Systems and Control
Threshold Pricing for Distributed Scheduling of Flexible Demands in Energy Communities
Minjae Jeon, Lang Tong and Qing Zhao
This paper develops a price-based distributed scheduling in an energy community whose members own behind-the-meter renewable generation with deferrable EV charging and price-elastic thermostatic loads. A coordinator transacts with the distribution utility under a Net Energy Metering tariff and broadcasts a community price to which each household responds in its own interest, giving a bilevel
stochastic dynamic program that is intractable in general. Our main result characterizes that the joint optimal centralized policy is a two-threshold policy on the community's aggregate renewable generation. Building on this structure, we adopt the Threshold Pricing Rule, which is uniform, individually rational, revenue adequate, and asymptotically optimal in terms of community welfare under a light-traffic condition. Simulations using synthetic and real world data confirm asymptotic optimality and individual surplus gains.
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Cryptography and Security
X-WAD: eXplainable Web Anomaly Detection
Matteo Bitussi and Roberto Doriguzzi-Corin
The rapid growth of web-based services, particularly API-driven architectures, reflects an increasing reliance on distributed systems, exposing sensitive data to security risks and making the adoption of automated defensive mechanisms essential. In this context, where benign traffic predominates in real-world settings, modern defenses increasingly model normal behavior, relying on semi-supervised approaches trained on only normal data. However, ensuring the complete absence of anomalous instances in such training data is inherently difficult in practice, and mislabeled or contaminated attack samples can introduce
backdoors into the learned defense, causing the model to silently misclassify certain attack patterns as normal behavior. This paper investigates the effectiveness of
Transformer-based
Language Models (TLMs) in the detection of anomalies in HTTP requests, focussing on providing detailed explanations for the detected anomalies. The study employs
token-level logit-based surprisal mapping to provide both an anomaly score and a direct, detailed explanation via heatmap-like highlighting. The effectiveness of the proposed explainability approach is demonstrated by the discovery of labelling inconsistencies in a popular public
dataset, revealing how anomalous contamination in the training data had induced
backdoor-like failures in the detection models.
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Information Theory · Cross-listed from quant-ph
Conditional contraction coefficients and their applications to quantum networks
Christoph Hirche, Ian George, Theshani Nuradha et al. (+1 more)
Contraction coefficients quantify the loss of distinguishability induced by a channel and provide a strong form of the data-processing inequality. While standard contraction coefficients ignore auxiliary
quantum systems, existing extensions based on complete contraction coefficients require the compared states to have identical reference marginals. In this work, we introduce conditional contraction coefficients, a novel family that incorporates arbitrary
quantum reference systems by subtracting the distinguishability already present in the reference system. We develop a general framework for contraction coefficients with such
quantum side information, including the corresponding strong-data-processing-inequality (SDPI) constants, expansion coefficients, and relative contraction coefficients. For the trace distance, we show that the optimization can be restricted to orthogonal input states. For the
quantum relative
entropy, we prove that its conditional contraction coefficient is exactly equal to the contraction coefficient of the conditional mutual information, extending the classical correspondence between relative-
entropy contraction and mutual-information contraction to the setting with
quantum side information. More generally, we identify structural properties of divergences required for these results and discuss extensions beyond the relative
entropy. These results establish a unified framework for analyzing information contraction in
quantum network settings, where
quantum side information and distributed correlations are intrinsic features of the information-processing task. Applications include an extension of the Polyanskiy-Wu bounds on mutual information contraction, new perspectives on mixing times, and fundamental limits on
quantum memories.
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All authors Christoph Hirche, Ian George, Theshani Nuradha, Mark M. Wilde
Computer Vision
Ancient-Bench: A Comprehensive Multi-millennial, Multi-medium, and Multi-script Benchmark for Ancient Chinese Artifact Text Recognition
Hiuyi Cheng, Nuo Xu, Yuyi Zhang et al. (+9 more)
Ancient Chinese artifact text recognition is fundamental to heritage digitization, and
benchmarks for ancient texts are essential for evaluating current model capabilities. However, existing
benchmarks suffer from ”fragmentation”, manifested in limited temporal coverage, limited medium diversity, and incomplete script types. Therefore, we present Ancient-Bench, a comprehensive
benchmark of 2,700 images for ancient Chinese artifact text recognition, featuring three dimensions: Multi-millennial (spanning 3,000 years of character
evolution), Multi-medium (covering nine artifact categories), and Multi-script (encompassing seven historical script forms). To enable consistent and fair evaluation across heterogeneous media, we further define three annotation standards tailored to the medium-specific characteristics of ancient texts: symbol standardization, character standardization, and parsing standardization. Extensive experiments on Ancient-Bench covering general
Vision-
Language Models (VLMs) and OCR-specialist models reveal that ancient Chinese artifact text recognition remains fundamentally unsolved, with persistent challenges in variant characters, specialized symbols, and
hallucination. The
dataset is available at https://github.com/SCUT-DLVCLab/Ancient_Bench.
