Generative AI models lower the bar for content creation, making it easy for any user to create professional-looking images, text and music with minimal effort. This has enabled a new cottage industry around creation of "AI slop" mass quantities of mediocre content produced to generate revenue, often through misrepresentation as human-authored content, or scams involving automated scripts and fake consumption. While there are obvious parallels between the AI-slop industry and "traditional" email spam networks, it might be too early to determine if AI slop generation can grow into a similar self-sustaining industry. In this paper, we look specifically at the music industry, and explore the question: Can we prevent AI music slop from growing into a self-sustaining shadow industry? To answer this question, we characterize the current state of AI slop in music, and its pipeline from generation, distribution, and consumption by users on streaming platforms. By examining growth and engagement on Spotify, we confirm that AI music exhibits AI slop characteristics: the overwhelming majority (93%) of AI music receive few, if any listener plays, and are rarely recommended. AI musicians "spray and pray," releasing large volumes of music across multiple genres in hopes of generating a hit. We also explore the AI slop pipeline by generating and publishing our own AI tracks onto streaming through 11 indie music distributors. We find distributors have inconsistent and largely unenforced policies on AI music, making it surprisingly easy to publish mass produced AI songs. Finally, we consider AI music detection, and find that current methods lack accuracy or robustness. As generation costs decrease, we believe slop generation in music will become self-sustainable, unless concrete steps are taken by the music industry. We consider and discuss potential mitigation methods based on our findings.

Deploying Large Language Models (LLMs) requires exploring a large configuration space spanning parallelization strategies, batching techniques, and scheduling policies. Exhaustive measurement across this space is impractical, making latency prediction essential for system optimization. While NPUs have emerged as accelerators designed for LLM inference, no prediction methodology has been established for them. Specifically, applying prior work to LLM inference latency prediction on NPUs faces three challenges: undisclosed microarchitecture of commercial NPUs, unpredictable compiler optimizations, and latency non-linearity induced by bucketing. We present LENS, a latency estimator that predicts NPU inference latency without information on the microarchitecture or compiler, and captures the non-linear latency induced by bucketing. LENS profiles each bucket with two end-to-end (E2E) measurements and composes the results to predict latency for arbitrary input-output length combinations. We validate LENS across NPUs from multiple vendors, several LLMs, and diverse workloads, achieving a mean prediction error of 2.15\%. We further compare LENS against two methodologically related baselines, confirming the validity of its approach.

This paper presents a low-cost home automation system implemented in a municipal swimming pool to address various challenges, including security concerns, air quality control, gas leakage detection, energy consumption reduction, and temperature and humidity control on the pool deck. The system utilises Arduino microcontrollers with sensors and actuators, enabling real-time data collection and analysis. The project is divided into two phases: hardware assembly and data analysis. In the hardware assembly phase, the Arduino sends data to a web Application Programming Interface (API) and stores it in a time-series database, with results presented in an Android application. The data analysis phase involves statistical exploration using libraries such as Pandas, NumPy, and Matplotlib. The proposed system aims to enhance decision-making based on collected and analysed data.

Social media posts often include misinformative or misleading content, diminishing the expected credibility of content feeds. We present an optimization-based method to improve the credibility of news content on social media feeds by refining existing content rankings. This method is based on a dual-objective optimization approach that minimizes the Spearman's footrule distance to the original ranking to maintain the original content order while incorporating an additional linear cost objective to elevate the expected credibility of the content feed. Additionally, we propose a robust semi-automated pipeline for assigning credibility scores to content based on a mixture of retrieval-augmented score assignments and human-generated fact-checks. This semi-automated pipeline helps ground the credibility assignment using human-generated labels while ensuring the algorithm extends to posts with few or no human-generated labels. We showcase our approach through an experimental setup using real-world data collected over X (Twitter), where we assign the credibility scores based on a mixture of user-generated community notes and retrieval augmented generation. The method we present leads to at most 7% deviation in both optimization objectives from the Pareto optimal front with known initial ranking values. Additionally, the algorithm allows for incorporating different measures for source credibility, making it applicable across various social media platforms.

