The rapid advancement of deep learning is reshaping the hardware design landscape toward AI tasks, posing fundamental challenges for HPC workloads such as atomistic simulation. Here we present SMC-AI, a general algorithmic framework that extends the SMC-X method for efficient canonical Monte Carlo simulation on AI accelerators, including GPUs and NPUs, while maintaining extreme scalability. The implementation of SMC-AI on an NPU cluster reaches unprecedented performance, achieving MC simulation of 4 trillion atoms on 4096 NPU dies. This represents the largest ML-accelerated atomistic simulation reported, delivering 32X system size and 1.3X throughput than previous records, with a relatively small computational budget. Excellent strong and weak scaling efficiency are reached for both the NPU and GPU implementation. By decoupling ML models from simulation, SMC-AI creates an abstraction that facilitates integration and porting of diverse ML models, laying a foundation for the future development of scalable scientific software.

Active colloidal particles provide versatile model systems for exploring non-equilibrium physics in motile matter. To date, most experimental realizations have focused on spherical particles, largely due to fabrication constraints. However, theoretical and computational studies have long predicted that particle anisotropy and flexibility can dramatically enrich single-particle dynamics, interparticle interactions, and emergent collective behavior. Here, we highlight recent advances in the fabrication of anisotropic active particles and architectures enabled by the unprecedented design freedom of 3D microprinting. We discuss how additive manufacturing is expanding the accessible parameter space of active soft matter, allowing precise control over shape, location of active forces, and functionality at the microscale. These developments establish new model platforms for uncovering fundamental principles of active and soft matter, and simultaneously pave the way toward microrobotic systems with programmable dynamics and emergent functionalities.

It is known that the external irradiation of protoplanetary disks by nearby massive stars can result in mass loss that impacts the disk evolution, however the dynamical impact of external irradiation upon the disk itself has not been explored in detail. We aim to investigate the dynamical effect of asymmetric external irradiation on the structure of such disks. We perform two-dimensional multi-fluid radiation hydrodynamical simulations of protoplanetary disks subject to external irradiation using the PLUTO code, with external irradiation modeled as a plane-parallel flux and a simplified nonaxisymmetric heating rate corresponding to the thermal reemission from hot material within the region marginally optically thick to the external irradiation. We find that a nearby massive star can, under certain conditions, induce significant dynamical effects on a protoplanetary disk, including a shadowed region, pronounced spiral arms in gas, and rings and gaps in dust. The dynamics are caused by the temperature asymmetry driven and maintained by external irradiation, akin to the well-established mechanism of shadow-induced spirals and rings in disk with shadowing from their inner regions. Our results show that if an external temperature asymmetry can be induced it can have a significant dynamical impact on the disk itself (in addition to the well-studied mass loss and truncation effects due to external irradiation), possibly even driving substructure. This prompts further investigation with detailed, dynamical radiative transfer models.

The Gottesman-Kitaev-Preskill (GKP) code is a promising bosonic candidate for realizing fault-tolerant quantum computation. Among existing error-correction protocols for GKP code, the Steane-type scheme is a canonical and widely adopted paradigm, yet its intrinsic noise propagation pattern limits further performance improvement. In this work, we propose a preprocessing-based Steane-type (P-Steane) scheme, which introduces a tunable preprocessing stage with squeezing parameters $a$ and $b$ to actively reshape noise propagation, thereby constituting a parameter framework. This framework spans a spectrum of protocols beyond existing methods, reproducing the performance of both the ME-Steane scheme ($a=1$, $b=1$) and the teleportation-based scheme ($a=1/\sqrt{2}$, $b=\sqrt{2}$) as special cases. Crucially, in the small-noise regime and when the data qubit is noisier than the ancilla qubits, P-Steane scheme achieves the minimum product of position- and momentum-quadrature output noise variances when $2a = b$, and consistently outperforms the ME-Steane scheme within a specific squeezing-parameter range under this condition.