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All authors Hiuyi Cheng, Nuo Xu, Yuyi Zhang, Xuhan Zheng, Wei Pan, Jing Zhang, Dezhi Peng, Minghui Liao, Yihua Teng, Jihao Wu, Haoyu Ren, Lianwen Jin
Computer Vision
Magpie: Real-Time World Renderer for Interactive Games
Xiaoyu Zhan, Xinyu Wang, Xiaohong Zhang et al. (+6 more)
Modern game development relies heavily on conventional graphics pipelines. High-quality visual content requires modeling, material authoring, animation, lighting, effects, and runtime optimization, making asset production expensive and extending the development cycle of game prototypes. Recently,
video foundation models are beginning to change film and
video production, but games differ from linear media, they require not only continuous and realistic imagery, but also stable and reproducible gameplay rules, object states, and interaction outcomes. We present Magpie, a real-time generative world-rendering system for interactive games. Magpie separates gameplay execution from visual generation. Designers define scenes and rules in a game engine. At runtime, the Game Engine resolves player actions and maintains world state, while an independent Render Server generates visual output from white-box frames produced by the engine. Magpie provides a system-level implementation path for applying generative models to real-time game rendering. It preserves gameplay designability and reproducibility, and reduces the dependence of early game prototypes on complete visual assets.
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All authors Xiaoyu Zhan, Xinyu Wang, Xiaohong Zhang, Huanjie Zhu, Tengjiao Sun, Pengcheng Fang, Jiaxing Yu, Yanwen Guo, Dongjie Fu
Artificial Intelligence
Calibrated Enough to Know, Not Calibrated to Act: Fabricated Evidence Makes LLM Agents Commit to the Unknowable
Pranav Aggarwal
An LLM
agent shown a professional-looking
market panel commits to a directional call on a provably unpredictable question far more often than one asked the bare question: across 12 frontier models, commitment rises from 6.5% to 54.0% as evidence is escalated. It commits just as readily when every number on the panel is invented: fabricating the entire display, so nothing the model can see is true except the question itself, still lifts commitment from 24.5% to 36.8%, statistically indistinguishable from the 37.6% produced by genuine
market data. What unlocks confident action is not information but the authority of its packaging. The failure is narrow and locatable. Incapacity is not the answer: on matched answerable questions attached to the same panels, the same models answer essentially always, at near-perfect accuracy. Nor is it belief - stated probabilities barely move across the gradient that swings action by 48 points, and score worse than a climatological baseline. Missing judgment isn't it either: asked to classify a question's knowability before acting, models call it irreducible 90% of the time and then commit on just 0.4% of those. The act/don't-act gate is what fails, and the effect is concentrated in a few models rather than universal. Because the gate is separable, it can be trained. Supervised fine-tuning of a 3B model on 540 synthetic cases, predominantly dice, coins, jars and timers, drives commitment to 0.0% on the original cases and transfers to three unseen domains. It does not survive everything: the gate holds exactly when the response format leaves room to reason, and rigid formats that remove that room leave the model confident and wrong on questions it otherwise answers correctly. The gate is trainable and context-fragile, and deployment needs both halves of that sentence.
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Computation and Language
Prediction of Prediction (PoP): Inter-Layer Activation Fusion for Single-Pass Hallucination Detection in Large Language Models
Himal Badu
Autoregressive large
language models (LLMs) routinely generate factually incorrect outputs with high decoding confidence, limiting their deployment in high-stakes workflows. Existing output-stage uncertainty metrics can fail when models are overconfident on false assertions, while multi-sample verification pipelines introduce substantial
memory and latency overhead. This work evaluates whether internal hidden-state transition dynamics during generation can signal factual errors without auxiliary decoding calls. We introduce Prediction of Prediction (PoP), a mechanism that captures layer-transition uncertainty by fusing intermediate hidden representations across depth during a single forward pass. Evaluated on the TruthfulQA
benchmark using autoregressive
transformer backbones, PoP achieves an area under the receiver operating characteristic curve (AUROC) of 75.5% for factual-correctness classification. The mechanism operates within the base forward pass, adding less than 1.2% runtime latency and requiring zero additional generation passes. The numerical results are reported from the author-verified experimental implementation and are bounded by the evaluation scope described below.
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Cryptography and Security
Physical-Layer Fingerprint-Space Capacity Analysis for 100BASE-TX Devices in IIoT
Chenming Zhang and Aiqun Hu
Industrial Internet of Things (IIoT) networks widely adopt Ethernet technologies, such as 100BASE-TX, for industrial communications. As industrial networks continue to scale, reliable device authentication becomes increasingly important for preventing device impersonation and unauthorized access. Physical-layer fingerprinting (PLF) exploits device-dependent fingerprint features in transmitted signals and provides a hardware-based approach for terminal authentication. However, the distinguishable space supported by 100BASE-TX physical-layer fingerprints and its capacity boundary remain largely unexplored. To analyze the capacity of physical-layer fingerprints, this paper proposes a nonlinear and impulse-response model (NAIM) that characterizes device-dependent waveform differences in 100BASE-TX transmitted waveforms. The nonlinear component captures steady-state level deviations, while the impulse-response component describes the transition response during level transitions. The 100BASE-TX transmitter waveform requirements, the observation resolution determined by noise and analog-to-digital conversion (ADC) quantization, and the target bit-error ratio (BER) constrain the admissible fingerprint space. Under the NAIM model, the fingerprint-space capacity of 100BASE-TX terminals is derived as approximately $2.96\times10^{10}$ distinguishable states. Experiments on signals collected from 48 NICs under two cable conditions estimate a Gaussian-equivalent empirical capacity from the measured inter-device and within-device variations. Under the 5-m cable condition, empirical capacity and closed-set identification consistently rank the three NIC models, and a larger empirical capacity yields higher identification accuracy. These results demonstrate that the proposed capacity analysis provides a pre-deployment assessment for physical-layer fingerprinting in IIoT.
