arXiv:2407.11217v4 Announce Type: replace
Abstract: Clustering problems such as $k$-means and $k$-median are staples of unsupervised learning, and many algorithmic techniques have been developed to tackle their numerous aspects.
In this paper, we focus on the class of greedy approximation algorithm, that attracted less attention than local-search or primal-dual counterparts. In particular, we study the recursive greedy algorithm developed by Mettu and Plaxton [SIAM J. Comp 2003]. We provide a simplification of the algorithm, allowing for faster implementation, in graph metrics or in Euclidean space, where our algorithm matches or improves the state-of-the-art.
Science Journals
arXiv:2607.07052v1 Announce Type: new
Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats agent exploration as a discovery mechanism rather than a permanent execution model. It defines a three-stage execution taxonomy, from fully agent-orchestrated to hybrid to fully deterministic workflows, together with an evidence-based promotion mechanism that converts repeatedly validated agent behaviors into cheaper and more reproducible deterministic workflows, while automatically demoting workflows that regress. Evaluated on a production cloud networking AIOps system processing tens of thousands of incidents per month, the approach increased deterministic execution from 0% to 45% over eight months, reduced per-incident agent costs by more than 70% despite doubling incident volume, and improved safety through greater reproducibility and auditability. The paper also presents the execution taxonomy, promotion and demotion criteria, trace extraction methodology, economic model, safety considerations, and discusses limitations and threats to validity.
arXiv:2607.07596v1 Announce Type: new
Abstract: Electronic voting must keep individual ballots private while letting anyone verify the final tally. This paper presents an architecture that meets both goals without a trusted key dealer: each voter encrypts a ballot in the browser with a self-generated secret key under the Paillier additive homomorphic cryptosystem, and no party ever holds every key. Two server roles divide the tally. A collector combines the voters' per-ballot auxiliary values into a single group element; an aggregator uses that element to cancel the voters' random masks inside the homomorphic product and recover the exact vote sum, learning the result but no individual ballot. The Solana blockchain records every ciphertext immutably and enforces the election lifecycle, while a native C library (libtommath) performs the heavy modular arithmetic. We state six assumptions under which the protocol is correct and prove product homomorphism, mask cancellation, and sum recovery; privacy rests on the Decisional Composite Residuosity (DCR) assumption for the additive layer together with a Diffie-Hellman-style assumption on the masking base. A bit-packing scheme places an entire multi-candidate ballot in one ciphertext, cutting client work, on-chain transactions, storage, and tally cost by a factor of k (the candidate count); the slot width b is free, with only k*b bounded by log_2(N). With b = 25 and a 255-bit modulus the scheme supports ten candidates and up to 2^25 - 1 = 33,554,431 votes per candidate, about 335 million ballots, and tallies 50,000 ballots in under one second. Finally, running the collector and aggregator inside attested secure enclaves makes the tally tamper-resistant and prevents cross-role collusion to deanonymize voters. The proof-of-concept implementation is open-source; a worked numerical example in the appendix reproduces the full pipeline.
arXiv:2411.01683v3 Announce Type: replace
Abstract: Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to comprehend the actions of other road users. This paper presents ROAD-Waymo, an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes, provided as a layer upon the (US) Waymo Open dataset. Considerably larger and more challenging than any existing dataset (and encompassing multiple cities), it comes with 198k annotated video frames, 54k agent tubes, 3.9M bounding boxes and a total of 12.4M labels. The integrity of the dataset has been confirmed and enhanced via a novel annotation pipeline designed for automatically identifying violations of requirements specifically designed for this dataset. As ROAD-Waymo is compatible with the original (UK) ROAD dataset, it provides the opportunity to tackle domain adaptation between real-world road scenarios in different countries within a novel benchmark: ROAD++.
