OpenRaiser/Intern-Atlas huggingface dataset:
Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure
Venue: arXiv cs.LG 2025
Year: 2025

Graph neural networks are becoming increasingly popular in the field of machine learning due to their unique ability to process data structured in graphs. They have also been applied in safety-critical environments where perturbations inherently occur. However, these perturbations require us to formally verify neural networks before their deployment in safety-critical environments as neural networks are prone to adversarial attacks. While there exists research on the formal verification of neural networks, there is no work verifying the robustness of generic graph convolutional network architectures with uncertainty in the node features and in the graph structure over multiple message-passing steps. This work addresses this research gap by explicitly preserving the non-convex dependencies of all elements in the underlying computations through reachability analysis with (matrix) polynomial zonotopes. We demonstrate our approach on three popular benchmark datasets.
On Function-Correcting Codes
Venue: arXiv cs.IT 2025
Year: 2025

Function-correcting codes were introduced in the work "Function-Correcting Codes" (FCC) by Lenz et al. 2023, which provides a graphical representation for the problem of constructing function-correcting codes. We use this function dependent graph to get a lower bound on the redundancy required for function correction codes. By considering the function to be a bijection, such an approach leads to a lower bound on the redundancy required for classical systematic error correcting codes (ECCs). We propose a range of parameters for which the bound is tight. For single error correcting codes, we show that this bound is at least as good as a bound proposed by Zinoviev, Litsyn, and Laihonen in 1998. Thus, this framework helps to study systematic classical error correcting codes. Further, we study the structure of this function dependent graph for linear functions, which leads to bounds on the redundancy of linear-function correcting codes. We show that the Plotkin-like bound for function-correcting codes proposed by Lenz et.al 2023 is simplified for linear functions. We identify a class of linear functions for which an upper bound proposed by Lenz et al., is tight and also identify a class of functions for which coset-wise coding is equivalent to a lower dimensional classical error correction problem.
Lost in Magnitudes: Exploring Visualization Designs for Large Value Ranges
Venue: arXiv cs.HC 2025
Year: 2025

We explore the design of visualizations for values spanning multiple orders of magnitude; we call them Orders of Magnitude Values (OMVs). Visualization researchers have shown that separating OMVs into two components, the mantissa and the exponent, and encoding them separately overcomes limitations of linear and logarithmic scales. However, only a small number of such visualizations have been tested, and the design guidelines for visualizing the mantissa and exponent separately remain under-explored. To initiate this exploration, better understand the factors influencing the effectiveness of these visualizations, and create guidelines, we adopt a multi-stage workflow. We introduce a design space for visualizing mantissa and exponent, systematically generating and qualitatively evaluating all possible visualizations within it. From this evaluation, we derive guidelines. We select two visualizations that align with our guidelines and test them using a crowdsourcing experiment, showing they facilitate quantitative comparisons and increase confidence in interpretation compared to the state-of-the-art.
CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and Detection
Venue: arXiv cs.CV 2025
Year: 2025

Feature pyramids have been widely adopted in convolutional neural networks and transformers for tasks in medical image segmentation. However, existing models generally focus on the Encoder-side Transformer for feature extraction. We further explore the potential in improving the feature decoder with a well-designed architecture. We propose Cross Feature Pyramid Transformer decoder (CFPFormer), a novel decoder block that integrates feature pyramids and transformers. Even though transformer-like architecture impress with outstanding performance in segmentation, the concerns to reduce the redundancy and training costs still exist. Specifically, by leveraging patch embedding, cross-layer feature concatenation mechanisms, CFPFormer enhances feature extraction capabilities while complexity issue is mitigated by our Gaussian Attention. Benefiting from Transformer structure and U-shaped connections, our work is capable of capturing long-range dependencies and effectively up-sample feature maps. Experimental results are provided to evaluate CFPFormer on medical image segmentation datasets, demonstrating the efficacy and effectiveness. With a ResNet50 backbone, our method achieves 92.02\% Dice Score, highlighting the efficacy of our methods. Notably, our VGG-based model outperformed baselines with more complex ViT and Swin Transformer backbone.
Deep Diffusion Deterministic Policy Gradient based Performance Optimization for Wi-Fi Networks
Venue: arXiv cs.NI 2025
Year: 2025

