Numerical Analysis
Low-Order Refined Preconditioning for Spectral/hp Element Method for Complex, 3D Geometries
Parv Khurana, Henrik Wüstenberg, David Moxey et al. (+3 more)
Low-order refined (LOR) preconditioning replaces a high-order operator with a low-order discretisation on a refined nodal mesh. For tensor-product elements, the two operators are spectrally equivalent with bounds independent of the
polynomial order $P$, but the construction does not extend directly to simplex and mixed-element discretisations. This work makes two contributions: it extends LOR preconditioning to simplex and mixed-element discretisations, including triangular, tetrahedral, and prismatic elements, and establishes a generalised Vandermonde transformation linking the modal and nodal LOR formulations, showing that the resulting preconditioned spectra and Krylov convergence are independent of the high-order basis. Numerical experiments show controlled iteration growth on triangular meshes despite increasing condition number, and controlled iteration counts up to $P=5$ on tetrahedral, prismatic, and mixed-element meshes. A single algebraic multigrid V-cycle per outer iteration gives the best balance of iteration count and cost. The method is applied to a production incompressible Navier-Stokes simulation of a race-car front-wing and wheel configuration, discretised on a mesh of $2.87\times10^6$ mixed prismatic and tetrahedral elements giving $32.2\times10^6$ pressure degrees of freedom at $P=3$. LOR reduces the mean pressure conjugate gradient (CG) iteration count from $235.3$ to $5.5$, and the pressure-solve time over 1000 timesteps by 16.1%, relative to the default production static-condensation diagonal preconditioner in Nektar++. The one-time cost of constructing the LOR preconditioner is amortised over the production simulation.
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All authors Parv Khurana, Henrik Wüstenberg, David Moxey, Athanasios Chatzopoulos, Julien Hoessler, Spencer J. Sherwin
Robotics
Learning to Leverage Compliance: A Policy-Admittance Learning Framework for Robotic Insertion
Chongren Wang, Minghe Li, Honghua Dai et al. (+3 more)
Policy learning and compliant control offer a promising route to reliable autonomous assembly under pose errors and contact uncertainty. However, combining them does not ensure coordination: the policy may continue pushing against contact while the controller yields, producing sustained loading with limited progress. To address this problem, we propose LeCo (Leverage Compliance), a policy-admittance learning framework that guides a visual policy through execution-time interaction under fixed admittance. A multirate feedback mechanism aggregates high-rate contact-interaction records into policy-transition rewards. An integrated conflict cost then characterizes sustained policy-loading/controller-unloading opposition, while a directional high-force tail cost captures continued-loading events within a transition. Together with task completion, these costs encourage the policy to leverage compliance with less unproductive loading. We evaluate LeCo on four real connector-assembly tasks, obtaining an aggregate success rate of 94%. Across tasks, mean successful-trial resultant-force and torque peaks decrease by approximately 30% and 64% relative to the comparison baseline. Reward ablation further shows that adding conflict shaping reduces median successful-trial contact-conditioned conflict density by approximately 53%. These results support learning to leverage fixed compliance by turning multirate policy-admittance interaction into complementary reward signals for effective, lower-load insertion.
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All authors Chongren Wang, Minghe Li, Honghua Dai, Zhicheng Lin, Shiyang Wei, Xiaokui Yue
Computer Vision
Implicit Neural Representation for Hyperspectral Video Compression
Alfredo Scalera, Paul Murray and Jaime Zabalza
With the advent of snapshot cameras, hyperspectral
video is becoming more readily available. In recent years, new applications have emerged which have led to increasingly larger
datasets. However, hyperspectral
video compression remains in the early stages. In this study, we explore the use of implicit neural representation as a candidate solution. We propose a novel extension of an existing RGB
video compression model, achieving Bjøntegaard Delta PSNR gains of +4.99 dB and Bjøntegaard Delta rate of -88.88% compared to traditional hyperspectral image compression methods applied frame-by-frame. In addition to reconstruction quality, the effects on downstream task performance are measured in the form of object tracking success. Compared to
video compressed with methods based on principal component analysis and JPEG2000 in low data regimes, our proposed method improves tracking area under the curve by up to 23.42% and distance precision by up to 35.56% on examples from the HOT2026
dataset.
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Robotics
ExoLaN: Physics-Consistent Context-Aware Dynamics Learning for Exoskeletons
Lucas Schulze, Maximilian Schwarz, Jona Hoppe et al. (+2 more)
Task-agnostic assistive exoskeleton control based on human intention offers greater flexibility than conventional approaches that rely on predefined tasks or motion patterns. Human joint torque estimation enables task-agnostic assistance by characterizing user actions. Physics-consistent methods such as Deep Lagrangian Networks (DeLaN) have been applied to estimate the human torques in multi-user settings, but existing approaches cannot adapt to a specific user without retraining, and do not account for intermittent contacts during locomotion. We propose ExoLaN, a Context-Aware DeLaN for human-exoskeleton interaction that learns the full coupled system dynamics while adapting to changes in interaction context. ExoLaN combines temporal context with partial contact-force measurements from force-sensitive insoles to infer latent dynamics
embeddings and estimate generalized contact torques. On seven unseen users performing 21 unseen tasks, ExoLaN reduces torque estimation MSE by 7% compared to a black-box baseline. Beyond inverse dynamics, ExoLaN serves as a unified model that also enables accurate forward prediction: training with a multi-step prediction loss reduces acceleration MSE by 59% and long-horizon position and velocity errors by 60% and 93%, respectively, compared with a single-step loss. Moreover, the learned latent context captures task information without explicit task labels, making it a promising signal for task-aware assistive control.