Network slicing is a modern 5G technology that provides efficient network experience for diverse use cases. It is a technique for partitioning a single physical network infrastructure into multiple virtual networks, called slices, each equipped for specific services and requirements. In this work, we particularly deal with radio access network (RAN) slicing and resource allocation to RAN slices. In 5G, physical resource blocks (PRBs) being the fundamental units of radio resources, our main focus is to allocate PRBs to the slices efficiently. While addressing a spectrum of needs for multiple services or the same services with multi-priorities, we need to ensure two vital system properties: i) fairness to every service type (i.e., providing the required resources and a desired range of throughput) even after prioritizing a particular service type, and ii) PRB-optimality or minimizing the unused PRBs in slices. These serve as the core performance evaluation metrics for PRB-allocation in our work. We adopt the 3-layered hierarchical PRB-partitioning technique for allocating PRBs to network slices. The case-specific, AI-based solution of the state-of-the-art method lacks sufficient correctness to ensure consistent system performance. To achieve guaranteed correctness and completeness, we leverage formal methods and propose the first approach for a fair and optimal PRB distribution to RAN slices. We formally model the PRB-allocation problem as a 3-layered framework, FORSLICE, specifically by employing satisfiability modulo theories. Next, we apply formal verification to ensure that the desired system properties: fairness and PRB-optimality, are satisfied by the model. The proposed method offers an efficient, versatile and automated approach compatible with all 3-layered hierarchical network structure configurations, yielding significant system property improvements compared to the baseline.

Quantum communication protocols require efficient detection schemes to maximize the information transfer rate between the sender and the receiver. To this aim, we have demonstrated that weak-field receivers, merging wave-like and particle-like features, can be considered as a valid alternative to already existing receivers, such as optical homodyne detection. To better emphasize the potential of our receiver, in this work we consider a proof of concept for quaternary communication based on coherent states with the same amplitude and different phase values. The encoding in phase requires a fine control of phase noise obtained through a feedback system. The results achieved in terms of mutual information and secret key generation rate encourage further increase of the alphabet towards an approximately continuous phase modulation.

This paper describes a multi-region control framework for floating offshore wind farms. Specifically, we propose a novel generator torque controller that regulates rotor speed in Region 2, corresponding to wind speeds between the cut-in and rated values. In Region 3 (wind speeds at or above rated but below cut-out speed) we employ a PI-LQR for collective blade pitch. Control blending across the transitional wind speeds (Region 2.5) employs a sigmoid weighting function applied to the control variables. Two modeling paradigms are proposed for farm-level power tracking with rotor speed regularization: a nonlinear model predictive controller (NL-MPC) with a dynamic wake model, and a reduced order model predictive controller based on linear parameter varying turbine models with a time delay representation of wake advection (LPVTD-MPC). These approaches are evaluated over three wind inlet conditions using the PJM ancillary service certification criteria for participation in a secondary frequency regulation market. Results show that both approaches achieve scores of at least 89.9\% for the three different testing scenarios, which are well above the qualification threshold of 75\%. However, the LPVTD-MPC approach solves the problem in under half the time versus NL-MPC but with slightly larger fluctuations in farm-level power output, highlighting the trade-off between performance and computational tractability. The control framework is among the first to address multi-region wind turbine dynamics together with market driven power tracking objectives for floating offshore wind farms. Such multi-region control becomes increasingly necessary in the floating turbine setting where large (region spanning) wind speed variations are common due to wave induced platform pitching.

We study the impact of multiple vector-like fermions in SU(5) grand unified theory (GUT). Threshold effects from extra fermions allow the observed gauge couplings to be consistently matched to a single unified gauge coupling, and typically raise the unification scale to $M_\mathrm{GUT}\simeq 10^{15.5}\,\mathrm{GeV}$. Because the Standard Model fermions arise as admixtures of several GUT multiplets, the nucleon decay operator coefficients are further suppressed, leading to longer proton lifetimes than in conventional GUTs. We also find that the admixture of multiple GUT multiplets relaxes the rigid Yukawa relations of conventional GUTs and alleviates the bottom-tau unification problem. Overall, our analysis demonstrates that multi-fermion SU(5) GUTs provide a testable framework that simultaneously reconciles gauge coupling unification, realistic flavor structures, and proton stability. Our results highlight the importance of probing multiple proton-decay channels in next-generation experiments such as Hyper-Kamiokande to critically test this scenario.