Generative Diffusion Models (GDMs), have made significant strides in modeling complex data distributions across diverse domains. Meanwhile, Deep Reinforcement Learning (DRL) has demonstrated substantial improvements in optimizing Wi-Fi network performance. Wi-Fi optimization problems are highly challenging to model mathematically, and DRL methods can bypass complex mathematical modeling, while GDMs excel in handling complex data modeling. Therefore, combining DRL with GDMs can mutually enhance their capabilities. The current MAC layer access mechanism in Wi-Fi networks is the Distributed Coordination Function (DCF), which dramatically decreases in performance with a high number of terminals. In this study, we propose the Deep Diffusion Deterministic Policy Gradient (D3PG) algorithm, which integrates diffusion models with the Deep Deterministic Policy Gradient (DDPG) framework to optimize Wi-Fi network performance. To the best of our knowledge, this is the first work to apply such an integration in Wi-Fi performance optimization. We propose an access mechanism that jointly adjusts the contention window and the aggregation frame length based on the D3PG algorithm. Through simulations, we have demonstrated that this mechanism significantly outperforms existing Wi-Fi standards in dense Wi-Fi scenarios, maintaining performance even as the number of users increases sharply.
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation
Venue: arXiv cs.LG 2025
Year: 2025

We propose a neural operator framework, termed mixture density nonlinear manifold decoder (MD-NOMAD), for stochastic simulators. Our approach leverages an amalgamation of the pointwise operator learning neural architecture nonlinear manifold decoder (NOMAD) with mixture density-based methods to estimate conditional probability distributions for stochastic output functions. MD-NOMAD harnesses the ability of probabilistic mixture models to estimate complex probability and the high-dimensional scalability of pointwise neural operator NOMAD. We conduct empirical assessments on a wide array of stochastic ordinary and partial differential equations and present the corresponding results, which highlight the performance of the proposed framework.
Hardness and Tight Approximations of Demand Strip Packing
Venue: arXiv cs.DS 2025
Year: 2025

We settle the pseudo-polynomial complexity of the Demand Strip Packing (DSP) problem: Given a strip of fixed width and a set of items with widths and heights, the items must be placed inside the strip with the objective of minimizing the peak height. This problem has gained significant scientific interest due to its relevance in smart grids[Deppert et al.\ APPROX'21, Gálvez et al.\ APPROX'21]. Smart Grids are a modern form of electrical grid that provide opportunities for optimization. They are forecast to impact the future of energy provision significantly. Algorithms running in pseudo-polynomial time lend themselves to these applications as considered time intervals, such as days, are small. Moreover, such algorithms can provide superior approximation guarantees over those running in polynomial time. Consequently, they evoke scientific interest in related problems. We prove that Demand Strip Packing is strongly NP-hard for approximation ratios below $5/4$. Through this proof, we provide novel insights into the relation of packing and scheduling problems. Using these insights, we show a series of frameworks that solve both Demand Strip Packing and Parallel Task Scheduling optimally when increasing the strip's width or number of machines. Such alterations to problems are known as resource augmentation. Applications are found when penalty costs are prohibitively large. Finally, we provide a pseudo-polynomial time approximation algorithm for DSP with an approximation ratio of $(5/4+\varepsilon)$, which is nearly optimal assuming $P\neq NP$. The construction of this algorithm provides several insights into the structure of DSP solutions and uses novel techniques to restructure optimal solutions.
Intelligent Machines and Incomplete Information
Venue: arXiv cs.GT 2025
Year: 2025

The distribution of efficient individuals in the economy and the efforts that they will put in if they are hired, there are two important concerns for a technologically advanced firm. wants to open a new branch. The firm does not have information about the exact level of efficiency of an individual when she is hired. We call this situation incomplete information. The standard principal agent models assume that employees know their efficiency levels. Hence these models design incentive-compatible mechanisms. An incentive-compatible mechanism ensures that a participant does not have the incentive to misreport her efficiency level. This paper does not assume that employees know how efficient they are. This paper assumes that the production technology of the firm is intelligent, that is, the output of the machine reveals the efficiency levels of employees. Employees marginal contributions to the total output of the intelligent machine, the probability distribution of the levels of efficiency and employees costs of efforts together define a game of incomplete information. A characterization of ex-ante Nash Equilibrium is established. The results of the characterization formalize the relationship between the distribution of efficiency levels and the distribution of output.
The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms
Venue: arXiv cs.LG 2025
Year: 2025

When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their inputs, they typically fail to account for their level of familiarity with these novel observations. Prevailing works navigate test-time adaptation with the goal of curtailing model entropy, yet they unintentionally produce models that struggle with sub-optimal calibration-a dilemma we term the over-certainty phenomenon. This over-certainty in predictions can be particularly dangerous in the setting of domain shifts, as it may lead to misplaced trust. In this paper, we propose a solution that not only maintains accuracy but also addresses calibration by mitigating the over-certainty phenomenon. To do this, we introduce a certainty regularizer that dynamically adjusts pseudo-label confidence by accounting for both backbone entropy and logit norm. Our method achieves state-of-the-art performance in terms of Expected Calibration Error and Negative Log Likelihood, all while maintaining parity in accuracy.
muRelBench: MicroBenchmarks for Zonotope Domains
Venue: arXiv cs.LO 2025
Year: 2025