arXiv:2607.07675v1 Announce Type: new
Abstract: Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inherently prioritizes visual fidelity and creativity over computational efficiency and physical realism. In this work, we present LingBot-Video, a DiT-based video pretraining paradigm specifically tailored for embodied intelligence. From the architecture perspective, we adopt the Mixture-of-Experts (MoE), instead of dense, framework to achieve a better trade-off between modeling capacity and inference efficiency, and manage to scale it up from scratch. From the data perspective, we construct a data profiling engine that augments standard internet videos with extensive robot-oriented footage, encompassing manipulation, navigation, and egocentric perspectives, to equip the base model with an intrinsic understanding of actions and world dynamics. From the training perspective, we develop a multi-dimensional reward system to enforce the alignment regarding physical rationality and task completion, going beyond standard criteria such as aesthetics, prompt-following, and motion consistency. Comprehensive evaluations validate its performance and efficiency as a video foundation model. We contribute LingBot-Video as the inaugural large-scale, open-source MoE video foundation model to the community, in a pioneering effort to bridge digital creativity and physical actuation.
arXiv:2411.14038v3 Announce Type: replace
Abstract: Given a finite set $S$ of points in the plane and a finite set $\mathcal{D}$ of directions, a geometric spanning tree~$T$ of~$S$ is $\mathcal{D}$-monotone if every path in $T$ is monotone with respect to some direction in $\mathcal{D}$. We study the problem of computing, for a given point set $S$ and a given set $\mathcal{D}$ of directions, a minimum-length $\mathcal{D}$-monotone spanning tree of~$S$. We present a quadratic-time algorithm for two directions. More generally, we show that the problem belongs to the complexity class XP when parameterized by the number of directions. We further study, for a given positive integer $k$ and point set~$S$, the problem of finding a minimum-length $\mathcal{D}$-monotone spanning tree of $S$ over all possible sets~$\mathcal{D}$ of $k$ directions. We prove that this problem, too, is in XP when parameterized by~$k$, and present two algorithms that run in $O(n^2 \log n)$ and $O(n^6)$ time for $k=1$ and $k=2$, respectively, where $n$ is the number of points in~$S$.
Finally, in contrast to the classical Euclidean minimum spanning tree of a set of points, whose vertex degree is bounded by six, we show that for every even integer~$k$, there exists a point set~$S_k$ and a set $\mathcal{D}_k$ of $k$ directions such that any minimum-length $\mathcal{D}_k$-monotone spanning tree of $S_k$ has maximum vertex degree~$2k$.
arXiv:2501.02436v5 Announce Type: replace
Abstract: Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning. In this work, we propose a theoretical framework to analyze learning dynamics through the lens of dynamical systems theory. We redefine the notions of linearity and nonlinearity in neural networks by introducing two fundamental transformation units at the neuron level: order-preserving transformations and non-order-preserving transformations. Different transformation modes lead to distinct collective behaviors in weight vector organization, different modes of information extraction, and the emergence of qualitatively different learning phases. Transitions between these phases may occur during training, accounting for key phenomena such as grokking. To further characterize generalization and structural stability, we introduce the concept of attraction basins in both sample and weight spaces. The distribution of neurons with different transformation modes across layers, along with the structural characteristics of the two types of attraction basins, forms a set of core metrics for analyzing the performance of learning models. Hyperparameters such as depth, width, learning rate, and batch size act as control variables for fine-tuning these metrics. Our framework not only sheds light on the intrinsic advantages of deep learning, but also provides a novel perspective for optimizing network architectures and training strategies.
arXiv:2501.05711v4 Announce Type: replace
Abstract: Vision Language Models (VLMs) have achieved strong performance across a wide range of video understanding tasks. However, their viewpoint-invariant training limits their ability to infer egocentric properties, such as human-object interactions, from exocentric video observations. This limitation is particularly critical for applications such as Activities of Daily Living (ADL) monitoring, where understanding egocentric properties is essential but deploying wearable egocentric cameras is often impractical. We propose Ego2ExoVLM, a framework that enables VLMs to infer egocentric properties directly from exocentric videos by leveraging time-synchronized ego-exo video pairs during training. Our key insight is to treat the egocentric viewpoint as privileged supervision, providing rich interaction signals that are available only during training. Ego2ExoVLM consists of two complementary components: Ego2Exo Sequence Distillation, which transfers egocentric reasoning through a language-level sequence distillation objective, and Ego Adaptive Visual Tokens, which encourage the model to surface interaction-relevant cues within exocentric visual representations. To evaluate this capability, we introduce Ego-in-Exo Perception, a benchmark for assessing the understanding of egocentric properties from exocentric videos. We evaluate Ego2ExoVLM on 10 tasks spanning Ego-in-Exo Perception and existing ADL benchmarks, achieving state-of-the-art performance on the ADL-X benchmark suite and consistently outperforming strong baselines on our proposed benchmark. All code, models, and data will be released at https://github.com/dominickrei/EgoExo4ADL.
