arXiv:2607.06510v2 Announce Type: replace-cross Abstract: Spherical colloids with harsh repulsive forces have long been used as experimental analogs of the hard sphere model, with demonstrated good agreement with computer simulations for bulk and structural properties of the fluid, glass and crystal phases. However, an enigmatic discrepancy remains for the crystal-melt stiffness coefficient. Here we perform computer simulations of colloidal hard spheres under tunable buoyant mass and show that the long-standing discrepancy can be traced to a hitherto unrecognized gravity dependent contribution of the stiffness coefficient. This effect is one practical realization of a more general result for the external field dependence of stiffness coefficients of arbitrary interfaces.
Science Journals
arXiv:2607.06748v1 Announce Type: new Abstract: Smart home automation platforms increasingly rely on user-authored YAML configuration files to define device behaviors, but these files are prone to syntax, formatting, and semantic logic errors that can cause automation failures and safety risks. Existing YAML validators, static analysis tools, and general-purpose large language models offer limited support for end-to-end diagnosis and repair because they lack domain-specific understanding and validated correction workflows. This paper presents SmartHomeSecure, a prototype for automated detection and repair of Home Assistant configuration errors using lightweight program analysis and constraint-guided large language model generation. SmartHomeSecure parses YAML files, detects syntactic and common semantic errors, normalizes error context, applies deterministic auto-fixes for routine defects, and constructs constrained prompts that guide LLMs toward minimal and structurally valid repairs. The system is implemented as a modular web application with four layers: UI Shell, Feature Orchestrator, Domain Engine, and Integration Layer. Its repair pipeline was evaluated on 100 real-world Home Assistant YAML files with manually injected errors across five categories: syntax/parsing, indentation, mapping, sequence, and scalar quoting errors. Four models were tested: gpt-oss-20b, gpt-oss-120b, llama-3.1-8b, and llama-3.3-70b. Results show that three models achieved 100% error detection accuracy, with repair success rates ranging from 87% to 93%. Manual verification found no hallucinated or incorrect repairs among successful outputs. These findings suggest that combining domain-aware program analysis with constrained generative AI is a feasible approach for improving the reliability and usability of smart home configuration repair.
arXiv:2607.06542v2 Announce Type: replace Abstract: Dependency parsing consists of finding a tree representation for a sequence. Unsupervised dependency parsing aims to develop parsing methods without a gold standard during model training. In human languages, an unsupervised parser can be evaluated because some gold standard is usually available or can be created. For other species, a gold standard is unknown. Thus one may conclude that it is impossible to determine the accuracy of an unsupervised parser and, consequently, dependency parsing is unfeasible in other species. However, here we apply recent advances in network science to demonstrate that the proportion of correct edges retrieved by a parser must be high for the sequences of vocalizations or gestures that non-human primates produce due to the fast decay of the sequence length distribution. In contrast, human language sequences lack that property. Therefore, evaluation without a gold standard is feasible in non-human primates but a hard problem in humans.
arXiv:2607.07440v1 Announce Type: new Abstract: In parametrized linear systems $\mathsf{P}(\mu)\mathsf{x}=\mathsf{b}$ the system matrix $\mathsf{P}$ depends nonlinearly on a parameter $\mu$ and solutions are sought for many values of this parameter. We show that the compact rational Krylov (CORK) framework, originally introduced to solve nonlinear eigenvalue problems, can be used to efficiently produce approximate solutions to such a system for many values of the parameter at once. In this approach the parametrized system is first linearized, resulting in a large shifted linear system $(\boldsymbol{\mathsf{A}}-\mu\boldsymbol{\mathsf{B}})\boldsymbol{\mathsf{y}}=\boldsymbol{\mathsf{d}}$. We formulate a left- and right-preconditioned rational Krylov GMRES method for shifted linear systems and show how the CORK framework can be used to speed up these methods in the setting of parametrized linear systems. Additionally, we show how to incorporate a right-hand side $\mathsf{b}(\mu)$ that also depends on the parameter, how to choose the shifts to steer convergence and how to allow for inexact solves at these shifts throughout the iterations. As an application we consider the 'frequency sweeping' of Helmholtz scattering problems through the Boundary Element Method (BEM), enabled via an efficient representation of the dense but data-sparse wavenumber-dependent system matrix.
