arXiv:2605.14624v2 Announce Type: replace
Abstract: A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational energy cost of training them on GPUs. This paper examines the inferential step from "training is expensive" to "neural solvers are net-inefficient", which is where the critique actually goes wrong. Training the network costs a large fixed amount of GPU energy; running the metaheuristic costs a small amount of CPU energy on every instance, repeated as long as the solver is deployed. The two are not commensurable until a deployment volume is fixed. We define the Amortized Efficiency Threshold (AET) as the deployment volume above which a neural solver breaks even with a heuristic baseline in total energy or carbon, under an explicit constraint on solution quality. We show that the cumulative-energy ratio between the two solvers tends to a constant strictly below one whenever the network wins per instance, and that this limit does not depend on how the training cost was measured. An embodied-carbon term amortizes hardware fabrication symmetrically on both sides. We instantiate the framework on the CVRP environment at n=50 customers with the attention-based autoregressive solver of Kool et al. (2019), trained for 100 epochs on 20,000 instances over five random seeds, and HGS via PyVRP as the heuristic baseline. The measured operational crossover sits near 4.56e3 deployed instances at the median of a six-point baseline-budget sweep; the per-instance neural-to-heuristic ratio is 2.29e-3. The contribution is the framework, the open instrumentation, and the end-to-end measurement protocol. Code and benchmark pipeline are available at https://github.com/sohaibafifi/aet.
Science Journals
arXiv:2605.18645v1 Announce Type: new
Abstract: Retrieving the 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision. Existing methods rely on complex video setups or cues such as long-term point tracking or wide-baseline matching, but are frequently brittle under severe occlusions, rapid camera ego-motion, or weak local features. Learning-based methods, meanwhile, struggle to generalize beyond their training categories. We propose a category-agnostic optimization framework that treats articulated object understanding as a primitive-fitting problem. Geometric primitives serve as a proxy representation that avoids the pitfalls of unstable point tracks; a novel mechanism organizes them into coherent parts constrained by revolute and prismatic joints. Our formulation jointly optimizes part segmentation and joint parameters, recovering complex kinematics from a single casually captured video. A visibility-aware procedure handles partial observations and occlusions inherent to real-world data. We also propose the AiP-synth and AiP-real benchmarks, featuring significant camera motion and heavy occlusions, and outperform existing methods. Project page: https://aartykov.github.io/Articulation-in-Prime/
arXiv:2507.09148v2 Announce Type: replace-cross
Abstract: Sparse Principal Component Analysis (SPCA) is a fundamental technique for dimensionality reduction, and is NP-hard. In this paper, we introduce a randomized approximation algorithm for SPCA, which is based on the basic SDP relaxation. Our algorithm takes an (approximate) SDP solution, constructs one deterministic sparse solution and several randomized solutions, and outputs the best among them. Our algorithm has an approximation ratio of at most the sparsity constant with high probability, if called enough times. Under a technical assumption, which is consistently satisfied in our numerical tests, the average approximation ratio is also bounded by $\mathcal{O}(\log{d})$, where $d$ is the number of features. We show that this technical assumption is satisfied if the SDP solution is low-rank, or has exponentially decaying eigenvalues. We then present two classes of instances for which this technical assumption holds. We also demonstrate that in a covariance model, which generalizes the spiked Wishart model, the deterministic solution in our algorithm achieves a near-optimal approximation ratio. We demonstrate the efficacy of our algorithm through numerical tests on real-world datasets.
