arXiv:2605.29354v1 Announce Type: new
Abstract: LLM-powered coding agents increasingly participate in software development workflows by generating code, selecting dependencies, and producing package installation commands. This creates a new software supply chain risk: when an agent hallucinates a non-existent package, an attacker may register the hallucinated name and later compromise users who install it. Existing package hallucination attacks and defenses primarily focus on naturally occurring hallucinations, targeted dependency steering, or post-hoc package validation. In this paper, we introduce \emph{Neutral Prompting Attack} (NPA), a highly stealthy attack paradigm in which semantically benign instructions, such as encouraging imagination and exhaustiveness, increase package hallucination propensity without containing explicit malicious intent. Unlike targeted dependency steering, NPA does not specify an attacker-chosen package. Instead, it shifts the model's dependency generation behavior toward more speculative package names. We evaluate NPA across multiple coding-oriented LLMs and package hallucination benchmarks. Our results show that NPA increases both \emph{Hallucination ASR} and \emph{Pip Install ASR}, changes the distribution of hallucinated package names, and evades existing static-analysis, LLM-based, and agent-based Skill defenses. These findings reveal that harmless-looking prompts can covertly manipulate hallucination behavior and create downstream software supply chain risks.
Science Journals
arXiv:2605.29530v1 Announce Type: new
Abstract: Ethanol is conventionally perceived only as a pungent tastant, while the potential sweet properties of ethanol clusters have remained unrecognized. Here we show that ethanol tetramers exhibit a unique time dependent lingering sweetness, distinct from the immediate upupfront sweetness of conventional sweeteners. To objectively verify this phenomenon, we developed an impedance based bionic electronic tongue that monitors changes at a lipid polymer membrane interface. Sensory evaluation revealed increasing ethanol cluster content from 4.83% to 29.53% prolonged sweetness duration from 10s to 25s, whereas sweetness intensity remained low to moderate (1.0-1.7 on a 10 point scale). Using this electronic tongue, we achieved objective discrimination between upfront and lingering sweetness,full band phase angle scanning identified optimal frequencies of 0.933Hz (t=0.171s) for xylitol (fast binding and dissociation) and 0.03Hz (t=5.305s) for ethanol clusters (slow binding and dissociation). Phase angle responses correlated strongly with sensory sweetness duration. In natural aged Chinese Baijiu (1-20 years), low frequency phase angle signals positively correlated with sensory lingering sweetness scores, confirming that ethanol cluster accumulation during aging underpins the sweetness of Baijiu. This work expands the classic AH-B sweetness theory from single molecules to supramolecular clusters and provides an analytical platform for probing the temporal dimension of flavor perception.
arXiv:2605.29028v1 Announce Type: new
Abstract: Conditioned Sequence Models (CSMs) learn policies by treating return-to-go (RTG) as a control signal. However, existing CSMs often treat the RTGs as simple numerical inputs rather than aligning them with the performance of their policies. In this paper, we propose Q-ALIGN DT, a framework that enforces this alignment by ensuring the $Q$-value of the output policy is consistent with the input RTG. By leveraging a $Q$ function to provide dense guidance to CSMs and further fine-tuning it using an RTG-perturbation technique with the CSM, our method ensures that higher RTGs are consistently mapped to trajectories with higher expected returns. Theoretically, we show that Q-ALIGN DT can efficiently learn the desired policy and output a near-optimal one when the RTG is sufficiently high. Empirically, we demonstrate through extensive experiments that Q-ALIGN DT achieves superior controllability and performance across the D4RL benchmark. Remarkably, our model effectively learns a structured family of policies that maintains precise alignment and generalizes to tasks like velocity-tracking where prior methods fail.
