The "knee" of cosmic ray spectra may reflect the maximum energy accelerated by galactic cosmic ray sources or the limit of the galaxy's ability to bind cosmic rays. Measurements of individual energy spectra are a crucial tool to understand the origin of the knee. Energy reconstruction and composition identification are foundations of the individual energy spectra measurements. One of the main scientific goals of Large High Altitude Air Shower Observatory (LHAASO) is measuring the cosmic ray energy spectra and composition from ~10 TeV to ~EeV. In this work, a novel method for reconstructing energy and logarithm mass (lnA) based on a superposition model is introduced. Energy and lnA are reconstructed using two universal, composition- and energy-independent calibration lines. For zenith angle below 40 degree, the energy and lnA biases are within +-5% and +-0.3, respectively, across all compositions. The method uses particle densities-measured by LHAASO's electromagnetic and muon detectors at a fixed distance from the shower axis-rather than integrated particle counts in annular bands. The density-based approach improves resolution for both energy and lnA, especially for heavy nuclei. The resulting energy resolution ranges from below 5% to ~15% above 1 PeV, the best mass resolution for iron achieved is below 25% above 10 PeV. The hadronic model dependencies of energy and lnA are also reported. These dependencies scale with lg(E/A) and are nearly independent of primary composition.

Teaching assistants (TAs) are essential to grading and feedback provision in proof-based courses, yet these tasks are time-intensive and difficult to scale. Although Large Language Models (LLMs) have been studied for grading and feedback, their effectiveness in proof-based courses is still unknown. Before designing LLM-based systems for this context, a necessary prerequisite is to understand whether LLMs can meaningfully assist TAs with grading and feedback. As such, we present a multi-part case study functioning as a technology probe in an undergraduate proof-based course. We compare rubric-based grading decisions made by an LLM and TAs with varying levels of expertise and examine TAs' perceptions of feedback generated by an LLM. We find substantial disagreement between LLMs and TAs on grading decisions but that LLM-generated feedback can still be useful to TAs for submissions with major errors. We conclude by discussing design implications for human-AI grading and feedback systems in proof-based courses.

The electrical conductivity of high-pressure silicates profoundly influences the interior dynamics of rocky planets. Employing the Kubo-Greenwood formalism, we perform ab initio calculations of electronic conductivity in Fe-bearing post-perovskite under super-Earth mantle conditions, up to 4000 K and 500 GPa. Electronic structures are obtained via many-body perturbation theory, incorporating dynamical screening and correlations among localized Fe-3d orbitals. In contrast to (Fe,Mg)O, for which metallization has been reported at comparable conditions, our results indicate that post-perovskite with Earth-like Fe contents is unlikely to metallize in super-Earth mantles via band-gap closure, yielding negligible low-frequency conductivity. Any substantial conductivity would require non-electronic mechanisms, such as thermally activated small-polaron hopping, which fall beyond the scope of band conduction.

For a Weyl group $W$ and a $W$-permutohedron $P$, there are associated toric varieties $X_P$ and $X_{P/W_K}$ for any parabolic subgroup $W_K$ of $W$, since the quotient $P/W_K$ can be identified with a polytope inside $P$. We construct an explicit algebra isomorphism between $H^*(X_{P/W_K};\mathbb{Q})$ and $H^*(X_P;\mathbb{Q})^{W_K}$. We further generalize this isomorphism to intermediate lattices, to finite Coxeter groups, and to non-degenerate $W$-symmetric polytopes. Our results give affirmative answers to two open questions of Horiguchi--Masuda--Shareshain--Song.

Hexagonal boron nitride (hBN) and graphene are similar in many ways - they are isoelectronic, have the same structure, are chemically inert and show persistence. All of these properties are indicators of a deeper connection that has, thus far, been overlooked. Unlike graphene, which has been shown to be aromatic, it is not known if hBN is aromatic. In this density functional theory-based work, we investigate the aromaticity (or lack thereof ) of hBN. By employing the magnetic criterion, supported by group theoretic and energetic considerations, we show that hexagonal boron nitride is indeed aromatic, even if weakly so, as compared to graphene. Since aromaticity is used to understand physical and chemical properties of planar compounds, the picture developed in this work is important to bridging the gap between the physical and chemical understanding of hBN's properties.