PriGo: Test-Time Primitive Guidance to Diffusion and Flow Policies for Adaptive Robotic Manipulation
arXiv:2607.07076v1 Announce Type: new
Abstract: Imitation learning has enabled remarkable progress in robotic manipulation, especially with diffusion and flow-based policies that generate complex visuomotor behaviors directly from demonstrations. Yet, despite their strong performance, these policies often fail to generalize across tasks and environments. A key reason is that existing policies tend to imitate superficial action correlations rather than the underlying intent. Inspired by the compositional structure of human behaviors, we propose PriGo, a primitive-guided test-time adaptive framework for robust robotic manipulation. PriGo introduces PANet, a lightweight primitive prediction module that infers primitive distributions directly from observations. We further propose a differentiable primitive guidance mechanism that refines generated actions during inference, steering trajectories toward semantically consistent behaviors. Unlike prior primitive-conditioned approaches, PriGo operates entirely at test time and can be seamlessly integrated into pretrained diffusion and flow policies without retraining. Extensive experiments on LIBERO, CALVIN, SIMPLER, and real-world robotic tasks demonstrate that PriGo consistently improves robustness, long-horizon execution, and generalization ability across both diffusion and flow-based policies.
arXiv:2607.07151v1 Announce Type: new
Abstract: We investigate memory requirements for Nash and subgame-perfect equilibria in turn-based deterministic games with $\omega$-regular objectives. We prove that memoryless randomised (i.e., stationary) subgame-perfect equilibria always exist in games with reachability, safety, and 0-2 Muller objectives (i.e., Muller objectives for which accepting sets are either up- or downward closed), and any combination of these objectives. We provide an algorithm to construct such an equilibrium. We also show that randomisation may be required to construct memoryless equilibria in games with reachability or B\"uchi as well as safety or CoB\"uchi objectives, and that memoryless equilibria need not exist for any other class of Muller objectives (with respect to the Mostowski hierarchy).
arXiv:2607.07235v1 Announce Type: new
Abstract: Explainability remains a key issue in reinforcement learning (RL). Distilling an interpretable policy from an agent trained in a complex environment is particularly challenging when the action space is continuous. We introduce ORCAID, a novel method for extracting interpretable rule-based policies from RL agents operating in mixed continuous-discrete environments with continuous action spaces. Our main contribution is an efficient oblique decision tree training algorithm that partitions the state space by hyperplanes and fits local linear models. The key idea lies in a three-stage split search: efficient random initialization, local refinement, and backward elimination. Finally, adjacent leaves are merged to yield a concise set of interpretable rules describing a given deep RL policy. We evaluate ORCAID across multiple RL environments, demonstrating that the extracted rule-based policies maintain strong performance with a low number of parameters and can even be used to improve the performance of the original deep RL policy.
arXiv:2501.08610v2 Announce Type: replace
Abstract: As the Internet rapidly expands, the increasing complexity and diversity of network activities pose significant challenges to effective network governance and security regulation. Network traffic, which serves as a crucial data carrier of network activities, has become indispensable in this process. Network traffic detection aims to monitor, analyze, and evaluate the data flows transmitted across the network to ensure network security and optimize performance. However, existing network traffic detection methods generally suffer from several limitations: 1) a narrow focus on characterizing traffic features from a single perspective; 2) insufficient exploration of discriminative features for different traffic; 3) poor generalization to different traffic scenarios. To address these issues, we propose a multi-view correlation-aware framework named FlowID for network traffic detection. FlowID captures multi-view traffic features via temporal and interaction awareness, while a hypergraph encoder further explores higher-order relationships between flows. To overcome the challenges of data imbalance and label scarcity, we design a dual-contrastive proxy task, enhancing the framework's ability to differentiate between various traffic flows through flow-to-flow and group-to-group contrast. Extensive experiments on five real-world datasets demonstrate that FlowID significantly outperforms existing methods in accuracy, robustness, and generalization across diverse network scenarios, particularly in detecting malicious traffic.
