arXiv subjects

Dan Zhao

Publications and source records attributed to Dan Zhao.

At least 19 recordsLinked to original sources

arXiv2026

State-resolved multimodal contributions to stratospheric polar vortex predictability

The dynamical basis of stratospheric polar vortex (SPV) predictability remains unclear, particularly the relative roles of persistence, structural variability, and cross-level coupling. Here we combine eigen microstate theory with mesoscopic Granger-causality analysis of ERA5 geopotential height fields to quantify the predictive contributions of eigen microstates. We show that short-term predictability is dominated by the persistence of the leading stratospheric state, whereas higher-order stratospheric structures and tropospheric variability provide additional predictive information at subseasonal leads. Including these additional states increases the all-winter 30-day prediction correlation from 0.24 to 0.33 and reduces forecast error. These contributions exhibit strong lead-time dependence and vary between winters with and without sudden stratospheric warmings. Part of this predictive information overlaps with that contained in preceding 100 hPa eddy heat flux, linking state-resolved predictability to wave-related circulation variability. Our results unify SPV predictability within a multimodal, state-resolved framework and provide a physically interpretable pathway.

physics.ao-ph↗

arXiv2026

MLLMs Fail to Refuse when Using Tools Agentically

Agentic multimodal large language models (MLLMs) have recently pushed the frontier of visual reasoning by calling tools such as zooming and tagging. Despite the recent strong success of agentic MLLMs, this work uncovers a critical safety failure in the tool-use paradigm: agentic tool-using MLLMs become less capable of refusing harmful requests. Our experiments confirm that, across three popular safety benchmarks, all the top open- and closed-weight MLLMs we test exhibit significantly lower safety in tool-using settings than in non-tool settings, with a relative refusal failure rate increase of up to 68.7%. Based on analysis of 100,000+ responses, including extended experiments, we also propose two possible reasons for this safety degradation.

cs.AI↗

arXiv2026

Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning algorithms have enabled AI/ML methods to impact applications spanning atomic-scale simulations, materials and catalyst discovery, transport and thermodynamics, separations, process systems engineering, and industrial operations. This article provides a perspective on recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model lifecycle management. Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints. These approaches not only improve robustness and reliability, but also enable meaningful human-AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them. As the field advances toward increasingly autonomous, adaptive, and sustainable systems, the thoughtful integration of AI/ML with first-principles understanding and domain expertise will be essential to realizing their full potential across both research and industrial practice.

cs.AI↗

arXiv2026

PerfReasoning: How Well Do LLMs Reason on Hardware Performance?

Performance modeling is central to hardware design and software optimization, yet constructing these models requires structured reasoning about computation, data reuse, storage, and movement. We introduce PerfReasoning, a benchmark that evaluates LLMs both as direct performance reasoners and as generators of analytical performance-model code. Given workload, architecture, and mapping specifications, models compare mappings and predict off-chip traffic and buffer requirements. The strongest closed-source models exceed 90% on reasoning-based Q&A, and the best open-weight model reaches 82.4%. However, model construction is substantially harder: while GPT-5.6 Sol exceeds 80% pass rate, all other model configurations average below 45% and vary markedly across runs. Task-specific RL raises a 4B model's mapping-reasoning accuracy by 15.7 points, whereas feedback-free multi-round self-revision prompting is not reliably effective. PerfReasoning exposes the gap between plausible architectural reasoning and reliable performance-model construction. We will publicly release the benchmark to support reproducible evaluation and track future progress.

cs.AI↗

arXiv2026

Unveiling the nature of collective charge excitations in a cuprate superconductor

Emergent symmetry breakings in condensed matter systems are often intimately linked to collective excitations. For example, the intertwined spin-charge stripe order in cuprate superconductors is associated with spin and charge excitations. While the collective behavior of spin excitations is well established, the nature of charge excitations remains to be understood. Here we present a high-resolution resonant inelastic x-ray scattering (RIXS) study of charge excitations in the stripe-ordered cuprate La$_{1.675}$Eu$_{0.2}$Sr$_{0.125}$CuO$_4$. The RIXS spectra consist of both charge and phonon excitations around the charge ordering wave vector. By modeling the momentum-dependent phonon intensity, the charge-excitation spectral weight is extracted over a wide energy range. As such, we reveal the highly dispersive nature of the charge excitations, with an energy scale comparable to the spin excitations. Since charge order and superconductivity in cuprates are possibly driven by the same electronic correlations, determining the interaction strength underlying charge order is essential to establishing a comprehensive microscopic model of high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2026