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All authors Lucas Schulze, Maximilian Schwarz, Jona Hoppe, Jan Peters, Oleg Arenz
Networking
Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions
Daniel Flor, Danilo Pena, Hyago Lucas Oliveira et al. (+4 more)
Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.
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All authors Daniel Flor, Danilo Pena, Hyago Lucas Oliveira, Luan Pena, Vicente A. de Sousa, Jr., Allan Martins
Computer Vision
AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy
Edward Gaibor, Kyriaki-Margarita Bintsi, Chiara Mauri et al. (+9 more)
Accurate
segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive to obtain. Existing supervised axon
segmentation methods rely on target-domain annotations and can be brittle when tissue type, species, modality, or acquisition conditions change. We present AxonSynth, a domain-randomized synthetic-data framework for training 3D axon
segmentation models without manually annotated real training volumes. AxonSynth generates dense synthetic axon labels with orientation priors that reflect realistic fiber configurations and renders them with randomized density, contrast, bias fields, blur, and noise. A three-class 3D U-Net is trained to predict background, axon sheath and intra-axonal space. We evaluate zero-shot transfer on 10 held-out light-sheet microscopy (LSM) patches from macaque and human
brain samples labeled with one of three axonal markers, comparing against calibrated thresholding and Frangi filtering using overlap, corrected detection, false-positive, and
topology metrics. On macaque samples, AxonSynth achieved the best corrected Dice and corrected precision (0.826 and 0.851), compared with 0.765 and 0.754 for thresholding and 0.685 and 0.762 for Frangi. On human samples, corrected Dice was comparable to thresholding (0.857 vs. 0.868), while component-count error decreased from 22,504 to 3,377. Across all held-out patches, AxonSynth reduced component-count error in 10/10 patches and Euler-characteristic error in 8/10. These results show that synthetic-label domain randomization can reduce dependence on manual axon annotation while supporting synthetic-to-real 3D
segmentation.
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All authors Edward Gaibor, Kyriaki-Margarita Bintsi, Chiara Mauri, Emine Özen, Carmen Luz Leiva Ureta, Zayneb Bellatif, Chiara Maffei, Wenze Li, Zhuhao Wu, Elizabeth Hillman, Yaël Balbastre, Anastasia Yendiki
Artificial Intelligence
Compress What You See, Not What You Say: Anchored Context Distillation for Latent-Observation Software Engineering Agents
Zhensheng Zou, Guoqing Wang and Dan Hao
Tool observations dominate the context of software-engineering
agents, making long interaction histories costly to maintain. Existing context compression methods can discard information needed by later actions, while adapting
agents to soft-
token representations can compromise their original behavior. To reduce context while preserving action-critical information and
agent behavior, we combine Latent Observations, Hard Actions (LOHA), a context layout that separates compressed history from text needed for exact reference, with Anchored Context
Distillation (ACD), a training method that enables latent reading while constraining behavioral drift. LOHA compresses older tool observations into soft
tokens while retaining the
agent's own turns and the last K observations in text, providing compact access to historical information and exact access to recent content. To enable the
agent to use this representation, ACD distills the base model's full-text predictions into the latent view while anchoring its behavior on plain-text inputs to the same base model. On SWE-bench Verified, K=3 reduces context per call by 43% for Qwen3-4B and 57% for SWE-Master-4B-RL, with resolve rates of 12.1% and 21.8% versus 14.5% and 27.5% for their uncompressed bases. A single-run recency sweep reaches 14.4% and 23.0% at K=8, with larger windows generally favoring task performance over compression. Under a 32K-
token limit, Qwen3 with K=3 resolves 21.1% of a 199-instance subset versus 11.1% for the same adapted
agent using full text. In concurrent single-GPU serving, it achieves 1.9 times that full-text
agent's instance throughput.