We present \texttt{muRelBench}, a framework for synthetic benchmarks for weakly-relational abstract domains and their operations. This extensible microbenchmarking framework enables researchers to experimentally evaluate proposed algorithms for numerical abstract domains, such as closure,least-upper bound, and forget, enabling them to quickly prototype and validate performance improvements before considering more intensive experimentation. Additionally, the framework provides mechanisms for checking correctness properties for each of the benchmarks to ensure correctness within the synthetic benchmarks.
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Venue: arXiv cs.LG 2025
Year: 2025

Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real-world applications. Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods. This is motivated by the hypothesis that nodes in the graph tend to exhibit consistent behaviors with their neighborhoods. However, such consistency can be disrupted by graph anomalies in multiple ways. Most existing methods directly employ GNNs to learn representations, disregarding the negative impact of graph anomalies on GNNs, resulting in sub-optimal node representations and anomaly detection performance. While a few recent approaches have redesigned GNNs for graph anomaly detection under semi-supervised label guidance, how to address the adverse effects of graph anomalies on GNNs in unsupervised scenarios and learn effective representations for anomaly detection are still under-explored. To bridge this gap, in this paper, we propose a simple yet effective framework for Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection (G3AD). Specifically, G3AD first introduces two auxiliary networks along with correlation constraints to guard the GNNs against inconsistent information encoding. Furthermore, G3AD introduces an adaptive caching module to guard the GNNs from directly reconstructing the observed graph data that contains anomalies. Extensive experiments demonstrate that our G3AD can outperform twenty state-of-the-art methods on both synthetic and real-world graph anomaly datasets, with flexible generalization ability in different GNN backbones.
Regular Typed Unification
Venue: arXiv cs.LO 2025
Year: 2025

Here we define a new unification algorithm for terms interpreted in semantic domains denoted by a subclass of regular types here called deterministic regular types. This reflects our intention not to handle the semantic universe as a homogeneous collection of values, but instead, to partition it in a way that is similar to data types in programming languages. We first define the new unification algorithm which is based on constraint generation and constraint solving, and then prove its main properties: termination, soundness, and completeness with respect to the semantics. Finally, we discuss how to apply this algorithm to a dynamically typed version of Prolog.
A Notion of Uniqueness for the Adversarial Bayes Classifier
Venue: arXiv cs.LG 2025
Year: 2025

We propose a new notion of uniqueness for the adversarial Bayes classifier in the setting of binary classification. Analyzing this concept produces a simple procedure for computing all adversarial Bayes classifiers for a well-motivated family of one dimensional data distributions. This characterization is then leveraged to show that as the perturbation radius increases, certain notions of regularity for the adversarial Bayes classifiers improve. Furthermore, these results provide tools for understanding relationships between the Bayes and adversarial Bayes classifiers in one dimension.
Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes
Venue: arXiv cs.CL 2025
Year: 2025

Dual process theory posits that human cognition arises via two systems. System 1, which is a quick, emotional, and intuitive process, which is subject to cognitive biases, and System 2, is a slow, onerous, and deliberate process. Prior research in LLMs found that using chain-of-thought (CoT) prompting in LLMs, which has been often compared to System 2 reasoning, can lead to reduced gender bias. Along these lines, we investigate the relationship between bias, CoT prompting, a direct debiasing, and dual process theory modeling in LLMs. We compare zero-shot CoT, debiasing, and dual process theory-based prompting strategies on two bias datasets spanning nine different social bias categories. We incorporate human and machine personas to determine whether LLM modeling of the effects of dual process theory exist independent of explicit persona models or are tied to the LLM's modeling of human-like generation. We find that a human persona, debiasing, System 2, and CoT prompting all tend to reduce social biases in LLMs, though the best combination of features depends on the exact model and bias category -- resulting in up to a 33 percent drop in stereotypical judgments by an LLM.
Multiple-Target Detection in Cell-Free Massive MIMO-Assisted ISAC
Venue: arXiv cs.IT 2025
Year: 2025

We propose a distributed implementation for integrated sensing and communication (ISAC) backed by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations' requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via successive convex approximation approach. We further propose a low-complexity design, where AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of $100\%$ for different network setups with a satisfactory fairness among all UEs.
Exact and Approximate High-Multiplicity Scheduling on Identical Machines
Venue: arXiv cs.DS 2025
Year: 2025

