The atmospheres of super-Earths and sub-Neptunes can be strongly modified by chemical exchange with their molten interiors during long-lived magma ocean phases. Interpreting atmospheric observations requires fast models that self-consistently couple atmospheric chemistry to the composition of the planetary interior. We present an updated implementation of the global chemical equilibrium (GCE) framework from (Schlichting & Young 2022), which computes the equilibrium composition of a coupled metal-silicate-gas system. The numerical solver has been improved using a gradient-based optimizer, reducing the computational cost of solving the chemical network by more than two orders of magnitude and enabling large parameter studies. We apply the framework to a large synthetic population of planets and explore the imprint of bulk refractory composition of Mg, Si, and Fe on atmospheric properties. We consider planets with different masses, thermal states, and volatile inventories. We find that the atmospheric mass fraction and atmospheric metal mass fraction are primarily controlled by the temperature at the atmosphere-magma ocean interface and the planetary water budget, while the accreted hydrogen mass fraction plays a minor role because most hydrogen dissolves into the interior. For planets that accreted water, the refractory ratios Mg/Si and Fe/Si strongly influence carbon partitioning between the gas, silicate, and metal phases, producing large variations in atmospheric atmospheric metal mass fraction and C/O ratios. These results demonstrate that atmospheric compositions of sub-Neptunes depend sensitively on both the volatile inventory and the bulk composition of rocky material, providing new constraints for interpreting atmospheric observations. The new GCE code is open-source.

In this paper we derive a Bismut-Elworthy formula under assumptions weaker than the non degeneracy of the noise. By Bismut-Elworthy formula we mean a gradient type estimate on the transition semigroup of a stochastic differential equation in a possibly infinite dimensional Hilbert space. We also consider a nonlinear version of the Bismut formula for a backward stochastic differential equation, in analogy to what is done in \cite{futeBismut}, where a non-degenerate noise is considered. Our study is motivated by applications to stochastic wave equations and to stochastic damped wave equation.

We study an optimal threshold functional arising in binary classification for continuous biomarkers. While the ROC curve summarizes discriminatory performance across all thresholds, practical threshold selection must also account for disease prevalence and asymmetric misclassification costs. The classical Youden index corresponds to a symmetric special case and may therefore be suboptimal in realistic decision settings. In addition, biomarker distributions in serological and immunological studies often display skewness and heavy tails, making Gaussian ROC models inadequate. We develop a parametric framework for ROC analysis and optimal cutoff selection under the family of scale mixtures of skew-normal (SMSN) distributions, including the skew-normal and skew-t models. The ROC curve and AUC are estimated by plug-in maximum likelihood from separate group fits. The optimal cutoff is defined as the minimiser of a weighted misclassification risk, which yields a likelihood ratio equation extending the Youden criterion. Under a monotone likelihood ratio condition, we establish existence, uniqueness, and global optimality of the cutoff. We further study its local regularity as an implicitly defined functional of the model parameter and derive consistency, asymptotic normality, and a closed-form plug-in variance estimator. A central term in this variance is the local slope of the estimating equation at the optimal threshold, which acts as a local identifiability diagnostic. Monte Carlo experiments across six scenarios show that the asymptotic approximation is accurate and that Wald confidence intervals attain near nominal coverage. An application to SARS-CoV-2 serological data illustrates that the proposed cutoff can differ substantially from the Youden threshold and may reduce estimated misclassification risk by up to 63% under asymmetric decision settings.

Hadronic parity violation concerns the study of the interplay of the weak- and strong-interaction dynamics that yields low energy, parity-violating observables in systems of hadrons and nuclei. We explain its essential features, as well as our current understanding of its observed effects, describing recent theoretical and experimental progress in a pedagogical context. We provide a broad overview of ongoing research efforts to show how precision studies of few-nucleon systems can be extended to studies of complex nuclei and, ultimately, to new benchmarks for computations in the Standard Model, as well as to new searches for the dynamics beyond it.