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Computer Vision · Cross-listed from cond-mat.mtrl-sci
Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer
Thorsten Tegetmeyer-Kleine, Thomas Schmitt, Phillip Aquino et al. (+3 more)
Battery lifetime is central to sustainable electrification, yet the particle cracking that drives lithium-ion cathode aging is hard to measure: quantitative microscopy of this degradation is bottlenecked by annotation, because each destructive electron-microscopy cross-section spans hundreds of megapixels and pixel-level expert labelling requires hours per image. We show that a frozen self-supervised
vision-
transformer encoder, combined with a lightweight trainable decoder and iterative model-assisted annotation, turns this sparse labelling budget into population-scale degradation measurements. Applied to three 120-megapixel NMC cathode cross-sections representing initial, cycled-aged and calendar-aged states, the framework distinguishes intragranular cracks from early- and late-stage intergranular cracks and yields per-particle distributions of crack width, tortuosity and area fraction. Late intergranular crack coverage reaches 4.6% in the cycled sample versus 0.5% in the initial and calendar-aged samples, forming more tortuous, higher-coverage networks, consistent with degradation from repeated electrochemical cycling rather than elevated-temperature storage alone. A single destructive image yields the population-level statistics needed for lifetime-extending design, aging assessment and second-life decisions.
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All authors Thorsten Tegetmeyer-Kleine, Thomas Schmitt, Phillip Aquino, Christiane Rahe, Dirk Uwe Sauer, Weihan Li
Computation and Language
STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation
Yichen Dong, Hao Wang, Junhui Li et al. (+3 more)
Large
Language Models (LLMs) have enabled a shift from sentence-level to document-to-document (Doc2Doc) machine translation, promising improved global coherence. However, document-to-document generation in a single pass frequently suffers from structural misalignment, manifesting as sentence omissions or
hallucinations that violate the core requirement of source-target correspondence. To address this, we introduce Sentence Translation
Alignment Rate (
STAR), an auxiliary metric that explicitly quantifies sentence-level structural fidelity. Building on this, we propose
STAR-masked Preference Optimization (StarPO), a framework that ranks document-level hypotheses by structural quality and utilizes a dynamic
alignment mask to focus optimization on misaligned segments. Experimental results across news and literary domains demonstrate that StarPO significantly enhances translation quality and structural integrity. Notably, StarPO allows compact models to surpass the performance of massive proprietary systems like GPT-4o while maintaining superior
token efficiency.
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All authors Yichen Dong, Hao Wang, Junhui Li, Linlong Xu, Longyue Wang, Weihua Luo
Numerical Analysis
A multi-class kinetic traffic flow model: discrete-velocity formulation and diffusively-corrected macroscopic limits
Carmen Mezquita-Nieto, Paola Goatin and Axel Klar
This paper introduces a multi-class extension of a discrete-velocity kinetic traffic flow model based on a non-local Prigogine-Herman framework. We derive a hyperbolically scaled system of equations from a continuous kinetic formulation describing interactions between different vehicle classes through braking and relaxation terms. The model is then discretized with respect to the velocity variable for an arbitrary number of vehicle classes, and the structural properties of the resulting formulation are analyzed. In particular, we prove hyperbolicity and total linear degeneracy. Due to the non-conservative structure of the model, we employ a path-conservative finite volume scheme for the numerical approximation of the system. Finally, we derive the corresponding diffusively-corrected macroscopic multi-class model, investigate its stability and present numerical simulations on a single-lane road to illustrate the theoretical findings.
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Machine Learning
Wendong Li and Jochen Garcke
Robot crowd
navigation requires safe and efficient decision-making under dense, dynamic, and
multimodal human–
robot interactions. Existing reinforcement-learning methods typically output a single reactive action at each timestep, which limits their ability to represent diverse short-term avoidance strategies. We propose Planning
Diffusion Policy Optimization (PDPO), an offline-to-online reinforcement-learning framework that uses a
diffusion policy to generate short-horizon action chunks for crowd
navigation. PDPO is first pretrained on collision-avoidance demonstrations and then fine-tuned online with PPO by treating the denoising process as an internal decision process. During execution, the policy generates a five-step action chunk and applies it in a receding-horizon manner. Furthermore, we observe an evaluation artifact in common crowd-
navigation benchmarks: without explicit boundary constraints, learned
agents may leave the valid domain and bypass dense crowds. To address this, we introduce a setting in which boundary violations are treated as collisions. Experiments show that PDPO obtains an improved success rate over strong baselines, and ablations demonstrate that action chunks are especially important for the modified bounded
benchmark.
