OpenRaiser/Intern-Atlas huggingface dataset:
Model-Agnostic Correctness Assessment for LLM-Generated Code via Dynamic Internal Representation Selection
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have demonstrated impressive capabilities in code generation and are increasingly integrated into the software development process. However, ensuring the correctness of LLM-generated code remains a critical concern. Prior work has shown that the internal representations of LLMs encode meaningful signals for assessing code correctness. Nevertheless, the existing methods rely on representations from pre-selected/fixed layers and token positions, which could limit its generalizability across diverse model architectures and tasks. In this work, we introduce AUTOPROBE, a novel model-agnostic approach that dynamically selects the most informative internal representations for code correctness assessment. AUTOPROBE employs an attention-based mechanism to learn importance scores for hidden states, enabling it to focus on the most relevant features. These weighted representations are then aggregated and passed to a probing classifier to predict code correctness across multiple dimensions, including compilability, functionality, and security. To evaluate the performance of AUTOPROBE, we conduct extensive experiments across multiple benchmarks and code LLMs. Our experimental results show that AUTOPROBE consistently outperforms the baselines. For security assessment, AUTOPROBE surpasses the state-of-the-art white-box approach by 18%. For compilability and functionality assessment, AUTOPROBE demonstrates its highest robustness to code complexity, with the performance higher than the other approaches by up to 19% and 111%, respectively. These findings highlight that dynamically selecting important internal signals enables AUTOPROBE to serve as a robust and generalizable solution for assessing the correctness of code generated by various LLMs.
RAxSS: Retrieval-Augmented Sparse Sampling for Explainable Variable-Length Medical Time Series Classification
Venue: arXiv cs.LG 2025
Year: 2025

Medical time series analysis is challenging due to data sparsity, noise, and highly variable recording lengths. Prior work has shown that stochastic sparse sampling effectively handles variable-length signals, while retrieval-augmented approaches improve explainability and robustness to noise and weak temporal correlations. In this study, we generalize the stochastic sparse sampling framework for retrieval-informed classification. Specifically, we weight window predictions by within-channel similarity and aggregate them in probability space, yielding convex series-level scores and an explicit evidence trail for explainability. Our method achieves competitive iEEG classification performance and provides practitioners with greater transparency and explainability. We evaluate our method in iEEG recordings collected in four medical centers, demonstrating its potential for reliable and explainable clinical variable-length time series classification.
Single-Rod Brachiation Robot: Mechatronic Control Design and Validation of Prejump Phases
Venue: arXiv cs.RO 2025
Year: 2025

A complete mechatronic design of a minimal configuration brachiation robot is presented. The robot consists of a single rigid rod with gripper mechanisms attached to both ends. The grippers are used to hang the robot on a horizontal bar on which it swings or rotates. The motion is imposed by repositioning the robot's center of mass, which is performed using a crank-slide mechanism. Based on a non-linear model, an optimal control strategy is proposed, for repositioning the center of mass in a bang-bang manner. Consequently, utilizing the concept of input-output linearization, a continuous control strategy is proposed that takes into account the limited torque of the crank-slide mechanism and its geometry. An increased attention is paid to energy accumulation towards the subsequent jump stage of the brachiation. These two strategies are validated and compared in simulations. The continuous control strategy is then also implemented within a low-cost STM32-based control system, and both the swing and rotation stages of the brachiation motion are experimentally validated.
Low Recourse Arborescence Forests Under Uniformly Random Arcs
Venue: arXiv cs.DS 2025
Year: 2025

In this work, we study how to maintain a forest of arborescences of maximum arc cardinality under arc insertions while minimizing recourse -- the total number of arcs changed in the maintained solution. This problem is the "arborescence version'' of max cardinality matching. On the impossibility side, we observe that even in this insertion-only model, it is possible for $m$ adversarial arc arrivals to necessarily incur $Ω(m \cdot n)$ recourse, matching a trivial upper bound of $O(m \cdot n)$. On the possibility side, we give an algorithm with expected recourse $O(m \cdot \log^2 n)$ if all $m$ arcs arrive uniformly at random.
SoK: Kicking CAN Down the Road. Systematizing CAN Security Knowledge
Venue: arXiv cs.CR 2025
Year: 2025

