arXiv:2607.11576v1 Announce Type: new
Abstract: Accurate, spatially resolved flow field measurements are essential for the reliable assessment of hemodynamic quantities in cardiovascular research and clinical practice. Experimental techniques, such as 4D flow MRI, PIV, or Doppler ultrasound, often yield data that are sparse, noisy, or under-resolved, particularly near vessel walls and in regions of complex flow. This limits the fidelity of distributed or derived hemodynamic indicators such as the wall shear stress and the clinical utility of such measurements. To address these challenges, we propose a physics-informed neural network (PINN) framework that integrates the incompressible Navier-Stokes equations with velocity measurements coming from experimental flow field data. By embedding physical laws into data, PINN enhances the reconstruction of velocity fields, enables the estimation of unmeasured quantities such as pressure and wall shear stress, and improves the spatial resolution of hemodynamic indicators. We show the effectiveness of our approach using both in silico and experimental data. First, we apply our method to the FDA nozzle benchmark, leveraging both control particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Next, we apply our method to the more complex case of blood flow in an aneurysm model, exploiting in vitro 4D flow MRI data. In both cases, the synergy between data-driven learning and physics-based regularization yields results that align more closely with ground truth observations than standard CFD or pure data-driven approaches. Our findings highlight the potential of PINNs to improve the fidelity of under-resolved flow field measurements and yield spatially resolved hemodynamic indicators.
Science Journals
arXiv:2602.17686v5 Announce Type: replace
Abstract: Chain-of-Thought (CoT) reasoning has significantly improved LLMs' mathematical problem-solving capabilities, but distilling such capabilities into smaller models remains challenging due to the capacity mismatch between verbose teachers and compact students. Directly copying teachers' lengthy reasoning chains causes capacity overload, resulting in truncated outputs or repetitive failure. Existing remedies each sacrifice a critical property of CoT: implicit reasoning methods (e.g., compressing reasoning into hidden states) trade away interpretability and verifiability, while heuristic compression strategies (e.g., random step pruning) destroy logical integrity. To address this, we propose BRIDGE, a curriculum framework that first establishes structural understanding via masked reconstruction, then uses GRPO-based reinforcement learning to guide students in self-discovering the optimal balance between accuracy and brevity, and finally internalizes complex reasoning through teacher-guided rewriting on failure cases. On GSM8K, BRIDGE enables Qwen2.5-3B to achieve 11.29% accuracy improvement and 27.4% token reduction over the original model, outperforming instruction-tuned variants and distillation baselines. Zero-shot transfer experiments on SVAMP and MATH-500 further confirm the generalization of internalized reasoning. Our code and model checkpoints are publicly available at https://github.com/Applied-Machine-Learning-Lab/SDM2026_BRIDGE and https://huggingface.co/bowen0815/BRIDGE.
arXiv:2602.17993v2 Announce Type: replace
Abstract: Complex problems, whether in math, logic, or planning, are solved by humans through a sequence of steps where the result of one step informs the next. In this work, we adopt the perspective that the reasoning power of Transformers is fundamentally limited by a fixed maximum number of steps along any latent path of computation. To address this, we introduce Turbo Connection (TurboConn), a novel architecture that overcomes the fixed-depth constraint by routing multiple residual connections from the higher-layer hidden states of each token $t$ to the lower layers of token $t+1$. Fine-tuning pre-trained LLMs with our method not only yields accuracy gains of 0.9% to over 10% on benchmarks like GSM8K, Parity, and multi-step arithmetic, but also demonstrates that the density of these backward connections is critical; our dense interaction significantly outperforms "sparse" alternatives that only pass a single hidden state or vector. Notably, TurboConn can be integrated into pre-trained LLMs to overcome task-specific plateaus: while a fine-tuned Qwen-3-1.7B achieves only 53.78% on Parity, adding our architectural modification enables the model to reach 100% accuracy, all without the necessity to retrain the full model from scratch or sophisticated curriculum learning. Our results provide strong empirical evidence that the depth of the computational path is a key factor in reasoning ability, also offering a new mechanism to enhance LLMs without significantly affecting generation latency.
