Science & Mathematics

The increasing complexity of modern telecommunications networks introduces security challenges that require systematic threat analysis. However, a gap remains between architectural threat modeling frameworks, such as Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege (STRIDE), and operational knowledge bases, such as the MITRE Five-G Hierarchy of Threats (FiGHT). This gap limits the ability to connect architectural weaknesses with specific protocol behaviors and attack sequences. This paper presents Multi-stage Attack Surface Analysis and Reasoning (MASAR), a formal framework that bridges this gap through three layers: strategic threat mapping, logical attack path decomposition using directed acyclic graphs (DAGs), and operational synthesis of tactics, techniques, and procedures (TTPs). MASAR is evaluated using a 5G-ready IP Multimedia Subsystem (IMS) architecture, examining two signaling attack scenarios: early media billing bypass through Session Initiation Protocol (SIP) signaling and subscriber information disclosure and topology discovery through the Diameter Cx interface. MiniZinc constraint models are used to analyze the reachability of unsafe protocol states. The analysis demonstrates how MASAR connects architectural threats with specific protocol behaviors and supports security controls that account for network states in telecommunications systems.

High linear decodability does not entail causal controllability in continuous-latent text-to-speech (TTS). In VoxCPM2, position-matched probes and equal-norm interventions across 30 speakers and 60 directed dyads expose this separation: reference-audio states at the final residual acoustic language model layer (RALM L7) retain 0.970 probe accuracy, yet aligned edits yield 0/60 target-dominant dyads. Edits at early reference-audio positions produce the opposite pattern. At L0, pair-aligned updates increase target similarity by 0.326 and achieve target dominance in all 60 dyads, against at most 1/60 under any equal-norm direction or position control. Independent speaker encoders, retrieval against held-out speaker prototypes, and double-blind listening confirm that the shifted voice is heard as the intended target. Cross-architecture interventions in dots.tts distinguish backbone leverage from direct acoustic control: backbone edits displace the source without target attraction, while x-vector ablation reduces source similarity from 0.849 to 0.006. These findings identify two requirements for training-free speaker control: geometric specificity and a computational route that carries the intended speaker condition into acoustic synthesis.

As Large Language Models (LLMs) evolve into autonomous Deep Search and Deep Research Agents capable of executing long-horizon, multi-step tasks, evaluating their reliability becomes increasingly critical. However, existing evaluation paradigms predominantly focus on final task outcomes, leaving a significant gap in understanding the intermediate execution traces, where agents are highly susceptible to various errors. To address this critical gap, we introduce a comprehensive methodology and analytical framework designed specifically to diagnose errors in intermediate workflows. Building upon this framework, we establish a structured taxonomy that categorizes these vulnerabilities into two primary dimensions: System-Level Errors and Task-Level Errors. Through a systematic examination of step-by-step execution traces and agent-environment interactions, we reveal how these underlying errors affect overall performance. Finally, through empirical experiments and systematic analysis of the identified error types, we provide actionable design guidelines, aiming to enhance the robustness, reasoning capabilities, and reliability of Deep Search and Deep Research agents.

Mid-training (MT) is a training phase in LLM training pipelines that continues pretraining (PT) on smaller datasets of higher value. Taking the PT and MT phases as a whole, training usually starts with only PT data and then transitions completely to MT data. In this work, we view this complete transition from PT to MT data as a special case of a broader class of data mixing schedules that specify how the proportions of PT and MT tokens in each batch change over training. Under fixed total training compute and MT token budgets, we ask: Is this complete transition to MT data optimal for minimizing final MT loss? We show that the answer depends on the learning rate (LR) schedule: one data schedule may outperform another under one LR schedule, but changing only the LR schedule can reverse the outcome. Specifically, when the LR decays throughout continued training, introducing MT data earlier and mixing it with PT data lowers the final MT loss compared with placing all MT data at the end of training, but this result is reversed if we use constant LR. We therefore study how to jointly choose when to introduce MT data and when to decay the LR through ablation studies, and find that it is better to decay the LR only after MT data enters training. Finally, we develop a theoretical model that explains why the optimal data schedule for a fixed MT token budget depends on the LR schedule.