For decades, the Controller Area Network (CAN) has served as the primary in-vehicle bus (IVB) and extended its use to many non-vehicular systems. Over the past years, CAN security has been intensively scrutinized, yielding extensive research literature. Despite its wealth, the literature lacks structured systematization, complicating efforts to assess attack severity, defense efficacy, identify security gaps, or root causes. This leaves non experts uncertain about the relevancy of specific attacks or defenses to their systems, inadvertently portraying CAN as irredeemably insecure. Further, the introduction of new IVB technologies--CAN evolutions, add-ons, and alternative buses--with heightened security claims risks fostering the misconception that merely adopting these technologies resolves CAN's security challenges. This paper systematizes existing CAN security knowledge, presenting a comprehensive taxonomy and assessment models of attackers, attacks, and defenses. It identifies replicable attacks and defense gaps, investigating their root causes as inherent, accidental, unique, or universal. It then extrapolates these insights to emerging IVB technologies by formally analyzing three emerging IVBs to identify shared root causes with CAN and assess their ability to close security gaps. The findings challenge common perceptions, demonstrating that CAN is more securable than perceived, that most insecurity root causes are shared across IVBs, and that merely adopting newer IVB technology does not solve persistent security issues. The paper concludes by highlighting future research directions to secure IVB communication down the road.
ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its performance hinges on surrogate and acquisition function hyper-parameters that are often hand-tuned and fail to generalize across problem landscapes. We present ZeroShotOpt, a general-purpose, pretrained model for continuous black-box optimization tasks ranging from 2D to 20D. Our approach leverages offline reinforcement learning on large-scale optimization trajectories collected from 12 BO variants. To scale pretraining, we generate millions of synthetic Gaussian process-based functions with diverse landscapes, enabling the model to learn transferable optimization policies. As a result, ZeroShotOpt achieves robust zero-shot generalization on a wide array of unseen benchmarks, matching or surpassing the sample efficiency of leading global optimizers, including BO, while also offering a reusable foundation for future extensions and improvements. Our open-source code, dataset, and model are available at: https://github.com/jamisonmeindl/zeroshotopt
Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
Venue: arXiv cs.LG 2025
Year: 2025

This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using fully observable environments. The focus is on parametrized action (PA) spaces, eliminating the need for recurrent networks, with benchmarks Platform-v0 and Goal-v0 testing discrete actions linked to continuous action-parameter spaces. Hyperparameter optimization was performed with Microsoft NNI, ensuring reproducibility by modifying the codebase for GAC and TQC. Results show that Parameterized Action Greedy Actor-Critic (PAGAC) outperformed other algorithms, achieving the fastest training times and highest returns across benchmarks, completing 5,000 episodes in 41:24 for the Platform game and 24:04 for the Robot Soccer Goal game. Its speed and stability provide clear advantages in complex action spaces. Compared to PASAC and PATQC, PAGAC demonstrated superior efficiency and reliability, making it ideal for tasks requiring rapid convergence and robust performance. Future work could explore hybrid strategies combining entropy-regularization with truncation-based methods to enhance stability and expand investigations into generalizability.
An Exact Algorithm for Computing the Structure of Jordan Blocks
Venue: arXiv cs.SC 2025
Year: 2025

An efficient method is proposed for computing the structure of Jordan blocks of a matrix of integers or rational numbers by exact computation. We have given a method for computing Jordan chains of a matrix with exact computation. However, for deriving just the structure of Jordan chains, the algorithm can be reduced to increase its efficiency. We propose a modification of the algorithm for that purpose. Results of numerical experiments are given.
Geometry Meets Vision: Revisiting Pretrained Semantics in Distilled Fields
Venue: arXiv cs.CV 2025
Year: 2025

Semantic distillation in radiance fields has spurred significant advances in open-vocabulary robot policies, e.g., in manipulation and navigation, founded on pretrained semantics from large vision models. While prior work has demonstrated the effectiveness of visual-only semantic features (e.g., DINO and CLIP) in Gaussian Splatting and neural radiance fields, the potential benefit of geometry-grounding in distilled fields remains an open question. In principle, visual-geometry features seem very promising for spatial tasks such as pose estimation, prompting the question: Do geometry-grounded semantic features offer an edge in distilled fields? Specifically, we ask three critical questions: First, does spatial-grounding produce higher-fidelity geometry-aware semantic features? We find that image features from geometry-grounded backbones contain finer structural details compared to their counterparts. Secondly, does geometry-grounding improve semantic object localization? We observe no significant difference in this task. Thirdly, does geometry-grounding enable higher-accuracy radiance field inversion? Given the limitations of prior work and their lack of semantics integration, we propose a novel framework SPINE for inverting radiance fields without an initial guess, consisting of two core components: coarse inversion using distilled semantics, and fine inversion using photometric-based optimization. Surprisingly, we find that the pose estimation accuracy decreases with geometry-grounded features. Our results suggest that visual-only features offer greater versatility for a broader range of downstream tasks, although geometry-grounded features contain more geometric detail. Notably, our findings underscore the necessity of future research on effective strategies for geometry-grounding that augment the versatility and performance of pretrained semantic features.
Real Time Headway Predictions in Urban Rail Systems and Implications for Service Control: A Deep Learning Approach
Venue: arXiv cs.LG 2025
Year: 2025

