Science & Mathematics

Conventional communication systems are designed to transmit data reliably, focusing on metrics such as bit error rate, throughput, and channel capacity. However, these traditional measures do not evaluate whether the received information is actually useful for a specific downstream task. To address this limitation, semantic communication has emerged as a promising paradigm that prioritizes the meaning, context, and task relevance of transmitted information rather than raw bit level reconstruction. This paper presents a comprehensive review of the theoretical foundations, system architectures, learning techniques, and evaluation practices in semantic communications. We classify existing state-of-the-art algorithms ranging from deep joint source-channel coding (Deep JSCC) and task-oriented feature transmission to generative diffusion models and contrast their operational trade offs against conventional baselines. Furthermore, we examine key application domains, including remote sensing, autonomous vehicles, and healthcare IoT, while addressing open challenges in security, privacy, robustness, and standardized evaluation.

Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approaches have emerged, offering high-precision, targetless calibration capabilities. However, existing methods often suffer from a critical dataset shortcut phenomenon due to the lack of extrinsic diversity in their training data. Networks tend to overfit to fixed nominal extrinsic configurations, leading to severe performance degradation and poor generalization in real-world deployments. To address this limitation, we propose a highly generalizable data generation framework tailored for training LiDAR-camera calibration networks. Our framework leverages existing omnidirectional image datasets annotated with depth ground truth to synthesize multi-modal calibration data under rigorous extrinsic constraints. Extensive experiments demonstrate that the data generated by our pipeline significantly enhances the generalization and robustness of typical learning-based calibration networks. Furthermore, we conduct comprehensive real-world evaluations across various camera-LiDAR configurations, validating the zero-shot transfer capability of models trained exclusively on our synthetic data. To facilitate future research and community use, the code for data generation, the pre-trained models, and online benchmark datasets will be made publicly available.

When an LLM agent acts on behalf of a user, it is expected to act within the authorization scope the user grants. Existing agent evaluations largely measure task completion or harmful consequences and can therefore overlook actions that violate this delegation without producing conventional safety or privacy harms. We introduce AuthorizationBench, a benchmark of authorization alignment grounded in documented real-world cases. A scalable construction pipeline produces 286 executable tasks spanning situational triggers, the presence or absence of direct harmful consequences, and ten application domains. We execute every task with four models at two reasoning-effort settings across three agent harnesses in an authorization-aware sandbox that keeps authorized and out-of-authorization paths executable and observable, yielding 6,864 trajectories. Agents act out of authorization in 19.2% of trajectories, with rates ranging from 10.5% to 26.6% across the 24 configurations. Task completion does not imply authorization. Increasing reasoning effort leaves the aggregate rate nearly unchanged, with effects varying across models. On controlled pairs, agents act out of authorization at similar rates with (17.9%) and without (20.8%) direct harmful consequences, and more often under benign pressures than malicious injections (23.5% versus 17.6%, p=0.002). Our results show that task capability, additional reasoning, and consequence-oriented safety evaluation do not by themselves ensure authorization alignment. Moreover, agents that consult the principal before completing a task exhibit lower rates of out-of-authorization behavior (14.0% versus 21.6%, p

Task transition detection aims to identify when a person shifts from one task to another, providing a foundation for continuous health monitoring and timely context-aware interventions. While statistical and deep feature representations have demonstrated strong classification performance, they often rely on aggregated or latent features that are difficult to interpret in behavioural terms. Event-based features provide a compact and interpretable alternative by encoding behavioural and physiological patterns as discrete tokens with explicit semantic meaning, making them well suited for behavioural analysis in health informatics. Self-attention offers a mechanism for modelling interactions among such tokens; however, conventional self-attention classifiers primarily rely on token order and do not explicitly incorporate timing or duration information. As a result, informative temporal cues in event representations may not be fully exploited for task transition detection. This study proposes an event-based time-aware self-attention framework that represents behavioural signals as sequences of discrete events augmented with onset timestamps and durations. Event timing is encoded using a parameter-efficient kernel-based representation, while a novel type-aware duration embedding modulates information propagation according to event persistence. Experiments on a semi-naturalistic wearable dataset show that the proposed model outperforms existing event-based approaches, achieves performance beyond or comparable with statistical and deep-learning models with a more compact input representation, and provides improved interpretability through integrated-gradient-based analysis. These results indicate that explicit modelling of event timing and duration enhances both classification performance and interpretability, supporting reliable deployment in biomedical and health informatics applications.