Parent child tutoring is a collaborative learning setting with asymmetric roles, where parents guide children s problem solving while children engage in understanding and reasoning. However, most LLM based learning systems are designed for either single users or symmetric collaboration, leaving parent child tutoring with distinct instructional roles underexplored. Through a formative study, we find that effective parent child tutoring depends on preserving these distinct roles, with parents guiding the learning process and children remaining actively engaged in reasoning. We also identify recurring challenges when parents struggle to understand problem structure, lack sufficient knowledge to provide support, or encounter communication difficulties that disrupt shared understanding. To address these challenges, we present ParaTutor, a scaffolding system that provides different forms of support to parents and children. ParaTutor supports parents with guidance for tutoring and provides children with visual grounding for problem solving. We evaluate ParaTutor with 23 parent child dyads (children aged 10 to 12) under four tutoring conditions that vary how LLM assistance is delivered. Results show that generic LLM assistance tends to reduce the parent s role in tutoring, whereas ParaTutor better preserves parent led support and sustains children s participation in reasoning. These findings suggest that in multi users learning, the value of LLM support depends not only on model capability but also on how support is distributed across users with different roles. Our work contributes design implications for LLM systems that support family learning.

User mobility creates stochastic, time-varying backhaul demand that static capacity provisioning cannot match. We propose a closed-loop, load-aware hysteresis controller for hybrid fiber-IAB-FSO backhaul and show that energy drops faster than coverage: 8% to 44% energy savings cost only 0.9% to 6.7% coverage.

Metaheuristic optimization of hybrid fiber-IAB networks demonstrates that integrating user dynamics into topology design enables more adaptive and cost-efficient backhaul architectures, contributing to the development of scalable and flexible 6G network infrastructures.

Tabletop role-playing games provide a unique environment for interaction with artificial intelligence (AI) due to their complex and collaborative nature. We analyze Adventure AI, a podcast featuring human-AI interactions in Dungeons & Dragons play, to examine how AI is and can be used in tabletop role-playing gaming and how players perceive this use. We complete a qualitative analysis of three seasons of this podcast, from 2023 to 2025, reporting on the overarching themes of roles of AI, roles of humans, the evaluations and failures of AI, and its treatment as a person and character at the table. There are many aspects of the game where artificial intelligence succeeds, while there are others where it is less appropriate. This analysis gives a basis for future work on where artificial intelligence should and should not be used in gaming spaces.

In online vector balancing, vectors $t_1,\dots,t_n$ arrive one by one from a given set $T$ and the goal is to assign signs $s_1,\dots,s_n\in\{\pm1\}$ in an online manner so as to minimize the largest norm of any signed prefix sum $\sum_{i=1}^ks_i t_i$, $k \in [n]$. In this paper, we analyze the natural Euclidean greedy vector balancing algorithm for this problem: at each step $k$, the sign $s_k\in\{\pm1\}$ is chosen so that $s_k t_k$ has non-positive inner product with $\sum_{i=1}^{k-1} s_i\cdot t_i$. Our main result is the first finite bound, independent of the sequence length $n$, on the performance of greedy whenever $T$ is finite. When $T \subset \mathbb{R}^d$ consists of unit vectors, we prove that the signed sums produced by greedy have Euclidean norm at most $(2/δ_T)^{d-1}$, where $δ_T$ is the minimum non-zero distance between vectors in $T$ and subspaces spanned by vectors in $T$. The same upper bound holds when the sequences are composed of scaled down vectors in $T$. We also provide a simple set $T$ for which $Ω(\sqrt{d}/δ_T)$ is a lower bound. We analyze the greedy algorithm by proving the existence of a bounded convex $K_T$ that is $T$-absorbing: $\forall x\in K_T$ and $t \in\pm T$, $\langle x,t\rangle\leq0\Rightarrow x+t\in K_T$. We give an explicit construction of a set $K_T$ contained in a ball of radius $(2/δ_T)^{d-1}$, based on chains of subspaces spanned by vectors in $T$, which may be of independent interest. We generalize our greedy vector balancing bound to online vector partitioning, where the sequence $t_1,\dots,t_n$ must be partitioned in an online manner into $p$ subsequences. As an application, we prove a special case of a conjecture of Bosman et al. (arxiv:2402.19259), showing that a lexicographic version of total completion time scheduling under scenarios is polynomial time solvable when the number of scenarios is fixed.