Efficient real-time dispatching in urban metro systems is essential for ensuring service reliability, maximizing resource utilization, and improving passenger satisfaction. This study presents a novel deep learning framework centered on a Convolutional Long Short-Term Memory (ConvLSTM) model designed to predict the complex spatiotemporal propagation of train headways across an entire metro line. By directly incorporating planned terminal headways as a critical input alongside historical headway data, the proposed model accurately forecasts future headway dynamics, effectively capturing both their temporal evolution and spatial dependencies across all stations. This capability empowers dispatchers to evaluate the impact of various terminal headway control decisions without resorting to computationally intensive simulations. We introduce a flexible methodology to simulate diverse dispatcher strategies, ranging from maintaining even headways to implementing custom patterns derived from observed terminal departures. In contrast to existing research primarily focused on passenger load predictioning or atypical disruption scenarios, our approach emphasizes proactive operational control. Evaluated on a large-scale dataset from an urban metro line, the proposed ConvLSTM model demonstrates promising headway predictions, offering actionable insights for real-time decision-making. This framework provides rail operators with a powerful, computationally efficient tool to optimize dispatching strategies, thereby significantly improving service consistency and passenger satisfaction.
Learning Stability Certificate for Robotics in Real-World Environments
Venue: arXiv cs.RO 2025
Year: 2025

Stability certificates play a critical role in ensuring the safety and reliability of robotic systems. However, deriving these certificates for complex, unknown systems has traditionally required explicit knowledge of system dynamics, often making it a daunting task. This work introduces a novel framework that learns a Lyapunov function directly from trajectory data, enabling the certification of stability for autonomous systems without needing detailed system models. By parameterizing the Lyapunov candidate using a neural network and ensuring positive definiteness through Cholesky factorization, our approach automatically identifies whether the system is stable under the given trajectory. To address the challenges posed by noisy, real-world data, we allow for controlled violations of the stability condition, focusing on maintaining high confidence in the stability certification process. Our results demonstrate that this framework can provide data-driven stability guarantees, offering a robust method for certifying the safety of robotic systems in dynamic, real-world environments. This approach works without access to the internal control algorithms, making it applicable even in situations where system behavior is opaque or proprietary. The tool for learning the stability proof is open-sourced by this research: https://github.com/HansOersted/stability.
Algorithmic Tradeoff Exploration for Component Placement and Wire Routing in Nanomodular Electronics
Venue: arXiv cs.ET 2025
Year: 2025

Advances in fabrication technology have enabled modularizing electronic components at the micro- or nano-scale and composing these modules on demand into larger circuits. Micromodular and nanomodular electronics (ME and NE) open a new design space in electronics, promising a degree of flexibility, extensibility, and accessibility far superior to traditional monolithic methods. ME/NE leverage a multi-stage process of initial imprecise component deposition, followed by precise wire printing to compose them into a circuit. Due to imperfections in deposition, each circuit instance has a unique layout with its own component placement and wire routing solutions, putting the design automation process on the critical path. Moreover, high-performance nanomodular components enable the synthesis of larger heterogeneous circuits than traditional printed electronics, requiring more scalable algorithms. ME/NE thus introduce a tradeoff between the time-to-solution for placement/routing algorithms and the resulting total wire length, with the latter dictating circuit printing time. We explore this tradeoff by adapting standard partitioning, floorplanning, placement, and routing algorithms to the unique characteristics of ME/NE. Our evaluations demonstrate significant optimization headroom in different dimensions. For example, our tunable algorithms can deliver a $108\times$ improvement in end-to-end manufacturing time at the cost of $21\%$ increase in total wire length. Conversely, circuit quality/performance can be prioritized at the cost of increased manufacturing time, highlighting the value of the ability to dynamically navigate the tradeoff space according to the primary optimization metric.
The Computational Complexity of Almost Stable Clustering with Penalties
Venue: arXiv cs.CC 2025
Year: 2025