We demonstrate that chiral transport in a strongly magnetized electron-positron plasma can arise dynamically from dissipative pair-creation processes encoded in the imaginary part of the photon polarization tensor within one-loop finite-temperature quantum electrodynamics (QED). In the kinematic region corresponding to longitudinal photon absorption, real electron-positron pair production induces axial charge nonconservation and generates an electric current parallel to the magnetic field, without requiring the introduction of an external chiral chemical potential. This provides a microscopic mechanism for chiral magnetic transport, offering an alternative to hydrodynamic or anomaly-based effective descriptions in which chirality imbalance is typically introduced as an external input. We derive an explicit expression for the longitudinal magnetoconductivity associated with this process and show that it exhibits an approximately quadratic dependence on the magnetic field only within a restricted intermediate regime. This behavior emerges from the dominance of the lowest Landau levels as a characteristic of negative longitudinal magnetoresistance. We further analyze how Pauli blocking regulates the pair-creation phase-space and demonstrate that the dynamically generated chiral imbalance is suppressed at high frequencies, revealing a transition between chiral-active and non-chiral-active regimes. Our results connect microscopic QED processes with anomaly-related transport phenomena in strongly magnetized relativistic plasmas, where pair creation provides a dynamical source for chiral imbalance.

While deep learning offers powerful capabilities for scientific research, its application is often hindered by a lack of quantitative reliability. To address this, we introduce a probabilistic denoising framework that simultaneously extracts denoised signals and element-wise predictive uncertainties from noisy data. We demonstrate this approach on three-dimensional angle-resolved photoemission spectroscopy data, showing that the model reliably recovers the spectral features of a cuprate superconductor from Poisson-distributed noise with an average count of only 0.02 electrons per voxel. Crucially, we show that these predicted uncertainties can be propagated into subsequent superconducting gap analyses, enabling quantitative parameter extraction with scientifically meaningful error bars. Furthermore, we validate the broad applicability of our approach by successfully extending it to two-dimensional X-ray diffraction data. Ultimately, this approach establishes uncertainty-aware deep learning not merely as a visualization tool, but as a rigorous framework for scientific data analysis.

When assessing the potential impact of code-level vulnerabilities, e.g., discovered by automated analyzers, it is essential to consider them in the context of the system's security design. However, this is a challenging task due to the abstraction gap between security design, often specified using security DSLs, and implementation. As we will show, even security experts lack a complete understanding of this relationship. Intrigued by this gap (and the general disconnect between secure design and secure implementation) we present a study of 66 design-level security DSLs and 559 security checks from 36 code-level analyzers. We identify what concepts are common to both and capture them in the SecLan model, which has been validated by 22 security experts. Based on this, we investigate the relationship between DSLs and analyzers quantitatively and explore it qualitatively together with 9 security experts. We learn that there are few commonalities between design-level and implementation-level security; security checks are often described by overly general weaknesses, resulting in many non-obvious potential relationships between security DSLs and analyzers; and even security experts are overwhelmed by this complexity. We provide an empirical basis that helps practitioners and researchers better understand the gap and serves as a first step toward bridging it.

A non-trivial spinor field $ψ$ is called a generalized imaginary $\mathrm{Spin}^c$-Killing spinor if $\nabla^{g,A} _X ψ= iμX \cdot ψ$ for all vector fields $X$, where $μ$ is a real function that is not identically zero and $\nabla^{g,A}$ is the $\mathrm{Spin}^c$ Levi-Civita connection with $\mathrm{U}(1)$-connection $A$. Associated with $ψ$ is a vector field $V$, the Dirac current, defined by $g(V,X) = i \langle X\cdot ψ, ψ\rangle$. We prove that if $V$ vanishes somewhere and $\operatorname{dim} M \geq 3$, the manifold is locally isometric to real hyperbolic space. When $V$ never vanishes and $\operatorname{dim} M \geq 3$, we obtain a global geometric description of all $\mathrm{Spin}^c$-Riemannian manifolds carrying such spinors, under the assumption that either the normalized Dirac current $ξ= \frac{V}{|V|}$ is complete or the leaves of $\mathcal{D} = \ker(ξ^\flat)$ are complete. Finally, we reinterpret the case of type~I generalized imaginary $\mathrm{Spin}^c$-Killing spinors in terms of parallel spinors for a suitable connection with vectorial torsion.

