LLM agents have been widely adopted in real-world applications, relying on agent frameworks for workflow execution and multi-agent coordination. As these systems scale, understanding bugs in the underlying agent frameworks becomes critical. However, existing work mainly focuses on agent-level failures, overlooking framework-level bugs. To address this gap, we conduct an empirical study of 998 bug reports from CrewAI and LangChain, constructing a taxonomy of 15 root causes and 7 observable symptoms across five agent lifecycle stages: 'Agent Initialization','Perception', 'Self-Action', 'Mutual Interaction' and 'Evolution'. Our findings show that agent framework bugs mainly arise from 'API misuse', 'API incompatibility', and 'Documentation Desync', largely concentrated in the 'Self-Action' stage. Symptoms typically appear as 'Functional Error', 'Crash', and 'Build Failure', reflecting disruptions to task progression and control flow.

As quantum information systems mature, efficient and coherent transfer of quantum information through noisy channels becomes increasingly important. We examine how passive environment-assisted quantum communication enhances direct quantum information transfer efficiency. A bosonic pure-loss channel, modeled as transmission through a beam splitter with a vacuum input state at the dark port, has zero quantum capacity when transmissivity is below 50%. Quantum communication through the channel can be enhanced by passive environment assistance, achieved via the selection of an appropriate input state for the ancilla port. Although ideal Gottesman-Kitaev-Preskill (GKP) states enable perfect quantum information transmission at arbitrarily small transmissivity, they are challenging to realize experimentally. We therefore explore more experimentally accessible non-Gaussian ancilla states, such as Fock, cat, and squeezed cat states, and numerically determine the optimal encoding and decoding strategies. We also construct analytical schemes that yield high-fidelity transmission and good information rates.

Congestion pricing has emerged as an effective tool for mitigating traffic congestion, yet implementing welfare or revenue-optimal dynamic tolls is often impractical. Most real-world congestion pricing deployments, including New York City's recent program, rely on significantly simpler, often static, tolls. This discrepancy motivates the question of how much revenue and welfare loss there is when real-world traffic systems use static rather than optimal dynamic pricing. We address this question by analyzing the performance gap between static (simple) and dynamic (optimal) congestion pricing schemes in two canonical frameworks: Vickrey's bottleneck model with a public transit outside option and its city-scale extension based on the Macroscopic Fundamental Diagram (MFD). In both models, we first characterize the revenue-optimal static and dynamic tolling policies, which have received limited attention in prior work. In the worst-case, revenue-optimal static tolls achieve at least half of the dynamic optimal revenue and at most twice the minimum achievable system cost across a wide range of practically relevant parameter regimes, with stronger and more general guarantees in the bottleneck model than in the MFD model. We further corroborate our theoretical guarantees with numerical results based on real-world datasets from the San Francisco Bay Area and New York City, which demonstrate that static tolls achieve roughly 80-90% of the dynamic optimal revenue while incurring at most a 8-20% higher total system cost than the minimum achievable system cost.

Over the past few decades, data from leading neutrino experiments have firmly established neutrino oscillation, implying non-zero neutrino masses and leptonic mixing and thereby providing confirmed evidence of physics beyond the Standard Model. On the backdrop of the precision era of neutrino oscillation, this thesis underscores its relevance by demonstrating the physics reach of the forthcoming long-baseline experiments -- Deep Underground Neutrino Experiment (DUNE) and Hyper-Kamiokande (Hyper-K) -- to establish non-maximal $θ_{23}$, resolve the correct $θ_{23}$ octant, and improve the precision on $θ_{23}$ and $Δm^2_{31}$ by efficiently breaking parameter degeneracies. This is enabled by DUNE's high-resolution LArTPC detector and its wide-band beam, achieving sensitivity at a high confidence level compared to the global fits of world neutrino data. The combined analysis of DUNE and Hyper-K not only significantly enhances sensitivity to these phenomenological studies but also demonstrates their capabilities at lower exposures when operated together, relative to their nominal individual exposures. In addition, we investigate the impact of flavor-dependent long-range interactions arising from anomaly-free U(1)' extensions of the Standard Model, showing that although subdominant long-range interactions can substantially influence the sensitivity and precision of oscillation parameter measurements, the complementary strengths of DUNE and Hyper-K mitigate these challenges to a large extent.