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Computer Vision
ReViCo: Unveiling the Limitations of VLMs in Visual Text Understanding via Error Correction
Bojun Zhang, Junhong Liang, Feifei Zhai et al. (+2 more)
Vision Language Models (VLMs) have shown great success in general visual tasks, yet they still struggle to deeply understand text within images. In this paper, we introduce ReViCo (Real Visual Correction), a
benchmark designed to evaluate VLM text understanding through a novel task of visual text error correction. ReViCo challenges models to identify and fix text errors in real-world images, which requires a profound understanding of the interplay between visual text and its surrounding visual context. We
benchmark various VLMs using two distinct paradigms:
prompt-based strategy and targeted model training, both aimed at pushing the limits of current models. Our experiments reveal a striking performance gap between even the best VLMs and human, and further analysis also shows that most models struggle to accurately perceive the visual text, resulting in frequent correction errors. By highlighting these gaps, ReViCo provides a new
benchmark foundation for developing more robust and text-aware VLMs.
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All authors Bojun Zhang, Junhong Liang, Feifei Zhai, Fengxian Ji, Yu Zhou
Machine Learning
Inductive Correlation Clustering with Graph Neural Networks
Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan et al. (+1 more)
Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of clusters. Given $n$ objects and a pairwise similarity function, the goal is to cluster the objects so that similar objects are put in the same cluster and dissimilar objects are put in different clusters. Despite its versatility, existing CC
algorithms suffer from significant scalability issues and are inherently transductive: i.e., the
algorithm must be executed from scratch for any new problem instance. In this work, we bridge this gap by leveraging Graph Neural Networks (GNNs) to solve Inductive Correlation Clustering, a novel generalization of the CC problem designed to handle unseen graph instances. By learning to exploit common structural patterns and node features during training, our framework generalizes to new graphs drawn from the same distribution with minimal computational overhead with respect to standard
algorithms. We demonstrate the effectiveness and scalability of our approach through extensive experiments. Our framework not only excels in the inductive setting, e.g., lowering the inference time up to $5$ order of magnitude, while maintaining an approximation ratio within $~10\%$ of the best baseline solution, but also achieves competitive results on standard (transductive) CC
benchmarks. Finally, we showcase a practical application of our framework as a learnable pooling mechanism for graph classification. Our results indicate that our method serves as an efficient pooling layer, enhancing the ability of GNNs to capture hierarchical structural information in networks.
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All authors Francesco Paolo Nerini, Francesco Bonchi, Arijit Khan, André Panisson
Machine Learning
Ultra Low-Power, Lightweight, Probabilistic RSS-Based Path Reconstruction: A System for Landscape-Scale Bee Tracking
Christopher J. Noroozi, Joseph L. Woodgate, Michael Mangan et al. (+1 more)
Applications in fields such as movement ecology, Internet of Things or robotics share the need for systems that localize devices that are too small and power constrained to implement GNSS (Global
Navigation Satellite Systems). Alternative low-power localization methods often rely on only measurements of RSS (Received Signal Strength) to infer the AoA (Angle of Arrival) of a transmitted
radio frequency signal, but are limited by range and the power demand of the large number of RSS measurements required to infer an accurate AoA. In this paper we address these issues with a novel RSS-based method for tracking ultra lightweight and low-power moving receivers across a complex landscape, achieved by using a minimal number of RSS measurements from simple rotating high-gain transmitters with a range of 300m, and applying probabilistic modelling to infer their AoA. The receiver's movement path is then modelled using a Gaussian process and reconstructed using doubly
stochastic variational inference, resulting in approximately 15m accuracy tracking of receivers weighing 38mg (including power source) over a scalable landscape range while consuming less than 180uW, increased to approximately 10m accuracy at less than 600uW by taking more RSS measurements. We anticipate that this method will support fields such as the behavioural study of flying insect species, which we demonstrate by applying the system to track Bombus terrestris nest return flights.
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All authors Christopher J. Noroozi, Joseph L. Woodgate, Michael Mangan, Michael T. Smith
Robotics
Planning a Shared Modular Fixture Layout Across Robotic Disassembly Stages
Haohui Pan, Takuya Kiyokawa and Kensuke Harada
Stable support remains challenging in robotic disassembly of irregularly shaped products. As components are progressively removed, the available support surfaces, mass distribution, and task loads change throughout the process. A fixture layout designed for one workpiece state may therefore become infeasible at later stages, motivating unified support planning over the complete disassembly sequence. This paper presents a modular
vacuum-based fixturing system that plans one shared support configuration for the complete disassembly sequence of a screwdriver or shaver, allowing each sequence to proceed without fixture reconfiguration. To search the mixed continuous–discrete layout space under repeated cross-stage evaluation, a denoising
diffusion probabilistic model generates physics-informed initial configurations that are refined through
Bayesian optimization. Robotic screw and component-removal experiments verified the disassembly feasibility of the planned layouts, while 11 directional-load tests quantified their stability. Comparisons between the measured operational loads and directional responses yielded mean empirical stability margins of 66.9% for the screwdriver and 81.6% for the shaver. These results demonstrate that a product-specific shared layout can provide stable support throughout the tested robotic disassembly sequence.