arXiv:2605.29430v1 Announce Type: new
Abstract: Automatic speech recognition (ASR) is a core component of human--computer interaction and an increasingly important front-end for LLM-based assistants and agents. However, most current ASR systems still follow a single-pass paradigm, which is poorly aligned with human communication, where misunderstandings are resolved through iterative clarification and refinement. This mismatch makes it difficult to correct meaning-critical errors once they occur. Meanwhile, token-level metrics such as WER or CER cannot adequately reflect such a problem. To address these limitations, we formulate \emph{Interactive ASR} as a multi-turn refinement task and propose \textbf{Agentic ASR}, a closed-loop framework that combines a single-pass ASR front-end with semantic correction, intent routing, and reasoning-based editing. We further introduce the \textbf{Sentence-level Semantic Error Rate} ($S^2ER$), an LLM-based semantic evaluation metric, together with an \textbf{Interactive Simulation System} for scalable and reproducible benchmarking. Experiments on multilingual, named-entity-intensive, and code-switching benchmarks show that iterative interaction consistently reduces semantic errors, with much larger gains in $S^2ER$ than in conventional token-level metrics. Human--AI alignment and ablation studies further validate the reliability of the semantic judge and the robustness of the proposed framework. The code is available at: https://interactiveasr.github.io/ and the live demo is available at https://i-asr.sjtuxlance.com/
arXiv:2605.30059v1 Announce Type: new
Abstract: We connect stochastic resetting from non-equilibrium statistical physics with ridge regularization in statistical learning. For linear gradient flow, resetting to the origin at rate $r$ produces stationary mean $(X^\top X+rI)^{-1}X^\top y$, exactly the ridge estimator with penalty $\lambda=r$. This uses the known Laplace-transform relationship between ridge regression and exponential-time averaging of gradient flow, with the exponential time now interpreted as the stationary age associated with Poisson resetting. We then extend this identity to general renewal reset laws: the exponential reset time distribution is the unique renewal law whose stationary mean reproduces scalar ridge in every eigendirection as an exact filter identity for every positive curvature, while non-exponential renewal laws generate alternative spectral filters. At the fluctuation level, we study a separate additive Ornstein-Uhlenbeck extension with constant diffusion, interpreted as a stylized SGD approximation. In this setting, the equality holds only at the level of the mean, since the reset process has a nonzero stationary covariance from accumulated OU noise and reset-timing variance, whereas deterministic ridge is a fixed estimator with the same center. Stylized experiments compare the deterministic renewal-induced filters directly and illustrate when filters induced by non-exponential reset-time laws can differ predictively from ridge. The results for the stationary mean and the induced spectral filters are established for continuous-time gradient flow with isotropic resetting on quadratic objectives; the covariance and risk formulas additionally assume additive noise with state-independent covariance.
arXiv:2605.29380v1 Announce Type: new
Abstract: Mainstream strategies for finetuning pretrained multimodal models often degrade out-of-distribution (OOD) robustness, a phenomenon known as catastrophic forgetting. In this paper, we develop a theoretical framework for multimodal contrastive finetuning, yielding closed-form solutions and a geometric decomposition for each strategy. This framework shows that self-distillation is more effective than other regularization approaches to retain the knowledge of the pretrained model. Our analysis reveals a largely overlooked limitation: standard Exponential Moving Average (EMA) teachers, widely used in robust finetuning, suffer from collapse. To solve this, we prove that a Weighted Moving Average (WMA) teacher maintains a persistent regularizing force over finite horizons and yields bias-free convergence in the task subspace while preserving orthogonal knowledge. These insights motivate **TRACER** (**T**rajectory-**R**obust **A**nchoring for **C**ontrastive **E**ncoder **R**egularization), which combines contrastive learning with WMA-guided multi-perspective distillation. Extensive experiments on CLIP finetuning demonstrate consistent OOD accuracy and calibration gains across three backbone architectures, and comprehensive ablations confirm that TRACER is both principled and robust to hyperparameter choices. Code is available at [https://github.com/HesamAsad/TRACER](https://github.com/HesamAsad/TRACER).
arXiv:2406.10238v2 Announce Type: replace
Abstract: An infodemic refers to an enormous amount of true information and misinformation disseminated during a disease outbreak. Detecting misinformation at the early stage of an infodemic is key to reduce its harm to public health. An early stage infodemic is characterized by a large volume of unlabeled information concerning a disease. As a result, conventional misinformation detection methods are not suitable for this misinformation detection task because they rely on labeled information in the infodemic domain to train their models. To address this limitation, state-of-the-art methods learn their models using labeled information in other domains to detect misinformation in the infodemic domain. The efficacy of these methods depends on their ability to mitigate both covariate shift (i.e., differences in feature distributions) and concept shift (i.e., differences in labeling patterns) between the infodemic domain and the domains from which they leverage labeled information. However, these methods focus on mitigating covariate shift but overlook concept shift, rendering them less effective for the task. In response, we theoretically show the necessity of tackling both covariate and concept shifts as well as how to operationalize each of them. Built on the theoretical analysis, we develop a novel misinformation detection method that addresses both covariate and concept shifts. Using real-world datasets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over state-of-the-art misinformation detection methods as well as prevalent domain adaptation methods that can be tailored to solve the misinformation detection task.