arXiv:2607.06804v1 Announce Type: new
Abstract: We present ECO/CPO-DAG, a domain-specific accountability protocol for adversarial supply chains that formalizes contradiction detection as a supplemental validation layer rather than a consensus or truth-establishing mechanism. Participants publish signed Event Claim Objects (ECOs) into a causally ordered, append-only directed acyclic graph (DAG) whose edges encode happened-before relations. When two claims about the same subject violate a domain constraint, any observer can compile a Contradiction Proof Object (CPO), a self-verifying object binding the two signed claims and the violated rule, which, on public verification, triggers economic slashing of a determinately blamed party. We map constraints to GS1 EPCIS 2.0 event semantics (spatial uniqueness, temporal monotonicity, quantity conservation, quality monotonicity, regulatory validity), so detection targets inconsistencies that are meaningful in practice. Selective disclosure via commitment schemes and, optionally, zero-knowledge contradiction proofs lets parties withhold claim contents until a challenge forces the minimal opening. We give an analytical treatment: an independent-observer detection model $1-(1-p_{\min})^h$, a deterrence condition $S>g(1-p)/(kp)$ under $k$-party collusion, and a storage estimate of order 1 GB per participant per year under stated assumptions. The protocol's boundary is explicit: it detects provable contradictions, not consistent lies; a party that never contradicts itself is invisible to it, so the layer complements, and does not replace, source verification and oracle aggregation. A single-machine reference implementation corroborates the detection model, with the predicted coverage band overlapping the measured 95% confidence interval at every observer count, and records zero false accusations; the fully zero-knowledge CPO, multi-party propagation, and adaptive-adversary evasion remain analytical.
arXiv:2607.06979v1 Announce Type: new
Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refresh cycles due to the complexity of coordinating massive client populations. For applications such as feed ranking, ad targeting, and personalized recommendation, model freshness: the ability to rapidly adapt to new user-local data is critical for maximizing objectives like click-through rate. This lag leaves models stale and unresponsive to volatile data distributions driven by viral trends and shifting user intent. Bridging this gap requires addressing three challenges overlooked by existing FL systems: transient client availability, dynamic data heterogeneity, and delays between model predictions and observable outcomes. We present FeLiX, an FL orchestration framework that minimizes wall-clock time-to-target accuracy on live interaction streams. FeLiX introduces three primitives: (i) streaming-aware availability tiers that leverage lightweight telemetry to identify ready clients at scale; (ii) fresh-utility selection, a dual-tier mechanism that prioritizes statistically valuable updates from devices able to meet tight refresh deadlines; and (iii) informativeness-aware, delay-robust aggregation that incorporates late, high-value updates containing ground-truth outcomes without biasing the global model toward stale distributions. Unlike prior systems that rely on unrealistic oracular knowledge of client availability, FeLiX achieves near-oracular performance in real-world settings. Across CIFAR-10, Google Speech, and realistic low-availability traces, FeLiX reduces wall-clock time-to-target accuracy by up to 2.37X while reducing communication bandwidth by 1.30X compared to state-of-the-art synchronous and asynchronous FL baselines.
arXiv:2607.07276v1 Announce Type: new
Abstract: The certainty equivalence (CE) principle underpins a wide range of control architectures by enabling the separation of estimation and control design. While this property holds for linear systems, its validity in nonlinear settings remains limited and often implicitly assumed. This paper revisits CE from a nonlinear perspective, showing that estimated states induce an intrinsic coupling between estimation and tracking dynamics. By analyzing the closed-loop system in tracking-error coordinates, we demonstrate that nonlinear state dependence gives rise to higher-order interaction terms during aggressive transients. Motivated by this limitation, we propose an estimation-aware (EA) control paradigm that incorporates estimation quality into the feedback law to isolate estimation-induced loops. The formulation remains filtering-agnostic while preserving general applicability to smooth, underactuated nonlinear systems. We derive analytical conditions guaranteeing bounded tracking under uncertainty, validating the framework under high-fidelity quadrotor flight simulation along complex 3D trajectories at speeds up to 57.6 km/h. Frequency-domain evaluations demonstrate that the EA law extends tracking bandwidth by 39% and improves stability margins by up to 55%, effectively mitigating severe cross-couplings to offer a robust alternative to classical CE-based designs.