Decentralized stochastic optimization has emerged as a fundamental paradigm for large-scale machine learning. However, practical implementations often rely on biased gradient estimators arising from communication compression or inexact local oracles, which severely degrade convergence in the presence of data heterogeneity. To address the challenge, we propose Decentralized Momentum Tracking with Biased Gradients (Biased-DMT), a novel decentralized algorithm designed to operate reliably under biased gradient information. We establish a comprehensive convergence theory for Biased-DMT in nonconvex settings and show that it achieves linear speedup with respect to the number of agents. The theoretical analysis shows that Biased-DMT decouples the effects of network topology from data heterogeneity, enabling robust performance even in sparse communication networks. Notably, when the gradient oracle introduces only absolute bias, the proposed method eliminates the structural heterogeneity error and converges to the exact physical error floor. For the case of relative bias, we further characterize the convergence limit and show that the remaining error is an unavoidable physical consequence of locally injected noise. Extensive numerical experiments corroborate our theoretical analysis and demonstrate the practical effectiveness of Biased-DMT across a range of decentralized learning scenarios.

A single coloring channel is defined by a subset of letters it allows to pass through, while deleting all others. A sequence of coloring channels provides multiple views of the same transmitted letter sequence, forming a type of sequence-reconstruction problem useful for protein identification and information storage at the molecular level. We provide exact capacities of several sequences of coloring channels: uniform sunflowers, two arbitrary intersecting sets, and paths. We also show how this capacity depends solely on a related graph we define, called the pairs graph. Using this equivalence, we prove lower and upper bounds on the capacity, and a tailored bound for a coloring-channel sequence forming a cycle. In particular, for an alphabet of size $4$, these results give the exact capacity of all coloring-channel sequences except for a cycle of length $4$, for which we only provide bounds.

In the non-relativistic limit, helimagnetic order is always associated with odd-parity magnetism. That is, for single-particle states the expectation value of the electronic spin is odd in crystal momentum, which implies direct control of the spin by means of electric fields. However, the theoretical description of helimagnets is hindered by the fact that the spiral pitch may require large super cells or even be incommensurate with the lattice. In the this letter we show that such issues may be remedied by use of the Generalized Bloch theorem. It allows one to describe (by models or first principles) the system in terms of the primitive unit cell, from which all relevant properties can be obtained by downfolding in reciprocal space. We exemplify the procedure using MnI$_2$ and NiI$_2$, which are known type II multiferroics having spiral order and the helimagnetic metal MnTe$_2$. We analyze how the magnitude of spin splitting depends on orbital composition of bands, and we show that spin splitting is maximized for states having large odd-orbital ($p$-type) character. It is straightforward to generalize the framework to handle response functions for helimagnets using only the primitive unit cell and the present downfolding procedure thus strongly facilitate theoretical progress in the field.

We present a theory of the Josephson current in superconductor-normal metal-superconductor (SNS) junctions in the presence of generic spin-dependent fields, such as spin-orbit coupling (SOC), Zeeman fields, and altermagnetism. We consider systems with arbitrary disorder strength, going beyond the usual diffusive and ballistic approximations. Using the linearized quasiclassical Eilenberger equation, we derive a compact expression for the Josephson current, which is then applied to various situations of experimental interest. First, we investigate the evolution of the Josephson critical current in an applied magnetic field in the presence of Rashba and Dresselhaus SOC, and discuss how this dependence can be used to probe SOC in the junction. We then study the anomalous Josephson ($φ_0$) effect in systems with Rashba SOC and show that it remains robust over a wide range of disorder strength, and can even be enhanced by moderate disorder in sufficiently long junctions. Finally, we investigate the Josephson current in disordered junctions with altermagnets, and show how the $0$-$π$ transition in such systems is suppressed by disorder. Our results may be useful for describing experimental setups with high-mobility samples, which nevertheless always contain some amount of disorder, and where neither purely ballistic nor diffusive approximations are adequate.