arXiv:2602.18757v3 Announce Type: replace
Abstract: Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single average driving style, neglecting individual differences. Achieving personalized E2E-AD faces challenges across three levels: limited real-world datasets with individual-level annotations, a lack of quantitative metrics for evaluating personal driving styles, and the absence of algorithms that can learn stylized representations from users' trajectories. To address these gaps, we propose Person2Drive, a comprehensive personalized E2E-AD platform and benchmark. It includes an open-source, flexible data collection system that simulates realistic scenarios to generate scalable, diverse personalized driving datasets; style vector-based evaluation metrics with Maximum Mean Discrepancy and KL divergence to comprehensively quantify individual driving behaviors; and a personalized E2E-AD framework with a style reward model that efficiently adapts E2E models for safe and individualized driving. Crucially, our framework enables plug-and-play personalization by fine-tuning only the trajectory prediction head, preserving the pretrained base model and ensuring safety. Extensive experiments demonstrate that Person2Drive enables fine-grained analysis and effective personalization, while preserving driving performance and success rate even in challenging scenarios.
arXiv:2602.19598v2 Announce Type: replace
Abstract: Eye-tracking-while-reading corpora are a valuable resource for many different disciplines and use cases. Use cases range from studying the cognitive processes underlying reading to machine-learning-based applications, such as gaze-based assessments of reading comprehension. The past decades have seen an increase in the number and size of eye-tracking-while-reading datasets as well as increasing diversity with regard to the stimulus languages covered, the linguistic background of the participants, or accompanying psychometric or demographic data. The spread of data across different disciplines and the lack of data sharing standards across the communities lead to many existing datasets that cannot be easily reused due to a lack of interoperability. In this work, we aim at creating more transparency and clarity with regards to existing datasets and their features across different disciplines by i) presenting an extensive overview of existing datasets, ii) simplifying the sharing of newly created datasets by publishing a living overview online, https://t.uzh.ch/1Yh, presenting over 55 features for each dataset, and iii) integrating all publicly available datasets into the Python package pymovements which offers an eye-tracking datasets library. By doing so, we aim to strengthen the FAIR principles in eye-tracking-while-reading research and promote good scientific practices, such as reproducing and replicating studies.
arXiv:2607.11429v1 Announce Type: new
Abstract: TR 38.901-based channel models such as Sionna are reliable, but generating many multi-user channel realizations remains expensive. This paper asks a practical question: can a trained generative model produce multi-user TR 38.901 channels faster than Sionna without losing the spatial correlations imposed by user geometry? To answer this question, we propose a physics-aware, geometry-conditioned SetGAN trained on Sionna reference data. The method separates large-scale received power from normalized small-scale fading, compresses the latter with principal component analysis, and learns the conditional channel distribution in a latent space while preserving geometry-dependent correlations. On the UMa/NLoS benchmark, the model keeps the received-power distributions close to the reference, with about 0.41 dB Wasserstein distance, and reproduces spatial-consistency profiles with mean deviations below 0.03 on median curves versus distance. In addition, it reduces elapsed generation time by a factor of 3.45 and CPU-total cost by a factor of 6.15 relative to Sionna under matched user positions in the fixed-position CPU-vs-CPU benchmark. These results show that a trained generative model can substantially accelerate TR 38.901 channel generation without breaking the spatial consistency needed to evaluate multi-user systems.
arXiv:2607.11432v1 Announce Type: new
Abstract: In this work, we study the reinforcement learning (RL) problem from pairwise trajectory comparisons provided by a human expert. We generalize preference-based RL by formalizing a novel setting in which the expert can also label trajectory pairs as incomparable, i.e., when neither trajectory dominates the other. We introduce the learning problem and the desiderata that its solution should satisfy. Then, we propose a novel Bradley-Terry-inspired rationality model that effectively captures incomparabilities and infers a multi-dimensional reward function, and we study its properties. We provide a sample complexity analysis for learning the model parameters when a dataset is available. Finally, we evaluate our model's ability to reconstruct a reward function that aligns with the expert's comparisons in simulated environments and to recover the Pareto frontier of policies, along with a robustness analysis across varying levels of expert rationality.
arXiv:2602.19951v2 Announce Type: replace
Abstract: Metaprogramming enables the generation of performant code, while gradual typing facilitates the smooth migration from untyped scripts to robust statically typed programs. However, combining these features with imperative state - specifically mutable references - introduces the classic peril of scope extrusion, where code fragments containing free variables escape their defining lexical context. While static type systems have employed environment classifiers to successfully tamed this interaction, enforcing these invariants in a gradual language remains an open challenge.
