contribution. The annihilation rates depend weakly on the tested light-cone distribution amplitudes and light-quark masses. Relativistic corrections reduce the $Υ(1S)$ rates by a few percent but enhance the $Υ(2S)$ rates by approximately $17\%$--$30\%$. The ratio of the $γη^{\prime}$ and $γη$ branching fractions is sensitive to the light-meson mixing parameters, with predictions differing by about a factor of $5$ between two representative schemes. At a scale of half the quarkonium mass, however, the $Υ(1S)\rightarrowγη^{\prime}$ central predictions in the two mixing schemes exceed the experimental upper limit by factors of approximately $5$ and $7$. We also examine the heavy-pseudoscalar admixtures in the light mesons. The $η_c$ contribution is small, whereas $η_b$ mixing can substantially modify the rates through interference. Destructive interference alleviates the discrepancy. With the scale set to the quarkonium mass, the prediction in the mixing scheme inferred from transition form factors is compatible with the upper limit within the input uncertainty.

hep-ph↗
arXiv2026

Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement

Reinforcement learning (RL) has substantially improved the reasoning capabilities of vision-language models (VLMs), yet it can induce chain-of-thought (CoT) obfuscation, an operational, non-intentional regime where models achieve high accuracy while producing reasoning traces that are less grounded and harder to monitor. While prior work documents this decay at the behavioral level, the underlying representational correlates of such drift remain poorly understood. In this work, we find that obfuscation is accompanied by a representational shift, where RL-induced optimization makes task-agnostic template features less separable from visually grounded content in the model's activation space. Based on this insight, we propose TAME, an activation-level intervention framework that leverages Sparse Autoencoders (SAEs) to regularize VLM internal features during RL training. Specifically, TAME utilizes an anti-obfuscation feedback loop to reduce behavioral obfuscation and applies an asymmetric SAE constraint to specific internal directions, penalizing template activations while preserving visual evidence. By directly intervening on the features associated with obfuscation, our method improves the transparency and visual grounding of reasoning traces. Experiments on the VIRL-39k and SPA-VL benchmarks across two model families show that TAME improves CoT monitorability (by up to +30.9 points on VIRL and +16.7 points on SPA-VL over GRPO), while largely preserving task accuracy and general-capability performance across four standard VLM benchmarks, with task-specific trade-offs. Our code is available at https://github.com/henrymao2004/TAME.

cs.AI↗
arXiv2026

PHD-Hand: A Printable Humanoid Dexterous Hand with Linkage-Driven 3-DOA Digits

Currently, dexterous humanoid robot hands are widely considered the final hardware bottleneck that hampers generalist robots from physically assisting people at home and work. As conventional tendon-driven designs suffer from high cost, demanding maintenance, poor reliability, and short durability, alternative linkage-driven designs have increasingly gained attention. Despite their potential to avoid the weaknesses of tendon-driven hands, integrating multiple degrees of actuation (DOA) in a single digit with linkage drivetrains is a challenge for design and fabrication. Only a few studies have managed to present functional humanoid hands that are completely linkage-driven and have at least three DOAs per digit without compromising actuator placement and other design preferences. Our work introduces a new linkage-driven hand featuring a unique flexure hinge. Together with elastic drive rods made of spring steel, the linkage drivetrains are significantly simplified. Unlike other linkage-driven designs of similar dexterity, our design does not require many miniature moving parts. As a result, the hand can be fabricated easily with a budget 3D printer. This not only significantly lowers the barrier to entry for using and developing dexterous humanoid robot hand functions, but also allows the hands to be sized much closer to regular human hands. The design offers desirable kinematic advantages as well. A prototype is evaluated for grasping and object manipulation. In addition, this paper presents our latest findings on kinematics and control of the hand.

cs.RO↗
arXiv2026

Aluminum wide-range EOS sensitivity in Z-pinch dynamic-hohlraum liner-foam impact and outgoing radiation

In a Z-pinch dynamic hohlraum, an aluminum liner collides with a central foam and converts kinetic energy into radiation, and the liner-foam response depends on the aluminum equation of state (EOS). We construct a wide-range aluminum equation-of-state table from density functional theory (DFT) simulations over solid-liquid-plasma regions, denoted DFT-eos. Mixed deterministic-stochastic finite-temperature DFT (mDFT) gives the electron thermal contribution; quantum molecular dynamics (QMD) with classical calibration gives the ion part. We perform one-dimensional cylindrical radiation magnetohydrodynamics (MHD) simulations at a peak current of 50 MA and rise times of 220-300 ns on a single-layer light foam load. Opacity and electrical conductivity are fixed; only the liner Al EOS is changed between DFT-eos and the quotidian EOS (QEOS). Impact time and peak liner kinetic energy change little between the two tables. Larger differences appear in flight-segment density, the contact state at impact, and the time history of outgoing radiation power at the outer interface.