Stellar streams trace the gravitational potential of their host galaxies and offer a direct probe of dark matter halo geometry. Cosmological simulations predict that halo shapes depend on both baryonic physics and the nature of dark matter, yet observational constraints on halo flattening and orientation remain limited, especially for individual galaxies. We present Potamides, which utilizes the curvature of extragalactic stellar streams to derive constraints on halo shapes. We apply Potamides to 15 stellar streams from the Stellar Stream Legacy Survey to infer the projected axis ratios and orientation of their host halos. We find that some streams in our sample exclude large regions of halo flattenings and halo orientations. Systems with edge-on wrapping loops or sharp turning points yield the strongest constraints, whereas great circle-like streams remain largely uninformative. All streams in our sample support a spherical halo for a given flattening direction. These results demonstrate that stream morphology can provide halo shape constraints for individual external galaxies. With upcoming surveys (such as Euclid, Rubin, Roman, and ARRAKIHS) expected to discover large numbers of stellar streams, this curvature-based technique will enable rapid statistical tests of dark matter and baryonic physics through the shapes and alignments of halos and disks across cosmic time.

We present measurements on a metastable state in $\mathrm{Th}^{2+}$ with the electronic configuration $6d^2\,{}^3P_0\,(5090\ \mathrm{cm^{-1}})$. This is motivated by the prospect of using the state in laser excitation of the low-energy $^{229}$Th nuclear resonance independent from the leading hyperfine interactions. The $6d^2\,{}^3P_0$ state has no dipole-allowed radiative decay channel and is connected to the second ground state $6d^2\,{}^3F_2\,(63\ \mathrm{cm^{-1}})$ through an electric quadrupole transition only. We populate the state by laser excitation at 484~nm via a higher excited level and detect its population in laser-induced fluorescence. The isotope shift of the $J=0$ level between $^{232}\mathrm{Th}^{2+}$ and $^{229}\mathrm{Th}^{2+}$ is determined as a measure of the interaction of electronic and nuclear charge distributions. The lifetime of the level in our ion trap with buffer gas is limited by collisional mixing with the nearby state $5f6d\,{}^3G_3\,(5060\ \mathrm{cm^{-1}})$. In ultrahigh vacuum, it could serve as a hyperfine-free nuclear clock that is largely immune to field-induced frequency shifts via the electron shell.

Sampling-based model predictive control (MPC) algorithms, such as model predictive path integral (MPPI), enable approximate, gradient-free solutions to optimal control problems by drawing samples from a proposal distribution, evaluating their trajectory costs, and updating the proposal parameters accordingly. However, these approaches typically rely on heuristics for adjusting hyperparameters, such as temperature or momentum, or manual tuning. We propose a trust region formulation for sampling-based MPC that constrains updates of the proposal distribution via a principled Kullback--Leibler (KL) divergence bound and, optionally, an entropy lower bound. This replaces heuristic hyperparameter adaptation with values that are optimal w.r.t. the underlying Lagrangian. We further improve sample efficiency and convergence by combining the trust region update with deterministic localized cumulative distribution (LCD)-based sampling. Experiments on two benchmark environments demonstrate that the proposed trust region update achieves faster convergence and better sample efficiency in low-sample and low-iteration regimes, especially when paired with deterministic LCD-based sampling.

Near-fields around nanophotonic structures and waveguides can be used to optically interface particles ranging from atoms and molecules to microscopic biological and synthetic particles. Due to the strong, non-linear dependence of the near-field coupling strength on the particles' position, a change of the spread of the particles' position will change their mean coupling strength. When the particles are trapped, this position spread depends on their temperature, generally leading to temperature-dependent coupling. Here, we experimentally demonstrate that this effect renders optical probing of trapped particles with near fields an inherently transient process. Specifically, we trap cold atoms in a two-color dipole trap surrounding an optical nanofiber and probe them with the evanescent field of guided, resonant light. The scattering of this probe light heats up the atoms, leading to a decrease of the coupling strength as well as loss of atoms. We observe both effects via a concurrent decrease of the absorption signal. In addition, we demonstrate that the coupling strength can be recovered by cooling the atoms back to their initial temperature. Our findings are relevant for numerous situations where stable coupling of trapped particles to a nanophotonic structure is required.

Stochastic unravelings provide a useful way to represent open quantum system dynamics in terms of pure state realizations, and have been widely studied both from a fundamental and from a computational point of view. They were initially formulated for Markovian dynamics described by the Gorini-Kossakowski-Sudarshan-Lindblad master equation. However, due to recent technological and experimental development, most physical relevant dynamics present temporal correlations beyond the Markov approximation. Such correlations cause decay rates to turn temporarily negative, thus requiring the generalization of stochastic unravelings from Markovian to non-Markovian scenarios. Indeed, many unraveling techniques have been introduced in this regime, and a comprehensive review of the different jump methods is currently missing. In this work, we provide an overview of widely used quantum jump unraveling techniques for non-Markovian systems and also discuss them in terms of their numerical efficiency, divisibility requirements, Hilbert space extension, and measurement interpretation.