This paper presents $\lambda^{\alpha,\star}_{\text{Ref}}$, the first gradual metaprogramming language that supports mutable references while guaranteeing scope safety. To put $\lambda^{\alpha,\star}_{\text{Ref}}$ on a firm foundation, we also develop its statically typed sister language, $\lambda^{\alpha}_{\text{Ref}}$, which introduces unrestricted subtyping for environment classifiers. We mechanize the proof of scope safety for $\lambda^{\alpha,\star}_{\text{Ref}}$, contributing the first mechanized proof of scope safety for a statically typed language. Our key innovation, however, is the dynamic enforcement of the environment classifier discipline in $\lambda^{\alpha,\star}_{\text{Ref}}$, enabling the language to mediate between statically verified scopes and dynamically verified scopes. The dynamic enforcement is carried out in a novel cast calculus $\mathrm{CC}^{\alpha,\star}_{\text{Ref}}$ that uses an extension of Henglein's Coercion Calculus to handle code types, classifier polymorphism, and subtype constraints. We prove that $\lambda^{\alpha,\star}_{\text{Ref}}$ satisfies type safety and scope safety. Finally, we provide a space-efficient implementation strategy for the dynamic scope checks, ensuring that the runtime overhead remains practical. All of our results are fully mechanized in Agda.
arXiv:2607.09751v1 Announce Type: new
Abstract: Machine Learning (ML) algorithms have been widely used to estimate agricultural variables across diverse contexts. However, because the quantity and quality of training data strongly influence performance of ML algorithms, their use can be constrained by limited or incomplete reference data. Synthetic Data Generation (SDG) offers a practical approach to address this issue by producing artificial but realistic samples that preserve key characteristics of the original data. Building on teacher-student knowledge transfer and in-context learning for tabular data, this study proposes a Task-Conditioned SDG (TCSDG) algorithm that pairs a Bayesian Network generator with a transformer-based tabular foundation model (TabICL). The proposed algorithm was evaluated on two agricultural prediction tasks: crop yield prediction and crop type classification. Six benchmark SDG algorithms were also utilized to compare their performance with that of TCSDG. Across twelve study sites, two training-data fractions, four multiplication ratios, and three predictive ML algorithms, augmenting the original data with TCSDG-generated synthetic data improved ML performance in 89% of the crop type classification experiments and 74% of the crop yield prediction experiments. TCSDG also substantially outperformed benchmark SDG algorithms and was the only method to consistently improve ML performance across both tasks at the aggregate level. The study demonstrates that carefully designed and processed synthetic data can improve ML performance in precision-agriculture applications. TCSDG offers a practical and extensible framework for generating synthetic data that supports downstream ML agricultural prediction. The full implementation of TCSDG is publicly available as open source at https://github.com/HamidEbrahimy/TCSDG.
arXiv:2607.10453v1 Announce Type: cross
Abstract: We study synchronous Boolean dynamics on finite loopless directed graphs in which a vertex is active at the next time step exactly when its number of active in-neighbors is prime. We call these systems Pulse Graphs. Let $L(n)$ denote the largest attractor period realizable on $n$ vertices. Exhaustive enumeration gives \[ L(1),\ldots,L(5)=1,1,1,3,9. \] Our main result determines the exponential order of the maximum period: \[ 2^{n-3}-1\leq L(n)\leq2^n-1 \qquad(n\geq5). \] The lower bound is obtained by implementing a maximal-length affine feedback register using prime-count logic gates. For $n\geq6$, the construction is loopless, has maximum in-degree five, and uses only $O(n)$ edges.
For complete directed graphs, we derive an exact update formula, classify all attractors as fixed points or complement two-cycles, prove that every orbit reaches its eventual attractor within three updates, and count the attractors explicitly. We also derive the activation probability under independent random inputs. For sparse random directed graphs, the associated prime-Poisson mean-field map undergoes a nondegenerate fold at \[ c_\ast\approx3.824963, \qquad \rho_\ast\approx0.368241, \] with local bistability immediately above the threshold.