arXiv:2607.07023v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is often treated as a capability-adaptation step, while alignment is attributed to later preference optimization or reinforcement learning. This separation is incomplete: when examples are scored and kept online during fine-tuning, the choice of which data to train on already changes the model's behavioral preferences. We study online data selection as an implicit alignment mechanism. Given the same base model, optimizer, and selected-token budget, we compare random, loss-based, quality-based, and diversity-based online selectors and measure the behavioral drift they induce without any preference optimization. The proposed evaluation tracks helpfulness, refusal rate, verbosity, truthfulness, sycophancy, calibration, and jailbreak robustness, together with diagnostics for which behavioral modes are over-represented in the selected data. We formalize online selection as a reweighted SFT objective whose weights define an implicit preference over response styles and safety postures, so that an online scorer plays the role usually assigned to a reward model. This view predicts that high-scoring data can systematically favor longer, more assertive, more compliant, or more refusal-prone behaviors depending on how the online score is defined. Empirically, selectors that are statistically indistinguishable in task accuracy diverge sharply in refusal rate, verbosity, and sycophancy, and we show that the direction of the shift is predictable from the attribute mixture of the selected data. We introduce Alignment Drift Auditing (ADA), a controlled protocol for quantifying selection-induced behavioral movement, and Alignment-Aware Selection (AAS), a diagnostic online selector that retains data efficiency while constraining drift along safety and style axes.
arXiv:2607.07026v1 Announce Type: new Abstract: Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific structures such as JSON schema-formatted function calls. Existing systems are designed for autoregressive models and assume left-to-right generation, masking out invalid next tokens at each step. Diffusion language models, however, break this assumption: they sample multiple positions simultaneously from a fully-factorized mean-field distribution at each denoising step. In this paper, we present an exact and tractable algorithm for sampling from the constrained mean-field posterior under any constraint expressible as a finite automaton. Viewing finite automata as graphical models, we obtain tractable representations of the constrained distribution that enable efficient inference. The approach guarantees constraint satisfaction by construction, supports both greedy and sampling-based decoding, and is compatible with parallel and block-wise decoding under arbitrary remasking schedules. Applying depth-reduction techniques from arithmetic circuit theory, we further reduce sampling depth from linear to logarithmic in the sequence length. Empirical evaluations on Dream-7B and LLaDA-8B show substantial accuracy gains across various tasks including function calling (xLAM, BFCL), planning (Sudoku, Countdown), text-to-SQL (Spider), and math reasoning (GSM-Symbolic), with little inference overhead relative to unconstrained decoding. For example, on BFCL-Live, our approach improves Dream-7B's greedy decoding accuracy from 63.9% to 71.5%, and stochastic sampling accuracy from 22.3% to 69.0%, where the unconstrained baseline collapses, with under 5% wall-clock overhead.
arXiv:2607.07040v1 Announce Type: new Abstract: How can we measure intelligence beyond human capability? Human-authored benchmarks saturate, and above human capability, examiners may not know which tasks are both hard and verifiable. We argue that this difficulty is inherent to absolute-scale evaluation and propose a new paradigm based on relative measurement in which models generate public challenges that separate other systems. Aggregating these outcomes yields an adversarial psychometric rating system that can scale with the systems being measured. We describe practical protocols that reduce incentives for private-information attacks, support judge-free adjudication, and naturally scale with agent capabilities. We instantiate the framework across verifiable and open-ended, non-verifiable domains, illustrating how model-generated evaluation can continue to measure systems beyond the human frontier.