Proximal operators with affine constraints arise in numerous models in nonconvex projection, composite optimization, and structured regularization. However, their efficient computation remains challenging due to the simultaneous presence of affine constraints and nonsmooth, possibly nonconvex objectives. In this work, we develop a unified dual-representability framework for analyzing and computing affine-constrained proximal mappings. Specifically, we introduce a multiplier inclusion formulation that connects the primal affine-constrained proximal problem to an unconstrained convex dual problem. Based on this formulation, we prove that, whenever the associated dual inclusion problem admits a solution, strong duality holds. For convex functions and a broad class of prox-regular nonconvex functions, we establish that dual representability holds under a simple subdifferential sum rule, and further develop a hierarchy of verifiable regularity conditions that guarantee this sum rule. In addition, we analyze the smoothness and strong convexity properties of the dual objective, providing a rigorous foundation that guarantees fast local convergence rates for efficient first- and second-order methods. Numerical experiments demonstrate that the proposed dual reformulation enables the reliable computation of globally optimal solutions for a range of large-scale nonconvex proximal and projection problems using existing convex optimization solvers.

Recent advances in intelligent network control have primarily relied on task-specific Artificial Intelligence (AI) models deployed separately within the Radio Access Network (RAN) and Core Network (CN). While effective for isolated models, these suffer from limited generalization, fragmented decision-making across network domains, and significant maintenance overhead due to frequent retraining. To address these limitations, we propose a novel AI agent-based RAN-CN converged intelligence framework that leverages a Large Language Model (LLM) integrated with the Reasoning and Acting (ReAct) paradigm. The proposed framework enables the AI agent to iteratively reason over real-time, cross-domain state information stored in a centralized monitoring database and to synthesize adaptive control policies through a closed-loop thought-action-observation process. Unlike conventional Machine Learning (ML) based approaches, it does not rely on model retraining. Instead, the AI agent dynamically queries and interprets structured network data to generate context-aware control decisions, allowing for fast and flexible adaptation to changing network conditions. Experimental results demonstrate the enhanced generalization capability and superior adaptability of the proposed framework to previously unseen network scenarios, highlighting its potential as a unified control intelligence for next-generation networks.

In this paper we classify all topological vector spaces with linear topology with the property that all algebraic automorphisms are continuous. Moreover, we prove some properties of these spaces.

We propose a sparse interpolation construction and a practical coarsening algorithm for the algebraic multigrid (AMG) method, tailored towards H(curl). Building on the generalized AMG framework, we introduce an interior/exterior splitting that yields both a refinement-based and a fully algebraic construction of the interpolation. The refinement-based approach follows geometric hierarchy, while the purely algebraic interpolation is constructed through a coarsening process that first coarsens a nodal dual problem and then builds coarse and fine variables using a matching algorithm. We establish the weak approximation property and the commuting relation under certain assumptions. Combined with matching block smoothers, the proposed interpolation yields an effective algebraic multilevel method. Numerical experiments show robustness under strong coefficient jumps, where the proposed methods substantially outperform standard geometric multigrid.

The impact of magnetic geometry on zonal-flow generation in ion temperature gradient driven turbulence is investigated by means of linear and nonlinear gyrokinetic simulations. The modulation of geodesic curvature on various configurations has revealed amplification of the zonal-flow intensity in relatively smaller geodesic curvature. Based on these findings, a nonlinear proxy model for explorations of a novel magnetic geometry to activate the zonal-flow dynamics is proposed.

Hall effect is an important phenomenon when a magnetic field is applied to materials. From the curve depicting the Hall resistance versus the magnetic field, crucial information such as carrier concentration can be extracted. If the curve exhibits a linear dependence up to rather high magnetic fields, it indicates that charge transport involves only a single type of carrier, and if a non-linear curve is measured, then the double-carrier model should be considered for fitting. However, this model involves four unknown parameters, including the concentration and mobility of the two carriers, resulting in that such fitting is usually non-unique, which significantly reduces the reliability and accuracy. In this work, a double-carrier platform was constructed on a probable excitonic insulator Ta2Pd3Te5, and the four-parameter fitting based on the double-carrier model was simplified to a single-parameter fitting by employing methods such as analyzing the shape of the Hall resistance curve and generating gate-induced Shubnikov-de Haas oscillations. Thus, we provide a reliable method for double-carrier fitting of Hall resistance and a new evidence for the existence of excitonic-insulator state in Ta2Pd3Te5.