NFV/SDN orchestration lets operators instantiate and steer traffic through virtual firewalls, IDS/IPS replicas, WAF clusters, zero-trust gateways, backup inspection paths, and migration targets on demand. Operators often treat these options as monotone improvements: more inspection capacity, lower nominal latency, or broader placement flexibility should not degrade the service. That intuition can fail even when the new option is locally attractive. We study a security-induced Braess paradox in service function chain (SFC) orchestration, where adding a defensive option worsens the post-adaptation equilibrium by concentrating traffic and adversarial value on shared security resources. We define Braessian security-management actions, derive a sufficient condition for paradox emergence under affine load-dependent VNF delay, and give a pre-deployment orchestration screen that rejects, caps, or reserves harmful options. A multi-tenant SFC experiment suite applies the model to four topology-derived settings: a fat-tree datacenter, NSFNET-style WAN, GEANT-style WAN, and edge/fog topology. Under default parameters in the Braessian regime identified by the theory, naive defensive expansion raises equilibrium service cost by 27.2-30.8% and increases risk concentration by factors of 6.1-9.7. Paradox-aware constrained use keeps the residual penalty below 1.9%, reduces service cost by 20.0-22.1% relative to naive expansion, and lowers a concentration-sensitive attack-loss proxy by 93.5% on average.

Cyber deception and Moving Target Defense are promising strategies that aim to disrupt adversaries by increasing uncertainty. However, sustaining long-lived, credible interactive sessions with adversaries remains an open challenge. Large Language Models (LLMs) offer a promising path toward more dynamic deception systems, but suffer from key limitations that fundamentally limit their applicability, including: lack of persistent state, output inconsistencies, hallucinations, latency, and susceptibility to behavioral subversion that may reveal the deception. We propose ShellGames, an SSH shell simulator based on LLM designed to address these limitations. ShellGames combines five complementary techniques: (i) Automatic Chain-of-Thought and few-shot learning to improve correctness; (ii) memory management to maintain system state coherency; (iii) speculative command execution to reduce response latency; (iv) smart routing of complex interactive commands to a sandboxed environment; and (v) subversion detection leveraging the constrained input-output domain of shell environments. To enable systematic evaluation, we introduce a standardized benchmarking protocol and dataset spanning correctness, consistency, state tracking, and robustness tasks. ShellGames achieves $0.898$ command accuracy on correctness ($+5.3pp$ over baselines), $0.918$ sequence-level accuracy on consistency ($+36pp$), $0.98$ state tracking accuracy ($+18.3pp$), and $0.95$ accuracy on robustness ($+37pp$). A user study with $n=20$ participants confirms that ShellGames achieves realism comparable to a real shell under free exploration and outperforms traditional honeypots on perceived command coverage.

As AI-generated code proliferates, formal verification, particularly through interactive theorem provers such as Rocq and Isabelle, becomes increasingly important for ensuring software correctness. However, producing machine-checked proofs in such provers remains a bottleneck. Existing solutions bring complementary strengths to proof automation: large language models (LLMs) can propose high-level proof strategies but lack local rigor, while automated tactics such as CoqHammer can reliably discharge many local goals but lack long-range planning capabilities. To combine the best of both worlds, we present Quarry, a planning-based proof synthesis framework that separates proof planning from proof execution. Specifically, Quarry asks an LLM to actively propose multiple proof decompositions with arbitrary sublemmas, type-checks them in Rocq under temporarily admitted sublemmas, and ranks candidates using a proof-state-based difficulty model that estimates hammer solvability. It then recursively proves sublemmas within a bounded budget, effectively turning long proofs into sequences of hammer-solvable obligations. We implement Quarry on top of SerAPI and CoqHammer and evaluate it using multiple frontier LLMs across multiple benchmarks. The experimental results show that planning-based decomposition with solvability-aware ranking substantially improves automation while maintaining predictable cost. Under a uniform 10-minute wall-clock budget, Quarry improves over the strongest baseline by 7% to 13% in success rate across three Rocq benchmarks. These results demonstrate that reliable proof automation can be achieved by coordinating neural planning with symbolic execution rather than replacing either.