arXiv:2607.07314v1 Announce Type: new
Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself. Recently, centralized Taking Away Training Data (TATD) attacks have shown that malicious training could abuse the memorization capacity of deep models to store and later recover training data. However, this memorization-based threat has not been systematically studied under FL environments, where multi-client averaging could overwrite encoded training data. In this paper, we study a white-box TATD attack in which a malicious server selects n target clients from K participating clients and actively writes private training data into the global model during federated training. We propose FedCVESA, a federated variant of Correlation Value Encoding Attack (CVEA), by adding a Pearson-correlation regularizer to the loss function of target clients, so that private training data are gradually encoded into selected model parameters, referred to as carrier parameters. To reduce the overwriting of carrier parameters during server aggregation, we further propose segmented aggregation over dispersed carrier parameters, preserving selected carrier parameters while keeping standard averaging on the remaining parameters. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 under Dirichlet non-IID partitions show that the proposed method can steal semantically meaningful private training images from the trained model while maintaining acceptable main-task utility in a controlled proof-of-concept setting. These results demonstrate that FL can become a parameter-level memorization channel for active TATD attack under the studied white-box malicious-server setting.
arXiv:2502.02815v3 Announce Type: replace
Abstract: Fair allocation of indivisible items among agents is a fundamental and extensively studied problem. However, fairness does not have a single universally accepted definition, leading to many competing fairness notions. Some of these notions are considered stronger or more desirable, but they are also more difficult to guarantee. In this work, we examine 22 different fairness notions and organize them into a hierarchy. Formally, we say that a notion $F_1$ implies another notion $F_2$ if every $F_1$-fair allocation is also $F_2$-fair. We give a near-complete picture of implications among fairness notions: for almost every pair of notions, we either prove an implication or give a counterexample demonstrating that the implication does not hold. Although some of these results are already known, many are new. We examine multiple settings, including the allocation of goods, chores, and mixed manna, and different valuation classes like additive, submodular, and subadditive. We believe this work clarifies the relative strengths and applicability of these notions, providing a foundation for future research in fair division. Moreover, we develop an inference engine to automate part of our work. It is available as a user-friendly web application and may have broader applications beyond fair division.
arXiv:2607.07317v1 Announce Type: new
Abstract: Light coupling into integrated microresonators is typically achieved using straight bus couplers that are optimized to achieve critical coupling at the pump wavelength but lack spectral engineering capabilities. Pulley couplers offer additional degrees of freedom and opportunities, but optimizing their spectral response requires intensive and time-consuming numerical simulations. To address this challenge, we developed a semi-analytical approach to quickly and efficiently calculate the coupling quality factor of pulley couplers. This enables the design of either broadband or wavelength-selective pulley couplers, depending on the target application. We illustrate the dispersion engineering capabilities offered by this new approach in the context of Kerr microcombs and experimentally validate it on the SiN photonic platform. We show that it is possible to achieve a non-dispersive broadband coupler, enabling the generation of frequency combs with similar spectral characteristics (span and intensity) irrespectively of the chosen pump wavelength.