arXiv:2607.11216v1 Announce Type: cross
Abstract: We present a quantum process calculus that can split the system state along process boundaries and follow the evolution of each process in isolation, without losing information about the joint state-a property we call spatial compositionality. Compositionality is the key to reasoning about any complex system, yet quantum process calculi have struggled to provide its spatial kind, which would enable analyzing a system one process at a time. Many a quantum process calculi have been proposed, but they invariably rely on a global state representation based on state vectors or density matrices, with no known way to split them without losing information about entanglement. We propose to model quantum states with Deutsch-Hayden descriptors instead, which provide a modular representation of qubit states and their evolution. We adapt these descriptors to allow arbitrary splitting and merging of the store of qubits, leading to an unusual process calculus in which qubit transfer messages carry the actual state of the qubit, where existing calculi transfer only a reference. The calculus gives localized views of system state visible to each process, which can be assembled back together into the joint state. We define a notion of process equivalence with extensive justification grounded in physics and show a bisimulation whose soundness proof is simplified by spatial compositionality. The calculus can model open systems entangled with external processes, and we demonstrate this capability on a fragment of the BB84 key distribution protocol. This exercise shows that Deutsch-Hayden descriptors can successfully track qubit movements across process and system boundaries, though it needs help from density matrices to reason about information flow.
arXiv:2602.21247v3 Announce Type: replace
Abstract: The fastest indexes for Approximate Nearest Neighbor Search today are also the slowest to build: graph-based methods like HNSW and Vamana achieve state-of-the-art query performance but have large construction times due to relying on random-access-heavy beam searches. We introduce PiPNN (Pick-in-Partitions Nearest Neighbors), an ultra-scalable graph construction algorithm that avoids this ``search bottleneck'' that existing graph-based methods suffer from.
PiPNN's core innovation is HashPrune, a novel online pruning algorithm which dynamically maintains sparse collections of edges. HashPrune enables PiPNN to partition the dataset into overlapping sub-problems, efficiently perform bulk distance comparisons via dense matrix multiplication kernels, and stream a subset of the edges into HashPrune. HashPrune guarantees bounded memory during index construction which permits PiPNN to build higher quality indices without the use of extra intermediate memory.
PiPNN builds state-of-the-art indexes up to 11.6x faster than Vamana (DiskANN) and up to 12.9x faster than HNSW. PiPNN is significantly more scalable than recent algorithms for fast graph construction. PiPNN builds indexes at least 19.1x faster than MIRAGE and 17.3x than FastKCNA while producing indexes that achieve higher query throughput. PiPNN enables us to build, for the first time, high-quality ANN indexes on billion-scale datasets in under 20 minutes using a single multicore machine.
arXiv:2607.09919v1 Announce Type: new
Abstract: As students increasingly use LLM tutors in computer science education, one question becomes especially important: what kind of response helps a student continue productively? Prior work has studied how students use LLMs in computer science education, but less is known about how tutoring response styles are associated with student follow-up across programming help-seeking contexts. This paper analyzes StudyChat (UMass, 2026), a public dataset of student and ChatGPT tutoring conversations from an artificial intelligence course. We transformed StudyChat into 16,851 assistant-response interactions from 203 students and 2,214 conversations. Using local LLM-assisted annotation with Gemma 4, we labeled student help-seeking situations, student state, assistant response style, and student next-turn outcome. Human validation showed 82\% agreement with the LLM-assisted labels (Cohen's $\kappa=.74$). We analyzed productive continuation and unresolved continuation across the full dataset and across help-seeking contexts. Globally, response style was significantly associated with productive continuation, $\chi^2(7)=100.39$, $p<.001$, $V=.078$, and unresolved continuation, $\chi^2(7)=125.77$, $p<.001$, $V=.087$, though effect sizes were small. Verification feedback had the highest productive-continuation rate (82.4\%), while direct answers had the lowest (62.7\%). Descriptively, response-style score ranges were smallest in low-confusion conceptual contexts (.017) and largest in high-cognitive-load contexts (.203). More detailed comparisons showed situation-dependent response patterns. For example, stepwise guidance was followed by greater confusion decrease in high-cognitive-load code requests, while direct answers were followed by more unresolved continuation in high-load debugging. These findings support context-aware evaluation and design of AI tutoring responses for programming education.
arXiv:2607.11601v1 Announce Type: new
Abstract: Blockchain governance, the set of processes by which decentralized protocols evolve, remains a fundamental challenge in balancing adaptability, security, and stakeholder representation. This technical report analyzes Cardano's Voltaire governance system, the on-chain framework introduced via CIP-1694 and enacted through the Chang hard fork in September 2024, and lays down a corresponding research program.