Low curvature trace, an effective rank condition, is a common explanation for why randomized zeroth-order methods scale to high-dimensional tasks such as language model fine-tuning. Can deterministic methods achieve the same savings? We show that they cannot. We study finding $\varepsilon$-stationary points of $L$-smooth nonconvex functions on $\mathbb{R}^d$ from exact function values, assuming that below the starting value the Hessian is dominated by a fixed matrix with trace at most $LE$; this is a fixed-matrix form of the effective rank condition used to explain zeroth-order fine-tuning. When $ET\le d$, the Gaussian two-point method that such fine-tuning uses needs only $O(ET)$ calls if it rejects steps that raise $f$, where $T=LΔ/\varepsilon^2$ is the iteration count of gradient descent from initial gap $Δ$, and we prove that every randomized method needs $Ω(ET)$ calls, so this cost is optimal up to constants. In contrast, every deterministic method needs $\widetildeΩ(d\min\{T,\sqrt{ET}\})$ calls, and adaptive subspace search attains $O(d\min\{T,\sqrt{ET}\})$: a deterministic method pays about $d$ calls per discovered direction, and the trace budget allows about $\sqrt{ET}$ directions. Determinism thus multiplies the cost by a factor of order $d/(E+\sqrt{ET})$ up to logarithmic factors, which grows linearly with $d$ for fixed $E$ and $T$; once $E$ exceeds $T$ by a logarithmic factor, deterministic methods need $Ω(dT)$ calls, as without any trace budget. Our lower bounds hold for $C^\infty$ functions with the matrix bound on all of $\mathbb{R}^d$, even when each call also returns gradient projections priced by rank.

Safety pretraining has emerged as a promising paradigm for embedding safety deeply into a model during pretraining, yielding robustness to subsequent fine-tuning that conventional safety post-training often lacks. We refer to this safety acquired during model pretraining as native safety. Yet native safety is expensive to acquire, confined to the model in which it was pretrained, and often accompanied by lower general utility. In this work, we rethink the practical value of safety-pretrained models and introduce, for the first time, the native safety reuse problem: Can native safety be transferred from a specialized, potentially smaller, safety-pretrained "teacher" to standard-pretrained "students," enabling them to inherit its robustness while preserving their general utility? We show that native safety can "travel" through on-policy distillation. A standard-pretrained student generates responses to safety-pretraining prompts, while a small safety-pretrained teacher supervises the student's token-level predictions along its own generated trajectories; cross-tokenizer alignment further enables transfer across model families. The resulting students exhibit substantially greater safety robustness than their safety-post-trained counterparts while retaining higher general utility than the teacher: subsequent fine-tuning raises the average attack success rate (ASR) of the 1.7B student by only 5.00%, compared with 11.58% for safety post-training, and its average utility of 48.20% far exceeds the aligned teacher's 27.42%. Unlike conventional large-to-small knowledge distillation, our framework enables selective, small-to-large safety transfer: it reuses the robust safety expertise of a smaller pretrained teacher without inheriting its utility limitations. These findings establish native safety as a reusable model property rather than one permanently tied to the originally pretrained model.

Memory-based temporal graph neural networks (TGN) rely on recurrent node memories to capture evolving interaction histories. However, temporal mini-batching introduces a trade-off between computational efficiency and temporal fidelity: when a node appears multiple times within the same batch, later interactions are evaluated using stale pre-batch memory because the intermediate memory updates have not yet been committed. This problem becomes more severe as temporal batch size increases. To overcome this challenge, we propose AnchorRM, an anchor-based residual memory mechanism that reconstructs event-specific intra-batch node states without executing sequential recurrent updates. AnchorRM treats the last committed node memory at the beginning of each batch as a pre-batch fixed anchor and uses causally preceding same-batch interactions to infer the missing state evolution relative to that anchor. The learned residual then calibrates the anchor to reconstruct an event-specific node memory for each interaction. Because each event-specific residual is predicted directly from the fixed anchor and its causal history, reconstructed states do not depend on previously reconstructed states and can therefore be computed in parallel within the batch while preserving temporal causality. Experiments across four temporal graph datasets and three memory-based backbones demonstrate that AnchorRM achieves an average 6.16x training speedup (up to 10.05x) and 28.67x batch scaling (up to 40x). These results demonstrate that AnchorRM substantially improves the scalability and training efficiency of memory-based temporal graph learning while maintaining competitive predictive performance.