This expository article is an introduction to logarithmic Gromov--Witten (GW) theory. We discuss how to study the GW theory of a smooth projective variety via simple normal crossings degenerations. We survey several approaches to constructing well-behaved, virtually smooth moduli spaces of stable maps to such degenerations. Each irreducible component of the special fiber of a degeneration determines a pair consisting of a variety and a normal crossings divisor, and these pairs carry their own logarithmic GW theory. We explain how the GW theory of the general fiber can be expressed in terms of the logarithmic GW theory of these pairs. Finally, we discuss applications to tautological classes on the moduli space of curves.

We compile JWST/NIRSpec prism and MIRI data for 249 Little Red Dots (LRDs) at 2.3<z<9.3, forming a representative spectroscopic subset of NIRCam-selected LRDs. We derive a median stacked spectrum covering rest-frame 0.09-1.2 $μ$m, with MIRI photometry extending the spectral energy distribution to 4 $μ$m. Four additional stacks for subsamples defined by optical-to-UV luminosity ratios show that LRDs form a heterogeneous population spanning diverse continuum slopes and line properties. Assuming LRDs host super-massive black holes (BHs) surrounded by dense gas clouds, and stars accompany this core, we infer masses of $M_{BH}\sim10^{6.0-6.5}$ M$_\odot$ and $M_\bigstar\sim10^{8.3}$ M$_\odot$, corresponding to BH-to-stellar mass ratios of 1-2%. The stacks show ubiquitous UV and optical FeII emission, indicating a direct view of the broad-line region and high (but sub-Eddington) accretion ($λ_{Edd}=0.6\pm0.2$). We find a significant stellar contribution in the far-UV, reaching $\sim80$% in the bluest systems. Possible Wolf-Rayet features (HeII$λ$4687, nitrogen lines) are identified, tracing a young (3-7 Myr) compact starburst event. We also detect strong Balmer breaks and atypical Balmer, Paschen, [OIII], and optical and near-infrared HeI line ratios, and an absorption at $\sim4550$ Angstrom (probably linked to FeII), all consistent with radiative-transfer effects in high-density gas with warm temperatures (4000-7000 K). We find a diversity of LRD flavors modulated by the luminosity ratio between between a short ($\lesssim20$ Myr) and intense phase of BH activity, the most extreme stage lasting $\sim3-7$ Myr, characterized by near-Eddington-limit radiation, and a nuclear and compact starburst dominated by massive stars (even super-massive, $\mathrm{M}_\mathrm{SMS}\sim10^{5}$ M$_\odot$), all embedded in dense gas with modest dust content producing a variety of optical depths.

The Transparency and Consent Framework (TCF), developed by the Interactive Advertising Bureau (IAB) Europe, provides a de facto standard for requesting, recording, and managing user consent from European end-users. This framework has previously been found to infringe European data protection law and has subsequently been regularly updated. Previous research on the TCF focused exclusively on web contexts, with no attention given to its implementation in mobile applications. No work has systematically studied the privacy implications of the TCF on Android apps. To address this gap, we investigate the prevalence of the TCF in popular Android apps from the Google Play Store, and assess whether these apps respect users' consent banner choices. By scraping and downloading 4482 of the most popular Google Play Store apps on an emulated Android device, we automatically determine which apps use the TCF, automatically interact with consent banners, and analyze the apps' traffic in two different stages, passive (post choices) and active (during banner interaction and post choices). We found that 576 (12.85%) of the 4482 downloadable apps in our dataset implemented the TCF, and we identified potential privacy violations within this subset. In 15 (2.6%) of these apps, users' choices are stored only when consent is granted. Users who refuse consent are shown the consent banner again each time they launch the app. Network traffic analysis conducted during the passive stage reveals that 66.2% of the analyzed TCF-based apps share personal data, through the Android Advertising ID (AAID), in the absence of a lawful basis for processing. 55.3% of apps analyzed during the active stage share AAID before users interact with the apps' consent banners, violating the prior consent requirement.