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Networking
ITS Fairy: Occlusion Assistance Selected Against a Recipient's Own Perception Reports
Yenan Wang, Oscar Karlsson, Elad Michael Schiller et al. (+2 more)
Cooperative perception can expose object state beyond a vehicle's onboard sensors, but sensing occlusion can still leave a local safety application without the objects its collision computation needs. To tackle this challenge, we present the ITS Fairy, an infrastructure-side Server Local Dynamic Map (S-LDM) service whose decision unit is the pair (recipient, missing conflict-relevant object): among objects absent from a recipient's CPM-derived reported awareness, it sends only those relevant to a Time of Closest Approach (TCA) conflict test. Comparable services predict what a vehicle can perceive; the ITS Fairy instead reads what it has already reported. The recipient inserts the selected state into its local LDM and uses its unchanged collision-avoidance controller. We evaluate this application-level mechanism in SUMO–ms-van3t–S-LDM emulation, since extended as VaN3Twin, using a sensing-occluded lane merge and four-way intersection scenario. At every main-sweep speed, the smallest assisted per-encounter minimum TCA exceeds the largest local-only value in the archived data. Additionally, assisted medians remain in the multi-second range where local-only operation repeatedly approaches zero. In the lane-merge robustness data, the median benefit persists at 80% configured assistance omission with 10 and 5 Hz analysis, but largely disappears at 100-120 km/h when 80% omission is combined with 1 Hz analysis. These results demonstrate the application-level value of supplying object state selected against what a recipient has itself reported. They are not a vehicular wireless-channel evaluation, and they do not quantify what selectivity saves relative to forwarding every nearby object.
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All authors Yenan Wang, Oscar Karlsson, Elad Michael Schiller, Francesco Raviglione, Claudio Casetti
Multiagent Systems
Towards Mitigating Fabricated Consensus: The Active Provenance Gate for Multi-Agent Debate Synthesis
Jakub Masłowski and Jarosław A. Chudziak
Large
language model-based multi-
agent debate (MAD) systems are being increasingly used as complex decision pipelines in distributed processes. However, their final synthesis phase still remains inadequately controlled. Even with detailed debate logs, summarizing models are prone to fabricating smoothly written debate consensus that is not grounded in the debate's history. To address this safety gap, this paper presents empirical research and studies if the introduction of active post-debate verification can mitigate the production of such factually unsupported summaries, while still providing valuable information. Furthermore, it is examined whether explicitly signalling divergence is preferable in the absence of a reliable compromise. The Active Provenance Gate (APG) is introduced as a post-debate verification layer that treats the source as a hard constraint, analysing the debate logs,
auditing each claim, and applying self-correction. In crisis simulations, the self-healing mechanism more than doubles the average data Provenance Fidelity in difficult condition scenarios, before the strict gate blocks unsupported claims and generates divergence reports. In the human study, a vast majority of the users (over 75%) preferred a report explicitly stating failure in critical scenarios, despite most of them perceiving fabricated consensus from the baseline system as more fluent. Our main contribution is the transition of data origin tracing from passive logging to active conditional blocking before publication.
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Robotics
CognitiveReality: Robot-Agnostic Semantic Gaussian Mapping with an LLM Agent for Immersive Collaborative VR Teleoperation
Timofei Kozlov, Dmitrii Maliukov, Andrey Marchenko et al. (+3 more)
A photorealistic 3D view tells a teleoperator where a
robot is, but not what the scene contains, how well each object has been observed, or how to turn pointing and
speech into
robot action. CognitiveReality turns a
robot's RGB-D stream into a live, semantically indexed Gaussian-TSDF map shared by an operator in virtual reality and a tool-using
language agent. One mapper binary serves any platform through configuration alone: it ingests poses from
robot SLAM, joint kinematics, motion capture or an inline visual tracker, bridges localization outages with a shadow tracker and keyframe-anchored PnP, and maintains open-vocabulary instance identities with per-object quality at 2 Hz.
Speech and controller rays are grounded against persistent scene objects through validated typed tools and operator-confirmed
robot actions. In the controlled
agent evaluation, the deployed local Qwen3-VL-8B router reaches 81.24% tool exact match, while merge-aware replay correctly redirects 101 absorbed object identifiers. On
robot data CognitiveReality exceeds a Gaussian-plus-SDF baseline by 2-8 dB; pose error through 5-40 s SLAM outages stays within 1-8 cm. Deployed live on two quadrupeds, the
agent executed 26 of 30
navigation requests and 20 of 20 re-observation requests, raising object quality by 2-5 dB.
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All authors Timofei Kozlov, Dmitrii Maliukov, Andrey Marchenko, Dmitrii Plotnikov, Miguel Altamirano Cabrera, Dzmitry Tsetserukou
Machine Learning
Evaluating the accuracy of KV cache reuse techniques
Samuel Cestola, Tianxiang Xia, Pengfei Zheng et al. (+4 more)
Position-independent KV cache reuse aims to reduce latency in
retrieval-augmented generation by reusing chunk-level KV caches across
prompts. We show that current evaluations of KV cache reuse techniques rely on measurements that fail to faithfully capture the loss of accuracy attributable to reuse, often artificially inflating the reported effectiveness. We also show that existing
datasets do not exhibit the reuse dynamics needed to thoroughly evaluate such techniques. To address these issues, we propose an evaluation methodology that measures this accuracy loss without ambiguity and we introduce Boxoffice, a tool that programmatically generates evaluation
datasets that exercise challenging KV cache reuse patterns.