Pressure-induced unconventional charge-density-wave states in kagome metal AV3Sb5 (A = K, Rb, Cs)

Since the discovery of charge density wave (CDW) and superconductivity, kagome metal AV3Sb5 (A = K, Rb, Cs) provides a new platform for exploring novel many-body quantum phenomena. In CsV3Sb5, a stripe-like CDW with commensurate wave vector q = 3/8 was observed under moderate pressures, which leads to a peculiar superconducting double-dome behavior in pressure-dependent phase diagram. Previous density functional theory (DFT) calculations indicate that the pressure-induced stripe-like CDW is beyond conventional phonon softening scenario, suggesting a nontrivial role of electronic correlations. However, an in-depth understanding for the pressure-induced unconventional CDW remains elusive. Here, we performed pressure-dependent 51V nuclear magnetic resonance (NMR) measurements on KV3Sb5 and RbV3Sb5. Although the superconducting double-dome behavior is absent in pressurized KV3Sb5 and RbV3Sb5, a pressure-induced CDW phase, ascribed to a possible incommensurate triple-Q CDW, is identified by NMR spectra in both materials, indicating that the pressure-induced unconventional CDW beyond DFT calculations is a common feature for kagome metal AV3Sb5. In contrast to the stripe-like CDW, the pressure-induced incommensurate triple-Q CDW does not strongly suppress the superconducting temperature (Tc) but coincide with an almost plateau behavior at intermediate pressure regime in the pressure-dependent superconducting phase diagram. Furthermore, by systematically analyzing the Korringa relation between Knight shift and nuclear spin-lattice relaxation rate in AV3Sb5, van Hove singularities (vHSs) driven electronic fluctuations are revealed as an effective knob for the pressure-induced unconventional CDW. Finally, our present findings underscore the pressure-induced unconventional CDW as a novel correlated quantum state in kagome metal AV3Sb5.

cond-mat.supr-con↗

arXiv2026

AI Security Leaderboard: Methodology, Results and Minimal Standard

The AI Security Leaderboard is an independent benchmark that ranks the safeguards of frontier AI models from least to most secure. It tests models against the FAR$.$AI Minimal Standard for Safeguards, which represents a minimum bar for security: meeting it does not guarantee a secure model, but failing to meet it guarantees a lack of state-of-the-art security. Version 1.0 covers severe misuse requests across chemical, biological, radiological, nuclear, and explosive (CBRNE) threats and offensive cybersecurity. In this report, we tested four leading models for universal jailbreaks in the context of this minimal standard, and found more than a hundredfold difference in security. Claude Fable 5 and GPT-5.6 Sol held against every attack we ran, with no universal jailbreak found; we estimate they would likely cost more than \$14,200 to jailbreak, if it is possible with this methodology at all. Meanwhile, we found hundreds of universal jailbreaks for Grok 4.5 and Gemini 3.1 Pro; each broke for under \$300, with universal jailbreaks in Grok's weakest domain, cybersecurity, accessible for as little as \$24. The gap is fixable: every weakness we found belongs to a known class of attack that already has a defense deployed in production models. The leaderboard will be updated on a rolling basis as new models are released, and the evaluation methodology and Minimal Standard will be periodically revised to take into account the latest capabilities and the state-of-the-art in safeguards. The leaderboard is available at leaderboard.far.ai.