Teaching assembly programming is a fundamental component of undergraduate computer science education, yet many students struggle with its abstract and low-level concepts. Existing learning tools, such as simulators and visualisers, support understanding by exposing machine states. However, they often limit students to passive observation and provide few opportunities for meaningful interaction. To address these limitations, we introduce Playsemble, a gamified learning system that transforms assembly instructions into interactive, game-like tasks in which students control Pac-Man to collect items, avoid ghosts, and reach targets. Playsemble integrates a code editor, a CPU emulator, and visual debugging tools within a browser-based environment, allowing students to work offline without installation or configuration. It also provides immediate formative feedback enhanced by large language models. We deployed Playsemble in an undergraduate computer architecture course with 107 students. The course featured a sequence of assignments of increasing complexity, covering core concepts such as register and memory manipulation, control structures including loops and conditionals, and arithmetic operations. Our findings suggest that Playsemble promotes active experimentation, sustained engagement, and deeper conceptual understanding through meaningful game-based learning experiences.

The basic building blocks of many forms of optical topologies are particle-like singularities in phase and polarisation, giving rise to lines of darkness that weave complex threads in 3D space. Although known for half a century since seminal work on dislocations in wave trains, their behaviour in complex media remains under debate, especially with respect to their relative stability. Here we show that polarisation and phase vortices behave identically in one-sided turbulent complex channels. We perform complementary numerical and experimental studies using atmospheric turbulence as a test case, demonstrating agreement and equivalent dynamics. Our work addresses open questions on optical topologies and will be relevant to their harnessing for applications such as sensing, communication, imaging, and information transfer in noisy or complex environments.

A set $D$ of vertices in a graph $G = (V, E)$ is a locating-dominating set (LD-set) if it is dominating and every two vertices $u$, $v$ of $V\setminus D$ satisfy $N(u) \cap D \neq N(v) \cap D$. Two disjoint sets $A,B\subset V(G)$ form a locating-dominating coalition (for short, an LD-coalition) in $G$ if none of them is an LD-set in $G$ but their union $A\cup B$ is an LD-set. A locating-dominating coalition partition (for short, an LDC-partition) is a vertex partition $Π$ such that every set of $Π$ is not an LD-set in $G,$ but forms an LD-coalition with another set of $Π$. The locating-domination coalition number of $G$, denoted by $C_{L}(G),$ equals the maximum cardinality of an LDC-partition of $G$. Our purpose in this paper is to initiate the study of locating-dominating coalitions in graphs. We first investigate the existence of LDC-partitions. We also obtain lower and upper bounds on $C_{L}(G)$. We characterize connected graphs $G$ of order $n\ge 3$ satisfying $C_L(G) = n,$ as well as those trees $T$ such that $C_L(T)=n-1$. In addition, we determine the exact values of $C_L(G)$ for some classes of graphs. Moreover, we investigate the computational complexity of the decision problem associated with locating-dominating coalition partitions. To the best of our knowledge, this is the first work that addresses the algorithmic complexity of a decision problem related to coalition partitions, not only for this locating-dominating model but for coalition partitions in general.

We report on the monolithic, two-step epitaxial growth of site-controlled InGaAs quantum dots via the buried stressor method with local quantum dot density variation. As a result of high fabrication accuracy, we achieve low lateral displacements of the individual buried stressor apertures of $ 17^{+19}_{-17}$~nm from mesa centers. We provide extensive micro-photoluminescence and cathodoluminescence characterization of the site-controlled quantum dots and give theoretical calculations, explaining the effect of the stressor aperture on the quantum dot emission properties, positioning, and density. We show reproducibility of the nucleation process for apertures of the same size and achieve precisely-positioned, low- and high-density quantum dot nucleation within one active layer growth step. The results presented in this work demonstrate the significant potential of the buried stressor concept in fabricating single photonic chips, simultaneously combining single-photon sources and microlasers featuring different local densities of site-controlled quantum dots, paving the way for highly functional source modules with applications in photonic quantum technology.

