OpenRaiser/Intern-Atlas huggingface dataset:
ARC-Fi: Exploiting Antenna Spatial Diversity for Label-Efficient Domain Generalization in Wi-Fi Sensing
Venue: arXiv cs.LG 2026
Year: 2026

Wi-Fi sensing systems are severely hindered by domain shifts when deployed in unseen real-world environments. While existing methods attempt to tackle this through Unsupervised Domain Adaptation (UDA) or Domain Generalization (DG), they critically rely on either inaccessible target data or prohibitively expensive, massive labeled source datasets. In practice, collecting abundant unlabeled Channel State Information (CSI) is feasible, whereas manual labeling is severely constrained. This realistic dilemma necessitates Semi-Supervised Domain Generalization (SSDG). To this end, we propose ARC-Fi, the first dedicated SSDG framework for Wi-Fi sensing. Directly applying conventional contrastive learning to CSI data inevitably triggers paradigm-specific "shortcut learning," causing models to memorize environmental backgrounds rather than gesture dynamics. To overcome this, ARC-Fi introduces a physics-informed data augmentation strategy: the Antenna Response Consistency (ARC) module. ARC exploits the intrinsic spatial diversity of multi-antenna systems, treating signals from co-located antennas as naturally semantics-preserving augmented views to explicitly block environmental shortcuts. Furthermore, we introduce a unified Semi-Supervised Contrastive Objective that leverages scarce labels and reliable pseudo-labels to align cross-domain features, effectively preventing the blind repulsion of same-class instances. Extensive experiments on the Widar and CSIDA datasets demonstrate that ARC-Fi establishes a new state-of-the-art, significantly outperforming existing UDA, DG, and SSDG methods. Ultimately, this work provides a physics-grounded, label-efficient solution, advancing the scalable deployment of robust real-world Wi-Fi sensing systems. Code is available at: https://github.com/KaoruMiyazono/UniCrossFi.
Automated Layout and Control Co-Design of Robust Multi-UAV Transportation Systems
Venue: arXiv cs.RO 2026
Year: 2026

The joint optimization of physical parameters and controllers in robotic systems is challenging. This is due to the difficulties of predicting the effect that changes in physical parameters have on final performances. At the same time, physical and morphological modifications can improve robot capabilities, perhaps completely unlocking new skills and tasks. We present a novel approach to co-optimize the physical layout and the control of a cooperative aerial transportation system. The goal is to achieve the most precise and robust flight when carrying a payload. We assume the agents are connected to the payload through rigid attachments, essentially transforming the whole system into a larger flying object with ``thrust modules" at the attachment locations of the quadcopters. We investigate the optimal arrangement of the thrust modules around the payload, so that the resulting system achieves the best disturbance rejection capabilities. We propose a novel metric of robustness inspired by H2 control, and propose an algorithm to optimize the layout of the vehicles around the object and their controller altogether. We experimentally validate the effectiveness of our approach using fleets of three and four quadcopters and payloads of diverse shapes.
Morphing Graph Drawings in the Presence of Point Obstacles
Venue: arXiv cs.CG 2026
Year: 2026

A crossing-free morph is a continuous deformation between two graph drawings that preserves straight-line pairwise noncrossing edges. Motivated by applications in 3D morphing problems, we initiate the study of morphing graph drawings in the plane in the presence of stationary point obstacles, which need to be avoided throughout the deformation. As our main result, we prove that it is NP-hard to decide whether such an obstacle-avoiding 2D morph between two given drawings of the same graph exists. In fact, this statement remains true even in the severely restricted special case where only three vertices have to change positions. This is in sharp contrast to the classical case without obstacles, where there is an efficiently verifiable (necessary and sufficient) criterion for the existence of a morph. Further, we provide several combinatorial results related to conditions under which the existence of a morph between two drawings of a graph can or cannot be prevented by the placement of a given number of point obstacles.
MAST: Model-Agnostic Sparsified Training
Venue: arXiv cs.LG 2026
Year: 2026

We introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function. Unlike traditional formulations, the proposed approach explicitly incorporates an initially pre-trained model and random sketch operators, allowing for sparsification of both the model and gradient during training. We establish the insightful properties of the proposed objective function and highlight its connections to the standard formulation. Furthermore, we present several variants of the Stochastic Gradient Descent (SGD) method adapted to the new problem formulation, including SGD with general sampling, a distributed version, and SGD with variance reduction techniques. We achieve tighter convergence rates and relax assumptions, bridging the gap between theoretical principles and practical applications, covering several important techniques such as Dropout and Sparse training. This work presents promising opportunities to enhance the theoretical understanding of model training through a sparsification-aware optimization approach.
Isomorphism for Tournaments of Small Twin Width
Venue: arXiv cs.DS 2026
Year: 2026

