Science & Mathematics

Large anisotropic magnetoresistance (AMR) usually relies on magnetic ordering, which fixes a preferred moment orientation and couples it to charge transport. Here, we demonstrate that pronounced AMR can instead arise from the anisotropic magnetic-field-induced polarization of dilute local moments in paramagnetic Fe-doped Ta4SiTe4, a quasi-one-dimensional van der Waals narrow-gap semiconductor. Magnetization measurements reveal Curie-like paramagnetism down to 2 K, with the c axis as the magnetic easy axis. The magnetization is described by an effective S = 1/2 model with an anisotropic g-factor ratio of g_c/g_ab = 1.43(1). With the electrical current applied along the c axis, the Fe-doped sample exhibits a large negative magnetoresistance for B//c and only a weak positive response for B//ab, whereas the undoped sample shows little magnetoresistance for either field orientation. The resulting AMR ratio reaches -33% at 8 K and 10 T and exhibits a nonmonotonic temperature dependence qualitatively captured by a minimal spin-disorder model. These results show that engineering the local environment of dilute magnetic ions provides a route to large paramagnetic AMR without long-range magnetic order, extending the materials design space for AMR beyond magnetically ordered systems.

Dense retrieval has become a fundamental paradigm for modern information retrieval, supporting efficient semantic search across a wide range of applications. Its effectiveness, however, relies critically on query embeddings that adequately capture users' information needs, which can be difficult for short, ambiguous, or underspecified queries. Feedback-based query reformulation approaches like pseudo-relevance feedback (PRF) alleviate this by incorporating additional evidence from retrieved or generated content. However, existing embedding-space approaches commonly aggregate or interpolate feedback vectors, implicitly treating them as complete target representations. Such pointwise fusion can unnecessarily suppress query corrections along directions not expressed by individual feedback items, even when these directions are supported by complementary evidence. To address this limitation, we propose Directional Posterior Feedback Fusion (DIFF), a query reformulation framework that models feedback as directional evidence rather than target points. DIFF represents feedback as confidence-weighted directional evidence in the tangent space of the original query and jointly infers a controlled query update through prior-regularized posterior fusion, without retraining the underlying dense retriever. Experiments across multiple dense retrievers and different feedback sources show that DIFF achieves the best overall retrieval performance, demonstrating the effectiveness of explicitly modeling how feedback should constrain query updates.

Over-the-air computation (AirComp) enables efficient wireless data aggregation by exploiting the superposition of signals transmitted by multiple source nodes, but its performance is sensitive to hardware impairments such as power amplifier (PA) nonlinearity. In this paper, we develop an analytical framework for coherent AirComp with PA nonlinearities and derive a closed-form expression for the receiver mean-squared error (MSE) under a memoryless polynomial PA model. Our numerical results show that power control optimized under the conventional linear-PA assumption can exhibit rapidly increasing MSE as the transmit power budget increases, whereas incorporating PA nonlinearity into the power control design prevents this degradation and maintains low MSE over the considered power range.

In this paper, we investigate approximate matrix multiplication through random sampling. Specifically, we propose using a non-uniform sampling distribution constructed from the leverage scores of the two matrices, and characterize the associated approximation error in terms of the matrix spectral norm; which is well-known to better characterize the downstream error when the matrix product operator is applied to an input vector. We provide the probability error bounds for the proposed algorithm are also given, and discuss connections to data-oblivious approximate matrix multiplication, and graph sparsification algorithms.

Long-horizon dialogue agents need to keep user memories stable enough for personalization while adapting when the user genuinely changes. Existing memory systems often append every observation or overwrite old entries by heuristic rules, which makes transient noise hard to distinguish from real belief drift. We introduce PreCoMem, a memory consolidation framework inspired by predictive coding that represents memory as confidence-weighted beliefs about the user's state. Its core signal, Effective Surprisal, combines semantic deviation and contradiction evidence, then downweights this signal when retrieval is diffuse and unreliable. According to effective surprisal, a two-threshold gate maps each turn to one of three deterministic updates: MAINTAIN reinforces supported beliefs, PROFILE stores ambiguous cues as low-confidence hypotheses, and CORRECT softly decays reliably contradicted facts. The PROFILE stage acts as an explicit waiting room, preventing premature commitment to noisy evidence. Experiments on LoCoMo, LongMemEval-S, and PersonaMem-v2 show consistent state-of-the-art accuracy; our new DiMoBench confirms that PreCoMem adapts to non-stationary belief drift without over-reacting to noise. The code is available at https://github.com/ZhongWishing/PreCoMem.

Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems. Prior studies have demonstrated that training on high-quality data can substantially improve model reasoning and downstream task performance. However, the upper bound of user simulation is still constrained by the model's reasoning paradigm: native chain-of-thought often degenerates into shallow pattern matching and fails to adequately model the behavioral factors underlying real user decisions. In this paper, we propose OmniSimulator, a novel training framework designed to improve the predictive ability of small LLMs for realistic user simulation on short-video platform environments. Specifically, OmniSimulator organizes behavior-oriented reasoning through a structured schema with five dimensions, carefully selects data from real user-behavior scenarios, and introduces a teacher model to generate high-quality reasoning traces under the proposed schema, helping small models learn behavior-relevant reasoning patterns rather than relying on superficial behavioral correlations. Based on this process, we construct 9,881 high-quality training instances from three months of historical interaction trajectories of 200 real users, averaging about 50 distilled examples per user. Experimental results show that OmniSimulator improves the average score of Qwen3-8B by 34.33%, achieves performance competitive with Claude-Sonnet-4.5, and supports smaller-scale, self-hosted deployment. These results suggest that learning structured behavior-oriented reasoning patterns is a promising direction towards scalable and high-fidelity LLM-based user simulation.

This paper proposes a refinement to existing vision-based portable pipelines for the recognition of graspable object parts. Such pipelines can effectively support automatic grasp selection and preshaping in upper-limb prosthetic devices. However, realistic scenarios in which multiple objects are present still represent a challenge. The proposed method exploits a minor modification to the conventional hardware setup and a custom training approach to extend the applicability of vision-based pipelines. A laser pointer is introduced in the setup as an effective mechanism for object disambiguation. Accordingly, a suitable retraining of the deep convolutional neural networks supporting recognition of graspable parts is implemented. A mixed reality strategy allows the reuse of available high-quality segmentation masks to generate realistic images procedurally, extending the range of scenarios supported by the final device. Experimental results confirm that the proposed pipeline improves target disambiguation accuracy by up to 9% compared with existing solutions, while preserving the computational cost.

Equivariant neural networks encode prescribed symmetries as powerful inductive biases, yet real-world data may exhibit symmetry-breaking structure that these priors cannot fully capture. Prior work has largely addressed this challenge through model-side equivariance relaxation, seeking an appropriate level of equivariance for the data. This raises a complementary question: must equivariance relaxation be adaptively controlled? To investigate this question, we introduce a residual path alongside a strictly equivariant model and deliberately restrict their interface to examine the transmission of task-relevant structure. Across five benchmarks, we observe both residual collapse and survival despite these restrictions; we call the latter phenomenon asymmetry tunneling. We further characterize this phenomenon through four complementary diagnostics. Our results suggest that adaptive control of equivariance relaxation is not a prerequisite for capturing potential symmetry-breaking structure.

Low-NFE generative ODE sampling is constrained not only by solver order, but also by how a fixed budget of network evaluations is distributed along the flow. We develop a density-level backward-error framework that identifies the component of a solver's modified-equation defect that changes the evolving marginal, while excluding solenoidal trajectory distortion that is invisible in Wasserstein tangent geometry. The resulting residual induces a solver-order-aware compute-allocation rule and, through gauge freedom, an objective for selecting numerically favorable velocity representatives of the same density path. This separates our approach from schedule search driven by trajectory or endpoint surrogates and from higher-order solver design. Exact Gaussian and mixture models establish the mechanism, including cases where curvature-based allocation fails and continuum-optimal transport is not discretization-optimal. On a pretrained CIFAR-10 EDM probability-flow ODE, density-aware allocation consistently improves fixed-update Euler sampling in the low-NFE regime. Matched-seed convergence to a dense Euler reference confirms that the method accelerates approximation of the same high-NFE flow rather than changing the asymptotic sampler.

We propose a new generative modeling framework grounded in forward-backward partial differential equations (PDEs). These equations arise naturally in mean-field game (MFG) theory, and are closely related to nonlinear Schrödinger systems. Our main contribution is to reinterpret this connection within the setting of generative modeling and to develop an efficient numerical methodology for its practical implementation. Specifically, we derive and characterize a nonlinear extension of the classical Schrödinger bridge problem through a coupled system of PDEs, yielding a model we call SBNL (Schrödinger Bridge NonLinear). To compute its solution, we design a simulation algorithm based on branching diffusions and the probabilistic representation of semilinear PDEs. The resulting method is embarrassingly parallel, scalable to high dimensions, and applicable to a broad class of nonlinear interaction functionals. We validate the approach through numerical experiments, including recovery of the classical Schrödinger bridge, nonlinear extensions with convex penalties (Gross-Pitaevskii) as well as potential interaction terms, and high-dimensional generative tasks such as image synthesis on the MNIST and FFHQ human face dataset. Furthermore, the same framework embeds score-based generative modeling, diffusion models, and tractable gaussian Schrödinger bridges.