Holographic multiple-input multiple-output (H-MIMO) systems represent a paradigm shift in wireless communications by enabling quasi-continuous apertures. Unlike conventional MIMO systems, H-MIMO with subwavelength antenna spacing operates in both far-field and near-field regimes, where classical discrete Fourier transform (DFT) representations fail to sufficiently capture the channel characteristics. To address this challenge, this article provides an overview of the emerging wavenumber-domain signal processing framework. Specifically, by leveraging spatial Fourier plane-wave decomposition to model H-MIMO channels, the wavenumber domain offers a unified and physically consistent basis for characterizing subwavelength-level spatial correlation and spherical wave propagation. This article first introduces the concept of H-MIMO and the wavenumber representation of H-MIMO channels. Next, it elaborates on wavenumber-domain signal processing technologies reported in the literature, including multiplexing, channel estimation, and waveform designs. Finally, it highlights open challenges and outlines future research directions in wavenumber-domain signal processing for next-generation wireless systems.

We present a new family of multi-centered rotating black hole solutions in 5D vacuum Einstein gravity, providing explicit examples of cohomogeneity-three spacetimes. It is well known that, in the presence of two commuting Killing vector fields, the theory reduces to 3D gravity coupled to an $SL(3,\mathbb{R})$ nonlinear sigma model with five scalar fields. We show that the scalar fields of the extremal Myers-Perry solution can be expressed in terms of two harmonic functions on 3D flat space, and that promoting these functions to include multiple sources yields explicit multi-centered extremal Myers-Perry black holes located at arbitrary positions. Each center forms a smooth $S^3$ Killing horizon, provided that the rotation parameters satisfy $|j_i|<1/2$. We further demonstrate that all curvature singularities are hidden behind the horizons and that no closed timelike curves arise on or outside the horizons. The solutions are asymptotically locally Minkowski in the sense that constant-time hypersurfaces are asymptotically locally Euclidean (ALE). As a concrete example, we consider a binary configuration, examine its rod structure, and demonstrate the absence of conical singularities between the two black holes, indicating that they are supported by an intermediate bubble region separating them.

The nature of dark matter and dark energy are among the central questions in cosmology. Strong gravitational lenses with multiple source planes provide a geometric probe of cosmology: the ratio of deflection angles at different redshifts depends only on angular-diameter distances, constraining the matter density $Ω_m$ and the dark energy equation of state $w$. However, constraints from this technique have historically lagged behind those from the CMB, SNe Ia, and BAO. In this work, we present new cosmological constraints from the Carousel Lens, a cluster-scale lens with more than 40 extended images from 11 spectroscopically confirmed sources. Its relaxed core and rich set of extended images behind the main halo make it particularly suitable for cosmological inference. Using the GIGA-Lens pipeline, we construct a pixel-level lens model including six HST-detected sources and four mass components. From this model, we obtain $w$CDM constraints of $Ω_m = 0.34^{+0.16}_{-0.13}$ and $w = -1.31^{+0.35}_{-0.32}$ from the Carousel Lens alone, accounting for both statistical and systematic uncertainties. We further project that including four additional known higher-redshift sources, assuming similar fractional uncertainties, could improve the constraining power by ~80%, bringing the precision close to that of the CMB and SNe Ia. For an evolving dark energy model ($w_0w_a$CDM), the Carousel Lens alone yields constraints comparable to the CMB, providing an independent and complementary probe alongside SN Ia and BAO. While currently systematic uncertainties dominate, which we quantify through simulations, our results demonstrate that relaxed multi-source-plane cluster lenses can deliver competitive cosmological constraints. Further improvements are expected from reductions in systematics and from incorporating higher-redshift sources (known and new) with high-resolution imaging.

Agentic AI prototypes are being deployed across domains with increasing speed, yet no methodology for their structured design, governance, and prospective evaluation has been established. Existing AI documentation practices and guidelines -- Model Cards, Datasheets, or NIST AI RMF -- are either retrospective or lack machine-readability and interoperability. We present the Agentic Automation Canvas (AAC), a structured framework for the prospective design of agentic systems and a tool to facilitate communication between their users and developers. The AAC captures six dimensions of an automation project: definition and scope; user expectations with quantified benefit metrics; developer feasibility assessments; governance staging; data access and sensitivity; and outcomes. The framework is implemented as a semantic web-compatible metadata schema with controlled vocabulary and mappings to established ontologies such as Schema$\mathrm{.}$org and W3C DCAT. It is made accessible through a privacy-preserving, fully client-side web application with real-time validation. Completed canvases export as FAIR-compliant RO-Crates, yielding versioned, shareable, and machine-interoperable project contracts between users and developers. We describe the schema design, benefit quantification model, and prospective application to diverse use cases from research, clinical, and institutional settings. The AAC and its web application are available as open-source code and interactive web form at https://aac.slolab.ai$\mathrm{.}$