Digital technologies are now central to children's learning, play, communication, identity formation, and social participation. Yet dominant approaches to children's online safety often rely on containment mechanisms, including bans, age gates, parental controls, monitoring, and screen-time restrictions. These approaches can be useful in specific contexts, but they often frame child protection primarily as a problem of restricting access to systems designed for adults. In this paper, we argue that this framing is inadequate for children's digital lives and insufficient as a security paradigm. We propose Child-fit security, a design paradigm in which technologies likely to be used by children treat a child as legitimate users, not attackers to be excluded, vulnerabilities to be patched, or risks to be managed. In this paradigm, children's wellbeing, development, privacy, safety, agency, and rights become core security requirements. This shifts the focus of protection from apps, accounts, and data to the child-system relationship, which means protecting both the child and their participation. We conceptualise child-fit security, contrast it with containment-oriented approaches, define its core principles, and discuss its implications for security design. We conclude by presenting a research agenda for making child-fit security operational.

Heterogeneous LLM serving stacks split scheduling into two layers that optimize in isolation: model routers pick a model from quality and cost signals while ignoring instance load, and serving load balancers optimize queues while ignoring quality. We present RouteBalance, a serving-aware scheduling layer that fuses both into a single online assignment over concrete model instances, jointly trading off quality, latency, and cost. A batched in-process predictor stack and dead-reckoned instance state keep the joint decision cheap on the request hot path ($\approx$32 ms at 12 req/s). On a 13-instance, 28-GPU heterogeneous cluster serving four model sizes, a single deployed RouteBalance stack traces the upper region of the three-way quality-cost-throughput frontier. Sweeping one weight vector reaches both the highest routing-decision quality (DeepEval $0.419$, $+0.013$ over the strongest baseline, $95\%$ CI $[{+}0.005,{+}0.022]$; the ordering holds when a second judge re-scores the actually served text) and, at its cost-priority corner, per-request cost that ties the cheapest baseline. With router engineering equalized against concurrent-scoring baseline variants we build, its balanced preset serves at $2.8$ s and $30$ req/s, leading $2.6$ to $4.1\times$ ahead of enhanced BEST-Route at high load. (Deploying those routers as published, one serial scoring call per request, makes them collapse $23\times$ under load, a deployment-architecture effect we isolate separately, not the routing result.) A four-arm isolation shows the benefit follows from pricing latency at model-selection time; the learned predictors contribute calibration and SLO headroom rather than the headline frontier. Code: https://github.com/AKafakA/route-balance

This work extends recent developments in studying human--machine interaction by scaling from individual game-theoretic models to a societal-level model. We adopt a Graphon Mean-Field Game (GMFG) that models the interaction among four groups of internally-homogeneous but externally-heterogeneous agents in a shared environment. Our results show that parasitism can masquerade as productive learning, with knowledge distribution and actions appearing healthy while being driven by machine coupling rather than independent investigation. To detect this, we measure the direction of information flow and belief entropy of the environment, revealing that human to machine channel dominates across all scenarios, with the asymmetry intensifying under parasitism. We further demonstrate that the system exhibits coexisting mutualistic and parasitic equilibria, where environmental noise can induce a tipping point that shifts agents past the cognitive cost barrier. These emergent phenomena are not designed into any individual agent but arise from the collective interaction structure, underscoring the need to study the sociology of humans and machines holistically as a complex system.