We investigate the complexity of stable (or perturbation-resilient) instances of $\mathrm{k-M\small{EANS}}$ and $\mathrm{k-M\small{EDIAN}}$ clustering problems in metrics with small doubling dimension. While these problems have been extensively studied under multiplicative perturbation resilience in low-dimensional Euclidean spaces (e.g., (Friggstad et al., 2019; Cohen-Addad and Schwiegelshohn, 2017)), we adopt a more general notion of stability, termed ``almost stable'', which is closer to the notion of $(α, \varepsilon)$-perturbation resilience introduced by Balcan and Liang (2016). Additionally, we extend our results to $\mathrm{k-M\small{EANS}}$/$\mathrm{k-M\small{EDIAN}}$ with penalties, where each data point is either assigned to a cluster centre or incurs a penalty. We show that certain special cases of almost stable $\mathrm{k-M\small{EANS}}$/$\mathrm{k-M\small{EDIAN}}$ (with penalties) are solvable in polynomial time. To complement this, we also examine the hardness of almost stable instances and $(1 + \frac{1}{poly(n)})$-stable instances of $\mathrm{k-M\small{EANS}}$/$\mathrm{k-M\small{EDIAN}}$ (with penalties), proving super-polynomial lower bounds on the runtime of any exact algorithm under the widely believed Exponential Time Hypothesis (ETH).
Calibrated Uncertainty Sampling for Active Learning
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of actively learning a classifier with a low calibration error. One of the most popular Acquisition Functions (AFs) in pool-based Active Learning (AL) is querying by the model's uncertainty. However, we recognize that an uncalibrated uncertainty model on the unlabeled pool may significantly affect the AF effectiveness, leading to sub-optimal generalization and high calibration error on unseen data. Deep Neural Networks (DNNs) make it even worse as the model uncertainty from DNN is usually uncalibrated. Therefore, we propose a new AF by estimating calibration errors and query samples with the highest calibration error before leveraging DNN uncertainty. Specifically, we utilize a kernel calibration error estimator under the covariate shift and formally show that AL with this AF eventually leads to a bounded calibration error on the unlabeled pool and unseen test data. Empirically, our proposed method surpasses other AF baselines by having a lower calibration and generalization error across pool-based AL settings.
Beyond Cons: Purely Relational Data Structures
Venue: arXiv cs.PL 2025
Year: 2025

We present {Kanren} (read: set-Kanren), an extension to miniKanren with constraints for reasoning about sets and association lists. {Kanren} includes first-class set objects, a functionally complete family of set-theoretic constraints (including membership, union, and disjointedness), and new constraints for reasoning about association lists with shadowing and scoped lookup. These additions allow programmers to describe collections declaratively and lazily, without relying on structural encodings and eager search over representation spaces. The result is improved expressiveness and operational behavior in programs that manipulate abstract data -- particularly interpreters -- by supporting set equality based on contents, enabling finite failure. We describe the design and implementation of {Kanren} in a constraint-enabled miniKanren system and illustrate its use in representative examples.
Optimized Degree Realization: Minimum Dominating Set & Maximum Matching
Venue: arXiv cs.DM 2025
Year: 2025

The Degree Realization problem requires, given a sequence $d$ of $n$ positive integers, to decide whether there exists a graph whose degrees correspond to $d$, and to construct such a graph if it exists. A more challenging variant of the problem arises when $d$ has many different realizations, and some of them may be more desirable than others. We study \emph{optimized realization} problems in which the goal is to compute a realization that optimizes some quality measure. Efficient algorithms are known for the problems of finding a realization with the maximum clique, the maximum independent set, or the minimum vertex cover. In this paper, we focus on two problems for which such algorithms were not known. The first is the Degree Realization with Minimum Dominating Set problem, where the goal is to find a realization whose minimum dominating set is minimized among all the realizations of the given sequence $d$. The second is the Degree Realization with Maximum Matching problem, where the goal is to find a realization with the largest matching among all the realizations of $d$. We present polynomial time realization algorithms for these two open problems. A related problem of interest and importance is \emph{characterizing} the sequences with a given value of the optimized function. This leads to an efficient computation of the optimized value without providing the realization that achieves that value. For the Maximum Matching problem, a succinct characterization of degree sequences with a maximum matching of a given size was known. This paper provides a succinct characterization of sequences with minimum dominating set of a given size.
Superposition disentanglement of neural representations reveals hidden alignment
Venue: arXiv cs.LG 2025
Year: 2025

The superposition hypothesis states that single neurons may participate in representing multiple features in order for the neural network to represent more features than it has neurons. In neuroscience and AI, representational alignment metrics measure the extent to which different deep neural networks (DNNs) or brains represent similar information. In this work, we explore a critical question: does superposition interact with alignment metrics in any undesirable way? We hypothesize that models which represent the same features in different superposition arrangements, i.e., their neurons have different linear combinations of the features, will interfere with predictive mapping metrics (semi-matching, soft-matching, linear regression), producing lower alignment than expected. We develop a theory for how permutation metrics are dependent on superposition arrangements. This is tested by training sparse autoencoders (SAEs) to disentangle superposition in toy models, where alignment scores are shown to typically increase when a model's base neurons are replaced with its sparse overcomplete latent codes. We find similar increases for DNN-DNN and DNN-brain linear regression alignment in the visual domain. Our results suggest that superposition disentanglement is necessary for mapping metrics to uncover the true representational alignment between neural networks.