cs.CR↗

arXiv2026

Spectral condensation in a finite nonequilibrium atmospheric transition

Order parameters are difficult to define in high-dimensional nonequilibrium systems that lack a Hamiltonian, a thermodynamic limit or an observed control coordinate. Here we show that such transitions can be diagnosed from the spectrum of occupations over data-derived eigen-microstates. We combine Eigen Microstate Theory with a Marchenko--Pastur random-matrix baseline to isolate an emergent sector, whose entropy quantifies competition among statistically significant collective states. As a finite atmospheric realization, we analyse 51 sudden stratospheric warmings in ERA5. The event-aligned ensemble undergoes spectral condensation, decondensation and recondensation: a polar-vortex state dominated by a few eigen-microstates gives way to a high-entropy regime of competing emergent states before selecting a reorganized weak-vortex state. A stochastic wave--mean-flow model, in which upward wave-activity flux provides a reduced control coordinate, reproduces the same entropy maximum, collapse and top-down timing. These results identify emergent-sector entropy as an order-parameter-like, state-based spectral diagnostic for non-Hamiltonian transitions and place polar-vortex breakdown within a broader class of finite nonequilibrium phase reorganizations.

physics.ao-ph↗

arXiv2026

UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors

Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications. To address the challenge posed by varied DRFs, several preliminary studies focus on the task of universal PET image denoising, aiming to train a universal model over low-dose data across DRFs. Nonetheless, these vanilla universal models often struggle with misaligned styles present in different DRF data, leading to the \textit{style elimination issue} with a significant over-smoothing effect. To deal with this issue, we innovatively introduce domain generalization to PET image denoising and propose a universal PET image denoising network (UniPET) to achieve high-quality PET image denoising across diverse DRFs. UniPET comprises two primary innovations: a style alignment network (SAN) and a region-aware learning strategy (RALS). Specifically, SAN utilizes style alignment techniques derived from domain generalization to align and recover styles across different DRFs, ensuring the model's generalizability across various DRFs while effectively preserving styles. Furthermore, to enhance style recovery, RALS distinguishes between flat and stylized regions, exclusively conducting adversarial learning on the latter, thereby more effectively guiding the model's focus towards learning stylized regions. It is demonstrated that our proposed UniPET can adaptively recover different DRF styles and achieve high-quality PET image denoising across DRFs. Comprehensive experiments show that UniPET exhibits comparable performance to individual DRF-specific models at specific DRFs and realizes state-of-the-art performance in universal PET image denoising quantitatively, perceptually, and clinically.

cs.CV↗

arXiv2026

Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference

Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert communication and routing-induced load imbalance. Existing placement methods reduce this cost by co-locating frequently co-activated experts; however, they derive a single deployment plan from globally aggregated routing traces, thereby averaging away the heterogeneous, task-specific co-activation patterns that actually drive communication in multi-task serving. We observe that expert co-activation is strongly task-conditioned: pairs tightly coupled in one task family are often uncorrelated in another, so effective deployment should group experts by task-aware co-activation rather than by a task-agnostic average. Based on this insight, we propose \emph{Task-Aware Coactivation Grouping} (TACG), a deployment-time framework that uses family-specific dispatch and co-activation traces to derive per-expert task-family preferences, reweights the co-activation graph so that intra-family locality dominates grouping, and assigns each expert to a primary GPU under exact capacity constraints. To keep the static placement robust under online workload skew, we further introduce \emph{Generic Expert Shared Replication} (GESR), a lightweight companion that identifies generic experts with consistently central co-activation profiles, replicates them across a small set of secondary GPUs, and applies locality- and load-aware selection at serving time. Experiments on three representative open-source MoE models demonstrate that our framework reduces the average communication cost by 31.39\% over the baseline, while preserving an average Jain fairness index of 0.9975. This advantage persists even under severe distribution shifts in the inference data, consistently outperforming strong baselines.

cs.LG↗

arXiv2026

Lagged sea-surface-temperature precursors of the leading PM2.5 mode in China

Fine particulate matter(PM2.5) pollution in China is strongly modulated bymeteorological variability, yet its seasonal predictability from oceanic signals remains unclear. Here we identify the leading PM2.5 variability mode over China and show that it is preceded by coherent sea-surface-temperature anomaly clusters by more than one season. These oceanic precursors influence summer PM2.5 mainly by altering precipitation and lowlevel ventilation, and winter PM2.5 by modulating boundary-layer height and near-surface stagnation. Using the four largest precursor regions, a simple regression model achieves significant independent prediction skill for both summer and winter PM2.5 variability. Our results reveal a physical pathway linking sea-surface-temperature memory to regional aerosol pollution and provide a basis for seasonal air-quality risk assessment.