Automated video-odor matching predicts scents aligned with audiovisual content for scent-enhanced media. Existing methods usually treat odor labels as determined only by scene content, but odor judgment also depends on individual olfactory profiles, including scent sensitivity, tolerance to unpleasant odors, and affective preference. Ignoring this observer context limits current systems' ability to predict scents that match perceived experience. We present OlfactProfile, a framework for profile-conditioned odor prediction from audiovisual content. Our results show that olfactory profiles are not beneficial by default: with matched feature backbones, naive profile concatenation and uniform profile modulation can degrade performance, while structured field-wise profile conditioning consistently improves prediction. Thus, the key challenge is not merely whether observer context is available, but how it is integrated into multimodal reasoning. To study this setting, we construct an audiovisual benchmark pairing temporally aligned odor annotations with annotator olfactory preference profiles. It contains 1,350 video clips, a 99-class scent vocabulary, and three semantic odor tracks: Foreground Odor, Background Odor, and Emotion Odor. We also propose OAR (Olfactory-Aware Routing), a multimodal fusion module that performs track-aware audiovisual routing with field-wise profile modulation, allowing profile dimensions to influence odor reasoning according to perceptual role. Experiments show that OlfactProfile outperforms supervised baselines and general-purpose multimodal large models, is competitive with odor experts in a small human comparison, and improves perceived scent fit in scent-enhanced applications without task-specific fine-tuning. Per-track analysis shows that gains are strongest for Background Odor and Emotion Odor, where observer-dependent judgment is most important.

Classical DistFlow equations for steady-state distribution network analysis fail to capture the inherent imbalances of three-phase systems arising from asymmetrical lines, loads, and distributed energy resources (DERs). This paper extends the classical power flow (PF) equations into a rigorous, non-approximated three-phase formulation, termed Dist3Flow. The proposed branch flow model (BFM) utilizes the real and imaginary components of nodal voltages and the active and reactive power flows as state variables. Lines are modelled by nonlinear forward and backward equations, while loads and DERs are represented via ZIP models and P-Q control, respectively. By incorporating specific boundary conditions at the terminal nodes, the formulation generalizes PF analysis to both radial and closed-ring topologies. The solution is obtained by using a backward/borward sweep (BFS) algorithm. The approach is validated against OpenDSS across various configurations, considering open-ring and closed-ring topologies with and without DERs.

Space heating remains the dominant energy consumer in buildings. While structural retrofitting can substantially reduce demand, it is often costly and time-intensive. As an alternative, this study investigates the potential of intelligent heating control strategies to reduce heat consumption with lower investment and faster implementation. Previous studies have shown that replacing conventional heating-curve-based controllers with model predictive controllers (MPCs) can reduce heating energy demand. Whereas most studies compare MPC to conventional control, this work evaluates two MPC strategies with different control objectives and quantifies their impact on indoor temperature tracking and heating demand. A virtual residential building model was developed in Python based on ISO 52016-1 to generate synthetic measurement data. A simplified resistance-capacitance (RC) model was parametrised using this dataset and used as the internal model for two MPC strategies implemented in MATLAB. The strategies differ only in their optimisation objective: one minimises quadratic heating power, while the other prioritises indoor temperature tracking for thermal comfort. Simulations over six days show that both strategies satisfy comfort and system constraints, but differ in energy use and temperature variation. The comfort-oriented controller achieves lower total heat consumption than the controller minimising heating power, which is attributed to the penalisation of high heating rates in the quadratic objective function. The results demonstrate the importance of objective function formulation in MPC design and show that high comfort levels can be maintained while achieving lower heating demand without structural modifications to the building envelope.

Controller synthesis in power electronics-based systems depends predominantly on the mathematical model of the system, which is a limitation when the actual system is complex and the mathematical model cannot capture all its dynamics. Model-free control addresses this limitation by using an ad-hoc simple model which is compensated by high-rate evaluation of dynamics in terms of their derivatives. However, application of the model-free control strategy to power electronics-based multi-time scale dynamical systems is challenging because of the derivative action needed to implement such control. Grid-connected power converters are examples of such systems, yet experimental validation has not been adequately addressed in the literature. This letter presents the validation of such control including the hardware implementation level. An intelligent proportional-integral (iPI) controller is synthesized and validated on a 16 kW experimental test bench. This proves the benefits of the approach in control of grid-connected power converters, among which their participation in the secondary voltage control.