arXiv:2605.18647v1 Announce Type: new
Abstract: Federated learning for intrusion detection rests on a flawed premise: that every participating institution contributes equally to the shared model. In practice, a financial institution with mature security controls and low vulnerability exposure produces fundamentally different data than a government agency running with weaker controls and higher exposure. Treating their local models as equivalent discards information that organisations already collect through standard risk management audits. Four governance indicators from the CRISC framework of ISACA, specifically control maturity (CMM), proportion of implemented controls (KCI), risk indicator activation frequency (KRI), and mean vulnerability score (CVSS), are combined here into an Institutional Coherence Index (ICC). This index enters a Nelder-Mead federated weight optimizer as a regularization prior, guiding weight assignment toward institutional quality without imposing any fixed allocation. Each node trains a hybrid local classifier combining Categorical and Gaussian Naive Bayes. The server combines local distributions as a real Mixture of Gaussians, preserving each node's statistical identity rather than collapsing it into a global parameter vector. Validation on NSL-KDD (2009), CIC-IDS2017 (2017), and UNSW-NB15 (2015), under seven Dirichlet heterogeneity levels, shows that the ICC-regularized proposal outperforms size-proportional federated averaging in all three datasets: F1-macro 0.9135 vs. 0.9076 (+0.0059), 0.7556 vs. 0.6771 (+0.0785), and 0.2110 vs. 0.2060 (+0.0050). Statistical significance holds in 70 of 94 configurations (McNemar, p < 0.05). In all three cases, the optimizer assigned the highest weight to the institutionally most mature node and the lowest to the least mature, without any explicit ordering constraint.
arXiv:2605.17363v1 Announce Type: cross
Abstract: Single-layer molybdenum disulfide ($MoS_2$) possesses significant potential for nanoscale optoelectronics, but achieving high-intensity, long-term-stable photoluminescence (PL) emission remains a challenge. In this work, we demonstrate a remarkably robust, more than 8-fold maximum enhancement in the PL intensity of exfoliated and CVD-grown single-layer $MoS_2$ via a non-destructive ultraviolet (UV) laser treatment method. This substantial increase in radiative efficiency is accompanied by a trion-to-neutral exciton transition in the PL signal and a corresponding blue shift of the Raman $E_{2g}^1$ and $A_{1g}$ vibrational modes, signaling successful electron depletion (p-doping) and formation of Mo-O bonds, respectively. Furthermore, we demonstrate precise spatial control over PL properties by confining PL treatment exclusively to the UV laser-treated area. Crucially, the enhanced PL performance shows exceptional longevity; the CVD sample and the exfoliated sample remained stable for the entire monitoring period (72 and 32 days, respectively) under ambient conditions. We further investigated UV laser treatment in a controlled-environment chamber under argon, nitrogen, and oxygen atmospheres, distinguishing the influence of oxygen as the PL treatment agent. These findings establish a reliable pathway for the permanent treatment of single-layer $MoS_2$ PL properties, an essential step toward practical, high-performance nanophotonic devices.
arXiv:2605.17621v1 Announce Type: cross
Abstract: Stochastic forces are usually postulated or obtained by eliminating environmental degrees of freedom. Here we identify a variational origin: fluctuating endpoint data in Hamilton's principle induce fluctuations of the on-shell action. Hamilton--Jacobi propagation transports this boundary imprint, whose gradient generates an effective Langevin force inherited from boundary-action fluctuations. The resulting force is not freely specifiable: its amplitude is filtered by the Hessian of Hamilton's principal function, yielding multiplicative and state-dependent noise. Homogeneous additive Langevin forcing is recovered only as a Markovian coarse-grained limit.
arXiv:2605.15641v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly used as general planners in embodied intelligence, enabling high level coordination and low level task planning for both single robot and multi-robot collaboration. This increasing reliance on embodied LLM planners also raises critical security concerns, since misaligned or manipulated instructions can be translated into physical actions. Prior work has studied such threats in single robot settings, while security risks in LLM controlled multi-robot collaboration, especially those propagated through inter robot communication, remain largely unexplored. To bridge this gap, we propose a novel attack paradigm for multi-robot system in which the adversary interacts with only a single entry robot. The compromised robot then propagates malicious intent through peer communication, leading to coordinated unsafe actions across the system. Our evaluation, covering high risk dimensions of dereliction of duty, privacy compromise, and public safety hazards, reveals a persistent safety alignment gap in multi-robot planners. We quantify this process with three metrics, obedience, infectiousness, and stealthiness. Experiments demonstrate both persistent attacker control and rapid propagation: obedience reaches 1.00 in the strongest cases, and infectiousness rises to 0.90. Notably, the attack is highly efficient, requiring as few as 3.0 rounds to compromise all the robots while maintaining a stealthiness score of 0.81. Such risks are amplified when robots must resolve trade offs in critical situations, such as emergencies or conflicts of rights, because the coordination mechanism can unintentionally allow adversarial instructions to override safety requirements. The code is available at https://github.com/TheFatInsect/InfectBot.