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All authors Samuel Cestola, Tianxiang Xia, Pengfei Zheng, Weiyan Zheng, Bo Wang, Yi Zhao, Diego Didona
Robotics
dRVG: Quadtree-Guided, Resolution-Complete Online Motion Planning for Polygonal Robots in Unknown Environments
Duo Zhang, Hechen Zhang, Junshan Huang et al. (+1 more)
We present the dynamic rotation-stacked visibility graph (dRVG), an online motion planner that guides polygonal
robots to specified goals in initially unknown, static environ- ments. It merges local roadmaps from successive observations to plan collision-free translations and rotations without a uniform position grid. A spatial quadtree schedules sensing goals across regions to reduce repeated visits while retaining all orientation configurations for routing. Under exact sensing and
geometric computation and
star-shaped
robot and envelope assumptions, dRVG with center scans is resolution-complete relative to full- map RVG at the same angular resolution. In experiments using footprint scans, dRVG solves all 140 cases across 20 difficult maps and seven angular resolutions within a 20 s planning budget, with a median planning time of 1.18 s at 360 orientation layers. Six microMVP demonstrations illustrate the complete online planning loop on a physical
robot.
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All authors Duo Zhang, Hechen Zhang, Junshan Huang, Jingjin Yu
Numerical Analysis
Anatomical Connectivity from Tractography-free Diffusion Tensor Imaging and Its Application to EEG Brain Imagin
Joonas Lahtinen
This proof-of-concept paper demonstrates how the Kalman filter, as a
brain imaging method, can be enhanced by incorporating raw
diffusion tensor imaging (DTI) data without tractography, in addition to
EEG recordings. An efficient
algorithm is designed to streamline the otherwise tedious process of inferring connectivity between
brain regions from DTI data. The DTI-based
evolution model is applied to the Kalman filter and the Standardized Kalman filter, and these are compared with their conventional counterparts using the random walk
evolution model. The numerical experiments use synthetic somatosensory and auditory evoked potentials. The results show that the DTI-modelled Kalman filter can obtain deep activity in the brainstem and thalamus. Moreover, the connection model limits the estimated spreads to the correct or nearest anatomical
brain region.
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Cryptography and Security
Context-Aware Functional Modeling for Android Third-Party Library Detection
Dihao Fan, Jian Zhang, Yasai Shi et al. (+5 more)
Third-party libraries (TPLs) are widely used in Android apps, but their reuse can introduce security risks and interfere with downstream program analyses. Existing Android TPL detection approaches face two key limitations: their hand-crafted features are fragile under aggressive code transformations, and their whole-library matching strategies are ineffective when apps retain only part of a TPL. In this paper, we propose LibFan, a learning-based Android TPL detection approach based on context-aware functional modeling. It realizes this modeling through two complementary components: context-aware contrastive learning at the method level and functional partitioning at the library level. At the method level, it learns semantic representations through contrastive training while incorporating outgoing call relationships and class-level context, improving robustness to obfuscation, shrinking, and optimization. At the library level, it partitions each TPL into functionally coherent units and determines library presence using the best-matching partition, thereby accommodating partial library reuse. To evaluate LibFan, we construct a new
benchmark comprising 200 apps and 46 vulnerable TPLs, with each app compiled under four transformation configurations. Under the most challenging R8 full mode, LibFan achieves F1 scores of 81.3% at the library level and 47.6% at the version level, representing relative improvements of 64.9% and 35.6% over the state of the art, respectively.
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All authors Dihao Fan, Jian Zhang, Yasai Shi, Chuan Luo, Xudong Liu, Yang Liu, Chunming Hu, Xu Wang
Networking
Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
Iago Diógenes do Rego and Vicente A. de Sousa Jr
This work explores interference coordination techniques (inter-
cell interference coordination, ICIC) based on fractional frequency reuse (FFR) as a solution for a multi-cellular scenario with user concentration varying over time. Initially, we present the problem of high user concentration along with their consequences. Next, the use of multiple-input multiple-output (
MIMO) and small
cells are discussed as classic solutions to the problem, leading to the introduction of fractional frequency reuse and existing ICIC techniques that use FFR. An exploratory analysis is presented in order to demonstrate the effectiveness of ICIC techniques in reducing co-channel interference, as well as to compare different techniques. A statistical study was conducted using one of the techniques from the first analysis in order to identify which of its parameters are relevant to the system performance. Additionally, another study is presented to highlight the impact of high user concentration in the proposed scenario. Because of the dynamic aspect of the system, this work proposes a solution based on machine learning. It consists of changing the ICIC parameters automatically to maintain the best possible signal-to-interference-plus-noise ratio (SINR) in a scenario with hotspots appearing over time. All investigations are based on ns-3 simulator prototyping. The results show that the proposed Q-Learning
algorithm increases the average SINR from all users and hotspot users when compared with a scenario without Q-Learning. The SINR from hotspot users is increased by 11.2% in the worst case scenario and by 180% in the best case
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Computation and Language
Sorry Robot, Happy Human: Vision-Language Models Read Only One of Two Legible Typographic Layers
Mert İncidelen, Yamen Kashkash, Asya Berker et al. (+1 more)
Vision-
language models (VLMs), despite their success in optical character recognition (OCR) tasks, are vulnerable to typographic attacks and have a fragile structure for images with multiple text layers. In this study, the DecoyBench
dataset was created using the Decoy Font method. The
dataset consists of 300 images, each containing text with sharp contour lines superimposed on another text with soft shading. Six recent closed-source models from three different model families were evaluated using this
dataset under two different prompting conditions (naive and guided) and at two different resolutions ($512\times512$ and $64\times64$). A validation study showed that human participants could read both text layers with high accuracy. In contrast, the models, with most variants and both prompting methods, read the contour text with near-human accuracy at high resolution, but almost never fully extracted the shading text. At low resolution, the contour text could not be read by either the models or humans, while the shading text could be extracted with high accuracy. The findings indicate that the evaluated VLMs exhibit a consistent behavioral limitation when processing typographic structures containing multiple spatial frequency layers.