In centralized, distributed, and federated learning with stochastic gradients and $n$ workers, it was recently shown that it is infeasible to find an $\varepsilon$-stationary point faster than $\tildeΩ\left(\min\left\{\frac{d κL Δ}{\varepsilon} + \frac{h L Δ}{\varepsilon} + \frac{h σ^2 L Δ}{n \varepsilon^2},\; \frac{h σ^2 L Δ}{\varepsilon^2} + \frac{h L Δ}{\varepsilon}\right\}\right)$ seconds in both homogeneous and heterogeneous settings under standard assumptions: $L$-smoothness, $σ^2$-bounded unbiased stochastic gradients, and lower boundedness of the function, i.e., $f(x) \geq f^*$ for all $x \in \mathbb{R}^d$, where $Δ= f(x^0) - f^*$, $h$ is the computation time, $κ$ is the communication speed between the workers and the server, and $d$ is the dimension of the iterates and gradients. This result is pessimistic since it does not allow a complexity in which both $\frac{d κL Δ}{\varepsilon}$ and $\frac{h σ^2 L Δ}{\varepsilon^2}$ improve with $n$, even when using random sparsification techniques; moreover, this lower bound can be matched by either non-distributed SGD or vanilla Synchronous SGD, which reduces the impact of recent progress in the design of compression-based methods. In this work, we challenge this limitation and propose new compressed methods, Inkheart SGD and M4, and show that under an additional structural assumption, which is necessary due to the lower bound and which does not restrict the class of considered problems, we achieve new state-of-the-art time complexities that break this pessimistic barrier and allow scaling with the number of workers $n$.

Turbulence has strong and seemingly random fluctuations. Assessing its repeatability is key to predicting flows in technology and nature, much of which decay as viscosity dissipates energy. Much has been done to this end since the work of Lorenz, but mostly in theory and simulations. Here we present experimental results from the Max Planck Variable Density Turbulence Tunnel where we generated decaying turbulence using an active grid, repeating the process with nominally identical initial conditions up to 30,000 times. In contrast with the case of stationary turbulence we found that the energy-carrying large scales show significant repeatability, irrespective of flow development time and turbulence strength. Small scales, however, can effectively be modeled by independent random variables, supporting current numerical approaches in which they are parametrised.

Conversational AI increasingly supports everyday decision-making, yet most systems rely on data-centric reasoning rather than the heuristic and interactional strategies people use in natural conversation. To ground design in actual human practice, we analyze 955 real-world Korean conversations (15,476 utterances) involving food and travel decisions, applying a decision-making codebook through an LLM-assisted coding pipeline. Our findings reveal that people prioritize satisficing over optimization, relying heavily on internal knowledge and interactional strategies to manage cognitive load. Critically, we identify a frequency-efficiency mismatch: the most prevalent heuristics sustain conversational flow during exploration, whereas infrequent, rule-based strategies are highly effective at driving resolution during exploitation. By mapping how these patterns transfer across the spectrum of human-AI interaction, this work provides empirical grounding consistent with cognitive theories of decision-making and offers design implications that align AI systems with human heuristic processes.

The lexical and syntactic disparities among different programming languages (e.g., Java and Python) pose significant challenges for multi-language software engineering tasks such as cross-language code clone detection and code retrieval, since queries or code snippets written in one programming language often fail to match equivalent artifacts in another. To bridge this gap between different programming languages, we proposed a novel approach to construct a multi-language shared semantic space, in which functionally equivalent source code written in different programming languages are close to each other. In this approach, we first map the Abstract Syntax Tree (AST) node labels of the code snippets written in different programming languages into a unified label set, thus compressing high-dimensional language-specific tokens into a common embedding space. Then, we employ a Graph Matching Network (GMN) to encode the paired AST graphs into "semantic vectors" that capture functional equivalence between programming languages in a unified code vector space. In such a way, we can eliminate the differences in syntax between different programming languages. To validate the effectiveness of this approach, we apply it to two downstream tasks, including cross-language clone detection and cross-language code retrieval. Experiments demonstrate that our approach substantially outperforms the state-of-the-art baselines in cross-language clone detection, improving Precision from 95.62% to 99.94%, Recall from 97.72% to 99.92%, and F1 score from 96.94% to 99.93%. In terms of cross-language code retrieval, our approach raises the average Mean Reciprocal Rank (MRR) from 0.4909 to 0.5547, showing an absolute gain of 0.0638 (13% relative improvement), which demonstrates its superior ability to rank correct code snippets high across multiple programming languages.