arXiv:2607.07351v1 Announce Type: new
Abstract: Electricity markets increasingly expose stochastic energy generators to arbitrage opportunities between the day-ahead and balancing markets, driven by widening price spreads. However, opportunistic bidding, deliberately deviating from the production forecast to exploit anticipated price spreads, carries significant risk, and existing frameworks rarely offer explainable, risk-aware decision support. We propose a predict-then-contextual-optimize framework that decomposes the day-ahead bidding decision into three explicit stages to decide, when to engage in arbitrage, in what direction, and to what extent. A probabilistic binary classifier with confidence thresholds determines whether the predicted price spread is sufficiently confident to justify an opportunistic bid. Otherwise, the trader defaults to an arbitrage-free bid equal to the power forecast. A linear decision policy learned for each class via contextual optimization determines the magnitude of the bid deviation from the power forecast. The framework accommodates both standalone renewable generation and hybrid power plants combining renewable generation with other assets, such as an electrolyzer. We evaluate the framework on a real wind farm in the European bidding zones DK1 and DE/LU using a rolling-window procedure and compare it against several benchmark bidding strategies. The results show that the proposed framework increases mean profit relative to an arbitrage-free benchmark, reaching an improvement of about 7% for the hybrid power plant in DK1. The largest gains occur when distributional drift between training and testing windows is low, while the co-located electrolyzer further increases arbitrage value by providing additional operational flexibility.
arXiv:2607.07366v1 Announce Type: new
Abstract: Compressed indexing aims to support fundamental string queries in space proportional to compressed input size. For grammar compression, a length-$n$ string $T \in \Sigma^n$ represented by a grammar of size $|G|$ can support random access in $O(|G|\log^{O(1)} n)$ space and $O(\log^{O(1)} n)$ time, and the same bounds are known for many other queries, including pattern matching, longest common extension, lexicographic predecessor/successor, the Burrows-Wheeler transform, suffix arrays, and suffix trees.
Frequency-related queries remain less understood. These include rank queries, which report the number of occurrences of a symbol $c \in \Sigma$ in a substring $T(b..e]$, and symbol occurrence queries, which ask whether $c$ occurs in $T(b..e]$. No fully general data structure is known for these queries with $O(|G|\log^{O(1)} n)$ space and $O(\log^{O(1)} n)$ query time.
We establish new conditional lower bounds for such problems. First, we show that answering rank and symbol occurrence queries on grammar-compressed texts in polylogarithmic time using an $O(|G|\log^{O(1)} n)$-space structure constructible in $O(|G|\log^{O(1)} n)$ time would imply an $O(n^2\log^{O(1)} n)$-time algorithm for Boolean Matrix Multiplication. The proof uses a more general lower bound for efficiently answering a batch of such queries. Second, we extend the exact lower bounds from straight-line programs to LZ78-compressed strings, a weaker compression model. Third, independently, we show that even additive approximations of rank queries on straight-line grammars would imply faster Boolean Matrix Multiplication algorithms. Finally, assuming the Orthogonal Vectors conjecture, we show that other frequency-related problems, including range distinct counting and range mode frequency, also cannot be efficiently supported in compressed space.
arXiv:2504.06318v4 Announce Type: replace
Abstract: Sociality borne by language, as is the predominant digital trace on text-based social media platforms, harbours the raw material for exploring a multitude of social phenomena. Distinctively, the messaging service Telegram provides functionalities that allow for socially interactive as well as one-to-many communication. Our Telegram dataset contains over 5,800 groups and channels and 63 million messages, originating from a data-hoarding initiative named the ``Schwurbelarchiv'' (from German schwurbeln: speaking nonsense). Uniquely, it includes the transcriptions of over 3 million audio and video files. While the raw data was previously archived on the Internet Archive by an anonymous data hoarder, it was stored in a format that is difficult to process and largely inaccessible for systematic research. Our contribution consists of parsing, cleaning, and validating this raw archive, pseudonymising user data, and transcribing roughly 126,000 hours of audio and video content, thereby transforming this data hoard into a structured, research-ready dataset. This dataset publication details the structure, scope, and methodological specifics of the Schwurbelarchiv, emphasising its relevance for further research on the German-language conspiracy-theory-related discourse. We validate its predominantly German origin by linguistic and temporal markers and situate it within the context of similar datasets. We describe process and extent of the transcription of multimedia files. Thanks to this effort the dataset uniquely supports analysis of text from originally multimodal sources like voice messages and videos to investigate online social dynamics and content dissemination. Researchers can employ this resource to explore societal dynamics related to misinformation, political extremism, opinion adaptation, and social network structures.