arXiv:2605.30272v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) formulate the solution of partial differential equations as residual minimization problems over neural network parameterizations. Although highly flexible, optimization of PINNs using modern variants of Stochastic Gradient Descent algorithms is expensive. On the other hand, iterative computation of PINN parameterization using the Gauss-Newton method suffers from convergence difficulties, dense Jacobian structures, and poor conditioning that limit the effectiveness of second-order optimization methods. In this work, we introduce IGA-ODIL, a spline-based residual minimization framework combining ideas from Optimizing DIscrete Loss (ODIL), robust variational residual minimization, and Isogeometric Analysis (IGA). Instead of neural-network parameterizations of PINNs, the unknown solution is represented by smooth B-spline basis functions, leading to sparse structured Jacobians and efficient Gauss--Newton optimization. We also derive robust residual formulations based on weighted Gram operators, making the loss function related with the true error. The resulting systems inherit locality, sparsity, and approximation-theoretic properties of classical finite element and isogeometric methods while preserving the residual-learning philosophy of scientific machine learning. The proposed methodology is evaluated on several benchmark problems, including Poisson equations, convection-dominated advection--diffusion equations, Helmholtz problems with highly oscillatory solutions, nonlinear Allen--Cahn equations, and inverse Helmholtz parameter identification. Numerical experiments demonstrate orders-of-magnitude speedups compared with PINNs and CRVPINNs while maintaining high accuracy and robustness.
arXiv:2509.23571v3 Announce Type: replace
Abstract: As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect and mitigate risks. Large Language Models (LLMs) offer promising capabilities for enhancing threat analysis. However, their effectiveness in real-world blue team threat-hunting scenarios remains insufficiently explored. This paper presents CyberTeam, a benchmark designed to guide LLMs in blue teaming practice. CyberTeam constructs a standardized workflow in two stages. First, it models realistic threat-hunting workflows by capturing the dependencies among analytical tasks from threat attribution to incident response. Next, each task is addressed through a set of operational modules tailored to its specific analytical requirements. This transforms threat hunting into a structured sequence of reasoning steps, with each step grounded in a discrete operation and ordered according to task-specific dependencies. Guided by this framework, LLMs are directed to perform threat-hunting tasks through modularized steps. Overall, CyberTeam integrates 30 tasks and 9 operational modules to guide LLMs through standardized threat analysis. We evaluate both leading LLMs and state-of-the-art cybersecurity agents, comparing CyberTeam against open-ended reasoning strategies. Our results highlight the improvements enabled by standardized design, while also revealing the limitations of open-ended reasoning in real-world threat hunting.
arXiv:2601.12553v2 Announce Type: replace
Abstract: Micro-Pattern Gas Detectors (MPGDs) are extensively employed in modern high-energy and nuclear Physics experiments because of their excellent spatial resolution, high rate capability, and operational stability. Among these, the Gas Electron Multiplier (GEM) has emerged as one of the most widely adopted MPGD technologies. Despite their widespread adoption, GEM detectors based on the conventional bi-conical hole geometry do not always achieve optimal performance, particularly in maximizing effective gain while suppressing ion backflow. One of the primary factors limiting a GEM's performance is ion backflow. The accumulation and gradual discharge of these ions might alter the local electric field, resulting in a temporary dead time and complicating responses to subsequent events. These limitations pose challenges for applications requiring high precision and stable long-term operation. In this work, we address these issues by investigating modified GEM geometries designed to enhance gain performance and reduce ion backflow, thereby improving overall detector performance. The current study investigates geometric optimization strategies for a triple-GEM detector to enhance performance, mitigate ion backflow, and augment gain. The detector structures were designed using the ANSYS Mechanical APDL, and the associated electrostatic field configurations were computed using the ANSYS Maxwell. A thorough investigation of gain and ion backflow calculations was carried out when the generated field maps were interfaced with Garfield$^{++}$. The potential enhancements in detector efficiency and stability that the proposed modifications to the GEM foil geometry offers a valuable insights for the design of next-generation gaseous detectors.