We prove that isomorphism of tournaments of twin width at most $k$ can be decided in time $k^{O(\log k)}n^{O(1)}$. This implies that the isomorphism problem for classes of tournaments of bounded or moderately growing twin width is in polynomial time. By comparison, there are classes of undirected graphs of bounded twin width that are isomorphism complete, that is, the isomorphism problem for the classes is as hard as the general graph isomorphism problem. Twin width is a graph parameter that has been introduced only recently (Bonnet et al., J. ACM 2022), but has received a lot of attention in structural graph theory since then. On directed graphs, it is functionally smaller than clique width. We prove that on tournaments (but not on general directed graphs) it is also functionally smaller than directed tree width (and thus, the same also holds for cut width and directed path width). Hence, our result implies that tournament isomorphism testing is also fixed-parameter tractable when parameterized by any of these parameters. Our isomorphism algorithm heavily employs group-theoretic techniques. This seems to be necessary: as a second main result, we show that the combinatorial Weisfeiler-Leman algorithm does not decide isomorphism of tournaments of twin width at most 35 if its dimension is $o(n)$. (Throughout this abstract, $n$ is the order of the input graphs.)
GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs
Venue: arXiv cs.CL 2026
Year: 2026

Despite the rapid progress of large language models (LLMs), knowledge graph-based question answering (KGQA) remains essential for producing verifiable and hallucination-resistant answers in many real-world settings where answer trustworthiness and computational efficiency are highly valued. However, most existing KGQA methods provide only final answers in the form of KG entities. Without explicit explanations -- ideally in the form of intermediate reasoning process over relevant KG triples, the QA results are difficult to inspect and interpret. Moreover, this limitation prevents the rich and verifiable knowledge encoded in KGs, which is a key advantage of KGQA over LLMs, from being fully leveraged. However, addressing this issue remains highly challenging due to the lack of annotated intermediate reasoning process and the requirement of high efficiency in KGQA. In this paper, we propose a novel Graph Neural Network-based Two-Step Reasoning method (GNN2R) that can efficiently retrieve both final answers and corresponding reasoning subgraphs as verifiable rationales, using only weak supervision from widely-available final answer annotations. We extensively evaluated GNN2R and demonstrated that GNN2R substantially outperforms existing state-of-the-art KGQA methods in terms of effectiveness, efficiency, and the quality of generated explanations. The complete code and pre-trained models are available at https://github.com/ruijie-wang-uzh/GNN2R.
ComplicaCode: Enhancing Disease Complication Detection in Electronic Health Records through ICD Path Generation
Venue: arXiv cs.LG 2026
Year: 2026

The target of Electronic Health Record (EHR) coding is to find the diagnostic codes according to the EHRs. In previous research, researchers have preferred to do multi-classification on the EHR coding task; most of them encode the EHR first and then process it to get the probability of each code based on the EHR representation. However, the question of complicating diseases is neglected among all these methods. In this paper, we propose a novel EHR coding framework, which is the first attempt at detecting complicating diseases, called ComplicaCode. This method refers to the idea of adversarial learning; a Path Generator and a Path Discriminator are designed to more efficiently finish the task of EHR coding. We propose a copy module to detect complicating diseases; by the proposed copy module and the adversarial learning strategy, we identify complicating diseases efficiently. Extensive experiments show that our method achieves a 57.30\% ratio of complicating diseases in predictions, and achieves the state-of-the-art performance among cnn-based baselines, it also surpasses transformer methods in the complication detection task, demonstrating the effectiveness of our proposed model. According to the ablation study, the proposed copy mechanism plays a crucial role in detecting complicating diseases.
Mend the gap: A smart repair algorithm for noisy polygonal tilings
Venue: arXiv cs.CG 2026
Year: 2026

Let $T^* = \{P^*_1, \ldots, P^*_N\}$ be a polygonal tiling of a simply connected region in the plane, and let $T = \{P_1, \ldots, P_N\}$ be a noisy version of $T^*$ obtained by making small perturbations to the coordinates of the vertices of the polygons in $T^*$. In general, $T$ will only be an approximate tiling, due to the presence of gaps and overlaps between the perturbed polygons in $T$. The areas of these gaps and overlaps are typically small relative to the areas of the polygons themselves. Suppose that we are given the approximate tiling $T$ and we wish to recover the tiling $T^*$. To address this problem, we introduce a new algorithm, called {\tt smart\_repair}, to modify the polygons in $T$ to produce a tiling $\widetilde{T} = \{\widetilde{P}_1, \ldots, \widetilde{P}_N\}$ that closely approximates $T^*$, with special attention given to reproducing the {\em adjacency relations} between the polygons in $T^*$ as closely as possible. The motivation for this algorithm comes from computational redistricting, where algorithms are used to build districts from smaller geographic units. Because districts in most U.S. states are required to be contiguous, these algorithms are fundamentally based on adjacency relations between units. Unfortunately, the best available map data for unit boundaries is often noisy, containing gaps and overlaps between units that can lead to substantial inaccuracies in the adjacency relations. Simple repair algorithms can exacerbate these inaccuracies, with the result that algorithmically drawn districts based on the ``repaired" units may be discontiguous, and hence not legally compliant. The algorithm presented here is specifically designed to avoid such problems. A Python implementation is publicly available as part of the MGGG Redistricting Lab's {\tt Maup} package, available at https://github.com/mggg/maup.
Context-aware Decoding Reduces Hallucination in Query-focused Summarization
Venue: arXiv cs.CL 2026
Year: 2026