We make two contributions. First, we provide a complete technical specification of Voltaire's mechanisms, including its three-body architecture, seven governance action types, voting rules, and its constitutional framework; this specification is sufficient for implementation or formal analysis. Second, we establish a research agenda for principled governance optimization, including design of an agent-based simulation platform, analysis of delegation dynamics, optimization of multi-objective parameters, and game-theoretic incentive design; we provide preliminary results, including a formal governance kernel: a minimal executable model capturing self-amending governance as a state-transition system and enabling rigorous safety and liveness analysis.
Our report offers a comprehensive technical overview and invites the research community to advance blockchain governance science through rigorous study of Voltaire as a live, large-scale experiment now managing a treasury valued at approximately \$235 million (1.47B ADA as of early July 2026).
arXiv:2607.11689v1 Announce Type: new
Abstract: Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences. However, progress remains fragmented: models use incompatible action spaces and prediction targets, datasets and tasks follow different conventions, and runtime systems expose limited interfaces for reuse and evaluation. We review the evolution toward WAMs and organize these limitations into three coupled gaps: model roles and representations, objectives and standardization, and system composition. Building on this analysis, we propose a co-evolution roadmap for physical intelligence centered on the \emph{embodied brain}, a long-term model target for integrating multimodal context, comparing candidate interventions, and issuing state-transition or capability requests rather than direct actuator commands. WAMs provide promising prototypes for its predictive functions, while a physical harness grounds model outputs through tools, controllers, verification, and trace logging. Shared contracts align heterogeneous models, data, tasks, and embodiments, and closed-loop post-training converts verified interaction into reusable experience. Together, these components define a modular physical-intelligence stack for adaptive and self-improving embodied agents.
arXiv:2607.11690v1 Announce Type: new
Abstract: Tactile sensing for social-physical human-robot interaction (spHRI) is designed in a hardware-driven manner, where predefined sensor configurations constrain coverage, spatial resolution, and the range of recognizable gestures. We propose a requirement-driven framework that derives sensing requirements, specifically spatial resolution and placement, directly from interaction data. Using a VR-based platform with haptic feedback, we collected high-resolution whole-body contact distributions across multiple social scenarios, from which we identified nine recurring social touch gestures. Eight gestures were selected for controlled data collection with 18 participants, yielding an open-source dataset of 5,520 trials. Analysis of contact distributions and simulated tactile encodings provides quantitative baselines for skin coverage and sensor density on a humanoid robot platform. While demonstrated on a single robot platform, the methodology is designed to be transferable to other robot morphologies, potentially enabling morphology-specific sensing requirements to be derived prior to hardware fabrication.
arXiv:2607.11744v1 Announce Type: new
Abstract: Vehicles are increasingly equipped with advanced V2X communication capabilities. While early V2X apps utilized services such as Cooperative Awareness Messages, recent developments have allowed more advanced applications including cooperative driving, shared perception, and sensor-sharing services. The broader mix of applications leads to heterogeneous requirements for latency and reliability. At the same time multiple communication technologies for V2X are available with pros and cons. Hybrid V2X communication can exploit the distinct advantages at the right moment to fulfill the requirements of the applications. This work studies the decision problem between cellular Uu link, NR-V2X PC5 sidelink, and the simultaneous use of both channels. We address this problem by using the multi-agent reinforcement learning algorithm MAPPO and compare it to five baselines consisting of a deep reinforcement learning (DRL) approach, a static decision tree approach and static channel selection strategies. The methods are evaluated in an urban scenario and with a set of selected communication use cases. The evaluation results show that when compared to the DRL approach, the on-time delivery ratio improves from 0.508 to 0.535 in a single-controlled-vehicle setting and from 0.548 to 0.567 when all vehicles follow the learned policy and reduces the training time by half. The gains result mainly from the advanced applications scenarios, as opposed to scenarios involving exclusively CAM messaging. This indicates future applications will benefit from such adaptive communication strategies and that multi-agent modelling is useful for addressing the underlying decision problem.
arXiv:2607.11749v1 Announce Type: new
Abstract: Large language models (LLMs) are rapidly reshaping workplace communication, yet whether AI-assisted writing changes how recipients actually behave, and through what channel, remains unknown. Here, in a randomized crossover field experiment, 121 employees across six companies sent work emails under three conditions over three weeks: unaided writing, GPT-5 rewriting in a playful tone, and GPT-5 rewriting in a professional tone. Across 16,880 emails, playful editing increased emotional positivity (B=+0.068, p<0.001), and professional editing decreased it (B=-0.041, p<0.001), yet neither condition directly altered open rates, reply rates, or response times. Instead, within-sender positivity strongly predicted both opening (OR=2.05) and replying (OR=3.32, p<0.001), a significant indirect pathway through which AI editing shaped behavior, in the absence of any direct effect. These findings suggest that AI-assisted communication shapes workplace engagement not through its use, but through the emotional tone of the language it produces.