arXiv:2605.30294v1 Announce Type: new
Abstract: We present RaFI, a CUDA and MPI based software framework that simplifies the task of building GPU-enabled data-parallel software where rays or similar work items need to migrate between different GPUs. RaFI provides a simple interface for CUDA kernels to forward such work items to other GPUs, while under the hood managing all the CUDA and MPI related work required to make this happen. We describe RaFI's motivation and implementation, and show its potential in several example applications.
arXiv:2510.09831v2 Announce Type: replace
Abstract: Permanent magnet electron optics offer many advantages over electromagnets, and are being increasingly used in high-energy (GeV) electron accelerator designs. Here, we identify the advantages of permanent magnet electron optics for low-energy (keV) electron accelerators. We explore the applications of a class of designs based on axially magnetized permanent magnets, which offer a variety of advantages such as short focal lengths (few mm), while also preventing apparent emittance growth resulting from starting particles in a magnetic field. The proposed design philosophy is applied to an accelerator based on the ultracold electron source. The design is shown to be `emittance preserving' even for very short focal lengths (~5 mm) at an emittance level better than 1 nm-rad, while the short beamline (12 cm) limits space-charge effects. Two remedies for the mitigation of typical manufacturing and alignment challenges are considered. The performance of the design (related to parasitic aberrations) is enhanced by the proposed techniques. Applications of this design philosophy can improve the performance of ultrafast electron diffraction setups with minimal manufacturing effort.
arXiv:2605.29392v1 Announce Type: new
Abstract: AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, rather than how task effort is distributed between users and tools. Here, we introduce offloading score, a measure of reliance that quantifies the fraction of cognitive effort offloaded to an AI tool. Offloading Score is simulation-based -- we construct a counterfactual workflow by estimating how the user would have completed the task without the tool, and then computing the fraction of steps saved by using the tool. We validate offloading score through intrinsic evaluations of metric validity, and a controlled user study ($n=40$) with developers performing programming tasks using AI tools. We vary time pressure to test whether reliance measures capture the known increase in reliance under time pressure. We show that offloading score detects significantly higher reliance in time-constrained settings ($+43\%$, $p=0.018$), while usage-based and self-reported baseline measures of reliance do not distinguish the conditions. We complement this with descriptive insights showing that higher reliance manifests as greater delegation of subtasks to the tool and more direct reuse of AI outputs. Finally, we demonstrate an approach of using offloading score in combination with target outcomes of a task (e.g., code understanding) to identify when reliance may be (in)appropriate. Our framework offers two contributions: an instrument users can apply to measure and reflect on their own reliance, and a quantitative signal that agent designers can utilize to mitigate overreliance.
arXiv:2605.29394v1 Announce Type: new
Abstract: While large language models (LLMs) excel at static scientific reasoning, they struggle to model the temporal structure of dynamic physical processes. We present EvoMD-LLM (Evolutionary Molecular Dynamics Large Language Model), a framework that reformulates species-level molecular dynamics as a symbolic temporal language modeling problem. Reactive MD trajectories are discretized into sequences of molecular events, where each token represents a chemical species augmented with its persistence duration, enabling standard autoregressive LLMs to learn compositional evolution over time through efficient fine-tuning. A key component of EvoMD-LLM is temporal scaffolding, which treats event duration as an explicit linguistic token and serves as a structured inductive bias, significantly reducing invalid or hallucinated molecular outputs compared to conventional sequence modeling approaches. We evaluate EvoMD-LLM on multiple temporal prediction tasks, achieving up to 66.14% accuracy and consistently outperforming sequential neural networks and language-based baselines. Beyond quantitative improvements, we qualitatively observe that the model is capable of generating interpretations for its own predictions by incorporating relevant chemical knowledge, even though it was not explicitly supervised with paired trajectory-explanation data. These results demonstrate that symbolic temporal language modeling provides an effective framework for grounding LLMs in dynamic physical simulations.
arXiv:2605.30031v1 Announce Type: new
Abstract: Large Audio Language Models (LALMs) expand jailbreak risks from token-level prompting to the full speech perception-to-reasoning pipeline, where unsafe behavior can be induced through semantics, acoustic style, signal artifacts, or internal representations. Existing work studies these risks under heterogeneous threat models and evaluation protocols, making it difficult to compare attack practicality or defense utility. This paper provides a unified taxonomy and a controlled empirical evaluation of LALM jailbreak attacks and defenses. We organize prior work into semantic, acoustic, signal, and embedding-layer attacks; guard-based, training-free, and training-based defenses; and cross-modal, audio-native, and interactive benchmarks. We then evaluate representative attacks and defenses across ten open-source LALMs, measuring not only attack success rate but also benign refusal and latency. Our results show that Acoustic Best-of-N reveals strong worst-case audio-space vulnerabilities, Narrative Framing is an effective low-latency semantic threat, and current defenses trade robustness against benign usability. These findings support cost- and utility-aware evaluation as a necessary complement to success-rate-only LALM safety benchmarks.