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All authors Mert İncidelen, Yamen Kashkash, Asya Berker, Murat Aydoğan
Sound
AFA-Net: A Differential Attention Approach for Auditory Attention Detection
Philip H. Lee, Shreeram Suresh Chandra, Karan Thakkar et al. (+1 more)
Auditory
Attention Detection (AAD) utilizes electroencephalographic (
EEG) signals to identify a target speaker in a multi-speaker environment. Despite considerable progress, existing deep learning architectures often lack explicit mechanisms for handling noisy
EEG data. To address this limitation, we propose Auditory Focus
Attention Networks (AFA-Net), a machine learning framework that replaces vanilla
attention with a simple yet flexible differential
attention mechanism to help focus on task-relevant neural activity. AFA-Net achieves an upward accuracy of 96.8% at the 2s decision window, while using substantially fewer parameters than most existing methods. To the best of our knowledge, AFA-Net is among the first frameworks to explicitly try to combat
EEG noise to improve AAD.
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All authors Philip H. Lee, Shreeram Suresh Chandra, Karan Thakkar, John H.L. Hansen
Machine Learning
Decodable In-Context State and Model Output Across Training
Manas Venkata Sai Ravulapalli and Samrath Singh Chadha
Prior work established that a probe can decode an in-context binding on model errors and that probe-guided steering can repair some of them. We follow probe accuracy, model output, and steering response across public pretraining and post-training checkpoints. Probe accuracy rises during Pythia pretraining, while probe-guided steering moves from negligible all-trial benefit to a larger benefit at two model sizes. Saved scores distinguish probe-correct errors with low and above-uniform model probability for the correct candidate. Oracle-target steering already repairs many early errors, but saved aggregates cannot separate target quality from intervention sensitivity. A held-out comparison of decoders trained on the final state or candidate logits finds no detected final-state advantage on late-checkpoint model errors. An information-theoretic counterexample explains why decodability on errors alone cannot establish discarded output information. The connection to downstream omissions remains open.
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Machine Learning
Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition
Philip H. Lee, Shreeram Suresh Chandra and John H.L. Hansen
Multimodal emotion recognition (MER) increasingly pairs
EEG with
speech, treating internal neural signals and external vocal expression as informative views of affect. In practice, naive fusion underperforms the stronger single modality, because
EEG artifacts inject noise that corrupts the shared representation. We introduce EmoSpeechBrain, a
multimodal framework built on the insight that noise suppression is a precondition for effective fusion. Its
EEG encoder uses differential
attention, taking the difference between two
attention maps to cancel shared noise and isolate discriminative neural activity. An
attention-based gating adapter aligns both modalities in a shared space and weights each one's contribution to the prediction. On two
datasets - PME4 and EAV, EmoSpeechBrain improves MER accuracy by up to 12.9% over other state-of-the-art (SOTA)
EEG encoders, and surpasses unimodal
speech and
EEG baselines by up to 13.1% and 23.1%. These results show that once
EEG noise is suppressed, fusion delivers gains that naive combination cannot.
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Cryptography and Security
Short Paper: Prefix Count Limits Can Increase First-Hit Discovery in Card Reissuance
Wasif Faisal and Suprava Saha Dibya
When a card number is compromised, an attacker may search for active numbers sharing its prefix. An issuer might respond by reissuing cards from heavily populated prefixes into less populated ones. We show that this intuitive count control can backfire. For fixed search regions, exposure weights, and total activity, we derive exactly when reducing the maximum prefix count increases the chance that a budgeted search finds an active number. In matched synthetic simulations over 50,000 candidates, targeted replacement meets the count limit in every run but raises supplied-12-digit-prefix discovery relative to equal-volume random replacement in three of twelve settings. Thus a lower prefix count does not by itself certify lower enumeration risk.
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Networking
Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models
Andrea Masini, Sudipta Acharya, Paolo Bellavista et al. (+2 more)
Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable network configurations remains complex, error-prone, and difficult to automate. This paper presents Intent2Tc, a closed-loop
language-model-driven framework that translates business-level traffic-shaping intents into declarative sub-intents and subsequently into validated, executable Linux traffic control (tc) configurations. The framework integrates an Active Queue Management (AQM)-based digital twin (DT) semantic model, automated metadata extraction, critique-driven refinement, and
Retrieval-Augmented Generation (RAG)-based knowledge reuse to improve semantic consistency and configuration reliability. We evaluate multiple open-source large
language models (LLMs) and small
language models (SLMs), together with Claude Sonnet-4.6, on 100 Request for Comments (RFC) 9315-compliant traffic-shaping intents. Across both translation stages, Intent2Tc achieves high semantic fidelity, configuration accuracy, and deployment readiness, with Claude Sonnet-4.6 reaching 0.98 semantic similarity, 1.0 semantic unit coverage, and 0.045 normalized edit distance. Furthermore, RAG reduces
token consumption and inference latency while enabling compact models such as Phi-4-mini to approach the performance of substantially larger models. Linux tc serves as the target configuration platform, demonstrating the practical applicability of the proposed framework.