The spectrum of the exotic three-proton (3p) emitter $^{17}$Na has been studied by detecting all in-flight decay products. Derived from the measured angular correlations $^{14}$O+p+p+p, a resonant peak has been discovered at the 3p-decay energy of 2.24($^{+0.17}_{-0.25}$) MeV, which likely corresponds to the $^{17}$Na ground state. This decay energy value is significantly smaller than the previous experimental upper limit. Our measured $^{14}$O-p correlations stemming from the ground state decay have been quantitatively described by a sequential 1p-2p emission from a $^{17}$Na resonance via the intermediate $^{16}$Ne ground state, which allowed to derive the upper limit of $^{17}$Na ground-state width of 0.6 MeV. A dramatic systematic decrease in the mirror energy differences of mirror nuclei pairs has been observed at almost all 3p emitters with known proton separation energy (such as $^{31}$K, $^{20}$Al, and $^{17}$Na), in sharp contrast to the behavior in less exotic nuclei. Such a lowering effect indicates a general trend in evolution of nuclear structure for light to medium mass nuclei beyond the proton drip line, which is often associated with strong isospin symmetry breaking.

This paper develops a family of fast, structure-preserving numerical algorithms for the nonlinear Maxwell-Ampere Nernst-Planck equations. For the first-order scheme, the Slotboom transformation rewrites the Nernst-Planck equation to enable positivity preservation. The backward Euler method and centered finite differences discretize the transformed system. Two correction strategies are introduced: one enforces Gauss's law via a displacement correction, and the other preserves Faraday's law through potential reconstruction. The fully discrete scheme exactly satisfies mass conservation, concentration positivity, energy dissipation, Gauss's law, and Faraday's law, with established error estimates. The second-order scheme adopts BDF2 time discretization while retaining the same structure-preserving strategies, exactly conserving mass, Gauss's law, and Faraday's law. Numerical experiments validate both schemes using analytical solutions, confirming convergence orders and positivity preservation. Simulations of ion transport with fixed charges demonstrate exact preservation of Gauss's and Faraday's laws over long-time evolution, reproducing electrostatic attraction, ion accumulation, and electric field screening. The results fully support the theoretical analysis and the schemes' stability and superior performance.

Achieving long-range ferrimagnetic order in purely organic systems remains a major challenge in molecular magnetism. Here we report the synthesis and characterization of heterospin-coupling motifs, formed by covalently linking spin-1/2 and spin-1 triangular nanographenes. A combined solution-phase and on-surface synthetic strategy yields three distinct compounds, whose structures are elucidated by bond-resolved scanning probe microscopy. Starting from a spin-1/2--spin-1 dimer as the elemental ferrimagnetic unit, we employ inelastic electron tunneling spectroscopy to resolve low-energy magnetic excitations and extract the parameters of the Heisenberg Hamiltonian. Extension to trimeric architectures results in two distinct spin configurations, with compensated ($S=0$) and uncompensated ($S=3/2$) ferrimagnetic ground states. The Heisenberg model accurately describes all magnetic transitions, offering direct insight into increasingly complex spin Hamiltonians. These findings establish a molecular platform for designing tunable heterospin systems with robust exchange interactions, opening routes toward multi-level spin encoding in qudit-based quantum technologies.

This study presents a rigorous comparative analysis of two label-free optical biosensing platforms, Bloch surface wave (BSW) and microring resonator (MRR), for the detection of SARS-CoV-2 antibodies in human serum. To ensure direct comparability, a new BSW readout system was established alongside an existing MRR platform, allowing assays to be conducted under nearly identical experimental conditions. Both sensors were functionalized with various SARS-CoV-2 Spike and Nucleocapsid protein variants to capture specific host antibodies. The results demonstrate that both platforms provide rapid, quantitative, and sensitive detection of anti-Spike and anti-Nucleocapsid antibodies without the need for secondary labels. Furthermore, the platforms show excellent agreement with longitudinal serology benchmarks and high repeatability across different biochip batches. This work establishes both BSW and MRR technologies as powerful, low-cost candidates for next-generation clinical diagnostics and serological surveillance.

Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the model to recover internally are now externalized into memory stores, reusable skills, interaction protocols, and the surrounding harness that makes these modules reliable in practice. This paper reviews that shift through the lens of externalization. Drawing on the idea of cognitive artifacts, we argue that agent infrastructure matters not merely because it adds auxiliary components, but because it transforms hard cognitive burdens into forms that the model can solve more reliably. Under this view, memory externalizes state across time, skills externalize procedural expertise, protocols externalize interaction structure, and harness engineering serves as the unification layer that coordinates them into governed execution. We trace a historical progression from weights to context to harness, analyze memory, skills, and protocols as three distinct but coupled forms of externalization, and examine how they interact inside a larger agent system. We further discuss the trade-off between parametric and externalized capability, identify emerging directions such as self-evolving harnesses and shared agent infrastructure, and discuss open challenges in evaluation, governance, and the long-term co-evolution of models and external infrastructure. The result is a systems-level framework for explaining why practical agent progress increasingly depends not only on stronger models, but on better external cognitive infrastructure.

Tarski's theorem states that every monotone function from a complete lattice to itself has a fixed point. We specifically consider the two-dimensional lattice $\mathcal{L}^2_n$ on points $\{1, \ldots, n\}^2$ and where $(x_1, y_1) \leq (x_2, y_2)$ if $x_1 \leq x_2$ and $y_1 \leq y_2$. We show that the quantum query complexity of finding a fixed point given query access to a monotone function on $\mathcal{L}^2_n$ is $Ω((\log n)^2)$, matching the classical deterministic upper bound. The proof consists of two main parts: a lower bound on the quantum query complexity of a composition of a class of functions including ordered search, and an extremely close relationship between finding Tarski fixed points and nested ordered search.

We present a variational free-energy formulation for distributionally robust decision-making with ambiguity in the generative model. The formulation, related to a broad range of learning and control frameworks, yields a minimax optimal control problem where maximization is over an uncertainty set that represents ambiguities. We prove that computing the optimal policy requires solving a non-convex minimization problem and propose an algorithm with convergence guarantees to find the solution. The effectiveness of our results is illustrated via simulations on a pendulum swing-up problem.

The interplay of multiorbital physics and nonlocal self-energy effects is studied within an effective three-orbital model for the high-pressure normal state of superconducting bilayer nickelate La$_3$Ni$_2$O$_7$. The model is solved within an advanced many-body framework capturing $k$-dependent correlations beyond dynamical mean-field theory. Different low-energy scenarios subtly depend on the strength of the interorbital interaction, either placing the notorious flat $γ$ quasiparticle band in the occupied part of the spectrum, or letting it cross the Fermi level. In the latter case, intriguing spin-polaron formation due to the scattering of electrons with paramagnon excitations takes place. This leads to bound states appearing as a shadow band with incoherent low-energy spectral weight below the Fermi level. Our results uncover additional competing states that exist in bilayer nickelates and could explain the controversy of recent angle-resolved photoemission experiments.

Decentralized optimization over directed networks is frequently challenged by asymmetric communication and the inherent high variance of stochastic gradients, which collectively cause severe oscillations and hinder algorithmic convergence. To address these challenges, we propose the Stochastic Momentum Tracking Push-Pull (SMTPP) algorithm, which tracks the momentum term rather than raw stochastic gradients within the Push-Pull architecture. This design successfully decouples the variance reduction capacity from the algebraic connectivity of the graph.Although the inherent topology mismatch of directed graphs precludes exact convergence under persistent stochastic noise, SMTPP rigorously compresses this unavoidable steady-state error floor into a minimal neighborhood determined by network connectivity and gradient variance. Furthermore, SMTPP guarantees convergence on any strongly connected directed graph. Extensive experiments on non-convex logistic regression demonstrate that the algorithm is highly robust to network connectivity. By effectively dampening topology-induced oscillations, SMTPP achieves convergence rates and overall performance that closely match those of centralized baselines, regardless of whether the network is sparse or dense.