arXiv:2605.29396v1 Announce Type: new
Abstract: Safety alignment for large language models (LLMs) aims to reduce harmful or unsafe behavior while preserving general utility. However, recent findings reveal that alignment effects can be fragile: lightweight post-alignment manipulations, such as parameter noise, activation noise, or quantization, can easily weaken the intended safety behavior. Prior efforts to improve robustness have primarily focused on data curation, modified alignment objectives, and safety-critical parameter identification, leaving the role of the optimizer itself largely unexplored.
In this paper, we are the first to study the robustness of safety alignment from the perspective of the base optimizer. This optimizer-centric view naturally points to zeroth-order optimization, which provides a robustness-oriented signal by evaluating safety alignment under perturbations. Based on this insight, we propose a hybrid framework that first performs standard first-order safety alignment and then applies zeroth-order refinement to improve robustness. Both theoretically and empirically, we show that only a few zeroth-order refinement steps can enhance robustness while preserving safety alignment. We further improve the efficiency of zeroth-order refinement by exploiting its inherent perturbation-based evaluations to estimate layer-wise robustness sensitivity, enabling the refinement process to concentrate updates on robustness-critical layers with modest training overhead.
arXiv:2605.29399v1 Announce Type: new
Abstract: A central challenge in affective computing is determining appropriate empathy levels for different interaction contexts. Prior work has characterized two poles: task-focused interactions, where empathy demand is near zero, and emotional disclosure, where empathy demand is high. This paper identifies a distinct intermediate type, decision support under stress, in which a sender faces a consequential choice while experiencing emotional difficulty. We hypothesize that this type elicits an asymmetric empathy profile: empathy comparable to emotional disclosure but instrumentality comparable to task-focused exchange. We test five hypotheses using 28,239 post-reply dyads from three Reddit advice communities, classified into three interaction types and scored for empathy depth, empathy form, and instrumental proportion using LLM-based annotation with pattern-based robustness checks. Results confirm the predicted asymmetric profile: decision-support-under-stress replies show significantly higher empathy than task-focused replies (M = 0.47 vs. 0.24, p < 0.001) while maintaining high instrumentality (0.83 vs. 0.77 for emotional disclosure, p < 0.001). Behavioral empathy dominates (36.6%), and community-validated response quality is negatively associated with empathic expression (r = -0.075, p < 0.001). Community norms modulate baselines substantially but preserve the structural ordering. These findings establish a human empathy baseline for this interaction type and have direct implications for calibrating empathic expression in affective AI systems.
arXiv:2510.22437v2 Announce Type: replace
Abstract: Large Reasoning Models (LRMs) solve complex tasks by generating long Chain-of-Thought (CoT) sequences; however, the emergent dynamics governing reasoning trajectories are not well understood and can lead to inconsistencies and reasoning pathologies. In this work, we propose to approximate LRM's emerging hierarchical reasoning dynamics as a trajectory within a Finite State Machine (FSM) transitioning among six abstract cognitive states. We demonstrate that these states and transitions can be captured in the latent state of the model. We believe that this representation can have different applications in the interpretability and optimization of LRM models. For example, by analyzing the topology of these transitions, we identify statistical shifts in reasoning strategies that help identify effective reasoning chains from those that fail. To illustrate these potential advantages, we propose Q-Value guided steering, a training-free inference-time control method that treats reasoning as a planning problem. We estimate the long-horizon utility of state transitions and apply sparse, orthogonal activation steering at sentence boundaries to align the CoT generation with optimal reasoning policies. Experiments across four benchmarks (AIME25, MATH-500, GSM8k, and GPQA Diamond) using three state-of-the-art open reasoning models demonstrate that Q-Value steering policy achieves significant performance gains with "surgical" efficiency, often requiring 25 times fewer interventions than greedy and weighted baselines, which suggests that reasoning can be effectively controlled by guiding high-level cognitive dynamics rather than micro-managing token generation. Code is available at: https://github.com/shahariar-shibli/CoT-FSM.