Codes of professional ethics guide and restrict specialists, who increasingly work with assistants built on large language models. Such an assistant can breach the user's code, or help the user breach it, without the user noticing. Evaluations reporting broad categories of harm cannot show how often this happens or which provision is breached. TenetBench pairs 3,433 requests conflicting with clauses of fifteen Russian-language professional codes with benign twins that differ only in the legitimacy of what is asked. An automated pipeline generates the tasks from the text of a code, so the corpus grows with every code added and a provision yields new tasks on every run. It also produced tasks and human-validated verdicts from two English codes of other jurisdictions. In human validation of the frozen pipeline, specialists rated 0.995 of the conflicting tasks plausible, and the model judge agreed with the adjudicated label at Gwet's AC1 of at least 0.894 on 1,860 elements. We evaluated fourteen model and reasoning-mode combinations on whether they produce the conflicting text, decline or warn. Eleven of the fourteen produce conflicting content on most such requests, mostly without a warning, and nearly three warnings in four accompany conflicting content. The models that conform more do so by declining while still serving the legitimate twin, and enabling reasoning halved the refusals of two models. We hope the dataset and pipeline will help evaluate how language models observe professional norms and let diagnostic datasets be built for particular professions and organisations.

Snake robots can traverse complex environments with limbless locomotion, but maneuvering in low-clearance environments remains challenging because a gait must maintain a small swept height while also providing planar steering. Existing rolling gaits preserve a low swept height body configuration but generally provide limited steering, whereas steering gaits often introduce vertical undulation and require additional clearance. With that in mind, this paper develops S-rolling, a steerable rolling gait that combines low swept height locomotion with planar maneuverability for twistable snake robots. S-rolling is designed using an S-shaped planar backbone curve, whose head and tail segments roll with independently controlled angular velocities, producing different motion modes. We derive its joint space gait equation, in which a central twist joint realizes the relative rotation between the two segments. We also develop a quasi-static kinematic model relating the segment rolling speeds to the linear and angular velocities of the robot. Real-robot experiments demonstrate the various motion modes, traversal through a 10 cm clearance test track, and maneuvering tasks beneath a large workpiece and a car chassis. In these low-clearance scenarios, S-rolling successfully performs approximately 90$^\circ$ and 180$^\circ$ heading changes, whereas the baseline rolling arc gait cannot change the robot heading.

We propose a joint source-channel coding (JSCC) framework for efficient wireless point cloud transmission. In the proposed scheme, the point cloud is first divided into local patches and transformed into compact latent representations, which are then mapped to symbols for transmission over a noisy channel. Our model incorporates an entropy model that exploits inter-patch spatial dependencies to estimate patch-level coding costs and allocate different numbers of symbols accordingly. Unlike existing patch-based point cloud JSCC methods that use the same transmission rate between patches, we adapt the channel usage of each patch latent representation. To exploit inter-patch dependencies without patch-by-patch autoregressive processing, we select a group of anchor patches whose features provide contextual information for entropy estimation of non-anchor ones. The framework is trained using a reconstruction loss and an entropy-based rate surrogate. Experiments on ModelNet40 demonstrate overall rate-distortion improvements over existing baselines across channel conditions.

The scarcity of labeled underwater acoustic data limits the development of data-driven models for marine acoustic monitoring, motivating the use of self-supervised learning to exploit the abundance of unlabeled recordings. However, large-scale data alone does not guarantee effective representation learning, because model performance is strongly influenced by the distribution of the training data. To facilitate such training data, this study introduces a scalable curation pipeline that curates 3,000 hours from 41 years of raw NOAA hydrophone recordings, producing MarineSet, a large-scale and diverse dataset designed for underwater acoustic learning. A baseline model, MarineNet, trained on MarineSet achieves competitive performance on marine mammal call and ship-type classification, highlighting the value of systematic data curation for developing self-supervised learning models in underwater acoustics.

Serendipity is important to design for, yet it remains difficult to measure and evaluate. No validated questionnaire exists, so studies either borrow ResQue's novelty scale, which does not measure serendipity, or build their own items, which makes findings hard to compare and synthesize. Building on our earlier conceptualization of experienced serendipity as a fortuitous encounter with content that is both refreshing and enriching, we developed the Serendiptionnaire, a questionnaire that operationalizes our framework. We conducted twenty-seven cognitive interviews over three iterations with respondents who varied in age, education level, country of birth, and English proficiency, while they browsed recommendations on platforms such as Instagram, TikTok, YouTube, and Spotify. After each iteration, we revised the items until respondents interpreted them as intended. The resulting instrument consists of modular scales that researchers can combine to target the specific flavor of serendipity they want to study or design for. The Serendiptionnaire offers a ground truth for validating system-centered serendipity metrics and LLM-based evaluations, and it provides a nuanced evaluation of users' experienced serendipity.