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Neural and Evolutionary Computing
ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification
M. Middleton, H. Kayan, B. Sen Bhattacharya et al. (+7 more)
Object classification in event-based computer
vision is a task that is attracting considerable research
attention. Event-based object classification is a fundamental task in the fields of security and applied computer
vision, which typically use synchronous frame-based cameras and computing pipelines for operation. This approach has several practical flaws. The size, weight and power consumption of the device could prohibit deployment at the extreme edge or in covert sensing environments. Besides this, there are security concerns inherent in cloud-based or other off-device computation approaches due to the requirement of sending and receiving potentially sensitive data. Furthermore, this transmission of data introduces latency and requires consistent connectivity to the cloud infrastructure to function. The use of Spiking Neural Networks (SNNs) hosted on neuromorphic devices attempts to solve several issues present in this conventional approach. Research into event-based object classification methods are hindered by the lack of high-quality
vision datasets to use. To this end, the ANTShapes simulation tool has been previously proposed to create and label event-based
vision datasets. In this paper, four novel
datasets of varying difficulties are created using the tool and are benchmarked against existing spiking
datasets commonly used for event-based
vision research (N-MNIST, CIFAR10-DVS, DVSGesture and POKER-DVS). Classification is performed using a convolutional SNN. This work simultaneously provides four
datasets with rich details for future experiments to use and validates the output of the ANTShapes
dataset simulation tool as being suitable for its purpose.
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All authors M. Middleton, H. Kayan, B. Sen Bhattacharya, T. Ali, E. Baikas, M. Vousden, C. Perera, O. Rhodes, E. Gheorghiu, M. A. Trefzer
Artificial Intelligence
BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI
Björn-Lennart Eger, Daniel Rose and Barbara Dinter
Conversational AI systems, such as chatbots and virtual assistants, are becoming increasingly important to digital business processes. However, the established Business Process Model and Notation (BPMN) standard faces challenges when representing dynamic, context-sensitive interactions. This paper addresses this methodological and practical research gap by developing a standard-compliant BPMN extension (BPMN4CAI). Using Design Science Research methodology, this paper develops an approach that systematically extends existing BPMN elements and incorporates specialized components. The applicability and relevance of the BPMN4CAI framework are demonstrated and evaluated through a case study. The results show that the BPMN4CAI extension facilitates adaptive decision-making processes, robust context management, and transparent interactions for Conversational AI within business processes.
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Software Engineering
AgentDV: Closed-Loop Agentic AI for Hardware Design Verification
Navya Goli, Junzhe Liu, Zhenge Jia et al. (+1 more)
Register-transfer level (RTL) verification consumes a major part of modern system-on-chip (SoC) development effort. Yet, recent LLM-based verification-code generation often fails to produce runnable, design-consistent, and coverage-producing testbenches. We present AgentDV, a closed-loop agentic AI framework for automated RTL verification environment generation. AgentDV transforms single-shot LLM testbench generation into a tool-grounded verification pipeline by combining LLM-guided analysis, testbench construction, simulation, coverage measurement, and iterative refinement. The framework introduces three key ideas: 1) runnability filtering to reject invalid generated environments, 2) CSR-grounded checking to reduce hallucinated signals and incorrect expected behavior, and 3) coverage-guided iteration to regenerate tests based on measured verification gaps. We evaluate AgentDV using three LLMs on challenge DUTs and public OpenTitan peripheral and security IP blocks. From our analysis, we observed that direct single-shot prompting fails to produce a valid coverage-producing environment on
benchmarks. AgentDV achieves 100% pass rate on four DUTs and an average of 80.9% pass rate on all DUTs using Claude Sonnet 4.6. Similarly, an average of 58.7% and 60.6% pass rate is achieved for Llama and Qwen models, respectively. In addition, an average of 74.5%, 69.1%, and 64.9% of line coverage and 88.4%, 82.3%, and 76.7% of branch coverage for the
benchmarks under consideration for Claude Sonnet 4.6, Llama, and Qwen models, respectively.
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All authors Navya Goli, Junzhe Liu, Zhenge Jia, Umamaheswara Rao Tida
Artificial Intelligence
Thomson: Continual Learning of Frontier Models for SovereignAI
Shengzhuang Chen, Jerrod Parker, Yejin Bang et al. (+23 more)
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetry between developers and the diverse user base of modern AI. Recent public discourse acknowledges this concern, calling for SovereignAI (an organisation's capability to independently build, deploy and govern AI use), but offers little concrete advice on how this can be achieved in the short term under a diversity of funding settings. We argue that frontier performance is achievable by a wide range of institutions through Continual Learning on readily available open-weight models. Unlike limited approaches such as small-scale fine-tuning,
prompt engineering, or tool-augmentation of a frozen model, our approach exploits a modern mid- & post-training stack while introducing safeguards that preserve both plasticity and stability at each stage, making the minimal number of high-impact interventions on the parameters. This yields gains comparable to those typically seen across multiple successive model generations, at compute and personnel budgets substantially lower than commonly thought, making ownership of large parts of the SovereignAI stack (model, tool infrastructure, values & data
privacy) viable for far more actors. We demonstrate this with Thomson, a general-purpose frontier model trained with an enhanced focus on high-stakes professional work. Thomson performs competitively with recent frontier models across agentic tasks, safety, legal, tax & multilingualism, and large-scale Deep Research. Evaluations show a distinctive $\pi$-shaped pattern: distinct improvements across a wide range of capabilities, including those not explicitly targeted, while almost completely eliminating the forgetting problem common to narrow domain adaptation.