arXiv:2605.30001v1 Announce Type: new
Abstract: The transition toward Software-Defined Vehicles (SDVs) represents a major paradigm shift in vehicle design, transforming traditional hardware-centric systems into software-centric platforms capable of dynamic adaptation and continuous functional evolution. SDVs enable advanced capabilities such as Over-the-Air (OTA) updates, intelligent automation, and connected services driven by AI. This paper presents a comprehensive survey of the architectures, enabling technologies, and operational frameworks that define modern SDVs. It examines the evolution of vehicle architectures from distributed electronic control unit (ECU) systems to domain-based, zonal, and centralized computing platforms. Key enabling technologies are reviewed, including service-oriented software architectures, middleware, automation pipelines, artificial intelligence mechanisms, and cloud-based infrastructures. A structured taxonomy is introduced to organize SDV technologies into functional hardware, E/E architectures, software frameworks, automation mechanisms, and distributed infrastructure domains. The study also investigates the Software-Defined Internet of Vehicles (SDIoV) paradigm, integrating Software-Defined Networking (SDN) with edge and fog computing to support scalable vehicular communication and data processing. Furthermore, key technical challenges related to cybersecurity, interoperability, data management, and system scalability are discussed, along with emerging research directions and future development trends.
arXiv:2605.29999v1 Announce Type: new
Abstract: The accelerating growth of global data generation demands data storage platforms that offer high capacity, long lifespan, and low energy consumption beyond the limits of electronic memory technologies. Optical storage provides an attractive alternative. However, its density is fundamentally constrained by the optical diffraction limit and the limited scalability from the point-by-point laser writing, as well as thermal accumulation during high-speed writing. Here, we introduce a large-scale optical data storage scheme that is compatible with the progress in chip fabrication by combining electron-beam lithography (EBL) and ion implantation to deterministically encode high-density data. The approach achieves precise control of ion number and spatial distribution, enabling multi-bit grayscale encoding and wavelength division multiplexing with chip-scale patterning over millimeter areas. Wavelength-selective readout is performed using downconversion and upconversion fluorescence detection, allowing crosstalk-free retrieval of multiplexed data channels. We further develop a neural network-based super-resolution algorithm that reconstructs data beyond the diffraction limit, further increasing the effective storage density. Using this integrated framework, we achieve an optical data density of 10 Gbit/cm$^2$ with high fidelity. Our results establish a micro/nano-fabrication-compatible route to large-scale, high-density optical memory and provide a foundation for next-generation cold data optical storage technologies.
arXiv:2605.29995v1 Announce Type: new
Abstract: Superimposed pilot (SIP) transmission improves spectral efficiency by eliminating the dedicated pilot overhead required in orthogonal pilot (OP)-based schemes. However, SIP suffers from severe pilot-data coupling, which leads to a critical performance-complexity bottleneck at the receiver. To address this issue, this paper proposes a low-overhead transmission framework that revitalizes data-dependent superimposed training (DDST) with enhanced interference mitigation strategies. First, for quasi-static block-fading channels, an enhanced DDST receiver is developed to achieve non-iterative pilot-data decoupling by exploiting data-dependent algebraic structures. Second, to overcome the sensitivity of conventional DDST to channel variations and symbol misidentification in fast time-varying environments, a mix transmission scheme is developed. By strategically applying DDST to a subset of resource elements, the proposed scheme combines the interference-free transmission property of OP with the zero-pilot-overhead advantage of SIP, thereby improving demapping reliability and interference suppression. Furthermore, under the proposed mix scheme, a Vision Transformer-based neural receiver is designed to capture the orthogonal structure between pilots and perturbation-bearing data, as well as the underlying channel correlations, thereby relaxing the stringent quasi-static assumption required for interference disentanglement. Simulation results demonstrate that the proposed framework achieves significant performance gains in the low-to-medium SNR regime under time-varying channels while providing superior computational efficiency compared with state-of-the-art SIP receivers.