physics.ao-ph↗

arXiv2026

Constraining the Higgs potential using multi-Higgs production

The Higgs self-couplings remain only weakly constrained by current Large Hadron Collider (LHC) measurements, leaving ample room for physics beyond the Standard Model that could modify the structure of the Higgs potential. Multi-Higgs production processes provide a particularly sensitive probe of deviations in both the Higgs trilinear and quartic self-couplings. In this note, we summarize the current status of next-to-leading-order electroweak (EW) corrections to double-Higgs production computed within the Standard Model Effective Field Theory and Higgs Effective Field Theory frameworks, emphasizing how these calculations introduce sensitivity to the Higgs self-couplings beyond what is accessible at leading order. We discuss the key conceptual and technical differences between the two effective field theory approaches, including their treatment of higher-dimensional operators, renormalization procedures, and the structure of EW~two-loop amplitudes. Despite these differences, both approaches yield broadly consistent constraints, illustrating the complementarity of double- and triple-Higgs measurements. With the high-luminosity LHC and future high-energy colliders on the horizon, these developments and further advances provide an essential foundation for extracting increasingly precise information on the dynamics of EW symmetry breaking.

hep-ph↗

arXiv2026

Intelligence Delivery Network: Toward an Internet Architecture for the AI Age

The rapid emergence of AI-powered applications is reshaping the role of the Internet. Users increasingly rely on the network to obtain intelligence services derived from large foundation models, rather than merely to reach remote endpoints or retrieve specific content. Today's dominant deployment paradigm for AI services remains cloud-centric, where user requests are transmitted to remote data centers for centralized inference. Although operationally convenient, this paradigm suffers from latency and jitter, heavy wide-area traffic, limited utilization of distributed heterogeneous compute resources, and growing privacy and governance concerns. In this paper, we propose the Intelligence Delivery Network (IDN), an Internet architecture that treats AI capabilities as deliverable network services. The key idea is to position, select, reuse, and verify intelligence across cloud, regional, edge, and local environments according to demand locality, resource availability, and policy constraints. We present the system assumptions of IDN, define its core architectural mechanisms, and discuss how capability abstraction, compute resource integration, demand-driven deployment, service routing, state-aware caching, and trust management can jointly support distributed AI services. We believe that IDN provides a practical path toward an Internet architecture for the AI age, making AI capabilities more accessible, efficient, trustworthy, and responsive to diverse application needs.

cs.NI↗

arXiv2026

Stochastic resonance in higher-order networks driven by colored noise

We investigate stochastic resonance (SR) in an ensemble of coupled overdamped bistable oscillators driven by colored noise. The networks incorporate the weighted contributions of both pairwise coupling and 2-simplex coupling. Our findings show that these higher-order interactions further exacerbate the suppression effect of colored noise on SR, reducing the peak of resonance curves and shifting the optimal noise intensity toward higher values. To clarify the underlying mechanism, we establish a close connection between SR and the four-stage variation in network synchronization level. Specially, the synchronization extremes explain the effect of higher-order coupling and colored noise on SR. Our analysis reveals that higher-order interactions do not reverse, but primarily promote the spatial propagation of suppression effects due to colored noise.

cond-mat.stat-mech↗

arXiv2026

ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefense

Large language models (LLMs) are increasingly being deployed as software engineering agents that autonomously contribute to repositories. A major benefit these agents present is their ability to find and patch security vulnerabilities in the codebases they oversee. To estimate the capability of agents in this domain, we introduce ZeroDayBench, a benchmark where LLM agents find and patch 22 novel critical vulnerabilities in open-source codebases. We focus our efforts on three popular frontier agentic LLMs: GPT-5.2, Claude Sonnet 4.5, and Grok 4.1. We find that frontier LLMs are not yet capable of autonomously solving our tasks and observe some behavioral patterns that suggest how these models can be improved in the domain of proactive cyberdefense.