arXiv:2605.18120v1 Announce Type: cross
Abstract: Future 6G systems are expected to exploit upper midband spectrum in frequency range 3 (FR3) not only for high throughput communications, but also for sensing services such as localization, detection, and situational awareness. The following paper develops a concrete path from today's coverage-oriented deployments to FR3 networks that treat sensing as a native function. We first show how existing FR2 radars can be time-multiplexed and coordinated under a $6$G medium access control as radar-as-a-service, forming a bridge between legacy sensing and network-managed integrated sensing and communications (ISAC). We then propose a hierarchical FR3 beam-alignment strategy in which coarse access occurs at lower frequencies and refinement occurs at upper FR3, and quantify the resulting sensing and communication capabilities via range-angle Cram{\'{e}}r-Rao bounds in the near field. We identify intra- and inter-beam squint phenomena specific to wideband FR3 arrays, and discuss design approaches to mitigate them. On the signal-processing side, we argue that FR3 sensing cannot rely solely on pilot resources and discuss how much sensing information can be extracted from payload resource elements. We further highlight the role of calibrated FR3 channel simulators and real-time models as the core of wireless digital twins for training and evaluating ISAC algorithms, and discuss how massive MIMO and dense or distributed deployments at FR3 naturally act as large reconfigurable sensor arrays.
arXiv:2603.00652v4 Announce Type: replace-cross
Abstract: Semi-classical analysis is used to investigate synchronous quantum tunneling in a multidimensional potential energy surface (PES) characterized by four degenerate minima, serving as a foundational model for coupled vibrational modes. The primary challenge in such systems is the non-linear ``locking" of trajectories where degrees of freedom must traverse their respective barriers synchronously. Starting from the Feynman path integral in imaginary time, we analytically identify longitudinal, transverse, and diagonal instanton configurations that mediate competing tunneling pathways between minima. The translational zero mode for each trajectory is treated rigorously by transforming to a comoving rotating frame. By applying the Gelfand-Yaglom method to the functional determinant and utilizing graph theory to sum the multi-flavor dilute instanton gas , we derive coherent Rabi-type oscillations and exact ground-state tunneling splittings. Crucially, we identify a critical coupling regime where the discrete $D_4$ spatial symmetry of the minima undergoes a topological 'melting' transition into a continuous $O(2)$ rotational symmetry. These analytical results, validated against high-precision numerical diagonalization, provide a rigorous benchmark for multidimensional computational techniques, such as Ring Polymer Instanton (RPI) theory, particularly in the strongly coupled regime where standard discrete instanton approximations break down.
arXiv:2605.18660v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used as collaborative assistants, yet dominant NLP evaluation practices remain centered on aggregate metrics such as accuracy and fluency. These approaches often overlook behaviors that are critical in human-facing settings (e.g., consistency across multiple turns and iterative refinement). In this paper, we examine limitations of current NLP evaluation practices and introduce TCR, a structured framework for evaluating human--AI interaction using educational LLM assistants as an illustrative example. TCR emphasizes dimensions such as transparency, consistency, and refinement. We further present structured evaluation prompts and illustrative interaction examples demonstrating how structured evaluation can complement aggregate metrics and LLM-as-a-judge approaches. Our work highlights the need for more human-centered evaluation practices for interactive LLM systems.
arXiv:2605.18169v1 Announce Type: cross
Abstract: Multi-controlled Toffoli gates are fundamental building blocks in quantum computation, with applications in quantum arithmetic, simulation, and search algorithms. In fault-tolerant architectures, their realization is constrained by the high cost of non-Clifford resources, particularly in terms of T-count and T-depth. Recent advances have demonstrated that the use of ancillary qubits, relative-phase Toffoli gates, and dynamic circuit techniques can substantially reduce this overhead.