Clinical natural language processing includes diverse tasks such as information extraction, classification, and question answering. Developing one model for all tasks could simplify deployment and improve knowledge transfer. We evaluated whether small recurrent-depth language models can meet this need through joint fine-tuning and compared their performance with standard transformers. We fine-tuned Ouro1.4B-Thinking, Ouro-2.6B-Thinking, HRM-Text-1B, Qwen3-1.7B, and Qwen3-8B on 9 datasets covering information extraction, classification, inference, and question answering, and evaluated them on four additional datasets excluded from training. Across these 13 datasets, Ouro-1.4B-Thinking achieved higher performances than Qwen3-1.7B on 11 datasets; Ouro-2.6BThinking was competitive with Qwen3-8B on most tasks. Ouro models showed the largest gains on inference and question answering, whereas HRM-Text-1B showed mixed results. These findings support further study of recurrent-depth models for clinical tasks through controlled experiments to isolate architectural effects and investigate how recurrent computation influences reasoning, knowledge acquisition, and generalization.

When an enterprise must stop using a software component (for example, because of an interim court restraint in a technology patent dispute), the economic loss passes through a chain of technical mechanisms. Withdrawing the component exposes dependent capabilities. Fallback paths degrade throughput, correctness or feature coverage. Incident rates and recovery backlogs rise. Customers respond. Remediation programmes compete against the restraint clock, and planned launches are pushed back. This paper develops and verifies a \emph{technical--economic impact model} (TEIM) that quantifies this chain end to end as a probability distribution of present-value loss rather than as a point estimate. The technical layer combines (i)~a lattice-valued, fixed-point semantics of service dependencies with degraded fallbacks, (ii)~continuous-time Markov reliability models evaluated by uniformization and specified in continuous stochastic logic, and (iii)~a fluid queueing model of post-outage backlog. These feed (iv)~convex acute-outage loss functions with regulatory and contractual thresholds, (v)~compound-Poisson excess-incident processes, (vi)~survival-weighted functional, attrition and funding channels that provably avoid double counting, (vii)~a real-options valuation of forcibly delayed launches with an explicit closed form, and (viii)~a COCOMO~II--driven stochastic activity network for design-around and substitution strategies. We prove seven theorems and eight propositions.

Rapid and accurate hypocenter determination based on three-dimensional (3D) seismic velocity models is essential for seismic monitoring in subduction zones, yet it remains computationally demanding. HypoNet Nankai, a hypocenter determination package that uses physics-informed neural network (PINN) travel-time inference for a realistic 3D P-wave velocity model of the Nankai Trough subduction zone, was designed to address this problem, but supported only P-wave arrival times. Here, we present HypoNet Nankai PS, which extends the framework to both P- and S-wave travel times. We first constructed an S-wave velocity model from the existing P-wave model using empirical conversion relations for marine sediments and the crust. Because the resulting S-wave velocity structure has lower velocities and therefore considerably steeper travel-time gradients, the original PINN training procedure designed for the P-wave velocity structure was insufficient. We therefore revised the training procedure by reconsidering the distribution of collocation points, the NN architecture, and the optimizer. Benchmark comparisons with the fast marching method showed that the revised approach substantially reduced the root-mean-square deviations for both P- and S-waves compared with the original algorithm. Recovery tests using synthetic earthquakes were also performed. When applied to 223 earthquakes recorded by DONET stations off the Kii Peninsula, the system yielded hypocenters generally consistent with the JMA catalog beneath the network, suggesting that the 3D velocity model captures travel-time variations accommodated by JMA's 1D model and station corrections. Farther seaward, the estimated hypocenters were substantially shallower than those in the JMA catalog and were located in the oceanic crust and uppermost mantle, consistent with previous relocations that used DONET data and 3D velocity models.