arXiv:2602.21255v2 Announce Type: replace
Abstract: We establish a general equilibrium theory for systems of large language model (LLM) agents operating under centralized orchestration. The framework is a production economy in the sense of Arrow-Debreu (1954), extended to infinite-dimensional commodity spaces following Bewley (1972). Each LLM agent is modeled as a firm whose production set Y a $\subset$ H = L 2 ([0, T ], R R ) represents the feasible metric trajectories determined by its frozen model weights. The orchestrator is the consumer, choosing a routing policy over the agent DAG to maximize system welfare subject to a budget constraint evaluated at functional prices p $\in$ H A . These prices-elements of the Hilbert dual of the commodity space-assign a shadow value to each metric of each agent at each instant. We prove, via Brouwer's theorem applied to a finitedimensional approximation V K $\subset$ H, that every such economy admits at least one general equilibrium (p * , y * , $\pi$ * ). A functional Walras' law holds as a theorem: the value of functional excess demand is zero for all prices, as a consequence of the consumer's budget constraint-not by construction. We further establish Pareto optimality (First Welfare Theorem), decentralizability of Pareto optima (Second Welfare Theorem), and uniqueness with geometric convergence under a contraction condition (Banach). The orchestration dynamics constitute a Walrasian t{\^a}tonnement that converges globally under the contraction condition, unlike classical t{\^a}tonnement (Scarf, 1960). The framework admits a DSGE interpretation with SLO parameters as policy rates.
arXiv:2607.10196v1 Announce Type: new
Abstract: Access to sufficiently large biomedical datasets remains a major obstacle for machine learning in Raman spectroscopy-based diagnostics. In particular, for glioma analysis, datasets are typically small and heterogeneous, affected by acquisition-specific variability. This work investigates the utility of deep generative augmentation in such a small-cohort setting. We analyze glioma biopsy spectra acquired from 58 tumor samples and consider both binary IDH-status classification and 6-class methylation subtype classification problems. To address the limited size and imbalance of the dataset, we develop a conditional variational autoencoder ($\beta$-CVAE) capable of generating class-conditioned synthetic Raman spectra. The generated data are evaluated in Train-on-Synthetic, Test-on-Real (TS/TR) and Train-on-Synthetic+Real, Test-on-Real (TSR/TR) settings under a strict patient-isolated cross-validation protocol. Models trained exclusively on synthetic data underperform models trained on real spectra, indicating a substantial domain gap between synthetic and real distributions. However, augmenting the real training data with synthetic spectra consistently improves classification performance across multiple models. These findings indicate that, even with a limited number of independent patient samples, generative models can capture sufficient structure to provide useful regularization for downstream classifiers. We also investigate a reconstruction-based inference strategy, termed Classification by Reconstruction (CbR), in which class prediction is based on reconstruction error under different class conditions. Overall, the results support the use of deep generative augmentation as a practical strategy for improving machine learning robustness in Raman spectroscopy applications characterized by limited biomedical datasets.
arXiv:2607.11480v1 Announce Type: new
Abstract: An exact asymptotic expression is derived for the surface charge density associated with a steady current in closed loops or wire segments of infinitesimal cross section connected to a battery. Except for vanishingly thin boundary layers at the ends of the wire, and irrespective of the conductor shape, the charge density varies linearly with arc length, as does the electrostatic potential along the curve. This proportionality generalizes earlier results and provides a clear physical picture of charge accumulation in electrical circuits.