physics.plasm-ph↗
arXiv2026

Arcturus: A Cloud Overlay Network for Global Accelerator with Enhanced Performance and Stability

Global Accelerator (GA) services play a vital role in ensuring low-latency, high-reliability communication for real-time interactive applications. However, existing GA offerings are tightly bound to specific cloud providers, resulting in high costs, rigid deployment, and limited flexibility, especially for large-scale or budget-sensitive deployments. Arcturus is a cloud-native GA framework that revisits the design of GA systems by leveraging low-cost, heterogeneous cloud resources across multiple providers. Rather than relying on fixed, high-end infrastructure, Arcturus dynamically constructs its acceleration network and balances performance, stability, and resource efficiency. To achieve this, Arcturus introduces a two-plane design: a forwarding plane that builds a proxy network with adaptive control, and a scheduling plane that coordinates load and routing through lightweight, quantitative optimization. Evaluations under millions of RPS show that Arcturus outperforms commercial GA services by up to 1.7X in acceleration performance, reduces cost by 71%, and maintains over 80% resource efficiency--demonstrating efficient use of cloud resources at scale.

cs.NI↗
arXiv2026

Global Structure-Constrained 4D prestack inversion

Prestack seismic inversion often suffers from inherent ill-posedness and is significantly affected by complex factors such as the wavelets, parameter coupling, heterogeneous media, fault displacements, and stratigraphic variations, making it difficult for the conventional inversion methods to accurately recover subsurface information. To address these challenges, this paper proposes a global inversion method based on multi-dimensional matrix multiplication to optimize the inversion results under structure constraints. This method first represents the inversion model as a four-dimensional tensor representing spatial and parameter dimensions. By performing multi-dimensional matrix multiplication operations in various directions of the model, a multi-dimensional tensor of observed data is effectively constructed. Secondly, multi-dimensional regularization are introduced. By analyzing the impact of different dimensional constraints on the inversion results, targeted parameter selection and adjustment are achieved, significantly enhancing the stability of the inversion process and effectively suppressing noise. Finally, stratigraphic and fault constraints extracted from seismic interpretation are incorporated to improve the geological coherence and reasonableness of the inversion results. Both synthetic and field data experiments demonstrate that the proposed method effectively enhances the robustness of inversion in complex structural areas while maintaining high computational efficiency, validating its effectiveness in practical applications.

physics.geo-ph↗
arXiv2026

Penetrating the Reality Wall: Long-Range and Through-Wall AR/VR Privacy Eavesdropping via Wireless Power Side Channel

Over the past decade, Metaverse technology (AR/VR) has seen widespread adoption in numerous areas, including gaming, social networking, and online collaboration. Although recent studies have exposed vulnerabilities in VR systems, they often apply methods (e.g., pre-installed malicious apps or scripts) from conventional mobile platforms directly, demonstrating impractical attacks that fail to investigate unique privacy leakages specific to VR platforms. In this paper, we present an orthogonal attempt to analyze the attack surface of the power side channel on AR/VR's network connection by capturing the through-wall wireless power in the network transmission medium (e.g., Wi-Fi signals) non-intrusively in a comparatively long distance (e.g., 5.0m-8.0m) and further reconstructing multi-level privacy-aware user behaviors. We propose MetaSpy to validate the feasibility and practicality of our disclosed wireless power-based traffic analysis attack. It first captures through-wall wireless power transmitted from the Wi-Fi router of an adjacent room, identifies the device type, then infers (i) user interactions such as browsing websites, running AR/VR apps, and performing various in-app activities, (ii) unseen VR-specific activities in VR games (e.g., Population: ONE), and (iii) avatar-based user re-identification and virtual keystroke inference corresponding to head movements. We evaluated the effectiveness of MetaSpy in four commercial-off-the-shelf (COTS) VR headsets, and the empirical results demonstrate its competitive accuracy in unveiling multi-level user privacy within virtual scenes in through-wall settings at a longer distance (1.8x-80x) than most of the recent studies, and it shows resilience to a series of external interference and impact factors. In addition, we also propose and evaluate effective countermeasures to protect data confidentiality in AR/VR wireless transmission.

cs.CR↗