cs.CR↗

arXiv2026

WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference

The growing computational demands of large language models (LLMs) make efficient inference and activation strategies increasingly critical. While recent approaches, such as Mixture-of-Experts (MoE), leverage selective activation but require specialized training, training-free sparse activation methods offer broader applicability and superior resource efficiency through their plug-and-play design. However, many existing methods rely solely on hidden state magnitudes to determine activation, resulting in high approximation errors and suboptimal inference accuracy. To address these limitations, we propose WINA (Weight Informed Neuron Activation), a novel, simple, and training-free sparse activation framework that jointly considers hidden state magnitudes and the column-wise $\ell_2$-norms of weight matrices. We show that this leads to a sparsification strategy that obtains optimal approximation error bounds with theoretical guarantees tighter than existing techniques. Empirically, WINA also outperforms state-of-the-art methods (e.g., TEAL) by up to $2.94\%$ in average performance at the same sparsity levels, across a diverse set of LLM architectures and datasets. These results position WINA as a new performance frontier for training-free sparse activation in LLM inference, advancing training-free sparse activation methods and setting a robust baseline for efficient inference. The source code is available at https://github.com/microsoft/wina.

cs.LG↗

arXiv2026

StableQAT: Stable Quantization-Aware Training at Ultra-Low Bitwidths

Quantization-aware training (QAT) is essential for deploying large models under strict memory and latency constraints, yet achieving stable and robust optimization at ultra-low bitwidths remains challenging. Common approaches based on the straight-through estimator (STE) or soft quantizers often suffer from gradient mismatch, instability, or high computational overhead. As such, we propose StableQAT, a unified and efficient QAT framework that stabilizes training in ultra low-bit settings via a novel, lightweight, and theoretically grounded surrogate for backpropagation derived from a discrete Fourier analysis of the rounding operator. StableQAT strictly generalizes STE as the latter arises as a special case of our more expressive surrogate family, yielding smooth, bounded, and inexpensive gradients that improve QAT training performance and stability across various hyperparameter choices. In experiments, StableQAT exhibits stable and efficient QAT at 2-4 bit regimes, demonstrating improved training stability, robustness, and superior performance with negligible training overhead against standard QAT techniques. Our code is available at https://github.com/microsoft/StableQAT.

cs.LG↗

arXiv2026

Exotic vortex states at high magnetic fields in a quasi-two-dimensional FeSe-based superconductor

Owing to strong electronic correlations, high-temperature superconductivity always exhibits intricate intertwinement with various competing electronic orders in phase diagrams, such as spin/charge density waves (S/CDWs). In cuprate superconductors,the intertwinement of superconductivity and CDW order could strongly affect the fundamental properties of superconductivity, such as the critical temperature(Tc) and critical magnetic field(Hc). Recent high-field transport measurements indicate that when quantum fluctuations become important at low temperatures and high magnetic fields, the CDW order also reshapes the vortex states, which leads to fragile superconductivity with extremely low critical current(Jc). Here, by performing comprehensive high-field transport measurements, the H-T phase diagram of vortex states is mapped to H = 33 T in a quasi-two-dimensional FeSe-based superconductor (TBA+)xFeSe with a zero-resistivity transition temperature above 40 K. Our results indicate that (TBA+)xFeSe is an extremely type II superconductor with significant thermal fluctuations.At low temperatures, high magnetic fields cause the vortex solid state to exhibit similar current-dependent zero-resistance behavior as the fragile superconductivity in cuprate superconductors with CDW order. When the vortex solid state is melted with increasing temperature, a superconducting regime with vortex-like phase fluctuations emerges as an intermediate state, which features finite longitudinal resistance and vanishing Hall resistance. At higher temperatures, a vortex liquid state with finite Hall resistance eventually appears due to thermal fluctuations. All these observations suggest exotic vortex states beyond the classical paradigm of vortex matter.

cond-mat.supr-con↗