arXiv:2607.11779v1 Announce Type: new
Abstract: Robot-assisted endovascular intervention can potentially reduce radiation exposure, improve surgeon ergonomics, enable telesurgery, support active assistance and autonomy, and enhance procedural precision. However, existing systems often suffer from limited procedural coverage because constrained patient-side setups, restricted flexibility, and complex instrument exchange hinder clinical workflow integration. This work presents a compact robotic system for endovascular interventions that enables continuous translational and rotational manipulation of standard endovascular instruments. The system consists of two alternating carts with pneumatically actuated membrane grippers integrated into rotating gripper gears. Its top-loading design allows rapid exchange of instruments such as guidewires and catheters without changing the robotic setup. A leader-follower control strategy enables continuous motion despite the finite stroke of each cart. The system was evaluated in motion-tracking experiments with guidewires and catheters and in an in vitro vascular phantom. The motion-tracking experiments showed generally smooth translational and rotational motion profiles. Across all tested guidewire and catheter experiments, the mean relative tracking errors were 3.6% for translational motion and 4.1% for rotational motion. In the vascular phantom, robot-assisted navigation reached the target in most trials, demonstrating the feasibility of the proposed manipulation concept under in vitro conditions. The presented robotic system demonstrates technical feasibility for continuous manipulation of standard endovascular instruments in bench-top and in vitro experiments. The compact top-loading design may ease instrument exchange and clinical workflow integration. Future work will focus on improving gripping performance, actuation speed, force feedback, and evaluation in more clinically realistic settings.
arXiv:2607.09773v1 Announce Type: new
Abstract: Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments. Although imitation learning and offline trajectory refinement provide strong priors, static traces cannot cover the causal feedback loop of real computer use: each action changes the screen state, future action space, and recovery options. EvoCUA-1.5 extends self-evolving computer-use agents from offline experience learning to online reinforcement learning, where policies interact with executable sandbox environments and improve from verifiable task outcomes. Online RL in this setting requires more than directly reusing single-turn language-RL recipes. Multi-turn interaction introduces context-managed observations, sparse terminal rewards, variable-length trajectories, and slow environment feedback. EvoCUA-1.5 addresses these challenges with Step-Level Policy Optimization (STEPO), which preserves trajectory-level advantage balance after decomposition into step-level samples; policy-aware filtering and pass-rate calibration over verifiable synthesized tasks; Dynamic Tri-Adaptive Curriculum (DTAC), which combines learnable tasks, difficult positive replay, and controlled infeasible-task exposure; and a fully asynchronous RL infrastructure with staleness control and mini-group batching. Experiments show that these components improve training stability and downstream performance. EvoCUA-1.5 achieves 63.2\% success on OSWorld-Verified, outperforming comparable 32B/35B-scale open-weight baselines and even approaching models with significantly larger parameter counts. Overall, EvoCUA-1.5 provides a practical framework for scaling online RL in multi-turn computer-use agents.
arXiv:2607.10709v1 Announce Type: new
Abstract: Large Language Model (LLM) services introduce a fundamental privacy challenge. Sensitive information may be inferred not only from explicit identifiers, such as names or phone numbers, but also from contextual associations among otherwise innocuous spans. Existing sanitizers typically assign privacy or utility signals to individual spans without explicitly modeling pairwise relationships among them. In this paper, we propose PromptGraph, a graph-guided prompt-sanitization approach for privacy-preserving LLM inference. PromptGraph estimates privacy leakage at the span level and utility-relevant contextual dependencies between pairs of spans. It represents each prompt as an attributed graph, in which nodes carry span-level privacy scores and edges encode contextual dependencies needed to preserve utility. The sanitization objective selects a protected span set that maximizes privacy gain while penalizing the loss of contextual dependencies. This formulation explicitly balances privacy and utility when contextual evidence is hidden. Protected spans are sanitized locally, and returned placeholders are restored only after passing local consistency checks. We conduct extensive experiments showing that PromptGraph achieves a more favorable balance between privacy and utility than prompt-privacy baselines.
arXiv:2607.11785v1 Announce Type: new
Abstract: Indoor robotics research increasingly relies on micro-UAVs whose airframe, electronics, and control software are fully open to modification. Off-the-shelf platforms rarely expose the low-level access required for such modifications, while building a custom alternative typically requires substantial engineering effort before flight testing can begin, leaving many laboratories to work within constraints that limit the scope of their research. We present MIRA (Modular Indoor Research Architecture), a low-cost, open-source micro-UAV for indoor research built around a replicable 3D-printed PLA airframe and a containerized low-level software package managing the companion-to-autopilot communication bridge via Micro XRCE-DDS. Designed as a white-box architecture, core subsystems are individually replaceable without firmware refactoring, supporting local fabrication and component substitution from existing lab inventory. We characterize MIRA through manual flight in position-control mode within an optical motion-capture volume, where the communication pipeline sustains a median companion-to-autopilot latency of 0.02 ms and power spectral density analysis confirms the structural vibration energy stays concentrated in a narrow 90 to 110 Hz band, isolated from the sub-20 Hz control bandwidth and within the autopilot safety thresholds.