We introduce a theory of orthogonal polynomials on the unit sphere of the quaternions based on the notion of a $q$-positive measure (which originated in a work of Alpay, Colombo, the second author and Sabadini). The results we extend to this setting include the Szegő recurrences, the Zeros Theorem for orthogonal polynomials, the Szegő--Verblunsky theorem, and Baxter's theorem; to obtain these results, we utilise the Verblunsky coefficients (or Schur parameters) of Alpay, Colombo and Sabadini and a number of established results in the matricial setting. Our approach also requires matrix-valued analogues of Schur's recurrences for the coefficients of a Schur function and of Verblunsky's formula for the moments of a measure, which appear to be new.

Given a full column rank $M \in \Z^{\ell \times m}$ and an $F \in \Z^{n \times m}$ we present an algorithm to compute the $n \times n$ basis in Hermite form of the integer lattice comprised of all rows $p \in \Z^{1 \times n}$ such that $pF \in \Z^{1 \times m}$ is in the integer lattice generated by the rows of $M$. The algorithm is randomized of the Las Vegas type, that is, it can fail with probability at most $1/2$, but if fail is not returned it guarantees to produce the correct result. When $M$ is square and $F=I_m$, then the computed basis is the Hermite normal form of $M$, and the algorithm uses about the same number of bit operations as required to multiply together two matrices of the same dimension and size of entries as $M$.

We explore the ferroic properties of methylammonium lead iodide perovskite solar cells by Piezoresponse Force Microscopy (PFM). In vertical and horizontal PFM imaging, we find domains of alternating polarization with a width of 90 nm which we identify as polarized ferroelectric domains. High-resolution photo-conductive atomic force micrographs under illumination also show alternating charge carrier extraction patterns which we attribute to the local vertical polarization components within the ferroelectric domains. The correlation of the sample properties with Atomic Force and Kelvin Probe Force Micrographs evidence the piezo-electric nature of the domains.

Variational Monte Carlo calculations have recently reached state-of-the-art accuracy in the approximation of ground state properties of quantum many-body systems. Making use of flexible neural quantum states and automatic differentiation has bypassed traditional computational obstacles such as reliance on basis sets. In this paper, we propose a neural quantum state architecture capable of representing symmetric bosonic wavefunctions in Fock space, enabling the study of systems with variable particle number. By supplementing our variational state with Monte Carlo sampling and geometric optimization, we demonstrate competitive variational energies across an array of one- and two-dimensional systems, converging to the physical boson number under a set chemical potential. Our approach enables accurate estimates of one-body reduced density matrices, opening access to observables such as condensate fractions and radial density profiles from first principles. Our method opens the door to numerical predictions of key measurable quantities in practical grand canonical systems.

Gravitational memory provides a distinctive low-frequency probe of gravity, but explicit merger studies beyond general relativity remain limited. In this work, we investigate memory from binary black hole mergers in Ricci-coupled scalar-Gauss-Bonnet gravity, a natural extension of scalar-Gauss-Bonnet theory that admits an additional scalar breathing polarization. Based on numerical-relativity waveforms of binary black hole coalescences, we show that the change in the scalar charge of the system across merger generates a significant scalar-memory contribution. For a GW150914-like system, this effect modifies the memory signal in a gravitational-wave detector on the same observable timescale and by an amount comparable to the pure scalar-Gauss-Bonnet correction to tensor memory. Thus, it can substantially enhance the total deviation from the general-relativity prediction over a broad range of source and detector configurations. We argue that this identifies a general mechanism: whenever a compact-binary merger changes the asymptotic charge of an additional gravitational field, and that field sources an observable extra polarization, the resulting memory can provide a leading low-frequency signature of new gravitational physics.