Experiments at extreme strain rates and temperatures are critical for characterizing materials in high-speed applications. In this study, we develop a laser-driven particle impact platform capable of accelerating microparticles to supersonic velocities and impacting targets heated to temperatures approaching 2000 °C. The conventional laser-induced particle impact testing (LIPIT) system has been modified to enable high-temperature experiments through the integration of a resistive heating system and the development of a robust launch pad assembly suitable for accelerating particles in high-temperature environments. To eliminate the oxidation of materials at elevated temperatures, an optically accessible portable vacuum chamber has been developed and integrated into the setup. The capabilities of the system are demonstrated through a study of the temperature dependent particle impact cratering behavior of POCO graphite. With this new platform, high-velocity, high-temperature impact experiments can be performed in a controlled environment, supporting the investigation of materials under extreme conditions.

Geometrical design is a crucial and challenging strategy for improving the performance of type-II superconductors, because the proper placement of intended defects in the current path contribute to flux pinning, a reduction in dissipation, and an increase in achievable current density. Topology optimization is currently one of the most powerful approaches used to determine consistent structural geometries. Therefore, a topology optimization approach is presented to inversely design structural geometries of low- and high-temperature type-II superconductors with superconductor-dielectric/vacuum interfaces. In the presented approach, the magnetic response of type-II superconductors is modeled using the Ginzburg-Landau theory, where the temporal evolution of the order parameter and vector potential is described by the time-dependent Ginzburg-Landau equations under the Weyl gauge.

We consider the closed neighborhood ideal of square of the path graph and study some of its algebraic and homological invariants. We compute the height, the projective dimension and the Castelnuovo-Mumford regularity. We prove that these ideals are sequentially Cohen-Macaulay and characterize when they are Cohen-Macaulay.

This study investigates the relationship between interocular distance relative to overall facial width (width ratio) and perceived subjective beauty in male faces. Building on the methodology of Pallett et al. (2010), who found that average proportions in female faces were rated as most attractive, the current study aimed to test this hypothesis in male faces. Faces from the Chicago Face Database (Ma et al., 2015) were morphed into average faces within three groups (with low, medium, and high width ratios), each composed of 96 or 97 individual images. These three average faces were then systematically manipulated in their width ratios across three levels in both directions, respectively, resulting in a total of 21 comparable faces. The use of multiple base faces served as a control for potential artifacts of image processing. Consequently, comparisons were restricted to within-group pairs to avoid confounding by co-varying facial features (e.g., skin tone), which precluded direct cross-condition comparisons but ensured internal validity. In a two-alternative forced-choice task, participants selected the more beautiful face from each pair. The data were analyzed using a Bayesian model which enables inference of the width ratio perceived as most beautiful. Results support the hypothesis that averageness in facial proportions correlates with higher perceived attractiveness. The study highlights the importance of controlling for image manipulation, including attempts at methodological implementation, and of considering ethnicity as a potential moderating variable. These findings offer a data-driven foundation for understanding facial aesthetics and cognitive processes of human perception, with applications in advertising, artificial face generation, and plastic surgery.

When a particle contacts a surface of another material, it is commonly believed that the particle acquires an impact charge that scales inversely with its pre-impact charge and whose polarity is set by the materials. We show that this belief holds for conductive particles but fails for polymers. For polymers, the impact charge increases linearly with the particle's pre-impact charge. Its polarity is not determined by the materials but by the pre-impact particle charge relative to a divergence point at which the net charge transfer reverses. We attribute this divergence to the attraction of surrounding ions to the particle surface. These attracted ions carry polarity opposite to that of the particle, and their amount scales with the particle charge. They transfer to the opposing surface during contact, thereby defining the impact charge. We propose a phenomenological model for the divergent impact charge arising from this mechanism. Finally, we reexamine previous measurements and show that they support this mechanism.

