OpenRaiser/Intern-Atlas huggingface dataset:
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
Venue: arXiv cs.CL 2026
Year: 2026

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, the central challenge lies in the effective selection of exemplars for facilitating in-context learning. In this study, we introduce a framework that leverages Dual Queries and Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars for in-context learning. Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge. Moreover, for the second query, LoRe employs dimensionality reduction techniques to refine exemplar selection, ensuring close alignment with the input question's knowledge. Through extensive experiments, we demonstrate that DQ-LoRe significantly outperforms prior state-of-the-art methods in the automatic selection of exemplars for GPT-4, enhancing performance from 92.5% to 94.2%. Our comprehensive analysis further reveals that DQ-LoRe consistently outperforms retrieval-based approaches in terms of both performance and adaptability, especially in scenarios characterized by distribution shifts. DQ-LoRe pushes the boundary of in-context learning and opens up new avenues for addressing complex reasoning challenges. Our code is released at https://github.com/menik1126/DQ-LoRe
Crystal-GFN: sampling crystals with desirable properties and constraints
Venue: arXiv cs.LG 2026
Year: 2026

The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challenges. It has, for instance, the potential to significantly improve the efficiency of renewable energy production and storage, thereby making substantial contributions to climate crisis mitigation strategies. In this paper, we introduce Crystal-GFN, a generative model of crystal structures possessing desirable properties and constraints. Operating as a multi-environment, continuous-discrete GFlowNet, it sequentially samples structural attributes of crystalline materials, namely space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physicochemical and geometric hard constraints. We demonstrate the capabilities of Crystal-GFN to efficiently discover diverse and valid crystals with various properties: low predicted formation energy (median -3.2 eV/atom), band gap close to a target value and high density. Overall, Crystal-GFN is a crystal generation method that addresses several existing challenges in the literature and opens promising paths for accelerating materials discovery with machine learning.
Revitalizing education through ict: a short overview of japan's current landscape
Venue: arXiv cs.CY 2026
Year: 2026

The domain of Information and Communication Technology (ICT) education has garnered significant consideration in recent times. However, several challenges are inherent to this area of study, including monetary expense, temporal factors, pedagogical environment, teacher training programs, incentive, syllabus design, and health-related concerns. This paper presents an analysis of the difficulties encountered in the realm of ICT education in Japan, taking into account ten different perspectives. A peer-reviewed article of this Preprint also exists "Fujita, T. (2023). REVITALIZING EDUCATION THROUGH ICT: A SHORT OVERVIEW OF JAPAN'S CURRENT LANDSCAPE. European Journal of Social Sciences Studies, 8(5)."
MCPNS: A Macropixel Collocated Position and Its Neighbors Search for Plenoptic 2.0 Video Coding
Venue: arXiv cs.MM 2026
Year: 2026

Plenoptic 2.0 cameras enable high-resolution light field capture by incorporating focused optical designs that differ fundamentally from traditional plenoptic 1.0 systems. These structural differences produce distinct motion characteristics that challenge existing motion estimation (ME) algorithms. In this paper, we first conduct a comprehensive statistical analysis on real captured datasets to identify the primary differences in motion vector distributions among conventional, plenoptic 1.0, and plenoptic 2.0 videos. Building on these observations, we propose a novel fast ME algorithm specifically designed for plenoptic 2.0 video coding. The proposed method performs a joint search over macropixel collocated positions (MCPs) and their neighboring regions to effectively handle the large motion deviations typically observed in plenoptic 2.0 sequences. To further improve efficiency, we introduce a macropixel-level diamond search pattern (MLDSP) that follows the center-biased motion-vector distribution at the macropixel resolution, along with a fast MCP neighbor search restricted to the top K number of MCPs with the lowest distortion costs. Experimental results demonstrate that the proposed algorithm achieves better bitrate savings and computational complexity reductions compared to existing ME methods.
LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks
Venue: arXiv cs.CR 2026
Year: 2026