arXiv:2607.06632v1 Announce Type: new
Abstract: Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy systems literature, adversarial attacks have been studied with a focus on problems regarding the electricity grid. Such problems include forecasting and grid state estimation, where adversarial attacks are also known as false data injection attacks. Only few studies have analyzed the potential impact that adversarial attacks have on the demand side. We analyze how manipulated price forecasts impact the decision-making in industrial demand response. To this end, we design adversarial attacks that aim to deteriorate the output of electricity price forecasting models and solve scheduling optimization problems of energy-intensive production processes using the distorted price forecasts. We make use of a generalized process model to investigate the vulnerability to adversarial attacks for a range of production scheduling problems with different levels of process flexibility. We find that adversarial attacks can erode the profits gained from demand response. However, when perturbations are limited in extent (so that they are hard to detect by the human user), demand response preserves about 90\% of its financial advantage compared to steady-state process operation. Further, we find that the impact of adversarial attacks on demand response does not only depend on the magnitude of the perturbations but rather on the orientation of the adversarial perturbations. Therefore, we argue that attack analyses should explicitly incorporate the sensitivities of scheduling optimization models into the attack design to enable more rigorous assessments of decision-making under adversarial attacks.
arXiv:2607.07403v1 Announce Type: new
Abstract: Vision Language Models (VLMs) and Vision Language Action (VLA) models have shown promise in robotic control. Yet, they face significant challenges regarding explainability, generalization, and compute requirements. This paper presents a Multi-Agent System (MAS) architecture that addresses these limitations by deploying specialized agents on onboard hardware - eliminating dependence on external compute. The system controls a multi-purpose autonomous mobile manipulator in a simulated industrial warehouse, fulfilling five task categories: safety inspection, warehouse maintenance, warehouse search, package quality verification, and responding to human requests. Compact VLMs (3-20B parameters) are used throughout, with fine-tuning applied to improve package inspection accuracy. A novel "Megamind" orchestration agent mitigates context retention issues inherent to long-horizon planning with smaller models. The system was validated in a hardware-in-the-loop simulation using an AMD Ryzen(TM) AI mini PC. Results demonstrate that a fully onboard MAS architecture is a viable, cost-efficient alternative to cloud-dependent deployments, with strong potential for real-world transfer. The simulation environment has been released as open source under the Apache 2.0 licence.
arXiv:2607.06754v1 Announce Type: new
Abstract: Co-designing with families for environmental sustainability relies on participatory imagination, yet habitual family roles and uneven participation, especially between adults and young children, often constrain it. A second challenge is continuity: workshop relationships and embodied ways of working do not easily survive into the final design, where artefacts travel more readily than roles or interactional dynamics. We report on a nationally toured mixed-reality installation developed through applied-theatre-led co-design with families. Across three workshops and user testing, applied theatre methods supported families to co-create narratives, artefacts, and interactional roles that shaped the public event. We show how theatrical co-design can rebalance child-adult participation through playful status shifts, and how selected workshop dynamics can be re-staged within a public mixed-reality installation. We contribute a theatrical account of participatory design in which designers work not only with artefacts and ideas, but with roles, rhythms, authority, and the social conditions that support collective imagination.
arXiv:2607.07415v1 Announce Type: new
Abstract: Positional determinacy of vertex-colored parity games was proved in the 1990s, which directly implies positional determinacy of edge-colored parity games. In 2006, it was shown that if a prefix-independent color-based objective ensures that every edge-colored two-player turn-based game is positionally determined, this objective is equivalent to a parity objective. We prove a similar result for vertex-colored games, namely that the following are equivalent for any prefix-independent objective $W$ over a finite set of colors:
- $W$ is positionally determined on all vertex-colored one-player games.
- $W$ is positionally determined on all vertex-colored two-player games.
- $W$ is equivalent to a parity objective on ordrerd pairs of colors.
We prove that finiteness of the color set is required for our equivalence to hold. Beyond this $1$-to-$2$-player lift, the technique that we develop to handle the pairs of colors establishes a promising 2-way correspondence between edge-colored games and vertex-colored games.