arXiv:2607.07373v1 Announce Type: new Abstract: In biological circuits, sequential neural activity evolves along dynamic, low-dimensional manifolds to enable flexible behavior. Spiking network models link aspects of this sequential activity to features of manifold geometry through specific circuit mechanisms, making dynamic neural manifolds parameterizable, and thereby offering an explainable framework for neural computation. Extending this framework to neuromorphic engineering, we present an implementation on the SpiNNaker 2 chip for real-time, closed-loop control. By allowing sensory inputs to modulate heterogeneous inhibition, gain, and transient currents, our architecture drives rapid subspace rotations to switch between behaviors, as well as fine-grained trajectory control within them. We validate this via a robotic simulation where an agent uses sensory feedback to dynamically reconfigure its manifold geometry to navigate through a maze. Our results establish dynamic manifolds as a feasible approach for explainable neuromorphic architectures and a substrate for investigating biological neural dynamics.
arXiv:2607.07375v1 Announce Type: new Abstract: Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness, input-output Jacobians, and the instability of inverse problems. Here, we focus on the spectral structure of intermediate linear transformations that propagate information through modern DNNs, an unexplored mechanism of adversarial vulnerability. Specifically, we investigate transformer-based vision-language models, whose linear layers admit interpretable spectral decompositions and whose widespread adoption makes understanding their robustness increasingly important. We propose a white-box spectral-subspace-guided attack (SSGRA) that aligns intermediate representations with the subspace spanned by the bottom right singular vectors. Our experiments show improved attack effectiveness over existing baselines. In addition, SSGRA offers a spectral interpretation of adversarial vulnerability in VLMs, providing insights for improving their robustness.
arXiv:2607.07481v1 Announce Type: new Abstract: Large language models are increasingly used to evaluate and support software engineering tasks, yet the validity of these evaluations is often undermined by uncertainty about whether benchmark instances were seen during pretraining. This can lead to data contamination, which may inflate performance and result in misleading conclusions about model capability. Despite this, the training corpora of many modern models are only partially disclosed, making direct decontamination infeasible. This creates a need for practical methods that can detect a large language models' prior exposure to training data without access to the full training corpus. To address this challenge, we organize the first Poisoned Chalice of LLM Evaluation Competition, co-located with the FSE-AIWare 2026 Competition Track. The competition frames contamination detection as a white-box membership inference task on source code and provides participants with curated datasets, target models, baseline attacks, and a final evaluation on a held-out model and dataset. This design encourages methods that generalize beyond superficial dataset artifacts and beyond a single training setting. This paper reports the setup and results of the competition. More broadly, the competition aims to catalyze the community around trustworthy LLM evaluation for software engineering.
arXiv:2607.07483v1 Announce Type: new Abstract: We study the problem of estimating the number of edges in an undirected, unweighted graph using sublinear query access. We consider a query model that preserves the structure of Independent Set (IS) queries, but augments their output with a random certificate: given a vertex subset, the oracle returns a uniformly random edge from the induced subgraph if one exists, and returns null otherwise. Using this access, we give a randomized algorithm that outputs a $(1 \pm \varepsilon)$-approximation to the number of edges with constant success probability using $\widetilde{O}(\log^{2} m)$ queries. This implies an exponential separation from both standard IS queries and global random edge-sampling models: estimating the number of edges using standard IS queries require $\widetilde{\Theta}\!\left(\min\left\{\sqrt{m},\, \frac{n}{\sqrt{m}}\right\}\right)$ queries, while direct random edge-sample access requires $\widetilde{\Theta}(\sqrt{m})$ samples. Beyond separation in query complexity, our algorithm is output-sensitive: its query complexity is polylogarithmic in the number of edges in the graph. This aligns with the classical objective in group testing, where one seeks algorithms that are both worst-case optimal and instance-adaptive. Conceptually, our model connects group testing, the decision-versus-counting dichotomy, graph property testing, and the "power of a random certificate", and can be viewed as a structured form of conditional sampling of edges in graphs.