We introduce the notion of minimal inversion sequences for a pattern $ρ$, which form the smallest set of inversion sequences whose avoidance is equivalent to the avoidance of $ρ$ for inversion sequences. We give a characterization of $ρ$-minimal inversion sequences based on the occurrences of the pattern $ρ$ they contain, and use it to find upper and lower bounds on the lengths of $ρ$-minimal inversion sequences. We provide some enumerative results on the exact number of minimal inversion sequences for some patterns, through a bijection with increasing trees, and some exhaustive generation. Lastly, we enumerate inversion sequences which are equal to their reduction, and find an interesting connection with poly-Bernoulli numbers.

Biological machines like molecular motors and enzymes operate in dynamic cycles representable as stochastic flows on networks. Current stochastic dynamics describes such flows on fixed networks. Here, we develop a scalable approach to network design in which local transition rates can be systematically varied to achieve global dynamical objectives. It is based on the fluctuation-response duality in the recent Caliber Force Theory -- a path-entropy variational formalism for nonequilibria. This approach scales efficiently with network complexity and gives new insights, for example revealing the transition from timing- to branching-dominated fluctuations in a kinesin motor model.

Spin qubits are typically operated in the lowest orbital of a quantum dot to minimize interference from nearby states. In valence-band hole systems, strong spin-orbit coupling links spin and orbital degrees of freedom, strongly influencing the hole $g$-factor, a key parameter for qubit control. We investigate the out-of-plane $g$-factor in Ge quantum dots using excitation (single-particle) and addition (many-body) spectra. Excitation spectra allow us to distinguish the pure Zeeman $g$-factor from orbital contributions to the magnetic field splitting of states despite the strong spin-orbit coupling. This distinction clarifies discrepancies between $g$-factors extracted with the two methods, for different orbital states and different hole numbers. Furthermore, we find gate-tunability of $g$-factors at the level of 15%, highlighting its relevance for all-electric qubit manipulation.

This paper studies how the theory of derived algebras (in the sense of Bhatt-Mathew and Raksit) interacts with formal derived geometry, specifically the formal derived stacks which show up in the theory of prismatization. As an application we prove some classification theorems for derived algebras in quasi-coherent sheaves on a certain class of filtered \emph{formal} stacks, which includes those whose quasi-coherent sheaves are prismatic gauges over a perfectoid ring. Along the way, among other things, we study the behavior of derived algebras along schematic quasi-affine morphisms in derived geometry, and for example, classify derived algebras on the source as precisely those derived algebras on the target which receive a map from the pushforward of the structure sheaf of the source. We also indicate how to extend some of our results to (formal) classifying stacks of diagonalizable group schemes. As an aside, we also show some classification theorems even for quasi-coherent sheaves on formal stacks which (to our knowledge) weren't available in the literature on derived geometry previously. These results are motivated by forthcoming work of the author but hoped to be generally useful.

We develop a coordinate invariant formalism which describes the mechanical and electromagnetic interaction of gravitational waves (GWs) with a wide class of resonant detectors. We solve the GW-modified equations of electrodynamics and elasticity with dynamic boundary conditions using an eigenmode expansion. Furthermore, we take damping effects and electromagnetic back-action on mechanical systems covariantly into account. The resulting coupling coefficients are particularly useful for high-frequency gravitational wave experiments using microwave cavities and allow a straightforward numerical implementation for arbitrary detector geometries.

We propose doubly occupied configuration interaction-quantum selected configuration interaction (DOCI-QSCI), which samples from the seniority-zero space. While the use of this space effectively doubles the qubit budget, equaling the number of spatial orbitals, this sector restriction can compromise quantitative accuracy. To compensate for this, we expand sampled bitstrings via their Cartesian product into a larger space that includes seniority-breaking determinants. The resulting wave function is also proposed using the trial state in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) to recover dynamical correlations across the full orbital space (DOCI-QSCI-AFQMC). We evaluate the proposed methods on the H6 chain, N2 dissociation, and the addition of singlet O2 to a BODIPY dye. For the H6 chain, DOCI-QSCI-AFQMC reproduces the accuracy of the level of the complete-active-space counterpart with the quantum device ibm kobe. For N2 and BODIPY-O2, with (14e, 28o) and up to (20e, 20o) active spaces, it yields reasonable results, whereas single-reference CCSD(T) fails qualitatively. These results demonstrate that the DOCI-QSCI doubles the orbital space accessible to conventional QSCI and subsequent ph-AFQMC post-processing delivers reasonably high accuracy.

Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this challenge: (i) a statistical stage that uses Granger causality to identify candidate leader-follower pairs from market-implied probability time series, and (ii) an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism based on event descriptions. Because causal ground truth is unobserved, we evaluate the ranked pairs using a fixed, signal-triggered trading protocol that maps relationship quality into realized profit and loss (PnL). On Kalshi Economics markets, our hybrid approach consistently outperforms the statistical baseline. Across rolling evaluations, the win rate increases from 51.4% to 54.5%. Crucially, the average magnitude of losing trades decreases substantially from 649 USD to 347 USD. This reduction is driven by the LLM's ability to filter out statistically fragile links that are prone to large losses, rather than relying on rare gains. These improvements remain stable across different trading configurations, indicating that the gains are not driven by specific parameter choices. Overall, the results suggest that LLMs function as semantic risk managers on top of statistical discovery, prioritizing lead-lag relationships that generalize under changing market conditions.

We prove the following results: (1) For every generic closed smooth curve in $\mathbb{R}^3$ there is a point with at least $6$ emanating normals to the curve. (2) For every generic closed piecewise linear curve in $\mathbb{R}^3$ there is a point with at least $8$ emanating normals to the curve. If the curve is knotted, there is a point with at least $10$ emanating normals. The proof is based on the Morse theory for the squared distance function and self intersections of the focal surface.

The strange metal remains one of the great unsolved problems for 21st century science. Since the early development of the marginal Fermi liquid phenomenology, it has been clear that progress requires detailed knowledge of the momentum- and frequency-dependent charge susceptibility, $χ(\mathbf{q},ω)$, particularly at large momenta. Electron energy-loss spectroscopy (EELS), performed in either reflection or transmission geometry, provides the most direct probe of $χ(\mathbf{q},ω)$. However, measurements over the past four decades have yielded conflicting results, with some studies reporting a dispersing RPA-like plasmon and others observing a strongly overdamped, incoherent response. Here we report a transmission EELS study of Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ (Bi-2212) that simultaneously achieves high energy resolution ($ΔE \approx 30$ meV) and high momentum resolution ($Δq \approx 0.01$ Å$^{-1}$). To address issues of reproducibility, measurements were repeated ten times on five different Bi-2212 flakes, benchmarked against aluminum, a well-characterized Fermi liquid, and quantitatively compared with prior studies spanning four decades. At momenta $q < 0.15$ Å$^{-1}$, we observe a highly damped plasmon whose linewidth is comparable to its energy. At larger momenta, $q > 0.15$ Å$^{-1}$, this excitation does not disperse but instead evolves into an incoherent continuum, with no evidence for the RPA-like dispersion reported in some earlier works. Comparison with recent RIXS measurements on Bi-based cuprates supports the view that Bi-2212 is an incoherent metal with strongly damped charge excitations.

Digital quantum computing promises to offer computational capabilities beyond the reach of classical systems, yet its capabilities are often challenged by scarce quantum resources. A critical bottleneck in this context is how to load classical or quantum data into quantum circuits efficiently. Approximate quantum loaders (AQLs) provide a viable solution to this problem by balancing fidelity and circuit complexity. However, most existing AQL methods are either heuristic or provide guarantees only for specific input types, and a general theoretical framework is still lacking. To address this gap, here we reformulate most AQL methods into a unified framework and establish information-theoretic bounds on their approximation error. Our analysis reveals that the achievable infidelity between the prepared state and target state scales linearly with the total entanglement entropy across subsystems when the loading circuit is applied to the target state. In light of this, we develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states. We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits. The results show that AQER consistently outperforms existing methods in both accuracy and gate efficiency. Our work paves the way for scalable quantum data processing and real-world quantum computing applications.