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All authors Shengzhuang Chen, Jerrod Parker, Yejin Bang, Andrew M. Bean, Nabeel Seedat, Stefan Winzeck, Daniil Glazko, Jannik Zgraggen, Fangyi Yu, Scott Arnott, Dietrich Trautmann, Luca Ciuffreda, Guglielmo Bonifazi, Davide Romano, Bradley Bell, Kirsty Fielding, Daniele Giofrè, Tom Zielund, Ipshita Chatterjee, Sneha Murthy Ghantasala, Manpreet Nanreh, John Scoville, Maciej Sakowicz, Wassim Seifeddine, Lukas Thede, Jonathan Richard Schwarz
Artificial Intelligence
When Tool Outputs Become Commands: Separating Action Induction from Runtime Authorization in Tool-Augmented LLM Agents
Xiaokun Guo, Zhen Xu, Dongdong Huo et al. (+5 more)
Tool-augmented LLM
agents must rely on untrusted runtime Observations to complete open-ended tasks; however, when tool outputs no longer merely provide data but begin to specify concrete actions, they effectively become “commands” that can drive real-world side effects beyond user intent. We argue that this risk arises from conflating action induction with execution authorization. To address this distinction, we propose SARA, which treats action induction and execution authorization as distinct runtime roles and separates action provenance from execution authority. On the Observation side, a context-isolated Action Probe exposes action-inducing semantics and persistently records action-origin provenance across steps as a review signal; on the execution side, actual tool calls are authorized only against the user objective and audited evidence from authorized successful executions, while satisfying goal, execution-chain, and argument-level support. To preserve this separation across multi-step execution, SARA applies No-History-Promotion to prevent historical recurrence from laundering action origins into execution authority. Across AgentDojo and AgentDyn, SARA limits ASR to no more than \(0.63\%\) across four primary evaluation settings while maintaining competitive task utility, and consistently reduces ASR across additional
Agent backbones.
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All authors Xiaokun Guo, Zhen Xu, Dongdong Huo, Yanqiu Zhang, Wei Wang, Qinfu Yang, Dongjin Yu, Yu Wang
Artificial Intelligence
Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection
Xiang Wang, Zhijun Cheng and Zhenyu Meng
In recent years, graph anomaly detection (GAD) based on frequency-domain filtering have achieved promising results. However, existing approaches still face three major challenges: First, they use static basic function to constructed graph filter which cannot effectively adapt to the frequency-domain distribution of graph data. Second, they fail to adequately consider the importance information of each attribute in the node feature vector, leading to the loss of fine-grained information. Third, they insufficiently utilize node labels for GAD. To address these issues, this paper proposes a novel graph anomaly detection method called JPGFN (Feature Transformation Enhanced Jacobi
Polynomial Graph Filtering Network). First, a Feature Separation Transformation Network (FSTNN) is developed to better learn fine-grained node features by feature separation and applying nonlinear transformations to node features across different dimensions. Second, an adaptive Jacobi
polynomial graph filtering module is constructed based on Jacobi
polynomials to adaptively capture complex frequency-domain features of graph signals. Finally, a node label constraint module is developed to facilitate the use of node labels and enhance the performance of GAD. Experimental results on multiple real-world
datasets demonstrate that the proposed method significantly outperforms mainstream approaches.
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Artificial Intelligence
GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL
Zike Yuan, Han Zhang, Jianzhi Yan et al. (+10 more)
Despite their potential in standardized graph tasks, Large
Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formulation. While deterministic graph tools are invariant to such shifts, extracting topological structures from noisy text is highly fragile for LLMs, which often overfit to surface patterns. Moreover, mitigating these parsing failures via multi-
agent systems incurs prohibitive latency. To address this, we propose GRAIN, a single-
agent framework optimized via reinforcement learning. GRAIN models
reasoning as a semantic parsing and tool-execution pipeline, guided by a Structure Invariance Reward. By validating extracted intermediate graphs against ground-truth
topologies, this reward forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts. We also introduce GRIT, a
benchmark evaluating sensitivity to such linguistic shifts. GRAIN outperforms multi-
agent baselines by 16.45% in accuracy with approximately 24% lower latency. Furthermore, it demonstrates superior structural generalization, halving the out-of-distribution (OOD) gap of SFT models (from 15.77% to 7.80%) and maintaining robustness on large-scale graphs beyond the training distribution.