arXiv:2509.18248v2 Announce Type: replace-cross Abstract: We theoretically explore a dynamical generalization of the Aubry-Andr\'e model in two dimensions formed by superimposing two square-lattice potentials. Motivated by the rich physics emerging at different twist angles between the two lattices at equilibrium, we introduce periodic twisting by continuously rotating one of the lattices with respect to the other in the plane. We demonstrate that the distinct time-dependent twisting in this system gives rise to an intricate form of periodic multi-frequency driving that changes with the distance from the rotation axis. We find that the incommensurate nature of the potential no longer plays the pivotal role as it does in the static case. Rather, the tunneling can be understood in terms of a local, spatially varying dynamical localization effect, which we show to yield ring-shaped states localized within the bulk that have interesting transport signatures. Quantifying the eigenstates with the Bott index and local Chern marker, we find that there is a zoo of states with non-trivial topological signatures, the most ubiquitous of which result in relatively uniform ring-shaped regions of the Chern marker. We investigate the origin of these effects from various angles and identify that hybridization between different delocalized ring states plays a vital role. Lastly, we discuss possible experimental realizations in quantum simulation settings. Our results open a new avenue of investigation with periodic twisting inducing a spatially varying multi-frequency drive.
arXiv:2607.06881v1 Announce Type: new Abstract: A multiple double is an unevaluated sum of doubles. An NVIDIA tensor core is a specialized high performance compute core for matrix multiplication. The Ampere A100, released in 2020, introduced tensor cores capable of 64-bit floating-point arithmetic. Every multiple double arithmetical operation requires renormalization, which involves branching, for which tensor cores are unsuited. To solve this problem caused by renormalization, we apply a solution similar to the Ozaki scheme [Ozaki et al, Numerical Algorithms, 2012]. Our software is available under the GPU GPL license on github.
arXiv:2607.07511v1 Announce Type: new Abstract: Business-to-X (B2X, "X" for business, customer, etc.) mobile applications ("apps") show various mobile-specific chances and challenges that must be ad-dressed in the whole development process. Knowledge about mobile app devel-opment models' usage remains restricted, although various process models for B2X app development have been published already. This article first reviewed available process models for mobile app development. In addition, 28 expert in-terviews with various stakeholders involved in typical B2X mobile app develop-ment processes were conducted to examine this research-practice knowledge di-vide. Since hybrid process models are often advantageous for B2X mobile app development, technical backgrounds or communication processes, are also cru-cial. Since no process model can be used unadopted, this study theorizes mobile-specific characteristics and challenges of app development process models to a reference model generally valuable for management decision support. The find-ings create avenues for better theorizing toward successful B2X mobile app de-velopment.
arXiv:2510.22081v2 Announce Type: replace-cross Abstract: We use particle simulations to reveal two distinct propulsion mechanisms for a scallop-like swimmer to propel itself in granular media by reciprocally flapping its wings. Based on the discrete element method, we examine the structure, kinematics, and contact forces of particles near the swimmer to quantify how jamming manifests as stagnant zones near the swimmer in a frictional granular medium, which are less intense during the opening stroke than the closing. This broken symmetry is quantified by the difference in the number of strong particle contact forces formed during opening and closing, which shows a linear relation with the swimmer's net displacement across various swimmer and medium configurations, all favoring the opening stroke. We identify a secondary propulsion mechanism in a dynamic regime with significant swimmer inertia, as the flapping period approaches the coasting time for a moving swimmer to come to rest under the medium resistance. In this case, the swimmer's net displacement is correlated to the ratio between these two time scales, and the swimming direction favors the closing stroke due to the smaller medium resistance as the swimmer coasts with closed wings.