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All authors Andrea Masini, Sudipta Acharya, Paolo Bellavista, Luca Foschini, Burak Kantarci
Robotics
Augmented Reality Interfaces for Human-Robot Collaboration: Development of a ROS 2-Based Sensor Streaming Framework and Validation via SLAM Algorithms
Alessandro Rubert, Stefano Ghidoni and Matteo Terreran
In recent years, Human-
Robot Collaboration (HRC) has taken on a central role in Industry 4.0 and collaborative robotics, demanding communication channels that are increasingly bidirectional, intuitive, and efficient. In this context, Augmented Reality (AR) presents itself as a fundamental enabling technology, capable of both displaying information to the operator and gathering spatial data about the surrounding environment. This thesis presents the development of a sensor streaming framework that connects the Magic Leap 2 AR headset with the ROS 2 (
Robot Operating System) ecosystem. Using the Unity development environment and the ROSTCP-Connector package, an on-board application for the headset was developed, capable of acquiring real-time data from the integrated sensors (pose tracking, cameras, and environmental sensors) and publishing it to dedicated ROS 2 topics. In order to test the accuracy, latency, and robustness of the generated data stream, the framework was validated using SLAM (Simultaneous Localization and Mapping)
algorithms known in the literature. The experimental results demonstrate that the proposed architecture ensures stable data transmission, laying the groundwork for safe real-time interaction and shared spatial awareness, and opening up new perspectives for the control and supervision of robotic systems in complex HRC scenarios.
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Operating Systems
ActKV: Efficient LLM Agents through Action-Guided KV Cache Management
Zihan Wang, Cheng Tang, Lei Gong et al. (+4 more)
Agentic LLM inference accumulates long KV caches across iterative observation-
reasoning-action loops, imposing substantial
memory overhead and limiting serving throughput. Existing compression methods emphasize overall output quality, overlooking the asymmetric importance of actions in driving task progress. Our key idea is to establish a compression criterion that values KV entries by their contribution to action generation and prioritizes action quality. However, iterative execution, dynamic
memory demands, and scattered action-critical entries pose challenges to eviction policies, budget allocation, and paged
memory integration. To this end, we propose ActKV, the first KV cache compression framework tailored for agentic LLM inference. (i) Action-oriented KV cache eviction exploits stable action access patterns to retain entries critical to future actions, supporting reliable task progress under compression. (ii) Confidence-driven adaptive budget allocation uses LLM's intrinsic confidence to adapt the budget to evolving action-critical
memory demands. (iii) Page-aware compression management standardizes compression into three primitives with customized kernels, realizing practical throughput gains. On long-trace tasks, ActKV retains an average of 98.53% of FullKV's accuracy with only 25.98% of its peak KV cache
memory. It also achieves 3.97 times and 3.58 times FullKV's
token and task throughput, delivering state-of-the-art performance.
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All authors Zihan Wang, Cheng Tang, Lei Gong, Chao Wang, Wenqi Lou, Teng Wang, Xuehai Zhou
Robotics
InternW0-$\Delta$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
Xingyu Miao, Zizun Li, Baole Fang et al. (+45 more)
World Action Models (WAMs) jointly model visual dynamics and action generation for generalist
robot manipulation. A central challenge is to integrate priors from large-scale pretrained models—including visual dynamics, scene semantics, geometry, and motion—into a unified framework for
robot action generation. We introduce InternW0-$\Delta$, a unified WAM pretrained on a heterogeneous corpus that outperforms prior methods across simulation
benchmarks and real-
robot platforms. InternW0-$\Delta$ combines pretrained visual dynamics, scene-level semantics, 4D
geometric and motion priors, and action generation within a Mixture-of-
Transformers (MoT) framework. A pretrained
video expert and an action expert interact under semantic guidance from a frozen VLM, while a pretrained 4D foundation model injects
geometric and motion priors through training-only
distillation. We further introduce Causal Imprint, which learns future-relevant scene changes from training-only future supervision and provides predictive representations directly to the action expert without future-
video rollout at inference. For large-scale joint training, we construct a heterogeneous corpus of
robot demonstrations, UMI data,
egocentric human demonstrations, and Ego2
Robot data, curated and aligned under a common state-action representation. The resulting corpus contains over 20K hours of processed training data, to our knowledge the largest open-source corpus of its kind. We pretrain InternW0-$\Delta$ on this corpus and demonstrate strong performance across simulation
benchmarks and real-
robot platforms. We will open source the training code, model weights, infrastructure, data-processing pipeline, and processed data where licenses permit. Project page: https://internrobotics.github.io/InternW0-Delta/