Goemans and Rothvoss (SODA'14) gave a framework for solving problems which can be described as finding a point in int$.$cone$(P\cap\mathbb{Z}^N)\cap Q$, where $P,Q\subset\mathbb{R}^N$ are (bounded) polyhedra. The running time for solving such a problem is $enc(P)^{2^{O(N)}}enc(Q)^{O(1)}$. This framework can be used to solve various scheduling problems, but the encoding length $enc(P)$ usually involves large parameters like the makespan. We describe three tools to improve the framework: - Problem-specific preprocessing can be used to greatly reduce $enc(P)$. - By solving a certain LP relaxation and then using the classical result by Frank and Tardos (J. Comb. '87), we get a more compact encoding of $P$ in general. - A result by Jansen and Klein (SODA'17) makes the running time depend on the number of vertices of the integer hull of $P$. We provide a new bound for this number that is similar to the one by Berndt et al. (SOSA'21) but better for our setting. For example, applied to the scheduling problem $P||C_{\max}$, these tools improve the running time from $(\log(C_{\max}))^{2^{O(d)}}enc(I)^{O(1)}$ to the possibly much better $(\log(p_{\max}))^{2^{O(d)}}enc(I)^{O(1)}$. Here, $p_{\max}$ is the largest processing time, $d$ is the number of different processing times, $C_{\max}$ is the makespan and $enc(I)$ is the encoding length of the instance. On the complexity side, we use reductions from the literature to provide new parameterized lower bounds for $P||C_{\max}$. Finally, we show that the big open question asked by Mnich and van Bevern (Comput. Oper. Res. '18) whether $P||C_{\max}$ is FPT w.r.t. the number of job types $d$ has the same answer as the question whether $Q||C_{\max}$ is FPT w.r.t. the number of job and machine types $d+τ$ (all in high-multiplicity encoding). The same holds for objective $C_{\min}$.
Large Language Model Agent as a Mechanical Designer
Venue: arXiv cs.LG 2025
Year: 2025

Conventional mechanical design follows an iterative process in which initial concepts are refined through cycles of expert assessment and resource-intensive Finite Element Method (FEM) analysis to meet performance goals. While machine learning models have been developed to assist in parts of this process, they typically require large datasets, extensive training, and are often tailored to specific tasks, limiting their generalizability. To address these limitations, we propose a framework that leverages a pretrained Large Language Model (LLM) in conjunction with an FEM module to autonomously generate, evaluate, and refine structural designs based on performance specifications and numerical feedback. The LLM operates without domain-specific fine-tuning, using general reasoning to propose design candidates, interpret FEM-derived performance metrics, and apply structurally sound modifications. Using 2D truss structures as a testbed, we show that the LLM can effectively navigate highly discrete and multi-faceted design spaces, balance competing objectives, and identify convergence when further optimization yields diminishing returns. Compared to Non-dominated Sorting Genetic Algorithm II (NSGA-II), our method achieves faster convergence and fewer FEM evaluations. Experiments with varying temperature settings (0.5, 1.0, 1.2) and model sizes (GPT-4.1 and GPT-4.1-mini) indicate that smaller models yield higher constraint satisfaction with fewer steps, while lower temperatures enhance design consistency. These results establish LLMs as a promising new class of reasoning-based, natural language-driven optimizers for autonomous design and iterative structural refinement.
VIEW: Visual Imitation Learning with Waypoints
Venue: arXiv cs.RO 2025
Year: 2025

Robots can use Visual Imitation Learning (VIL) to learn manipulation tasks from video demonstrations. However, translating visual observations into actionable robot policies is challenging due to the high-dimensional nature of video data. This challenge is further exacerbated by the morphological differences between humans and robots, especially when the video demonstrations feature humans performing tasks. To address these problems we introduce Visual Imitation lEarning with Waypoints (VIEW), an algorithm that significantly enhances the sample efficiency of human-to-robot VIL. VIEW achieves this efficiency using a multi-pronged approach: extracting a condensed prior trajectory that captures the demonstrator's intent, employing an agent-agnostic reward function for feedback on the robot's actions, and utilizing an exploration algorithm that efficiently samples around waypoints in the extracted trajectory. VIEW also segments the human trajectory into grasp and task phases to further accelerate learning efficiency. Through comprehensive simulations and real-world experiments, VIEW demonstrates improved performance compared to current state-of-the-art VIL methods. VIEW enables robots to learn manipulation tasks involving multiple objects from arbitrarily long video demonstrations. Additionally, it can learn standard manipulation tasks such as pushing or moving objects from a single video demonstration in under 30 minutes, with fewer than 20 real-world rollouts. Code and videos here: https://collab.me.vt.edu/view/