When two massive stars orbit each other, their winds create a shock cone. In some cases, an evolved, carbon-rich Wolf-Rayet (WR) star's wind collides with that of an orbiting OB star, condensing into dust downstream. This dust is then seen as large spiral structures that eventually move into the interstellar medium. Among these colliding wind binaries, the archetype system WR104 has become an enigma. Aperture masking interferometry with Keck revealed an evolving face-on dust spiral with multiple rungs of dust visible from years of observations. In contrast to direct imagery, recent spectroscopic results implied that the orbit must have an inclination quite different from the face-on geometry. We examined the ASAS and ASAS-SN photometry to put further constraints on the geometry of the orbit. Through a phase-binning of the light curve, we find that the recent g-band light curve is brightest at a time when the OB star is in front of the WR star in our line of sight, with the lowest flux happening at the opposite conjunction. We fit the light curve with an illustrative model for scattering eclipses, which then allows us to infer an inclination of the system of $(41.8^{+13.0}_{-14.9})^\circ$. This inclination agrees with the recent spectroscopic orbit and presents challenges to previous interpretations of high-angular resolution images of the dust plume. We provide a qualitative geometric model for the dust plume to reconcile these results and show how WR104 can provide a means to study the properties of WR dust in detail.

We present a microscopic, parameter-free approach for computing the photoluminescence spectra of a single semiconductor nanocrystal. The method derives exciton-phonon coupling directly from the semi-empirical pseudopotential framework and systematically incorporates both diagonal and off-diagonal interactions, expanded to second-order in the phonon modes. The dipole-dipole correlation function was calculated using a Dyson expansion within the Kubo-Toyozawa formalism, enabling a consistent description of the role of pure dephasing and population-transfer on the photoluminescence spectral features. Applied to CdSe/CdS core-shell nanocrystals, the approach quantitatively reproduces experimental photoluminescence spectra over a wide temperature range, revealing that quadratic phonon couplings account for nearly half of the homogeneous linewidth above 100-150 K, while off-diagonal couplings leading to exciton thermalization play only a minor role and only as T approaches 300K.

Atomistic simulations are widely used to investigate reactive processes but are often limited by the rare event problem due to kinetic bottlenecks. We recently introduced an enhanced sampling approach based on the committor function, machine-learned following a variational principle. This method combines a transition-state-oriented bias potential, expressed as a functional of the committor, with a metadynamics-like bias along a committor-based collective variable, enabling uniform exploration of reaction pathways. In its original formulation, the committor is represented by a neural network that takes physical descriptors as input and is trained by minimizing a functional involving gradients with respect to atomic coordinates, which can be computationally demanding in some cases. Here, we propose a simplified learning criterion formulated entirely in the descriptor space, which bypasses the need for explicit and costly coordinate gradients and provides a relaxed upper bound to the original variational principle. Although this approach does not formally target the exact committor, we show that it retains robust sampling performance while significantly reducing computational costs, thus enabling the study of processes that would be practically unfeasible using the original formulation.

Function contract generation is a classical problem in program analysis that targets the automated analysis of functions in a program with multiple procedures. The problem is fundamental in inter-procedural analysis where properties of functions are first obtained via the generation of function contracts and then the generated contracts are used as building blocks to analyze the whole program. Typical objectives in function contract generation include pre-/post-conditions and assigns information (that specifies the modification information over program variables and memory segments during function execution). In programs with array manipulations, a crucial point in function contract generation is the treatment of array segments that imposes challenges in inferring invariants and assigns information over such segments. To address this challenge, we propose a novel symbolic execution framework that carries invariants and assigns information over contiguous segments of arrays. We implement our framework as a prototype within LLVM, and further integrate our prototype with the ACSL assertion format and the Frama-C software verification platform. Experimental evaluation over a variety of benchmarks from the literature and functions from realistic libraries shows that our framework is capable of handling array manipulating functions that indeed involve the carry of array information and are beyond existing approaches.

As a first step towards a refined description of the asymptotic of the Weil-Petersson metric on the moduli space of polarized Calabi-Yau manifolds we investigate the concrete case of abelian varieties by linking such asymptotic with the multi-scale collapsing limits of the parametrized flat tori, as explicitly classified by Odaka.

Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing DL fuzzing techniques have made progress in detecting crashes, they inherently struggle to detect silent bugs due to the lack of effective test programs and corresponding oracles. Building on the observation that historical bug reports contain rich, underutilized information about silent bugs, we leverage large language models (LLMs) to perform versatile yet controlled bug transfer for silent bug fuzzing. Specifically, our approach uses LLMs to extract context-aware bug patterns from historical issues, match semantically related Application Programming Interfaces (APIs) using functionality-based embeddings, and synthesize test cases with customized oracles. This enables proactive detection of silent bugs by transferring high-risk contexts and oracle designs from known buggy APIs to functionally similar target APIs. To ensure the reliability of our context-aware bug transfer, we introduce an LLM-powered self-validation module that systematically evaluates the validity of each transferred bug instance. We implement this methodology in a tool named TransFuzz and evaluate it on three mainstream DL libraries: PyTorch, TensorFlow, and MindSpore. TransFuzz successfully discovers 79 previously unknown bugs (12 confirmed as Common Vulnerabilities and Exposures (CVEs)) in 10 bug types, demonstrating its effectiveness and generalizability in migrating DL library bug discovery capabilities.