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All authors Zike Yuan, Han Zhang, Jianzhi Yan, Le Liu, Cai Ke, Huozhi Zhou, Jian Xie, Jiran Yin, Yukun Cao, Yue Yu, Hui Wang, Ming Liu, Bing Qin
Cryptography and Security
Safety Does Not Compose: Non-Decaying Loop State for Autonomous LLM Agents
Chenhao Wu, Haoxuan Jia, Yang Liu et al. (+11 more)
Large
language model
agents are increasingly deployed as autonomous loops. Starting from one human goal, such a system repeatedly discovers work, plans, executes tool calls, verifies outcomes and persists state across many unattended iterations. The
agent safeguards in wide use, however, are defined over a single trajectory, and their safety state is re-initialized when the next trajectory begins. We show that this is a failure of composition rather than an implementation detail. Our central result is a separation: against an attack whose evidence is fragmented across several iterations, every trajectory-scoped monitor has a true-positive rate equal to its false-positive rate, however expressive it is, because the evidence it would need never appears in the window it sees, whereas a monitor retaining cross-iteration state separates the two perfectly. We further show that the obvious repair of carrying a geometrically decaying risk score is insufficient, because the cooling-off period a patient adversary must wait is a constant that does not grow with the horizon $N$. We then present LoopHarness, which restores a persistent, non-decaying safety state at the loop level. Under mediated commits and an arbiter detection floor $\delta_M$, it bounds the expected number of unauthorized irreversible actions by $B+m-1+m/\delta_M$, a constant in $N$, of which the $B+m-1$ term is decided by a model-free rule and therefore survives a fully colluding verifier. We give a complete evaluation protocol on native
Agent-SafetyBench tasks with paired clean and attacked episodes, an outer-state attack suite whose decisive evidence exists only across iterations, per-module ablations, and an adaptive white-box red team.
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All authors Chenhao Wu, Haoxuan Jia, Yang Liu, Yingguang Yang, Yuhan Lin, Chongyang Zhang, Hao Zheng, Yulin Huang, Jianshen Zhang, Yongzhi Qi, Shang Luo, Kefu Xu, Jifeng Zhu, Bin Chong
Machine Learning · Cross-listed from eess.SP
Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces
Kyriakos Stylianopoulos, Mattia Fabiani, Giulia Torcolacci et al. (+2 more)
The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred data. This paper presents an eXtremely Large (XL) Multiple-Input Multiple-Output (
MIMO) system that operates as an Extreme Learning Machine (ELM) to execute Over-The-Air (OTA) binary classification. To reduce hardware complexity, the receiver is equipped with cascaded metasurfaces terminating in a single
radio-frequency chain. A front metasurface layer applies a fixed nonlinear response to the incoming signal, acting as the ELM's activation function. Subsequent tunable linear metasurface layers physically approximate the trained network weights directly in the wave domain. Numerical evaluations across diverse
datasets showcase that our XL
MIMO architecture achieves classification accuracy comparable to idealized digital models, thereby proving the viability of low-complexity, wave-domain OTA learning.
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All authors Kyriakos Stylianopoulos, Mattia Fabiani, Giulia Torcolacci, Davide Dardari, George C. Alexandropoulos
Computation and Language
Said Aloud, Read Different: Cross-Modal Instability in Multimodal Models
Basel Mousi, Fahim Dalvi, Shammur Chowdhury et al. (+2 more)
Multimodal foundation models are increasingly used in
speech-first assistants that must interpret spoken queries and produce visually grounded decisions. Yet it remains unclear whether semantically equivalent queries yield consistent judgments across modality (text vs.
speech) and
language (English vs. Arabic). We introduce a
speech-augmented visually grounded contrastive triplet
benchmark spanning 10,150 culturally grounded images from 18 MENA countries, where each image is paired with one supported statement and two plausible but unsupported alternatives. We define contrastive instability as the conditional rate at which a model fails to resolve all statements within a triplet, isolating fragmented
reasoning from complete failure. Evaluating recent
multimodal models under text and
speech in English and Arabic, we find that modality and
language shifts introduce substantial triplet-level inconsistencies that are not fully captured by aggregate accuracy, with
speech amplifying partial failures. We make the
benchmark publicly available to the community.
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All authors Basel Mousi, Fahim Dalvi, Shammur Chowdhury, Firoj Alam, Nadir Durrani
Cryptography and Security
SLIDE: Shuffle Shamir Secret Shares Uniformly with Linear Online Communication and Guaranteed Output Delivery
Jiacheng Gao, Moyang Xie, Yuan Zhang et al. (+1 more)
We revisit shuffle protocols for Shamir secret sharing. Existing constructions either produce non-uniform shuffles or incur high communication and round complexity, sometimes exponential in the number of parties. We propose two new shuffle protocols that achieve uniform shuffling with communication complexity $O((k+l)n^2m\log m/\log k)$ for an $m$-by-$l$
matrix shared among $n$ parties, where $k\leq m$ is a tunable parameter. The first protocol is concretely efficient, while the second achieves the best-known $O(nml)$ online communication and $O(n)$ rounds. Experiments show significant improvements in online efficiency and total cost over prior work. Our key technical ingredient is a novel permutation sharing technique that represents permutations using smaller permutation
matrices, making their application significantly more efficient. The first protocol applies independent secret permutations sequentially, while the second builds on shuffle correlation to achieve optimal online complexity. We further extend shuffle correlation to support guaranteed output delivery with linear online communication, yielding SLIDE, the first protocol to achieve both $O(nml)$ online communication and guaranteed output delivery. Our constructions rely only on basic Shamir secret sharing over any field of size greater than $n$. As shuffling is a fundamental primitive for MPC tasks such as sorting and oblivious data structures, our results enable more efficient and scalable secure computation in practice.