Young massive star clusters (YMSCs) have been proposed as excellent candidates for the main sources of Galactic cosmic rays (CRs) up to the PeV range. The detection and study of gamma rays in the very-high-energy (E>100GeV) range has brought arguments in favour of this hypothesis. Current instruments have detected only a few YMSCs. Future observatories are expected to increase this number, providing a larger sample improving our ability to constrain the role of YMSCs in the origin of CRs. We study the population of TeV YMSCs detected and their properties, confronting simulations of the YMSC population to the observed sample, to address the fundamental questions concerning the spectrum of accelerated particles, the efficiency of CR production, and the fraction of the wind luminosity converted into turbulent magnetic fields. Using Monte Carlo methods, we simulate the Galactic population of YMSCs in the gamma-ray domain and confront our simulations to the catalogue of sources of the systematic survey of the Galactic plane performed by H.E.S.S. (HGPS) and the First LHAASO Catalogue of Gamma-Ray Sources. We systematically explore the parameter space of our model, including the slope of accelerated particles $α$, the CR efficiency $η_{\rm CR}$, the fraction of the wind luminosity converted into turbulent magnetic field $η_{\rm b}$, and the diffusion regime. We found 5 possible sets of parameters for which >75% of realisations agree with the combined data from the HGPS and LHAASO 1st catalogue. Certain regions of the parameter space are strongly disfavoured, such as Bohm diffusion. Our model successfully reproduces the YMSC population observed in both catalogues. With future systematic surveys, e.g. the Cherenkov Telescope Array Observatory (CTAO), this approach will help break degeneracies and improve our understanding of particle acceleration at YMSC shocks in the Galaxy.

Reed [J.~Comb.~Theory B, 1999] showed that graphs of maximum degree $Δ\geq 10^{14}$ without $Δ$-cliques are $(Δ-1)$-colorable. We design a one-pass semi-streaming algorithm for computing such a coloring. Additionally, we prove that any one-pass $(Δ-k)$-coloring algorithm for $0\leq k < (Δ+1)/2$ requires $Ω(n(k+1))$ space.

The short-board effect, analogous to Liebig's Law of the Minimum, postulates that the collective performance of a team is constrained by its weakest component. This principle has profound implications for the optimization of collaboration in a variety of contexts, including management, education, and organizational structures. Despite its theoretical significance, empirical validation remains elusive due to challenges of assessing individual capabilities, controlling real-world variables, and data biases towards successful outcomes, as well as high employee turnover.To address this absence of knowledge, we employ multi-agents driven by large language models to simulate a teamwork with standard operating procedure, revealing the relationship between individual capability and collective team performance.In homogeneous team configurations, three capability regimes are observed, particularly the Sisyphus predicament state at the critical capability threshold characterized by extensive ineffective efforts and pseudo-high efficiency. Furthermore, with a single weak link quantifying the short-board effect, we highlight different impacts across core and non-core members on the team performance.More importantly, when the team exhibits multiple weak links, a cumulative product effect emerges, demonstrating that team performance is shaped by the aggregated impact of all weaknesses rather than the weakest link solely.This suggests that mitigation strategies should extend beyond the remediation of individual weak links.These findings rigorously elaborate the short-board theory and provide actionable insights to optimize team management, organizational operations, and supply chain resilience.

Modern retrieval systems increasingly require integrating approximate nearest neighbor search (ANNS) with complex attribute filtering to handle hybrid queries in applications such as recommendation systems and retrieval-augmented generation (RAG). While HNSW-based inline-filtering methods show promise, existing approaches struggle to deliver high throughput under low-selectivity scenarios while balancing search efficiency, filtering generality, and index connectivity. To address these challenges, we propose FAVOR, an efficient filter-agnostic vector ANNS that supports arbitrary filtering conditions while maintaining stable performance across varying selectivity levels. FAVOR introduces three novel features: (1) an integrated architecture that unifies selectivity estimation and filtered ANNS execution, providing a cohesive solution for hybrid vector-attribute queries; (2) a HNSW-based inline-filtering algorithm that introduces an exclusion distance mechanism to dynamically reshape the vector distance distribution, pushing non-target vectors away from the query while promoting valid candidates toward the query, thus improving search efficiency without compromising generality or graph connectivity; and (3) a selectivity-driven search selector that estimates query selectivity and dynamically routes queries between a pre-filtering brute-force algorithm for low-selectivity cases and an optimized HNSW-based search algorithm for other scenarios, ensuring consistent performance. Extensive experiments on real-world datasets demonstrate that FAVOR achieves a 1.3-5$\times$ higher QPS at $Recall@10 = 95\%$ compared to state-of-the-art methods for arbitrary filtering conditions, while maintaining competitive performance even against tailored solutions in some filtering conditions.