Stance classifiers make errors that can be costly to identify under limited human-review budgets. Prioritizing potentially incorrect predictions remains challenging, particularly across heterogeneous base models. We introduce B-CARE (Blind Classwise Assessment of Relative Evidence for Stance Error Ranking), a prediction-blind framework for identifying high-risk stance predictions. Given only a target and text, a fixed LLM verifier assigns ordinal evidence scores to all candidate stance labels without observing the base model's prediction or confidence. B-CARE constructs a comparative risk score by contrasting the strongest alternative-class evidence with the evidence supporting the predicted class. The resulting classwise evidence profile can be reused across multiple base models without training a separate error detector or calibrating risk scores. We evaluate B-CARE on four stance benchmarks and three heterogeneous base LLMs, covering 12 dataset-model settings and 5,206 unique test instances. Evaluation includes error-ranking discrimination (AUROC and AUPRC), selective prediction (E-AURC), and review effectiveness (ErrorCapture at fixed review budgets). B-CARE achieves a mean AUROC of 0.735 and a mean AUPRC of 0.524, capturing 41.06% of errors within the highest-risk 20% of predictions. Although B-CARE does not uniformly outperform strong confidence-based baselines, it provides a reusable, complementary evidence signal for prioritizing potentially incorrect stance predictions across heterogeneous models.

Zero-shot cross-lingual voice cloning often carries the reference speaker's accent into the target language. We propose closed-loop dual-reference guidance, which pairs the original reference with a reusable native anchor constructed by voice conversion (VC). The references initialize separate language-model (LM) states that guide a shared acoustic trajectory; each completed patch then updates both states, keeping their generated histories aligned with the emitted speech. Anchor construction runs once per speaker, and online synthesis uses a frozen TTS model without per-utterance VC. On 20 held-out French speakers, the fixed setting $λ^{\!*}=0.33$ reduces residual French LID mass by 73% versus direct cloning, at an ECAPA similarity cost of only $0.042$; relative to anchor-only cloning, it recovers $0.155$. The method achieves the lowest mean WER ($0.009$) and highest mean GOP ($4.750$) among the held-out test arms. In blinded preference tests, native-English listeners favor it over direct cloning on pronunciation naturalness, and over both anchor-only cloning and a TTS$\to$VC cascade on speaker identity. Online RTF is approximately 26% lower than the cascade's ($0.59$ vs. $0.80$), and the same setting reduces Spanish LID mass by 56% without re-tuning.

Long-context continual pre-training (LCCP) is widely used to improve long-context capability, but how its benefits scale with the number of tokens used for LCCP remains unclear. We investigate this question through modeling, monitoring, and validating the benefits of scaling LCCP. First, we establish an empirical additive power law relating long-context performance to model size and the respective token budgets of short-context pre-training and LCCP. Experiments across five model scales and more than one hundred short- and long-context budget configurations show that the fitted law reproduces the observed trends and extrapolates well to held-out model scales. Second, we introduce \textsc{Constant-Last4}, a training and observation protocol that averages the four most recent consecutive checkpoints from a constant-learning-rate run to enable stable monitoring along a single extensible training trajectory. Under this protocol, the same scaling-law form fits well and accurately predicts the LCCP performance trajectory of a 1.22B-parameter model trained on approximately 100B LCCP tokens. Finally, downstream evaluations along this trajectory show continued improvements in long-context performance, with no clear sign of saturation within the evaluated range. These gains persist after both 128K context extension and supervised fine-tuning, with benefits extending to long-context understanding and agentic tasks. Together, these results argue for continuing to scale LCCP rather than stopping early: its gains are stably observable along an extensible trajectory, quantitatively predictable, and transferable to downstream tasks.