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All authors Xingyu Miao, Zizun Li, Baole Fang, Kaiwen Song, Tenghui Wang, Hanxue Zhang, Yating Wang, Xudong Li, Yuping He, Xueyuan Wei, Chao Gao, Xijie Yang, Yingxiang Xu, Kerui Ren, Wenqi Guo, Jianjun Zhou, Xinzhe Wang, Weiguang Zhao, Ni Yang, Zetao Cai, Yufei Xue, Hengjie Li, Zeyu He, Yuanzhen Zhou, Rong Fu, Jianyang Zhang, Siwei Cui, Fuxian Huang, Yunsong Zhou, Xing Gao, Yifei Yao, Qiaojun Yu, Kailin Li, Ming Zhou, Mu Huang, Xinyue Li, Wenze Cui, Bingqi Jiang, Xueyue Zhu, Junting Dong, Haoyu Guo, Tao Lu, Mulin Yu, Bowen Zhou, Bin Zhao, Tianfan Xue, Weinan Zhang, Chunhua Shen
Human-Computer Interaction
PANEL: An Open-Source, Self-Hosted Web Platform for Human Evaluation of Generative Models
Matteo Spanio, Andrea Poltronieri and Martín Rocamora
Human judgement is the reference measure for evaluating generative models, yet the software used to collect it lags behing the methodology. Researchers adapt listening-test frameworks designed for perceptual protocols such as MUSHRA, rely on closed commercial survey platforms, or implement single-use web applications. Live arenas such as Chatbot Arena and Music Arena rank publicly deployed systems at scale, but do not support controlled comparisons of a laboratory's own models with its own participants. We present PANEL, an open-source, self-hosted platform for such studies. A study is authored in the browser and distributed as a single link, with audio,
video, image, and text stimuli, seven question types, and screening and skip logic. The platform reports per-question summaries, across-condition significance tests, pairwise win rates and Bradley–Terry scores, and supports power analysis from pilot data. Consent versioning, self-service withdrawal, retention enforcement, and audit logging support GDPR-compliant operation. Each study exports as a machine-readable specification. PANEL is available at https://github.com/matteospanio/panel.
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Computational Engineering
AlphaOpsBench: Benchmarking End-to-End Alpha Strategy Operationalization in Prediction Markets
Huaiyu Jia, Mingxuan Zhao, Jincheng Gao et al. (+4 more)
Large
language models increasingly generate quantitative trading strategies, yet existing
benchmarks assume standardized assets, numerical features, or directly compilable strategy representations—assumptions that prediction-
market strategies violate, since a coarse idea may leave the traded outcome, causal information source, signal definition, threshold, sizing, order policy, exit, and settlement behavior unspecified. We introduce AlphaOpsBench, which evaluates end-to-end operationalization from source-grounded economic hypotheses to auditable executable programs over 581 source-preserving strategy records and a lifecycle-scale Polymarket
dataset with 1.28 million binary
markets, 183.6 million cleaned executions, settlement evidence, and limit-order-book history, comparing Direct generation against a Staged design-then-code protocol. In a corrected independent-generation study over 36 controlled tasks and 24 preregistered real strategies, strict end-to-end validity remains rare: Direct and Staged obtain 35/180 and 20/180 canonical passes on the controlled cohort and no confirmed pass on the real cohort, and repeated generations vary substantially in model-owned economic choices. By contrast, 775,725 of 783,655 scheduled historical replays complete, showing that replayability is a far weaker property than source-faithful operationalization. Financial outcomes depend on the declared execution model and available historical evidence, and fee and liquidity experiments show that execution costs alter subsequent trading paths rather than acting only as ex-post deductions. AlphaOpsBench thus separates strategy fidelity, behavioral validity, historical executability, and financial performance in an evidence-aware
benchmark for LLM-based quantitative research in prediction
markets.
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All authors Huaiyu Jia, Mingxuan Zhao, Jincheng Gao, Zifan Peng, Wentao Zhang, Siguang Li, Shuo Sun
Distributed Computing
Adaptive Switching Between Leader-Based and Leaderless BFT Protocols
Sudip Bhujel, Yue Li, Ning Zhang et al. (+3 more)
Byzantine fault-tolerant (BFT) protocols are known for providing operational consistency and resilience in distributed systems. However, evolving network conditions, often driven by the network's inherent dynamism or
adversarial influence, make it suboptimal to rely on a static protocol at all times. Existing BFT protocol adaptation solutions switch only among leader-based protocols and coordinate each switch through a separate consensus round, leaving them ineffective at handling severe asynchrony or situations in which an adaptive adversary targets the network's leader. We propose BFTide, a protocol adaptation architecture that enables a BFT system to intelligently and swiftly switch to a suitable protocol as network conditions shift. BFTide integrates a novel protocol switching layer that embeds protocol transition logic into the ongoing BFT operation, enabling safe and low-overhead transitions between partially synchronous leader-based protocols and asynchronous leaderless protocols. It further incorporates an offline-trained reinforcement learning policy that allows nodes to propose protocols at runtime based on observed system metrics. Experimental results show that BFTide reduces transaction latency under adverse network conditions compared with static BFT protocols and the state-of-the-art BFT protocol adaptation scheme BFTBrain (NSDI'25), while maintaining comparable throughput. The switching layer adds a modest 10-21% overhead to median latency when idle and requires no separate consensus round per switch.