Ferroelectrics are polar materials whose polarization can be switched by applying electric fields; they offer unique opportunities to develop performant photostrictive materials, i.e., materials that can deform under visible light illumination. Naturally devoid of inversion symmetry, they exhibit original photogalvanic effects such as the Bulk Photovoltaic Effect, which relies on ``hot'' photoexcited carriers. It has long been thought that the electric field generated by this effect may couple to the natural piezoelectric abilities of ferroelectrics to provide large photoinduced deformations. However, due to competing effects, such as thermal dilatation, deformation potential, polarization, or depolarizing-field screening by \textit{thermalized} carriers, it remains unclear which microscopic phenomena govern the photoinduced deformations in classical ferroelectric materials. Here, we demonstrate the largest photoinduced deformation measured in a ferroelectric thin film. Reaching 1 %, this giant photostriction likely originates from the contribution of thermalized photoinduced carriers.

The polygenic risk scores (PRS) have emerged as an important methodology for quantifying genetic predisposition to complex traits and clinical disease. Significant progress has been made in applying PRS to conditions such as obesity, cancer, and type 2 diabetes (T2DM). Studies have demonstrated that PRS can effectively identify individuals at high risk, thereby enabling early screening, personalized treatment, and targeted interventions for diseases with a genetic predisposition. One current limitation of PRS, however, is the lack of interpretability tools. To address this problem for T2DM, researchers at the Graduate School of Data Science at the Seoul National University introduced eXplainable PRS (XPRS). This visualization tool decomposes PRSs into gene-level and single-nucleotide polymorphism (SNP) contribution scores via Shapley Additive Explanations (SHAP), providing granular insights into the specific genetic factors driving an individual's risk profile. We used a co-design approach to assess XPRS trustworthiness by considering legal, medical, ethical, and technical robustness during early design and potential clinical use. For that, we used Z-inspection, an ethically aligned Trustworthy AI co-design methodology, and piloted the Council of Europe's Human Rights, Democracy, and the Rule of Law Impact Assessment for AI Systems (HUDERIA) (Council of Europe (CAI) 2025). The findings of this use-case comprise a comprehensive set of ethical, legal, and technical lessons learned. These insights, identified by a multidisciplinary team of experts (ethics, legal, human rights, computer science, and medical), serve as a framework for designers to navigate future challenges with this and other AI systems. The findings also provide a useful reference for researchers developing explainability frameworks for PRS in diverse clinical contexts.

We investigate a quantum integrated communication and computation (QICC) scheme for a single-mode bosonic multiple-access channel (MAC) with coherent-state signalling. By exploiting the natural superposition property of the quantum MAC, a common receiver simultaneously performs over-the-air computation (OAC) on the analogue symbols transmitted by one set of devices and decodes multiple-access data from another. The joint design of the transmit power control and the receive coefficient leads to a non-convex optimization problem that maximizes computation accuracy under a prescribed sum-rate communication constraint. To address this challenge, we develop a low-complexity alternating-optimization framework that incorporates: (i) closed-form linear minimum-mean square error updates for the receive coefficient, (ii) monotonicity properties of the quantum sum-rate constraint, and (iii) projected-gradient refinements for the communication powers. The proposed QICC scheme achieves an effective computation-communication trade-off with fast convergence and low computational complexity.

We obtain a higher dimensional analogue of a classical theorem which states that a polygonally cellulated $2$-sphere in $\mathbb{R}^3$, such that each vertex has even degree, is $2$-face-colourable. In order to formulate our result, we introduce the notion of combinatorially sphere-like ranked posets, which are ranked posets that generalise combinatorial spheres. We prove that, in a combinatorially sphere-like ranked poset $S$ of rank $k$, if each element of rank $(k-2)$ is covered by an even number of elements, then the maximum ranked elements of $S$ admit a proper $2$-colouring, i.e., any two adjacent maximum ranked elements have different colours.

We establish a Liouville-type inequality for the values, at a common nonzero algebraic point, of arbitrary Mahler Mq-functions. As an application, we prove that no such value is a Liouville number, or even a U -number. This solves a long-standing problem in the field.