In this work, we investigate the decomposition of large Toffoli gates using 3- and 4-input relative-phase Toffoli gates in the presence of a single clean ancilla and conditionally clean ancillas. We derive explicit resource bounds for Clifford+T implementations incorporating dynamic-circuit-based uncomputation and measurement-conditioned corrections. Our analysis emphasizes T-depth reduction under fixed CX and T-count overhead, ensuring relevance for near-term devices. We show that introducing 4-input relative-phase Toffoli gates enables significant T-depth reductions through enhanced parallelism while maintaining favorable ancilla requirements. We further validate our theoretical results through experimental evaluation and comparative analysis with existing approaches.
arXiv:2305.07152v4 Announce Type: replace
Abstract: Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms that will enable them. With these goals in mind we have invited the surgical data science community to participate in a yearly competition hosted through the Medical Imaging Computing and Computer Assisted Interventions (MICCAI) conference. With varying changes from year to year, we have challenged the community to solve difficult machine learning problems in the context of advanced RA applications. Here we document the results of these challenges, focusing on surgical tool localization (SurgToolLoc) and surgical visual understanding (SurgVU). The publicly released dataset that accompanies these challenges is detailed in a separate paper arXiv:2501.09209 [1].
arXiv:2605.18662v1 Announce Type: new
Abstract: Noise-tolerant PAC learning of linear models has been of central interests in machine learning community since the last century. In recent years, many computationally-efficient algorithms have been proposed for the problem of learning linear threshold functions under multiple noise models. Yet, when the problem is considered under multiclass learning settings, i.e. when the number of classes $k$ is at least $3$, it is unknown whether there exist computationally-efficient PAC learning algorithms when the data sets are maliciously corrupted. In this paper, we consider that the marginal distribution is a mixture of bounded variance distributions and the data sets satisfy a margin condition at the same time. We show that there exists a computationally-efficient algorithm that PAC learns multiclass linear classifiers $\{h_w:x\mapsto \arg\max_{y\in[k]}w_y\cdot x, x\in \mathbb{R}^d, w\in\mathbb{R}^{kd}\}$ using at most $O(k^2\cdot (d\log d+\log k))$ samples even under a constant rate of nasty noise. Our algorithm consists of two main ingredients: a cluster-based pruning scheme and a standard multiclass hinge loss minimization program. Even in the special case of binary setting, i.e. $k=2$, our result is strictly stronger than all prior works.
arXiv:2309.11965v4 Announce Type: replace
Abstract: In this paper, coordination control of discrete event systems under joint sensor and actuator attacks is investigated. Sensor attacks are described by a set of attack languages using a proposed ALTER model. Several local supervisors are used to control the system. The goal is to design local supervisors to ensure safety of the system even under cyber attacks (CA). The necessary and sufficient conditions for the existence of such supervisors are derived in terms of conditional decomposability, CA-controllability and CA-observability. A method is developed to calculate local state estimates under sensor attacks. Two methods are also developed to design local supervisors, one for discrete event systems satisfying conditional decomposability, CA-controllability and CA-observability, and one for discrete event systems satisfying conditional decomposability only. The approach works for both stealthy and non-stealthy attacks. A practical example is given to illustrate the results.