Coding agents are increasingly deployed to autonomously maintain software, including to resolve user-reported issues: a bug report comes in and the agent creates a patch to address it. However, in any real-world deployment, they will encounter stale bug reports about issues that have already been resolved. Agents should recognize this and abstain from modifying the code to avoid accumulating technical debt. To systematically evaluate whether current agents do so, we introduce FixedBench, a code benchmark with 200 human-verified coding tasks in which no code changes are required, testing five recent models across four agent harnesses. We find that even state-of-the-art models fail, proposing undesirable changes (excluding tests and documentation) in $35$ to $65\%$ of cases. Explicit instructions to reproduce the issue before patching partially address this issue but introduce a new failure mode: when an issue is partially fixed, they abstain even though a patch would still be needed. More broadly, our results indicate that LLMs fall prey to an action bias: they choose to act even if inaction would be appropriate. To break this pattern, inaction needs to be explicitly framed as a path to success, which highlights an overreliance on human guidance implicit in current training objectives.

Satellite networks with dense low Earth orbit (LEO) constellations rely on aggressive spectrum reuse, making co-channel interference a dominant and rapidly varying factor that limits link availability and complicates spectrum sharing and compliance. Satellite radio map (RM) construction is therefore essential for interference cognition, yet it is challenging because the active satellite set is unknown, beam footprints and pointing are not directly observable, and received signal strength (RSS) measurements are difficult to calibrate under coupled link budget variations and noise. These latent uncertainties yield a severely underdetermined inverse problem with strong signature coherence, where existing methods often trade detection recall for precision and still fail to recover a faithful continuous RSS field. This paper proposes a beam-aware RM estimation framework that unifies active satellite identification and RSS field reconstruction through physics-consistent parametric modeling. An interpretable structural prior links geometry and beam shaping to spatial RSS formation, and an adaptive model order selection strategy infers the number of active satellites from measurements by balancing fit and complexity. Extensive experiments across varying signal to noise ratio (SNR), total satellite count, and active satellite count demonstrate consistently higher RSS spatial correlation, lower root mean squared error (RMSE), and improved F1 score, validating the proposed approach for interference-aware satellite RM construction in satellite networks.

The theory of constructed emotion defines social reality as the community-level consensus on emotion concepts assigned to interoceptive sensations arising from bodily allostasis and social interaction. In this study, we simulate this emergence process using a computational model that integrates symbol emergence with degrees of freedom in symbol interpretation and active inference. Two agents receive interoceptive signals, exchange inferred symbols, and simultaneously adapt their bodily control goals and symbol interpretations to each other. Experimental results show that the interoceptive prior preferences and symbol probability distributions of the two agents converge, confirming the emergence of social reality grounded in social consensus.

Self-encapsulated droplets floating at an oil--air interface undergo striking shape changes during evaporation, including flattening and localized loss of membrane tension leading to crumpling and wrinkling. Here we combine experiments, modeling and simulations to obtain predictive morphological maps. We perform contact-angle and evaporation experiments on water droplets coated by a hydrophobin protein film and floating in a fluorinated oil, providing reference profiles and volume-loss sequences for quantitative validation. We develop an axisymmetric mechanics framework in which equilibria follow from minimization of a total free energy combining surface energies, membrane strain energy and gravitational potential, subject to volume and contact-line constraints. A quasi-convex tension-relaxation rule accounts for compression-free states and enables coexistence of taut, wrinkled (one principal tension vanishes) and crumpled (both vanish) membrane domains. A finite element algorithm computes quasi-static morphing under volume reduction; key parameters are identified by fitting the reference contact-angle profile and then used without further tuning. The model reproduces the experimentally observed shape evolution and resolves the associated stress redistribution. Systematic parameter scans yield morphological phase diagrams governed by the Bond number, the oil--droplet surface-tension ratio and the density ratio. For buoyant droplets, crumpling relocates between exposed and submerged caps as parameters vary; for heavy droplets, a crossover to circumferential wrinkling along the immersed sidewall emerges. Wall-meniscus variations shift phase boundaries and can suppress bottom crumpling, consistent with wall-affected experiments.