arXiv:2607.07129v1 Announce Type: new Abstract: Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Leveraging this principle, we propose a generative learning-from-demonstration framework that enables compositional modeling of robotic behavior by connecting perception and motion through shared object-level representations. We render scenes from object-centric neural representations that integrate canonical neural fields with latent-conditioned deformations, capturing positional and geometric variations in a smooth, consistent, and interpretable way. For motion generation, a temporal mixture-of-experts (MoE) employs a gating mechanism to combine object-conditioned movement primitives over time, producing complete trajectories. This spatial-temporal compositionality maintains the data efficiency of movement primitives while grounding motion in visual structure, enabling systematic generalization across diverse scene configurations. In simulation, long-horizon manipulation tasks are successfully completed using the proposed model, which requires significantly less training data than other image-based baselines. Real-world experiments further demonstrate the method's robustness to noise, its ability to generalize at the category level through language-based segmentation models, and its capacity to operate directly on 3D scene representations.
arXiv:2607.06920v1 Announce Type: new Abstract: We describe the methodology used to alias the free-text author/committer identities of the entire World of Code (WoC) collection (version V2604, ~107M distinct author strings over ~6B commits) into canonical persons, extending the fingerprint-based anti-aliasing of ALFAA and the 38M-identity resolution of Fry et al. by an order of magnitude. At this scale the central problem is over-merge, not missed merges: a few bridge identities (bots, role accounts, placeholder emails, multi-author commit fields) transitively weld unrelated clusters through the global union step into million-member "mega-clusters." We report the full experimental record (more than twenty experiments, including unsuccessful ones) behind the deployed design. Node-level gates (information score, project spread, degree) preserve recall but cannot dissolve the mega-cluster; per-value blocklists of high-quality-but-shared attributes are recall-safe but cannot break a redundant mesh; the working composition is a betweenness cut over the exact union graph plus a per-edge classifier trained on 2.6M labels mined from GitHub no-reply identifiers. That classifier, filtering dormant cross-project shingle groups and joined by GitHub's own account assertions, then recovers the recall the precision work had foregone. Against human-adjudicated pairs the per-edge model transfers at AUC 0.99; end to end the largest cluster falls from 170,431 (and a predecessor's 3.0M) to under 7,000 w hile gold recall rises from 0.44 to 0.70 at increasing precision, and on an independent 21M-alias GitHub ground truth the final map outscores its predecessors and the published state of the art among global, privacy-preserving resolvers. The record doubles as a catalog of scale lessons: structural cuts do not transfer to edge sets they never saw, and recall-only and precision-only benchmarks invert verdicts unless read together.
arXiv:2607.07522v1 Announce Type: new Abstract: Controlled research on AI ideation typically compares independent agents, while field studies of human-AI collaboration sacrifice experimental control. We introduce a controlled, two-player extension of the Alternate Uses Test (AUT) that enables comparison of human-human and human-AI co-creation under matched interactive conditions, alongside calibrated non-interactive baselines. The platform supports decomposition of performance into three typically confounded factors: participant traits, partner perceptions, and content dynamics. An in-person pilot (N = 62) demonstrates its utility. Under matched time limits, originality with a GPT-4 partner is statistically equivalent to that with a human partner. Approach motivation (BAS Drive) moderates whether interactive partnership benefits originality, and self-reported cognitive outsourcing predicts lower originality specifically in human-human dyads. Prior exposure to highly creative ideas improves later performance, suggesting a "seeding" intervention. We release the platform, code, and dataset as a shared testbed for controlled studies of human-AI co-creation.