arXiv:2603.02531v2 Announce Type: replace
Abstract: Classifier-Free Guidance (CFG) improves sample quality in diffusion models, but its dual-pass inference and reliance on null-condition training limit its use in few-step regimes. Attention-space guidance has emerged as a complementary paradigm that addresses this gap, yet why prior sparse-vs-dense attention guidance works remains elusive. We address this by analyzing attention extrapolation through Modern Hopfield dynamics, proving two directional properties of the sparse-dense discrepancy under shared conditioning that together certify it as a directionally consistent acceleration signal. Building on this, we propose Geometry-Aware Attention Guidance (GAG), a training-free, plug-and-play extrapolation rule that decomposes the discrepancy into parallel and orthogonal components relative to the retrieval direction, amplifying the convergence-aligned component while suppressing off-manifold noise; stability follows from a weak contraction property. We further provide an interpretation of this extrapolation as first-order Anderson Acceleration in attention space, offering a unified perspective on attention extrapolation methods. GAG is a universal method that generalizes across architectures (UNet, MMDiT) and sampling regimes (multi-step, few-step), consistently improving generation quality on diverse backbones, including FLUX.1, the recent FLUX.2, and Qwen-Image, with minimal computational overhead.
arXiv:2603.03190v3 Announce Type: replace
Abstract: During music listening, cortical activity encodes both acoustic and expectation-related information. Prior work has shown that ANN representations resemble cortical representations and can serve as supervisory signals for EEG recognition. Here we show that distinguishing acoustic and expectation-related ANN representations as teacher targets improves EEG-based music identification. Models pretrained to predict either representation outperform non-pretrained baselines, and combining them yields complementary gains that exceed strong seed ensembles formed by varying random initializations. These findings show that teacher representation type shapes downstream performance and that representation learning can be guided by neural encoding. This work points toward advances in predictive music cognition and neural decoding. Our expectation representation, computed directly from raw signals without manual labels, reflects predictive structure beyond onset or pitch, enabling investigation of multilayer predictive encoding across diverse stimuli. Its scalability to large, diverse datasets further suggests potential for developing general-purpose EEG models grounded in cortical encoding principles.
arXiv:2603.03538v3 Announce Type: replace
Abstract: Large Language Models (LLMs) with chain-of-thought generation have demonstrated great potential for solving complex reasoning and planning tasks. However, the output of current LLMs is not fully reliable and needs careful verification. Even if LLMs get more accurate over time, learned verifiers can help increase trust, enforce safety constraints, and ensure alignment with personal preferences. A major challenge in learning verifiers, however, especially when their output will be used by the generator to improve its reasoning, is that the feedback loop between generator and verifier may produce substantial distribution shift. Motivated by this challenge, we propose an online learning framework for learning chain-of-thought verifiers that, given a problem and a sequence of reasoning steps, check the correctness of the solution. Highlighting the asymmetric role of soundness errors (failure in catching errors in a reasoning trace) and completeness errors (flagging correct reasoning steps as wrong), we introduce novel extensions of the Littlestone dimension which tightly characterize the mistake bounds for learning a verifier in the realizable setting. We provide optimal algorithms for finding the Pareto-frontier (the smallest total number of mistakes given a budget of soundness mistakes) as well as for minimizing a linear combination of asymmetric costs. We further show how our learned verifiers can be used to boost the accuracy of a collection of weak generators, and enable generation of proofs beyond what they were initially trained on. With the mild assumption that one of the generators can generate the next reasoning step correctly with some minimal probability, we show how to learn a strong generator with small error and abstention rates.