The coordinated attack problem models the challenge of coordinating a joint action within a bounded time by communicating over unreliable links. It was the first distributed computing problem proven unsolvable. Its analysis also revealed the importance of common knowledge, a central concept in epistemic logic. However, the randomized version of coordinated attack, which is solvable, has not, to the best of our knowledge, been studied through the lens of probabilistic epistemic logic, where processes generate randomness by flipping coins. We present an epistemic logic framework for studying randomized algorithms that execute for a bounded number of rounds. The framework applies to coordinated attack, approximate agreement, and consensus, and supports dynamic graph models: synchronous systems in which reliable processes execute a bounded number of rounds while an adversary determines which messages are lost. Our approach combines techniques from the logical characterization of dynamic networks and task solvability with ideas from probabilistic dynamic epistemic logic. It is inspired by the operational model of Varghese and Lynch for randomized coordinated attack. More broadly, the resulting notion of probabilistic epistemic task solvability provides a foundation for the epistemic study of randomized distributed computation. Using this framework, we analyze the Varghese-Lynch algorithm from a knowledge-theoretic perspective, providing a formal treatment of the algorithm and its lower bound. As a byproduct, we strengthen the lower bound and show it is tight. The proof relies on indistinguishability arguments, demonstrating that reasoning about knowledge remains essential in the probabilistic setting. We also formalize the notion of information level introduced by Varghese and Lynch, showing that it corresponds to a specific epistemic formula.

Biological plausibility is a key concept in neuromorphic computing and spiking neural networks, yet it remains inconsistently defined and difficult to quantify. In this work, we present an open-source framework for the automated assessment of biological plausibility in spiking neuron models. Our method builds on the idea of evaluating a model's ability to replicate canonical neuronal firing patterns observed in biological systems, following the classification proposed by Izhikevich. By encoding these patterns into objective functions and optimizing model parameters accordingly, our framework enables empirical assessment without requiring prior analytical modeling. Treating neuron models as black boxes, it provides a practical and flexible means of characterizing their dynamic capabilities. We demonstrate the effectiveness of the framework on several established models and a previously unexplored custom model. Implemented in Python and compatible with PyTorch and the Norse library, the framework is tailored for machine learning contexts. It is intended as a starting point for systematic research into the relationship between biological plausibility and network-level performance metrics such as accuracy, energy efficiency, robustness, and adaptability.

Coarse-grained thermal simulation tends to underestimate localized thermal issues, potentially missing critical hotspots. Accurate analysis, therefore, demands fine-grained information, which dramatically increases grid resolution and thus computational workload. Fortunately, the coefficient matrices are often sparse with regular sparsity patterns, offering optimization opportunities. However, existing general-purpose matrix solvers on GPUs rarely exploit these domain-specific properties, thereby encountering bottlenecks in data storage, memory access, parallelism, computational efficiency, and hardware utilization. Therefore, we propose CUTh-Solver, a co-designed GPU-accelerated Preconditioned Conjugate Gradient (PCG)-based sparse solver framework for Symmetric Positive Definite (SPD) systems arising from high-resolution steady-state and transient 3D IC thermal simulation. For data storage, CUTh-Solver condenses the Diagonal (DIA) storage format to remove redundancy. To optimize the memory access, CUTh-Solver employs diagonal-wise SpMV to achieve coalesced memory access. We further observe a critical conflict between parallelism and preconditioning quality and thus adopt a high-parallelism preconditioning strategy. To improve computational efficiency and hardware utilization, we employ an adaptive fine-grained mixed-precision strategy that leverages diverse floating-point units to avoid resource contention, enhancing throughput without compromising numerical stability. Experimental results show that CUTh-Solver achieves up to 25.8x speedup over GPU-accelerated COMSOL Multiphysics 6.4 and over 3x speedup over NVIDIA's native general-purpose libraries (AmgX, cuSPARSE, cuDSS). Ablation studies validate the individual contribution of each optimization. The code is available at: https://github.com/Chenghan-Wang/CUTh-Solver