We investigate the arrival time and the Faraday rotation of extragalactic electromagnetic signals from fast radio bursts (FRBs) propagating through chiral cosmic media within the framework of Maxwell-Carroll-Field-Jackiw (MCFJ) electrodynamics. By treating the interstellar medium as a cold, ionized chiral plasma, we derive the time delay between two traveling signals, expressing it in terms of modified dispersion measures (DMs) containing chiral contributions. The Faraday rotation angle is then written in terms of modified rotation measures (RMs). By combining the DMs and redshift data from a set of FRBs, we obtain constraints on the chiral parameter magnitude at the order of $10^{-26}$--$10^{-24}$ GeV. Using the Faraday rotation formulae and RM measurements, upper bounds as stringent as $10^{-43}$ GeV on the MCFJ parameters are also obtained.

As a characteristic property of all quantum systems, entanglement participates in many important quantum phenomena. In this proceeding, we employ it in the study of quantum field theories at finite density. We incorporate evaluations of entanglement entropy using the replica trick into MC simulations of $O(N)$ models at finite density with the worm algorithm and present some initial results for the nonlinear $O(4)$ model in 3 dimensions.

We present a supervised, probabilistic taxonomic classification of asteroid reflectance spectra from Gaia Data Release 3 (DR3). Using high-quality Gaia DR3 spectra and a reference set of spectra from the literature consisting exclusively of asteroids with robust spectroscopic taxonomic types, we construct a principal-component (PC) representation of the Gaia reflectances. For each major spectral complex (C, S, X) and several end-member classes (B, D, A, L, K, V), we model the distribution of reference objects in PC space using multivariate kernel density estimation (KDE). This yields likelihoods for each spectral class and provides a quantitative measure of classification confidence. Validation against a sample of objects with known spectral classes demonstrates good performance for classes with distinctive reflectance signatures, including the S-complex, D, V, and A types. Spectrally continuous classes (B-C-complex, K-L-S-complex, and X-complex) show the expected degrees of mixing given the limited wavelength range of Gaia's spectrophotometry. We further explore the compositional structure of six major asteroid collisional families using our Gaia-derived spectral classes, finding excellent agreement with ground-based spectroscopy and revealing enhanced detections of olivine-rich A type material in the Flora and Eunomia families, as well as new insights into the spectral diversity of the Tirela family. The resulting catalogue constitutes a fully probabilistic taxonomic classification for the full Gaia DR3 asteroid sample. It offers a resource for studying the compositional structure of the main belt, identifying family interlopers, and linking asteroid populations to meteorite groups, and establishes a methodological framework for future Gaia releases, in particular for the validation of the Gaia DR4, expected by the end of 2026.

This paper is about the Mackey analogy between the tempered representation theory of a real reductive group and that of its Cartan motion group. We consider the embedding of reduced C*-algebras constructed recently in connection with the Mackey bijection, and study its behavior on certain natural stratifications of the tempered duals. We formulate our result using a notion of stratified equivalence inspired by the study of the smooth dual of $p$-adic groups via the structure of Hecke algebras, in particular by the work of Aubert, Baum, Plymen and Solleveld. We derive related new topological properties of the Mackey bijection. We also analyze the behavior of the Mackey embedding on a stratification of reduced C*-algebras attached to a partition of the tempered dual into particularly elementary pieces, introduced in recent work of Bradd, Higson and Yuncken.