arXiv:2603.04727v2 Announce Type: replace
Abstract: Multimodal large language models (MLLMs) have demonstrated impressive general competence in video understanding, yet their reliability for real-world Video Anomaly Detection (VAD) remains largely unexplored. Unlike conventional pipelines relying on reconstruction or pose-based cues, MLLMs enable a paradigm shift: treating anomaly detection as a language-guided reasoning task. In this work, we systematically evaluate state-of-the-art MLLMs on the ShanghaiTech and CHAD benchmarks by reformulating VAD as a binary classification task under weak temporal supervision. We investigate how prompt specificity and temporal window lengths (1s--3s) influence performance, focusing on the precision--recall trade-off. Our findings reveal a pronounced conservative bias in zero-shot settings; while models exhibit high confidence, they disproportionately favor the 'normal' class, resulting in high precision but a recall collapse that limits practical utility. We demonstrate that class-specific instructions can significantly shift this decision boundary, improving the peak F1-score on ShanghaiTech from 0.09 to 0.64, yet recall remains a critical bottleneck. These results highlight a significant performance gap for MLLMs in noisy environments and provide a foundation for future work in recall-oriented prompting and model calibration for open-world surveillance, which demands complex video understanding and reasoning.
arXiv:2603.23194v2 Announce Type: replace
Abstract: Achieving real-time physics-based animation that generalizes across diverse 3D shapes and discretizations remains a fundamental challenge. We introduce PhysSkin, a physics-informed framework that addresses this challenge. In the spirit of Linear Blend Skinning, we learn continuous skinning fields as basis functions lifting motion subspace coordinates to full-space deformation, with subspace defined by handle transformations. To generate mesh-free, discretization-agnostic, and physically consistent skinning fields that generalize well across diverse 3D shapes, PhysSkin employs a new neural skinning fields autoencoder which consists of a transformer-based encoder and a cross-attention decoder. Furthermore, we also develop a novel physics-informed self-supervised learning strategy that incorporates on-the-fly skinning-field normalization and conflict-aware gradient correction, enabling effective balancing of energy minimization, spatial smoothness, and orthogonality constraints. PhysSkin shows outstanding performance on generalizable neural skinning and enables real-time physics-based animation.
arXiv:2603.29868v2 Announce Type: replace
Abstract: The reliability of autonomous systems depends on their robustness, i.e., their ability to meet their objectives under uncertainty. In this paper, we study spatiotemporal robustness of temporal logic specifications evaluated over discrete-time signals. Existing work has proposed robust semantics that capture not only Boolean satisfiability, but also the geometric distance from unsatisfiability, corresponding to admissible spatial perturbations of a given signal. In contrast, we propose spatiotemporal robustness (STR), which captures admissible spatial and temporal perturbations jointly. This notion is particularly informative for interacting systems, such as multi-agent robotics, smart cities, and air traffic control. We define STR as a multi-objective reasoning problem, formalized via a partial order over spatial and temporal perturbations. This perspective has two key advantages: (1) STR can be interpreted as a Pareto-optimal set that characterizes all admissible spatiotemporal perturbations, and (2) STR can be computed using tools from multi-objective optimization. To navigate computational challenges, we propose robust semantics for STR that are sound in the sense of suitably under-approximating STR while being computationally tractable. Finally, we present monitoring algorithms for STR using these robust semantics. To the best of our knowledge, this is the first work to deal with robustness across multiple dimensions via multi-objective reasoning.
arXiv:2605.18673v1 Announce Type: new
Abstract: Major deployed generative AI advertising systems preserve a visible boundary between commercial content and AI-generated responses. Yet empirical research shows that ads woven directly into large language model (LLM) outputs often go undetected by users. We argue that generative AI fundamentally changes advertising: rather than placing products into discrete slots, it enables interventions on the generative process itself, which induce commercial influence through less observable channels. This reframes generative AI advertising as a problem of trustworthy intervention rather than content placement. We introduce a taxonomy organized by influence tier, corresponding to interventions on progressively more latent variables: product mentions, information framing, behavioral redirection, and long-term preference shaping; and show how these tiers instantiate across modalities and system architectures, including retrieval-augmented generation and agentic pipelines where upstream decisions can sharply constrain downstream outcomes. Both major deployed systems and designed mechanisms concentrate on the most observable and easiest-to-govern tier, while the forms of commercial influence most consequential for user autonomy remain poorly understood and lack frameworks for detection, measurement, or disclosure. The central challenge is whether commercial influence in generative systems can be made trustworthy, i.e., attributable, measurable, contestable, and aligned with user welfare.