Penetration-testing is crucial for identifying system vulnerabilities, with privilege-escalation being a critical subtask to gain elevated access to protected resources. Language Models (LLMs) presents new avenues for automating these security practices by emulating human behavior. However, a comprehensive understanding of LLMs' efficacy and limitations in performing autonomous Linux privilege-escalation attacks remains under-explored. To address this gap, we introduce hackingBuddyGPT, a fully automated LLM-driven prototype designed for autonomous Linux privilege-escalation. We curated a novel, publicly available Linux privilege-escalation benchmark, enabling controlled and reproducible evaluation. Our empirical analysis assesses the quantitative success rates and qualitative operational behaviors of various LLMs -- GPT-3.5-Turbo, GPT-4-Turbo, and Llama3 -- against baselines of human professional pen-testers and traditional automated tools. We investigate the impact of context management strategies, different context sizes, and various high-level guidance mechanisms on LLM performance. Results show that GPT-4-Turbo demonstrates high efficacy, successfully exploiting 33-83% of vulnerabilities, a performance comparable to human pen-testers (75%). In contrast, local models like Llama3 exhibited limited success (0-33%), and GPT-3.5-Turbo achieved moderate rates (16-50%). We show that both high-level guidance and state-management through LLM-driven reflection significantly boost LLM success rates. Qualitative analysis reveals both LLMs' strengths and weaknesses in generating valid commands and highlights challenges in common-sense reasoning, error handling, and multi-step exploitation, particularly with temporal dependencies. Cost analysis indicates that GPT-4-Turbo can achieve human-comparable performance at competitive costs, especially with optimized context management.
Simple Mechanisms for Representing, Indexing and Manipulating Concepts
Venue: arXiv cs.LG 2026
Year: 2026

Supervised and unsupervised learning using deep neural networks typically aims to exploit the underlying structure in the training data; this structure is often explained using a latent generative process that produces the data, and the generative process is often hierarchical, involving latent concepts. Despite the significant work on understanding the learning of the latent structure and underlying concepts using theory and experiments, a framework that mathematically captures the definition of a concept and provides ways to operate on concepts is missing. In this work, we propose to characterize a simple primitive concept by the zero set of a collection of polynomials and use moment statistics of the data to uniquely represent the concepts; we show how this view can be used to obtain a signature of the concept. These signatures can be used to discover a common structure across the set of concepts and could recursively produce the signature of higher-level concepts from the signatures of lower-level concepts. To utilize such desired properties, we propose a method by keeping a dictionary of concepts and show that the proposed method can learn different types of hierarchical structures of the data.
Predictive Monitoring against Pattern Regular Languages
Venue: arXiv cs.PL 2026
Year: 2026

In this paper, we focus on the problem of dynamically analysing concurrent software against high-level temporal specifications. Existing techniques for runtime monitoring against such specifications are primarily designed for sequential software and remain inadequate in the presence of concurrency -- violations may be observed only in intricate thread interleavings, requiring many re-runs of the underlying software. Towards this, we study the problem of predictive runtime monitoring, inspired by the analogous problem of predictive data race detection studied extensively recently. The predictive runtime monitoring question asks, given an execution $σ$, if it can be soundly reordered to expose violations of a specification. In this paper, we focus on specifications that are given in regular languages. Our notion of reorderings is trace equivalence, where an execution is considered a reordering of another if it can be obtained from the latter by successively commuting adjacent independent actions. We first show that the problem of predictive admits a super-linear lower bound of $O(n^α)$, where $n$ is the number of events in the execution, and $α$ is a parameter describing the degree of commutativity. As a result, predictive runtime monitoring even in this setting is unlikely to be efficiently solvable. Towards this, we identify a sub-class of regular languages, called pattern languages (and their extension generalized pattern languages). Pattern languages can naturally express specific ordering of some number of (labelled) events, and have been inspired by popular empirical hypotheses, the `small bug depth' hypothesis. More importantly, we show that for pattern (and generalized pattern) languages, the predictive monitoring problem can be solved using a constant-space streaming linear-time algorithm.
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
Venue: arXiv cs.LG 2026
Year: 2026

signSGD is popular in nonconvex optimization due to its communication efficiency. Yet, existing analyses typically assume data are sampled with replacement in each iteration, contradicting a common practical implementation where data are randomly reshuffled and sequentially fed into the algorithm. This gap leaves the theoretical understanding of the more practical algorithm, signSGD with random reshuffling (SignRR), largely unexplored. We develop the first analysis of SignRR to identify the core technical challenge that prevents a thorough convergence analysis of this method. In particular, given a dataset of size $n$ and $T$ epochs, we show that the expected gradient norm of SignRR is upper bounded by $O(\log(nT)/\sqrt{nT} + σ)$, where $σ$ is the averaged conditional mean square error that may not vanish. To tackle this limitation, we develop two new sign-based algorithms under random reshuffling: SignRVR, which incorporates variance-reduced gradients, and SignRVM, which integrates momentum-based updates. Both algorithms achieve a faster convergence rate of ${O}(\log(nT)/\sqrt{nT} +\log(nT)\sqrt{n}/\sqrt{T})$. We further extend our algorithms to a distributed setting, with a convergence rate of ${O}(\log(n_0T)/\sqrt{n_0T} +\log (n_0T)\sqrt{n_0}/\sqrt{T})$, where $n_0$ is the size of the dataset of a single machine. These results mark the first step towards the theoretical understanding of practical implementation of sign-based optimization algorithms. Finally, we back up our theoretical findings through experiments on simulated and real-world problems, verifying that randomly reshuffled sign methods match or surpass existing baselines.
Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise
Venue: arXiv cs.LG 2026
Year: 2026