Context: Traceability is a key quality attribute of artifacts that are used in knowledge-intensive tasks and supports software engineers in producing higher-quality software. Despite its clear benefits, traceability is often neglected in practice due to challenges such as granularity of traces, lack of a common artifact structure, and unclear responsibility. The Taxonomic Trace Links (TTL) approach connects source and target artifacts through a domain-specific taxonomy, aiming to address these common traceability challenges. Objective: In this study, we empirically evaluate TTL in an industrial setting to identify its strengths and weaknesses for real-world adoption. Method: We conducted a mixed-methods study at Ericsson involving one of its software products. Quantitative and qualitative data were collected across two traceability use cases. We established trace links between 463 business use cases, 64 test cases, and 277 ISO-standard requirements. Additionally, we held three focus group sessions with practitioners. Results: We identified two practically relevant scenarios where traceability is required and evaluated TTL in each. Overall, practitioners found TTL to be a useful solution for one of the scenarios, while less useful for the other. However, developing a domain-specific taxonomy and managing heterogeneous artifact structures were noted as significant challenges. Moreover, the precision of the classifier that is used to create trace links needs to be improved to make the solution practical. Conclusion: TTL is a promising approach that can be adopted in practice and enables traceability use cases. However, TTL is not a replacement for traditional trace links, but rather complements them to enable more traceability use cases and encourage the early creation of trace links.

Modern Large Language Models (LLMs) operate fundamentally as Bounded-Input Bounded-Output (BIBO) systems. They remain in a passive state until explicitly prompted, computing localized responses without intrinsic temporal continuity. While effective for isolated tasks, this reactive paradigm presents a critical bottleneck for engineering autonomous artificial intelligence. Current multi-agent frameworks attempt to distribute cognitive load but frequently rely on static memory pools and passive message passing, which inevitably leads to cognitive stagnation and homogeneous deadlocks during extended execution. To address this structural limitation, we propose Global Workspace Agents (GWA), a cognitive architecture inspired by Global Workspace Theory. GWA transitions multi-agent coordination from a passive data structure to an active, event-driven discrete dynamical system. By coupling a central broadcast hub with a heterogeneous swarm of functionally constrained agents, the system maintains a continuous cognitive cycle. Furthermore, we introduce an entropy-based intrinsic drive mechanism that mathematically quantifies semantic diversity, dynamically regulating generation temperature to autonomously break reasoning deadlocks. Coupled with a dual-layer memory bifurcation strategy to ensure long-term cognitive continuity, GWA provides a robust, reproducible engineering framework for sustained, self-directed LLM agency.

I investigate the quantum dynamics of a spin-$1/2$ particle in a static, spherically symmetric Einstein-Gauss-Bonnet (EGB) black-hole spacetime within the Hamiltonian framework. Starting from the Dirac equation in curved spacetime, formulated via the tetrad formalism and the associated spin connection, we construct the corresponding Dirac Hamiltonian in the EGB background. Using this Hamiltonian, we derive the Heisenberg equations of motion for the position and momentum operators, obtaining explicit expressions for the velocity and force operators. This operator-based approach provides a direct description of particle dynamics beyond classical geodesic motion, incorporating both relativistic and quantum effects. We show that the resulting force operator contains corrections explicitly dependent on the Gauss-Bonnet coupling parameter $ξ$, which encode higher-curvature modifications of the gravitational interaction at the quantum level. In particular, the effective radial force deviates from its general relativistic counterpart by terms that become significant in the strong-field regime.

Photometric missions such as Kepler and TESS have generated millions of light curves covering almost the entire sky, offering unprecedented opportunities to study stellar variability and advance our understanding of the Universe. In this data-rich environment, machine learning has emerged as a powerful tool to efficiently and accurately process and classify light curves according to their type of stellar variability. In this work, we introduce ASTRAFier: a novel Transformer-based model for variability classification that integrates Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs). The model operates directly on time series without requiring feature engineering, creating an easy-to-maintain and efficient end-to-end classification framework. We train and validate our model using both Kepler and TESS light curves and, respectively, achieve a classification accuracy of $94.26\%$ on Kepler and $88.22\%$ on TESS. We demonstrate scalability by deploying our model on $\sim 2.8$ million TESS light curves from sectors 14, 15, and 26 (Kepler Field-of-View) delivered by MIT's Quick-look Pipeline (QLP) and release the resulting stellar variability catalog.