arXiv:2603.30021v2 Announce Type: replace
Abstract: Cities are often compared through scaling laws, usually expressed as power-law relations between population size and aggregate urban quantities related to infrastructure, socioeconomic activity, or environmental impacts. These laws are influential because their exponent is often interpreted as describing what happens when a city grows, with implications for urban theory, planning, and policy. Here, we show that this interpretation is generally misleading. An exponent measured by comparing many cities at one date does not, in general, describe the trajectory of any individual city. Instead, it reflects a statistical pattern produced by cities with different histories, constraints, institutions, and growth paths. Apparent sublinear or superlinear scaling can therefore arise even when individual cities follow simpler dynamics, as we show for the area--population relation. Cross-sectional scaling laws can reveal system-level regularities, but should not be used alone to infer growth mechanisms or guide policy for a given city.
arXiv:2604.06223v2 Announce Type: replace
Abstract: With the international business landscape becoming more crisis-ridden as risks proliferate, how do the professionals who implement corporate social initiatives in high-risk environments perceive their work, and what can this reveal about the forces shaping business engagement with society in crisis contexts? We present findings from a synthetic survey of 400 corporate professionals working on social impact in fragile and conflict-affected settings to understand conventional wisdoms and best practices on corporate strategy and activity in high-risk settings. Drawing on political corporate social responsibility (CSR), synthetic survey, and international business literatures, we test seven hypotheses about how regulatory environments, political polarization, sector characteristics, and organizational structures shape corporate social engagement in high-risk contexts. The synthetic results suggest that European professionals report significantly higher strategic integration of social impact across all measured dimensions, while US professionals overwhelmingly report that political polarization hinders social initiatives, yet this perception does not predict unreported social activities, complicating the emerging "quiet CSR" narrative. Extractive industry professionals deliver both the highest operational preparedness and the highest complicity awareness, a pattern we conceptualize as presence-dependent reflexivity. These patterns deliver a baseline to detect the theorized dynamics and offer preliminary theoretical propositions for future real-world empirical testing.
arXiv:2604.08205v2 Announce Type: replace
Abstract: Payment channel networks (PCNs) are a promising approach to making cryptocurrency transactions faster and more scalable. At their core, PCNs bypass the blockchain by routing transactions through intermediary channels. However, a channel can forward a transaction only if it has the necessary funds: the problem of keeping the channels balanced is a current bottleneck for the PCN's transaction throughput. This paper considers the problem of maximizing the number of transactions accepted by a channel in a PCN. Previous works either considered the associated optimization problem with all transactions known in advance or developed heuristics tested on particular transaction datasets. This work, however, considers the problem in its purely online form where the transactions are arbitrary and revealed one after the other. We show that the problem can be modeled as a new online knapsack variant where the items (transaction proposals) can be either positive or negative depending on the direction of the transaction. The main contribution of this paper is a deterministic online algorithm that is $O(\log B)$-competitive, where $B$ is the knapsack capacity (maximum allowed channel balance). We complement this result with an asymptotically matching lower bound of $\Omega(\log B)$ which holds for any randomized algorithm, demonstrating our algorithm's optimality.
arXiv:2605.18681v1 Announce Type: new
Abstract: Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to evaluate due to the lack of good ground-truth to compare against. We propose a framework that serves as a quantifiable metric for the quality of XAI methods, based on continuous input perturbation. Our metric formally considers the sufficiency and necessity of the attributed information to the model's decision-making, and we illustrate a range of cases where it aligns better with human intuitions of explanation quality than do existing metrics.
To exploit the properties of this metric, we also propose a novel XAI method, considering the case where we fine-tune a model using a differentiable approximation of the metric as a supervision signal. The result is an adapter module that can be trained on top of any black-box model to output causal explanations of the model's decision process, without degrading model performance. We show that the explanations generated by this method outperform those of competing XAI techniques according to a number of quantifiable metrics.