This study experimentally, numerically, and theoretically investigates the cavity/bubble dynamics and radiated acoustics during the water entry of a centimeter-scale cylindrical projectile with a conical nose. Experiments were conducted in a laboratory tank, employing synchronized high-speed imaging and hydrophone measurements to characterize the cavity closure modes and their resultant acoustic signatures across a range of Froude numbers. The acoustic signal features a weak radiated signal upon impact, followed by significant pressure oscillations spanning more than 20 cycles in the flow field after cavity elongation and pinch-off. A numerical model based on the Finite Volume Method (FVM) successfully captures these physical processes. Subsequently, a semi-theoretical model that incorporates the projectile's boundary effect is developed from potential flow theory. The model not only yields a dominant cavity oscillation frequency that agrees well with experimental data, but also reveals that the boundary effect leads to a cavity oscillation frequency markedly higher than the Minnaert frequency of an equivalent-volume ellipsoidal bubble containing an internal rigid core. The dominant cavity frequency falls nearly linearly with Fr, governed by nose geometry and projectile inertia. This study clarifies the underlying physics connecting cavity dynamics during water entry to underwater acoustic radiation.

We study boundary conditions for elliptic operators on non-compact manifolds with boundary via uniform K-homology, a version of K-homology sensitive to the large-scale geometry of the manifold. To that end, we develop the theory of relative uniform K-homology. We show that boundary conditions for uniformly elliptic differential operators define classes in the relative and non-relative uniform K-homology of the manifold, depending on the assumed regularity of the boundary condition. Moreover, we define and study a relative index map on relative uniform K-homology that combines uniform coarse information on the interior with secondary information on the boundary. As an application, we compute that on a spin manifold with product structure and uniformly positive scalar curvature on the boundary the image of the relative uniform K-homology class of the Dirac operator under this relative index map is closely connected to a uniform version of the higher $ρ$-invariant of the boundary. In particular, a delocalized APS-index theorem of Piazza and Schick is proved in the uniform setting.

Finding intermediate-mass black holes (IMBHs) and measuring their masses and spins are key to understanding massive black hole formation. White dwarf (WD)-IMBH binaries provide a unique probe because they emit both electromagnetic radiation and gravitational waves (GWs), thereby conveying richer information. However, such multi-messenger sources often enter the regime of strong gravity, where existing models fail to capture their relativistic dynamics. Here, we develop a fully relativistic model for the tidal response of a WD close to an IMBH and use it to study the secular orbital evolution as well as the GW signal. We find that for IMBHs more massive than 10^5 solar masses, tidal interaction becomes relativistic and sensitive to IMBH spin. The interaction generally dissipates binary orbital energy and angular momentum, but due to relativistic frame rotation, which reduces phase coherence across pericenter passages, the orbit-averaged tidal dissipation rate can be suppressed by up to about 50% relative to Newtonian predictions. Including tidal dissipation leads to more rapid damping of the orbital eccentricity, to the extent that the pericenter distance may even increase over time, potentially explaining quasi-periodic eruptions and secular orbital period growth. Such tidal effects accumulate into measurable phase and amplitude deviations in the GW signal. For typical space-based observations, the GW waveform mismatch can reach values of order 0.1 within 6 months. Our results indicate that relativistic tidal dissipation is both dynamically important and observationally essential for reliably predicting the multi-messenger signals of WD-IMBH systems.

Macroscopic quantum electrodynamics (MQED) provides a unified framework to describe quantum electromagnetic fields in the presence of arbitrary macroscopic environments. Central to this theory is the field correlation, which governs both radiative (e.g., Lamb shifts and the Purcell effect) and mechanical phenomena, such as van der Waals and Casimir forces. In this tutorial, we provide an overview of MQED and its extension to active media, highlighting fluctuation-induced forces as manifestations of gain-modified field correlations.

Since 2016, the term "misinformation" has become associated with a scientific paradigm that studies, at its core, people making, reading, and sharing false statements, usually on social media, and often warning of the harm to society resulting from the sum of many such events. By tracking the term through the academic literature, with special focus on the years 2011--2023, we connect the post-2016 paradigm with a strand of research dating to the Satanic panic of the 1980s. We argue that post-2016 misinformation research owes more to this intellectual lineage than is generally acknowledged, and we discuss the theoretical and practical implications of this connection. We conclude by drawing parallels between the Satanic panic and 2026, and, similarly, between misinformation research then and now.

We numerically investigate quantum circuit elementary-gate level instantiations of the standard Quantum Phase Estimation (QPE) algorithm for the task of computing the ground-state energy of a quantum magnet; the disordered fully-connected quantum Heisenberg spin glass model. We consider (classical simulations of) QPE circuit computations on relatively small quantum Hamiltonians ($3$ qubits) with up to $10$ phase bits of precision, using up to Trotter order $10$. We systematically study the inputs of QPE, specifically time evolution, Trotter order, Trotter steps, and initial state, and illustrate how these inputs practically determine how QPE operates. From this we outline a coherent set of quantum algorithm input and tuning guidelines. One of the notable properties we characterize is that QPE sampling of the optimal digitized phase converges to a fixed rate. This results in strong diminishing returns of optimal phase sampling rates which can occur when the Trotter error is surprisingly high.