Objective: To demonstrate and evaluate THERMO, a thermal-velocity optimization algorithm for autonomous electrosurgery, in ex vivo porcine tissue using the UR10e-based ASTR system. Methods: Thermography for Electrosurgical Rate Modulation via Optimization (ThERMO), a closed-loop algorithm that uses real-time thermal imaging data to minimize thermal injury while ensuring accurate cut margins, was tested on ex vivo porcine tissue. Histological analysis was performed to detect thermal injury extent at a cellular level, and 3D photogrammetric measurement was used to verify margin accuracy. ThERMO's performance was compared against two previously used fixed velocity methods. Results: ThERMO outperformed both fixed velocity comparison methods without requiring data-driven training or iterative tuning, reducing thermal injury by up to 52%, improving cut accuracy by up to 59%, and maintaining task completion speed. Conclusions: Closed-loop thermal feedback via ThERMO improves autonomous robotic electrosurgery by simultaneously reducing thermal injury and improving margin accuracy, reinforcing its value over fixed velocity approaches for tumor resection applications. Significance: This work advances autonomous robotic tumor resection by demonstrating that real-time closed-loop thermal feedback control can substantially reduce collateral tissue damage and improve surgical margin accuracy.

Generating high-fidelity, city-scale 3D environments plays a crucial role in applications such as virtual reality, gaming, and urban digital twins. However, the scarcity of real-world city-scale 3D training data forces existing methods to either stitch independently generated assets or learn from synthetic urban corpora, often sacrificing realism and global consistency. In contrast, 2D image data and their corresponding generative models are abundant and encode rich visual priors. To bridge this gap, we propose CityPaint, a novel framework that leverages 2D diffusion models to generate globally consistent city-scale 3D textures without relying on city-scale 3D training data. Specifically, CityPaint integrates geometric priors with the generative power of 2D diffusion models, introducing a robust top-$K$ camera baking strategy for iterative texture projection. To address the artifacts of 2D-to-3D projection, we further propose a decoupled refinement mechanism that separately optimizes terrain and building textures. Extensive experiments show that CityPaint produces globally coherent textures with fine local details and achieves favorable results against strong baselines in both visual quality and cross-view consistency.

Artificial radio emissions have historically been a major focus of technosignature searches. Here, we apply an anthropological lens to expand this scope to surface technosignatures: non-random, intentional patterning on planetary terrain, similar to long-lasting human-made landscapes and monuments on Earth. On geologically inactive bodies such as the Moon and Mars, artificial traces and patterns could potentially remain visible for hundreds of millions of years. We discuss two recent developments that make this search timely: high-resolution imaging datasets, such as those produced by the HiRISE camera on the Mars Reconnaissance Orbiter (MRO), and ML-based anomaly-detection methods. Finally, we list two possible approaches to developing a model capable of searching for these surface technosignatures: a supervised and an unsupervised one. Complemented by LiDAR, ground-penetrating radar, and reflectance spectroscopy, these methods would enable the first quantitative upper limits on surface technosignatures while yielding ancillary benefits for planetary science.

Recent multimodal LLM (MLLM)-based anomaly detection (AD) methods can generate textual explanations that help interpret their predictions. However, reliably and efficiently evaluating these explanations remains challenging. While LLM judges can assess explanations through structured rubrics, they often require substantial token generation, making evaluation costly and difficult to scale. We instead propose to anchor evaluation in the available ground truth by extracting verifiable claims from each explanation and verifying them against the corresponding annotations. We instantiate this idea as AnomalyJev, a lightweight framework that extracts verifiable claims from contextualised representations using ground-truth-defined queries and pointer based heads, without token generation. The claims are then verified against the ground truth to score faithfulness and completeness. For evaluation, we construct a human-annotated set of 640 explanations with over 1,600 expert ratings across in-domain and cross-dataset settings. Despite its compact design, AnomalyJev achieves stronger agreement with human annotators than most open MLLM judges and approaches that of flagship MLLM judges, at a fraction of their runtime and resources.

Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts. For responsible deployment of such systems, we desire insight into how much we can trust their individual outputs. A popular approach for estimating LLM confidence is to train probes on the hidden states of the LLM's tokens to predict the correctness of the model's output. These probes typically read hidden states from tokens that are already in the model's vocabulary. In this work, we introduce a new approach to confidence probes that learn a new readout token and utilize its hidden states to train the probe. We show that our learned readout approach ScratchProbe outperforms its existing-token counterpart on a variety of datasets and LLMs. We also extend our method to a multi-token approach which we show provides further performance improvements. Finally, since the learned readout changes only the position a probe reads from, any existing probe can adopt it. Applied to a state-of-the-art probe, it improves AUROC on every backbone, in domain and out of domain.