Intrusion detection systems (IDS) trained on wired-network benchmarks degrade sharply in real-world unmanned aerial vehicle (UAV) swarms, where mobility, fluctuating link quality, and decentralized routing reshape traffic distributions. Existing UAV-specific datasets also do not systematically vary these conditions, leaving no way to train or test an IDS against the very shift that defeats it. We present UAV-CAS, a large-scale labeled flow dataset for UAV-network intrusion detection, generated by a Containernet digital twin that is systematically calibrated against AERPAW testbed measurements. We have a four-layer calibration pipeline spanning altitude-dependent path loss, mission-specific mobility, the link-level performance chain, and end-to-end trace fidelity. UAV-CAS comprises 99,492 flows drawn from 1,024 configurations that span five attack families (DoS, DDoS, blackhole, wormhole, replay) and nine collaborative attack compositions. A diversity analysis shows that high-rate attacks separate from benign traffic up to an order of magnitude more strongly than in any prior benchmark, while stealth attacks deliberately blend with benign traffic. Across ten baseline IDS, binary attack detection saturates above $0.98$, confirming the dataset is learnable, whereas full attack-class identification remains hard -- per-class $F_1$ ranges from near zero to $0.82$ and falls into the single digits for stealth attacks. We release the dataset, simulator, and calibration data to support reproducible UAV intrusion-detection research.

This paper presents the unified Markov binary (UMB) format, an efficient, extensible, and well-supported explicit-state file format for representing a wide range of probabilistic systems. UMB addresses the problem that, while probabilistic model checking tools often support common high-level modelling languages, there is no effective mechanism for exchanging low-level model representations. In practice, textual, tool-specific formats are used, hampering interoperability and resulting in large overheads in writing and reading model files. UMB provides a clean, unified, and efficient solution, based on a general underlying mathematical model, and encoded using a small set of bit-level primitive data structures. The format has already been adopted by prominent tools and comes with a convenient Python library for reading, manipulating, creating, and validating models, plus infrastructure for cross-tool installation and continuous validation. We report on both the efficiency of the file format and the new practical use cases that it facilitates.

This work introduces ARES, a platform and open pilot dataset for auditing adaptive social engineering risks in LLM-mediated social decision-making through controlled social games. ARES supports human--human, human--AI, and AI--AI settings, combining configurable game templates, role-conditioned LLM agents, psychology-informed participant profiling, structured interaction trees, and synchronised behavioural and biometric acquisition, filtering, and deep-learning-based feature extraction. The pilot dataset was collected from 15 participants interacting with a role-conditioned GPT-5.4 agent in two concatenated games: an adapted Prisoner's Dilemma and an Ultimatum Game. It comprises 340 GB of raw and processed multimodal data across six streams: interaction logs, video, screen recordings, gaze logs, smartwatch signals, and game/questionnaire metadata. These data include interaction paths, written justifications, psychological profiles, subjective feedback, perceived counterpart identity, game outcomes, and derived behavioural, facial, and gaze features. Alongside the dataset, we provide descriptive analyses to characterise the pilot release. Rigorous risk evaluation is essential for the deployment of secure AI systems, as it enables the identification and mitigation of vulnerabilities, ensures the protection of sensitive data, and supports compliance with evolving regulatory and ethical standards in society.

Access to evidence-based psychotherapy remains limited worldwide, with long waitlists even in high-income regions. Recent advances in large language models (LLMs) offer potential for scalable mental health support when designed with clinical oversight and safety mechanisms. We present Mind Companion, an LLM-based embodied conversational agent integrating multi-layered psychological analysis with process-based therapy principles. The system performs real-time analysis of client statements across fact extraction, psychological flexibility process detection, emotion recognition, and safety monitoring. Analysis results are stored for supervising clinicians to inform therapeutic planning. Response generation incorporates retrieval-augmented generation from evidence-based therapeutic literature and context-aware prompting. Responses are delivered through an embodied avatar with synchronized speech synthesis and animation. We evaluated three LLM configurations (GPT-4.1-mini, GPT-5.2, Claude Sonnet 4.5) against therapist responses from real therapy sessions using automated LLM-judge assessment and expert evaluation with 11 professional psychotherapists. GPT-5.2 achieved higher ratings than human therapist responses across understanding, interpersonal effectiveness, collaboration, and therapeutic alignment in both evaluations, demonstrating the feasibility of LLM-based conversational agents as tools to complement clinical care.