Sparse Mixture-of-Experts (SMoE) models improve scalability by routing input tokens to only a subset of expert networks, but they often suffer from representation collapse, in which multiple experts converge toward similar functions and lose their intended diversity. Prior work has addressed this limitation either by improving router architectures or by introducing auxiliary loss terms that implicitly penalize similarity among experts. However, router-based approaches remain vulnerable to routing collapses, and loss-based approaches can conflict with the task's original objective during optimization. In this work, we propose GaP-MoE (Gradient Accumulation and Projection in MoE), which induces expert specialization by directly manipulating expert output gradients rather than modifying the loss function. At each training step, GaP-MoE stochastically selects one of the $TopK$ routed experts as a leader based on normalized routing scores, and projects the gradients of the remaining follower experts onto the orthogonal complement of the subspace spanned by the leader's recent gradients. This removes directional overlap between expert gradients without altering the task loss, allowing specialization to emerge as a direct consequence of the optimization dynamics rather than an externally imposed constraint. We evaluate GaP-MoE in both fine-tuning settings (BERT-based, on GLUE) and from-scratch pretraining settings (GPT-2-based, on WikiText-103, enwik8, and text8), and further confirm its compatibility as a drop-in module for existing SMoE architectures. Experimental results show that GaP-MoE achieves performance comparable to or better than standard SMoE.

Industries employ robots for repetitive tasks due to their high precision and performance, typically achieved through feedforward control actions based on an accurate robot description. However, an exact dynamic model can be difficult to obtain, especially for robots with elastic elements or when the payload changes. Learning-based feedforward approaches are promising for trajectory tracking under model uncertainties, but typically face generalization issues when task parameters vary. This paper proposes a novel method to generalize the feedforward control action with respect to payload parameters, independently of the underlying controller. Firstly, the minimal number of example objects needed for payload generalization is derived. Then, an algorithm is proposed to execute the same trajectory with a new payload, given only its parameters, without knowledge of the robot's dynamic model and even in the presence of elastic joints. The effectiveness of the proposed algorithm is validated in simulations and experiments on tracking tasks with different robots and controllers. Two sets of experiments are performed: on a standard Franka Emika Panda robot, and on a Franka Emika Panda robot equipped with an elastic wrist.

Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by whether they can recall stored facts or past interactions. Whether memory can drive genuine continual learning remains an open question. In this paper, we introduce Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves future actions, not merely what it retrieves. MCCL has three main parts. First, a three-axis taxonomy organizes memory by where state is stored (substrates), what form it takes (assets), and how it is managed (policies). This defines a two-loop lifecycle that updates memory state in an inner loop while refining management policies in an outer loop based on task feedback. Second, an evaluation protocol measures retention under interference, adaptation to change, and task transfer to isolate genuine behavioral gains from simple context expansion. Third, a memory scaling framework models how agent performance scales with experience, storage capacity, and compute budgets across non-parametric and parametric memory. Together, these components reframe agent memory from passive lookup to continuous learning, offering a principled route toward self-improving autonomous agents.

Deep learning based models accurately delineate tumours on dynamic contrast-enhanced MRI (DCE-MRI), but segmentation masks alone do not provide the rationales or reasoning used by radiologists to assess the lesion suspicion. This limits the explainability as well the clinical integration. We investigate whether clinically relevant descriptors can instead be derived from the segmentation masks and their dynamic contrast-enhancement series. We have used Breast Imaging Reporting and Data System (BI-RADS) guidelines as a structured framework. Using these guidelines we designed a BI-RADS decision tree with eight quantitative measurements using radiomics, together with the lesion size and component count, that determine the descriptors at eight nodes. Using these descriptors we generate natural-language rationale using a large language model. The model was constrained to express only the derived records from the decision tree. After applying this framework to the expert and automatic masks of each of the 1,506 cases in the MAMA-MIA dataset, we generated a reproducible corpus of 3,012 BI-RADS grounded rationales. We obtained a faithfulness score of 99.98% and a counterfactual accuracy of 99.3%, indicating that the generated rationales faithfully verbalises the measured descriptors and correctly reflects changes in the underlying record. Agreement between the descriptors from expert and automatic masks falls from 0.832 to 0.549 as mask quality falls from Good to Missed. The generated rationales are intended as draft representations for radiologist review and final sign-off rather than autonomous clinical reports.