arXiv:2605.29872v1 Announce Type: cross
Abstract: QEM is widely regarded as a plausible bridge from NISQ devices to FTQC. Yet the empirical studies used to assess the effectiveness of QEM techniques on concrete problems have received comparatively little scrutiny with respect to the validity of their conclusions. We systematically review 81 recent QEM papers using an eight-criterion framework covering statistical rigour, reproducibility, and reporting quality. Among the applicable papers, only 15 (25%) use inferential methods, while 25 (42%) report uncertainty only descriptively, without testing whether the claimed effects are statistically supported. To demonstrate the consequences of these omissions, we use ZNE as a representative and widely used case study and identify two compounding sources of artefacts in current QEM benchmarks. First, we observe parameter sensitivity: in a 132-configuration sweep, implicitly assumed choices such as scale factors, extrapolation method, and hardware calibration are not merely incidental but active, with variations changing conclusions from statistically significant improvement to statistically significant degradation. Second, we identify a drift-induced effectiveness illusion: in a 72-hour longitudinal study on real hardware, temporal drift alone can make the same ZNE configuration exhibit an effect size more than three times as large, depending solely on when it is executed, and also drastically reduces the effective number of independent observations. These findings do not imply that QEM methods are intrinsically unsound; rather, they show that current evaluation practice can make mitigation performance appear more robust than the evidence warrants. We therefore propose minimum reporting standards for QEM evaluations, including explicit parameter documentation, robustness checks, longitudinal drift assessment, and inferential statistical testing with effect-size reporting.
arXiv:2605.29926v1 Announce Type: new
Abstract: Accurate prediction of drug-target interactions (DTI) is critical for drug discovery. Existing methods often rely on single-modal representations (e.g., sequences or graphs) or combine only two modalities, overlooking 3D structural features. To address this challenge, we propose TriMod-DTI, a triple-modal contrastive learning framework that incorporates 1D sequences, 2D graphs, and 3D structures of drugs and proteins, obtaining the universal and complementary feature representations for DTI prediction. We design a Feature Extractor to capture drug and target features across the three modalities, thereby enriching their representations. We further propose a triple-modal contrastive learning strategy to align different modal representations of the same drug or protein in the latent space. By constructing cross-modal positive and negative sample pairs, this approach enhances the model's discriminative ability. Experiments on three benchmark datasets demonstrate that TriMod-DTI outperforms state-of-the-art methods. The ablation studies validate the contributions of each modality. Moreover, case studies highlight its practical potential for DTI prediction and drug discovery.
arXiv:2605.30210v1 Announce Type: new
Abstract: Chiroptical techniques for detecting and characterizing the chirality of matter and artificial nanostructures are traditionally based on their interaction with chiral light, typically circularly-polarized fields propagating in free space. More recently, these approaches have been extended to integrated photonic platforms, offering significant practical advantages. However, the generation of chiral guided light is challenging, it requires degeneracy of the fundamental modes of the integrated waveguide, which occurs at a single wavelength and limits broadband characterization. Here, we introduce and simulate numerically a new alternative integrated optical configuration inspired by the Stern-Gerlach experiment, in which a chiral sample is illuminated by a linearly polarized light beam. Such a beam exits from a dielectric waveguide that propagates the fundamental TE mode, thereby eliminating the need for circularly polarized excitation. On this basis, enantio-discrimination is achieved through a spatially resolved scheme using a symmetric arrangement of optical antennas on either side of the propagation axis. Our simulation results suggest that the selective scattering and absorption of the circular polarization component matching the chirality of the sample induces an imbalance of (spin) angular momentum of the optical field, resulting in a transverse deflection of the beam toward the corresponding side. This on-chip platform then provides a direct route to chiroptical functionalities under linearly-polarized illumination, enabling compact implementations of chiral sensing, spectroscopy, optical computing, and communication schemes.
arXiv:2605.28901v1 Announce Type: new
Abstract: This paper investigates the identification of observable low-frequency (LF) parameters of battery cell's equivalent circuit models (ECMs) using time-domain voltage and current measurements sampled at low frequency by built-in battery management systems (BMS) during operation. Accurate estimation of such parameters is challenging due to measurement resolution available in practical settings. To address this, a modeling and identification framework is proposed in which fractional constant phase element (CPE), commonly used to model LF diffusion phenomena of battery cells, is approximated in the time domain using a high-order RC network with a recursive definition. The parameter estimation problem is formulated as a constrained, non-convex least-squares problem in a discretized state-space representation. To improve robustness, parameter initialization strategies, bounds, and a procedure for selecting the number of RC branches are rigorously derived. The method is evaluated in a numerical study based on a power system application where the battery under the study provides primary frequency control to the grid. Under noise levels representative of typical BMS measurements, the proposed approach achieves, from time-domain measurements, accurate LF parameter estimation (including the CPE), with average errors below 1 %.