arXiv:2607.07161v1 Announce Type: new Abstract: The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy. To address this, we propose a novel, end-to-end adversarial spatio-frequency refinement network (ASFR-Net). Initially, a modality-invariant representation learner (MIR-Learner) guides the backbone to extract modality-invariant features, effectively bridging the primary domain gap. Subsequently, to address persistent residual modal differences, we design an innovative spatio-frequency synergistic enhancement module (SFEM), which identifies and suppresses sensor-specific noise and artifacts that are difficult to discern in the spatial domain by leveraging frequency-domain processing. Multi-level difference features are then computed from these refined representations and fed into a decoder equipped with cascaded hierarchical guided fusion module (HGFM) blocks to generate precise change maps. To alleviate the data scarcity in heterogeneous tasks, we construct and release a new high-resolution benchmark specifically focused on building changes: the visible-near-infrared heterogeneous change detection (VisNIR-HCD) dataset. It presents unique scientific challenges arising from deceptive visual similarity and non-linear spectral inversions, providing a robust platform for evaluating model generalization. Extensive experiments on VisNIR-HCD and public datasets demonstrate that ASFR-Net achieves state-of-the-art (SOTA) performance, significantly outperforming existing methods. The source code and the VisNIR-HCD dataset are publicly available at https://github.com/LuoYang2024/ASFR-Net.
arXiv:2506.08630v3 Announce Type: replace Abstract: A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utilizing contextual information about the properties of individual robots and exploiting their modular structure in the architecture of deep reinforcement learning agents. When the robots have highly dissimilar morphologies, however, this becomes a challenging problem, especially when the agent must generalize to new, unseen robots. In this paper, we posit that contextual features are often only partially available, but that they can be recovered through modular interactions. This can allow for better multi-robot control and generalization to contexts that are not seen during training. To this extent, we implement a transformer-based architecture with shared modular recurrence and evaluate its (generalization) performance on a large set of MuJoCo robots. The results show a substantial improvement in zero-shot generalization performance on robots with unseen dynamics, kinematics, and topologies, in four different environments.
Pickle: Precise, Flexible Cross-Core Last-level Cache Data Prefetching for Irregular Memory Accesses
arXiv:2511.19973v2 Announce Type: replace Abstract: Graph analytics and sparse scientific workloads are dominated by parallel chains of data-dependent, long-latency memory accesses whose patterns are difficult for hardware to infer yet straightforward to express in software. Conventional hardware prefetchers attempt to recover this information from address streams alone, but false positives lead to substantial memory traffic overhead. Software-assisted approaches offer greater flexibility but still consume core limited resources. We propose Pickle, a software-defined, hardware-managed lastlevel cache (LLC) prefetcher that follows the decoupled access/execute philosophy. Pickle serves as an independent access engine, fully decoupled from core resources, that executes prefetch kernels sliced from the original application to bring data into the shared LLC ahead of demand. We evaluate Pickle using full-system, cycle-level simulation of a cluster of 8 high-performance cores, running all GAP benchmark suite algorithms across nine real-world graphs and irregular-access dominated scientific applications from the NAS parallel benchmark suite. Over a no-prefetching baseline, Pickle achieves 1.49x geomean speedup with only 2% DRAM traffic overhead on graph algorithms, and 1.53x with a 4.5% memory traffic reduction on NAS scatter/gather kernels. For reference, the state-of-the-art coreprivate indirect prefetcher achieves 1.40x but incurs 43% DRAM traffic overhead on graph workloads, and 1.36x at zero traffic overhead on scatter/gather kernels, illustrating the challenge of inferring irregular access patterns without application-level context. Pickle also composes transparently with private cache prefetchers: combining it with the state-of-the-art indirect or a simple stride prefetcher yields 1.65x-1.66x and 1.72x-1.84x geomean speedup on graph and NAS scatter/gather workloads, respectively.
arXiv:2607.07534v1 Announce Type: new Abstract: We present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBot-World featuring four distinct upgrades. (1) Our model achieves an unbounded interaction horizon while maintaining consistent output quality, benefiting from a carefully crafted causal pretraining paradigm. (2) Through distilling a real-time variant from the base model, our system guarantees rapid response time, sufficient to drive 720p video streams at 60 fps. (3) Compared to the previous version, this update introduces highly diverse interactive elements, comprising a broader spectrum of actions (e.g., attacking, archery, spell-casting, and shooting) alongside a richer variety of text-driven events. (4) We pioneer the integration of an agentic harness within the domain of world modeling, wherein a pilot agent is tasked with planning and executing character behaviors, while a director agent is responsible for synthesizing novel environmental elements as the scene progresses. Additionally, to facilitate a shared experience, we develop an interface that permits multiple players to simultaneously immerse themselves in this vivid world simulator. We pair our primary 14B model with a lightweight 1.3B counterpart, which supports effortless deployment on a single GPU.