This paper introduces two key contributions aimed at improving the speed and quality of images generated through inverse diffusion processes. The first contribution involves reparameterizing the diffusion process in terms of the angle on a quarter-circular arc between the image and noise, specifically setting the conventional $\displaystyle \sqrt{\barα}=\cos(η)$. This reparameterization eliminates two singularities and allows for the expression of diffusion evolution as a well-behaved ordinary differential equation (ODE). In turn, this allows higher order ODE solvers such as Runge-Kutta methods to be used effectively. The second contribution is to directly estimate both the image ($\mathbf{x}_0$) and noise ($\mathbfε$) using our network, which enables more stable calculations of the update step in the inverse diffusion steps, as accurate estimation of both the image and noise are crucial at different stages of the process. Together with these changes, our model achieves faster generation, with the ability to converge on high-quality images more quickly, and higher quality of the generated images, as measured by metrics such as Frechet Inception Distance (FID), spatial Frechet Inception Distance (sFID), precision, and recall.
Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand
Venue: arXiv cs.RO 2026
Year: 2026

Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, especially when it comes to only partial observability and versatile grasping with multi-fingered hands. We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single depth image, and followed by a grasp predictor that is based on the predicted object shape. The shape completion network is based on VQDIF and predicts spatial occupancy values at arbitrary query points. As grasp predictor, we use our two-stage architecture that first generates hand poses using an autoregressive model and then regresses finger joint configurations per pose. Critical factors turn out to be sufficient data realism and augmentation, as well as special attention to difficult cases during training. Experiments on a physical robot platform demonstrate successful grasping of a wide range of household objects based on a depth image from a single viewpoint. The whole pipeline is fast, taking only about 1 s for completing the object's shape (0.7 s) and generating 1000 grasps (0.3 s).
Riemannian Laplace Approximation with the Fisher Metric
Venue: arXiv cs.LG 2026
Year: 2026

Laplace's method approximates a target density with a Gaussian distribution at its mode. It is computationally efficient and asymptotically exact for Bayesian inference due to the Bernstein-von Mises theorem, but for complex targets and finite-data posteriors it is often too crude an approximation. A recent generalization of the Laplace Approximation transforms the Gaussian approximation according to a chosen Riemannian geometry providing a richer approximation family, while still retaining computational efficiency. However, as shown here, its properties depend heavily on the chosen metric, indeed the metric adopted in previous work results in approximations that are overly narrow as well as being biased even at the limit of infinite data. We correct this shortcoming by developing the approximation family further, deriving two alternative variants that are exact at the limit of infinite data, extending the theoretical analysis of the method, and demonstrating practical improvements in a range of experiments.
Robust MAE-Driven NAS: From Mask Reconstruction to Architecture Innovation
Venue: arXiv cs.LG 2026
Year: 2026

Neural Architecture Search (NAS) relies heavily on labeled data, which is labor-intensive and time-consuming to obtain. In this paper, we propose a novel NAS method based on an unsupervised paradigm, specifically Masked Autoencoders (MAE), thereby eliminating the need for labeled data. By replacing the supervised learning objective with an image reconstruction task, our approach enables the efficient discovery of network architectures without compromising performance and generalization ability. Additionally, we address the problem of performance collapse encountered in the widely-used Differentiable Architecture Search (DARTS) in the unsupervised setting by designing a hierarchical decoder. Extensive experiments across various datasets demonstrate the effectiveness and robustness of our method, offering empirical evidence of its superiority over the counterparts.
Attacks on fairness in Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