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All authors Sudip Bhujel, Yue Li, Ning Zhang, Y. Thomas Hou, Wenjing Lou, Yang Xiao
Robotics
Modeling and Generative-AI-Based Design of Load-Adaptive Gravity Balancing Mechanisms
Ryotaro Kayawake, Kazuki Abe, Shota Miyake et al. (+2 more)
Load-adaptive
gravity balancing mechanisms (LA-GBMs) can accommodate various loading conditions by passively changing their characteristics in response to payload variations. However, their design is difficult because both the desired mechanism motion and static
equilibrium under variable payloads must be satisfied simultaneously. This study proposes a general design methodology for LA-GBMs that does not depend on specific mechanism architectures or mechanical elements. The necessary conditions for the potential fields of LA-GBMs are formulated, and two general forms are derived: an affine form representing the effect of payload mass and a factorized form representing state transitions associated with load adaptation and
gravity balancing. These forms are then provided to generative AI as design requirements to generate candidate potential functions. The generated functions are analytically verified in terms of their conformity to the two general forms and the conditions required for valid LA-GBMs. Furthermore, the obtained potential functions are decomposed into individual terms, and an example of a method for constructing an LA-GBM by combining springs, counterweights, and function-generating linkage mechanisms is presented. By using potential functions as an intermediate representation, the proposed framework enables the generation of LA-GBM design candidates without prescribing a mechanism architecture in advance. Mechanical realizability and manufacturability of the generated potential fields remain important issues for future work.
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All authors Ryotaro Kayawake, Kazuki Abe, Shota Miyake, Masahiro Watanabe, Kenjiro Tadakuma
Robotics
Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization
Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski et al. (+2 more)
End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on a vehicle, creating a fundamental mismatch between training and execution. Beyond the commonly studied effects of
covariate shift and causal confusion, we identify a complementary factor for this open-loop/closed-loop gap: waypoint-based supervision and displacement metrics do not ensure that the intermediate trajectory is physically coherent or easy for the controller to track. We observe that these inconsistencies concentrate primarily at intermediate waypoints, while the predicted endpoint remains comparatively reliable. Based on this observation, we introduce Endpoint-Constrained Optimization (ECO), a lightweight postprocessing layer that anchors the trajectory to the vehicle's executed history, preserves the policy's predicted endpoint, and reshapes the intermediate waypoints to improve feasibility. ECO requires no map, privileged simulator state, or additional training, and can be inserted between a broad range of waypoint-emitting policies and their controllers. Across two closed-loop simulators, it improves the aggregate closed-loop score of all six evaluated generative and regression-based driving policies, and the gains tend to increase with how often the base plans violate motion limits. On HUGSIM, ECO improves VaVAM from 18.1 to 31.0 HD-Score (+71%), achieving 1st place on the HUGSIM Closed-Loop Driving Challenge. Similarly, on AlpaSim, ECO increases the scene scores of VaVAM and DiffusionDrive by 123% and 22%, respectively. These results show that for a broad collection of end-to-end driving models, repairing the intermediate geometry of predicted trajectories without changing the policy's predicted endpoint can substantially improve closed-loop performance.
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All authors Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski, Boris Ivanovic, Kashyap Chitta
Computation and Language
Highlight-Then-Summarize: Learning to Compress Evidence for Long-Context Understanding
Zhaoyuan Xia, Qinghongbing Xie, Yung Xiang Hue et al. (+7 more)
Long-context understanding requires large
language models (LLMs) to reason over lengthy documents, conversations, and code, yet task-relevant evidence is often sparse and scattered amid substantial irrelevant and redundant content. We propose Highlight-Then-Summarize (H2S), a compress-then-reason paradigm that first identifies source-grounded, question-relevant evidence and then integrates it into a compact, question-conditioned summary before producing the final answer. To train this behavior, we construct H2S-
Dataset, comprising 6,647 examples from 11
benchmark families with an average context length of 43.9K
tokens, and introduce H2S-RL, which provides process-level rewards for evidence selection and summary construction in addition to final-answer correctness. We evaluate on H2S-Bench, a seven-task long-context suite. Under a shared 128K input and 4K output budget, H2S-14B achieves an average score of 32.60, outperforming Qwen3.8-27B by 10.17 points and obtaining the strongest overall result among the evaluated open-source models. H2S-14B also achieves the highest Evidence-Summary Quality score and retains 97.1% of its 16K-budget performance with only a 4K output budget. These results show that explicitly selecting and integrating evidence improves long-context
reasoning while enabling more compact generation.
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All authors Zhaoyuan Xia, Qinghongbing Xie, Yung Xiang Hue, Jianguang Jiang, Gaofeng Lu, Zhenyu Jiao, Xing Yuan, Dai Dai, Tong Mo, Long Zeng