arXiv:2604.14208v2 Announce Type: replace Abstract: We study the classification task of structured-light beams after propagation through a random turbulent medium. The received speckle patterns are generated by numerical simulation of a stochastic paraxial propagation model, and the classification task is formulated over a finite alphabet of 15 OAM source classes. We benchmark intensity and autocorrelation inputs using SimpleCNN and ResNet-18 as classifiers. We also quantify the effect of training-set size and receiver-window misalignment. Since additional propagated samples may be costly to obtain, we develop a class-conditioned diffusion model for generative augmentation of turbulence-degraded intensity images. The main contribution is a spectrum-aware diffusion objective: a pixel-domain loss combined with a Fourier-domain Bregman regularizer designed to preserve high-frequency speckle statistics. We prove that this hybrid objective is consistent with the posterior-mean regression target of the diffusion model and show that generated samples substantially improve low-data classification.
arXiv:2603.24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, largely due to their convenient closed-form expressions for choice probabilities. However, these models entail restrictive assumptions on the stochastic utility component, constraining our ability to capture realistic and theoretically grounded choice behavior$-$most notably, substitution patterns. In this work, we propose an amortized inference approach using a neural network emulator to approximate choice probabilities for general error distributions, including those with correlated errors. Our proposal includes a specialized neural network architecture and accompanying training procedures designed to respect the invariance properties of discrete choice models. We provide group-theoretic foundations for the architecture, including a proof of universal approximation given a minimal set of invariant features. Once trained, the emulator enables rapid likelihood evaluation and gradient computation. We use Sobolev training, augmenting the likelihood loss with a gradient-matching penalty so that the emulator learns both choice probabilities and their derivatives. We show that emulator-based maximum likelihood estimators are consistent and asymptotically normal under mild approximation conditions, and we provide sandwich standard errors that remain valid even with imperfect likelihood approximation. Simulations show significant gains over the GHK simulator in accuracy and speed.
arXiv:2604.10592v2 Announce Type: replace-cross Abstract: Quantum cloud providers can identify a user's algorithm and secret problem structure without ever seeing actual quantum data, simply by analyzing routine metadata collected for billing and system management. Existing confidentiality tools such as blind quantum computation and quantum homomorphic encryption protect the quantum payload itself, but they do not protect this classical orchestration metadata. This leaves an unexplored security risk in the logs generated when a large quantum program is split into smaller pieces to fit onto limited hardware, a process known as circuit cutting. These fragments leak sensitive information through what we term the topological transpilation penalty: the unavoidable depth and gate inflation added when a compiler reorganizes a program for a restricted hardware topology. Tests on a 156-qubit production Quantum Processing Unit (QPU) show that traditional timing side-channels fail in this setting, since hardware control-plane delays mask actual quantum execution time. The unique shape of the transpilation penalty acts instead as a persistent structural fingerprint for the hidden workload. Using 12,000 circuit fragments across eight algorithm families, our attack recovers algorithm family and Hamiltonian k-locality with near-perfect accuracy, achieving instance-disjoint AUC = 1.000 for both. This leakage persists under size-holdout evaluation on unseen circuit scales, with AUC = 0.987 and 0.986 respectively. The cutting mechanism is inferred with AUC = 0.991, and hardware topology is recovered well above chance with AUC = 0.818. These results show that circuit cutting exposes algorithmic intent, and potentially proprietary problem structure, through metadata alone, without any need to observe quantum data.