Federated Learning is an important emerging distributed training paradigm that keeps data private on clients. It is now well understood that by controlling only a small subset of FL clients, it is possible to introduce a backdoor to a federated learning model, in the presence of certain attributes. In this paper, we present a new type of attack that compromises the fairness of the trained model. Fairness is understood to be the attribute-level performance distribution of a trained model. It is particularly salient in domains where, for example, skewed accuracy discrimination between subpopulations could have disastrous consequences. We find that by employing a threat model similar to that of a backdoor attack, an attacker is able to influence the aggregated model to have an unfair performance distribution between any given set of attributes. Furthermore, we find that this attack is possible by controlling only a single client. While combating naturally induced unfairness in FL has previously been discussed in depth, its artificially induced kind has been neglected. We show that defending against attacks on fairness should be a critical consideration in any situation where unfairness in a trained model could benefit a user who participated in its training.
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Venue: arXiv cs.CV 2026
Year: 2026

Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adaptations (LoRA) have been proposed as a parameter-efficient way of achieving concept-driven personalization. While recent work explores the combination of separate LoRAs to achieve joint generation of learned styles and subjects, existing techniques do not reliably address the problem; they often compromise either subject fidelity or style fidelity. We propose ZipLoRA, a method to cheaply and effectively merge independently trained style and subject LoRAs in order to achieve generation of any user-provided subject in any user-provided style. Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize. Project page: https://ziplora.github.io
The Complexity of Second-order HyperLTL
Venue: arXiv cs.LO 2026
Year: 2026

We determine the complexity of second-order HyperLTL satisfiability, finite-state satisfiability, and model-checking: All three are equivalent to truth in third-order arithmetic. We also consider two fragments of second-order HyperLTL that have been introduced with the aim to facilitate effective model-checking by restricting the sets one can quantify over. The first one restricts second-order quantification to smallest/largest sets that satisfy a guard while the second one restricts second-order quantification further to least fixed points of (first-order) HyperLTL definable functions. All three problems for the first fragment are still equivalent to truth in third-order arithmetic while satisfiability for the second fragment is $Σ_1^2$-complete, and finite-state satisfiability and model-checking are equivalent to truth in second-order arithmetic. Finally, we also introduce closed-world semantics for second-order HyperLTL, where set quantification ranges only over subsets of the model, while set quantification in standard semantics ranges over arbitrary sets of traces. Here, satisfiability for the least fixed point fragment becomes $Σ_1^1$-complete, but all other results are unaffected.
Learning with Errors over Group Rings Constructed by Semi-direct Product
Venue: arXiv cs.CR 2026
Year: 2026

The Learning with Errors (\LWE) problem has been widely utilized as a foundation for numerous cryptographic tools over the years. In this study, we focus on an algebraic variant of the \LWE problem called \emph{Group ring} \LWE ($\GRLWE$). We select group rings (or their direct summands) that underlie specific families of finite groups constructed by taking the semi-direct product of two cyclic groups. Unlike the Ring-\LWE problem described in \cite{lyubashevsky2010ideal}, the multiplication operation in the group rings considered here is non-commutative. As an extension of Ring-$\LWE$, it maintains computational hardness and can be potentially applied in many cryptographic scenarios. In this paper, we present two polynomial-time quantum reductions. Firstly, we provide a quantum reduction from the worst-case shortest independent vectors problem (\SIVP) in ideal lattices with polynomial approximate factor to the search version of $\GRLWE$. This reduction requires that the underlying group ring possesses certain mild properties; Secondly, we present another quantum reduction for two types of group rings, where the worst-case \SIVP problem is directly reduced to the (average-case) decision $\GRLWE$ problem. The pseudorandomness of $\GRLWE$ samples guaranteed by this reduction can be consequently leveraged to construct semantically secure public-key cryptosystems.
Large Language Models for Travel Behavior Prediction
Venue: arXiv cs.LG 2026
Year: 2026

Travel behavior prediction is a core problem in transportation demand management and is traditionally addressed using numerical models calibrated on observed data. With recent advances in large language models (LLMs), new opportunities have emerged to model human decision-making through natural language reasoning. This study explores the use of LLMs for travel behavior prediction through two complementary frameworks. The first framework employs a zero-shot prompting strategy, where the prediction task, traveler attributes, and relevant domain knowledge are described in text, enabling the LLM to directly generate predictions without task-specific training data. The second framework uses LLM-generated text embeddings as high-level representations of travel scenarios, which are then combined with conventional supervised learning models to support prediction in small-sample settings. Empirical results show that both approaches achieve performance comparable to, and in some cases competitive with, classical models such as multinomial logit, random forest, and neural networks. These findings suggest that LLMs offer a flexible and data-efficient alternative for travel behavior prediction.