Infrared small target detection (IRSTD) is a challenging task due to the extremely small size of targets, low signal-to-noise ratios, and heavy background clutter in infrared imagery. While the progress of deep learning has advanced this field, existing methods primarily adopt discriminative paradigms that directly map images to target masks, overlooking the potential of generative modeling. In this paper, we propose DiffIRSTD, a novel generative framework that reformulates infrared small target detection as a background reconstruction problem. Instead of explicitly predicting target regions, we reinterpret small targets as noise-like signals and leverage a diffusion-based model to recover a clean, target-free background from a perturbed input image. The residual between the reconstructed background and the original image is then exploited to highlight candidate target regions, providing explicit cues for subsequent detection. To address the computational cost typically associated with diffusion sampling, we introduce a one-step denoising strategy that enables efficient background reconstruction while maintaining practical inference speed. Furthermore, we present a frequency-guided perception loss to preserve high-frequency background details and focus more on the reconstruction quality of target regions. More importantly, our DiffIRSTD operates as a plug-and-play framework, compatible with existing IRSTD models without increasing the computational complexity of detectors. Extensive experiments on IRSTD-1K, NUAA-SIRST, and NUDT-SIRST datasets demonstrate the superiority of our method, achieving state-of-the-art performance across three benchmarks.

X-ray and neutron scattering are indispensable for structural characterization across condensed-matter physics and materials science. Yet, because scattering experiments generally record intensities without phase information, distinct structures can yield similar scattering patterns, rendering the corresponding inverse problems non-unique. This ambiguity can be reduced by combining complementary measurements, but their integration into machine-learning frameworks remains challenging. Here we introduce a modular framework for probabilistic multi-measurement analysis based on prior-amortized flow matching. Single-observation posterior estimators are trained separately for individual observation types or physical probes and combined only at inference time through a compositional sampler. This late fusion approach allows pretrained single-observation models to be reused without dedicated joint retraining. As a case in point, we consider reflectometry, which provides depth-resolved, nanometer-scale information about thin films and interfaces. Our single-observation demonstrations span X-ray, time-of-flight neutron and polarized neutron reflectometry. In separate co-refinement studies, task-matched estimators enable homogeneous composition across multiple neutron contrasts and heterogeneous multimodal composition of X-ray and neutron measurements. Together, these results establish compositional flow matching as a modular approach to uncertainty-aware inversion and co-refinement, with potential applicability to other scattering problems in which multiple observations constrain a common physical structure while retaining measurement-specific parameters.

In this paper, we develop a dual-function waveform design framework for multiple-input multiple-output integrated sensing and communication (MIMO-ISAC) systems operating in congested electromagnetic environments. In such environments, the dual-function waveform must coexist with legacy systems, such as localized positioning sensors, maritime radar networks, and satellite communication links, that occupy specific frequency bands and angular sectors. To enable precision coexistence, we incorporate localized angular-spectral region constraints that suppress beamformed energy only within the angle-frequency regions occupied by each legacy system, while preserving the spatial degrees of freedom (DoFs) available outside those regions. The resulting waveform design problem is formulated as a nonconvex optimization problem and solved using a low-complexity majorization--minimization alternating direction method of multipliers (MM--ADMM) algorithm. Simulation results demonstrate that the proposed framework provides precise legacy-system protection while maintaining robust radar sensing and multi-user communication performance.

Species distribution models based on thinned point processes are now commonly used in ecology to analyze presence-only data collected through citizen science, including in spatio-temporal frameworks. A key challenge in these models is the separation of the ecological process from the observation process, which raises identifiability issues. Here, we investigate the theoretical and practical identifiability of a thinned Poisson point process model. We show that, with a log link for ecological intensity and a logit link for observation probability, the model can be structurally identifiable under simple conditions, provided that observation probability varies spatially. We then evaluate the benefits of using temporal replication within a spatio-temporal framework to improve estimation when ecological intensity and observation probability are weakly separated. We quantify these benefits through simulations. The results highlight persistent practical identifiability problems affecting the ecological and observation intercepts, especially when observation probabilities are low. However, in a spatio-temporal model with partial pooling, these parameters are estimated more accurately than in a single-year model, even when only a short time period is considered. Temporal replication also improves estimation of parameters shared across years. Finally, we illustrate the use of such a spatio-temporal model through a study of coypu (Myocastor coypus) distribution in Occitanie, France. Overall, our results show that temporal replication can partially alleviate practical identifiability problems in presence-only point process models when independent data sources are unavailable.