Modern large language model (LLM) serving clusters distribute inference requests across multiple worker processes on different GPUs, but failures are prevalent at scale. When a worker fails, the cluster simultaneously loses the failed worker's GPU-resident key-value (KV) caches and serving capacity, leaving surviving workers to absorb the redirected traffic while re-running interrupted requests from scratch. Existing fault-tolerant systems either restart interrupted requests from scratch or restore KV caches from checkpoints stored on a fixed neighboring worker, but both approaches route recovery work without considering current cluster load and leave the recovering worker idle during model reload. We present LUMEN, a fault-tolerant LLM serving system that treats recovery as a load-aware coordination problem across three decision points: checkpoint placement before failures, interrupted-request distribution at failure time, and serving capacity restoration during model reload. We evaluate LUMEN using both prototype experiments and large-scale simulations and demonstrate significant improvements in serving and recovery times.

Consumer mixed reality (MR) headsets seamlessly blend virtual content into physical environments with sufficient fidelity that users may be unable to distinguish virtual objects from physical ones. We identify this virtual-physical discrimination vulnerability as an exploitable security primitive. Through speculative design workshops with 12 experts from cybersecurity and MR/HCI, we develop a taxonomy of virtual-physical confusion attacks and implement four proof-of-concept attacks on Apple Vision Pro, evaluating them with 26 participants in realistic MR tasks. All four attacks altered user behavior, with success rates ranging from 85% to 100%, producing misdirected interactions, misjudged object identities, biased purchasing decisions, and altered navigation paths. Notably, the most successful attacks were also the hardest to detect according to participants' subjective ratings. Even participants who recognized virtual content still complied behaviorally, and no participant attributed anomalous events to adversarial causes. We propose platform-level provenance, interaction gating, and user education as countermeasures.

Network traffic classification enables website fingerprinting, intrusion detection, and Quality of Service management. However, developing methods that capture stable and generalizable traffic patterns under realistic deployment conditions remains challenging. We introduce FlowCLIP, a contrastive pretraining framework for domain name prediction from encrypted traffic using only side-channel features: packet inter-arrival times, packet sizes, and packet directions. FlowCLIP uses raw domain names as textual supervision by aligning traffic flow representations with domain name representations through a CLIP-style contrastive objective. The pretrained traffic encoder is then frozen and evaluated through linear probing on canonicalized domain name labels. We evaluate FlowCLIP on a large-scale QUIC traffic dataset using a time-based protocol, where models are trained on Week 1 traffic and evaluated on traffic from Weeks 2-4. FlowCLIP outperforms competitive machine learning baselines across later evaluation weeks, suggesting that raw domain names provide a textual supervision signal for learning transferable encrypted traffic representations.

A-mode ultrasound (US) is a promising sensing modality for Virtual Reality (VR) interaction, as it enables the mapping of muscular activity into control commands while retaining the benefits of wearable sensing. However, existing approaches still face limitations in terms of wearability and interaction complexity, often relying on external hardware such as cameras. In this work, we propose a fully wearable multimodal interface for real-time VR-interaction, based on concurrent US and inertial (accelerometry) sensing from the forearm and upper arm. The system is built on the WULPUS platform and integrates an end-to-end software framework for real-time acquisition, visualization, and communication with a Unity-based VR environment. A multimodal learning pipeline is introduced for concurrent hand pose and forearm position estimation in 2D space. The interface is evaluated through offline and online experiments with five subjects, during the execution of three functional tasks: cylinder grasping (gross motor) and relocation, marble pinching (fine motor) and relocation, and liquid pouring. For offline experiments, we collect 5 acquisition sessions across multiple days, achieving an average inter-session accuracy across subjects of 80$\pm$6\% for hand pose estimation and 77$\pm$7\% for forearm position estimation. Online validation with minimal fine-tuning (5 min) demonstrates success rates of 92.0$\pm$16.0\%, 88.0$\pm$9.8\%, and 96.0$\pm$8.0\% for the three tasks, respectively. With a power consumption of only 19.9~mW, our system enables more than 2.5 days of continuous use on a small 350 mAh LiPo battery without the need for recharge, enabling truly wearable, multimodal, and functionally meaningful VR interaction.