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All authors Jiacheng Gao, Moyang Xie, Yuan Zhang, Sheng Zhong
Computation and Language
Small Frequency Corrections Can Change What Survives KV Cache Compression
Hong Chen, Yudong Zeng, Yongwei Huang et al. (+5 more)
Compressing a key-value cache before its next question is known requires choosing what to retain without knowing which evidence will matter. Value energy measures entry strength but does not distinguish isolated keys from those with many similar neighbors. We introduce TwinKV, a training-free method that discounts value energy by nonlocal post-RoPE key frequency. Prefix
attention allocates head capacities, while retained entries preserve their original keys and values under an exact storage budget. Across four
language models, TwinKV exceeds five evaluated compressed baselines in mean score on LongBench, LooGLE, and RULER at 50% KV removal. Component controls isolate the frequency contribution. On Llama-3.2-1B RULER at 75% removal, normalized frequency weights average 0.95, yet change 7% of nonprotected retained positions and improve value-only retention by about 5.5 points under both uniform and adapted capacities. Permuting the weights within heads weakens this gain. These results show that modest frequency corrections can change retention and answering outcomes, with effects that depend on the model and task.
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All authors Hong Chen, Yudong Zeng, Yongwei Huang, Zuhao Ouyang, Dongnan Zheng, Junyan Zhang, Yubo Gao, Xuming Hu
Artificial Intelligence
TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation
Jingyi Zheng, Yule Liu, Zifan Peng et al. (+4 more)
Internet memes are a pervasive form of
multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore, adapting memes across cultures and
languages is a central challenge for enabling mutual understanding in online communication. Unlike ordinary translation or standalone text rewriting, cross-cultural meme transcreation must jointly preserve communicative intent, adapt culture-dependent meaning for the target audience, and maintain coherence between text and image. In this work, we first provide an explicit task analysis of cross-cultural meme transcreation and identify three core challenges: culture-specific knowledge understanding, intent and tone preservation, and
multimodal consistency. Based on this analysis, we propose a multi-
agent framework with specialized
agents that are coordinated to address these challenges through cultural adaptation, target text rewriting, revision, and conditional visual adjustment. The framework strengthens target text adaptation with coordinated feedback to handle difficult cases that require deeper cultural or visual intervention. We evaluate the framework on bidirectional Chinese-English meme transcreation using both human evaluation and LLM-as-a-Judge. Our method consistently outperforms all baselines across both evaluation settings. In human evaluation, it achieves the best performance on all four dimensions and delivers a 33.1% average improvement over the strongest baseline, while in LLM-as-a-Judge, it attains the highest Top-1 ranking rate (60% versus 26% for the second-best baseline). Further analysis indicates that each component contributes to the performance. Our error analysis suggests that the remaining bottlenecks lie in humor reconstruction and image-text
alignment rather than simple cultural knowledge gaps, pointing to future work on humor transfer.
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All authors Jingyi Zheng, Yule Liu, Zifan Peng, Tianyi Hu, Yuemeng Zhao, Xinhu Zheng, Xinlei He
Software Engineering
AROMA+: A Study of Factors Affecting Reproducible Builds in the Maven Ecosystem
Mehdi Keshani, Amirhossein Rahmati, Mohammad Hossein Aref et al. (+1 more)
Modern software engineering establishes software supply chains and relies on tools and libraries to improve productivity. However, reusing external software in a project presents a security risk when the source of the component is unknown or the consistency of a component cannot be verified. Reproducible builds present a mitigation strategy, as they can confirm the origin and consistency of reused components. A large reproducibility community has formed for Debian, but the reproducibility of the Maven ecosystem, the backbone of the Java supply chain, remains understudied in comparison. Reproducible Central is an initiative that curates a list of reproducible Maven libraries, but the list is limited and challenging to maintain due to manual efforts. Our research aims to support these efforts in the Maven ecosystem through automation. We investigate the feasibility of automatically finding the source code of a library from its Maven release and recovering information about the original release environment. Our tool, AROMA+, can obtain this critical information from the artifact and the source repository through several heuristics and we use the results for reproduction attempts of packages on Maven Central. Overall, our approach achieves an accuracy of up to 99.8% when compared field-by-field to the existing manual approach. In some instances, we even detected flaws in the manually maintained list, such as broken repository links. We reveal that automatic reproducibility is feasible for 32% of the packages on Maven Central using AROMA+, and 12% of these packages are fully reproducible. We demonstrate our ability to successfully reproduce new packages and have contributed some of them to the Reproducible Central repository. Additionally, we highlight actionable insights, outline future work in this area, and make our
dataset and tools publicly available.
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All authors Mehdi Keshani, Amirhossein Rahmati, Mohammad Hossein Aref, Abbas Heydarnoori