Identifying the boundary between classical and quantum computation is a central challenge in quantum information. In multi-qubit systems, entanglement and magic are the key resources underlying genuinely quantum behaviour. While entanglement is well understood, magic - essential for universal quantum computation - remains relatively poorly characterised. Here we show that determining membership in the stabilizer polytope, which defines the free states of magic-state resource theory, requires super-exponential time $\class{exp} ( n^2)$ in the number of qubits $n$, even approximately. We reduce the problem to solving a $3$-\class{SAT} instance on $n^2$ variables and, by invoking the exponential time hypothesis, the result follows. As a consequence, both quantifying and certifying magic are fundamentally intractable: any magic monotone for general states must be super-exponentially hard to compute, and deciding whether an operator is a valid magic witness is equally difficult. As a corollary, we establish the robustness of magic as computationally optimal among monotones. This barrier extends even to classically simulable regimes: deciding whether a state lies in the convex hull of states generated by a logarithmic number of non-Clifford gates is also super-exponentially hard. Together, these results reveal intrinsic computational limits on assessing classical simulability, distilling pathological magic states, and ultimately probing and exploiting magic as a quantum resource.

As the complexity of the HPC storage stack rapidly grows, domain scientists face increasing challenges in effectively utilizing HPC storage systems to achieve their desired I/O performance. To identify and address I/O issues, scientists largely rely on I/O experts to analyze their I/O traces and provide insights into potential problems. However, with a limited number of I/O experts and the growing demand for data-intensive applications, inaccessibility has become a major bottleneck, hindering scientists from maximizing their productivity. Rapid advances in LLMs make it possible to build an automated tool that brings trustworthy I/O performance diagnosis to domain scientists. However, key challenges remain, such as the inability to handle long context windows, a lack of accurate domain knowledge about HPC I/O, and the generation of hallucinations during complex interactions. In this work, we propose IOAgent as a systematic effort to address these challenges. IOAgent integrates a module-based pre-processor, a RAG-based domain knowledge integrator, and a tree-based merger to accurately diagnose I/O issues from a given Darshan trace file. Similar to an I/O expert, IOAgent provides detailed justifications and references for its diagnoses and offers an interactive interface for scientists to ask targeted follow-up questions. To evaluate IOAgent, we collected a diverse set of labeled job traces and released the first open diagnosis test suite, TraceBench. Using this test suite, we conducted extensive evaluations, demonstrating that IOAgent matches or outperforms state-of-the-art I/O diagnosis tools with accurate and useful diagnosis results. We also show that IOAgent is not tied to specific LLMs, performing similarly well with both proprietary and open-source LLMs. We believe IOAgent has the potential to become a powerful tool for scientists navigating complex HPC I/O subsystems in the future.

Heterogeneous nodes that combine multi-core CPUs with diverse accelerators are rapidly becoming the norm in both high-performance computing (HPC) and AI infrastructures. Exploiting these platforms, however, requires orchestrating several low-level accelerator APIs such as CUDA, SYCL, and Triton. In some occasions they can be combined with optimized vendor math libraries: e.g., cuBLAS and oneAPI. Each API or library introduces its own abstractions, execution semantics, and synchronization mechanisms. Combining them within a single application is therefore error-prone and labor-intensive. We propose reusing a task-based data-flow methodology together with Task-Aware APIs (TA-libs) to overcome these limitations and facilitate the seamless integration of multiple accelerator programming models, while still leveraging the best-in-class kernels offered by each API. Applications are expressed as a directed acyclic graph (DAG) of host tasks and device kernels managed by an OpenMP/OmpSs-2 runtime. We introduce Task-Aware SYCL (TASYCL) and leverage Task-Aware CUDA (TACUDA), which elevate individual accelerator invocations to first-class tasks. When multiple native runtimes coexist on the same multi-core CPU, they contend for threads, leading to oversubscription and performance variability. To address this, we unify their thread management under the nOS-V tasking and threading library, to which we contribute a new port of the PoCL (Portable OpenCL) runtime. These results demonstrate that task-aware libraries, coupled with the nOS-V library, enable a single application to harness multiple accelerator programming models transparently and efficiently. The proposed methodology is immediately applicable to current heterogeneous nodes and is readily extensible to future systems that integrate even richer combinations of CPUs, GPUs, FPGAs, and AI accelerators.