Query-focused summarization (QFS) aims to provide a summary of a single document/multi documents that can satisfy the information needs of a given query. It is useful for various real-world applications, such as abstractive snippet generation or more recent retrieval augmented generation (RAG). A prototypical QFS pipeline consists of a retriever (sparse or dense retrieval) and a generator (usually a large language model). However, applying large language models (LLM) potentially leads to hallucinations, especially when the evidence contradicts the prior belief of LLMs. There has been growing interest in developing new decoding methods to improve generation quality and reduce hallucination. In this work, we conduct a large-scale reproducibility study on one recently proposed decoding method\, -- \,Context-aware Decoding (CAD). In addition to replicating CAD's experiments on news summarization datasets, we include experiments on QFS datasets, and conduct more rigorous analysis on computational complexity and hyperparameter sensitivity. Experiments with eight different language models show that performance-wise, CAD improves QFS quality by (1) reducing factuality errors/hallucinations while (2) mostly retaining the match of lexical patterns, measured by ROUGE scores, while also at a cost of increased inference-time FLOPs and reduced decoding speed. The \href{https://github.com/zhichaoxu-shufe/context-aware-decoding-qfs}{code implementation} based on Huggingface Library is made available
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models
Venue: arXiv cs.IR 2026
Year: 2026

Denoising Diffusion Probabilistic Model (DDPM) has shown great competence in image and audio generation tasks. However, there exist few attempts to employ DDPM in the text generation, especially review generation under recommendation systems. Fueled by the predicted reviews explainability that justifies recommendations could assist users better understand the recommended items and increase the transparency of recommendation system, we propose a Diffusion Model-based Review Generation towards EXplainable Recommendation named Diffusion-EXR. Diffusion-EXR corrupts the sequence of review embeddings by incrementally introducing varied levels of Gaussian noise to the sequence of word embeddings and learns to reconstruct the original word representations in the reverse process. The nature of DDPM enables our lightweight Transformer backbone to perform excellently in the recommendation review generation task. Extensive experimental results have demonstrated that Diffusion-EXR can achieve state-of-the-art review generation for recommendation on two publicly available benchmark datasets.
Soft Contrastive Learning for Time Series
Venue: arXiv cs.LG 2026
Year: 2026

Contrastive learning has shown to be effective to learn representations from time series in a self-supervised way. However, contrasting similar time series instances or values from adjacent timestamps within a time series leads to ignore their inherent correlations, which results in deteriorating the quality of learned representations. To address this issue, we propose SoftCLT, a simple yet effective soft contrastive learning strategy for time series. This is achieved by introducing instance-wise and temporal contrastive loss with soft assignments ranging from zero to one. Specifically, we define soft assignments for 1) instance-wise contrastive loss by the distance between time series on the data space, and 2) temporal contrastive loss by the difference of timestamps. SoftCLT is a plug-and-play method for time series contrastive learning that improves the quality of learned representations without bells and whistles. In experiments, we demonstrate that SoftCLT consistently improves the performance in various downstream tasks including classification, semi-supervised learning, transfer learning, and anomaly detection, showing state-of-the-art performance. Code is available at this repository: https://github.com/seunghan96/softclt.
Efficient Interference Graph Estimation via Concurrent Flooding
Venue: arXiv cs.NI 2026
Year: 2026

Traditional wisdom for network management allocates network resources separately for the measurement and data transmission tasks. Heavy measurement tasks may take up resources for data transmission and significantly reduce network performance. It is therefore challenging for interference graphs, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, we propose to use power as a new dimension for interference graph estimation (IGE) and integrate IGE with concurrent flooding such that IGE can be done simultaneously with flooding using the same frequency-time resources. With controlled and real-world experiments, we show that it is feasible to efficiently achieve IGE via concurrent flooding on the commercial off-the-shelf (COTS) devices by controlling the transmit powers of nodes. We believe that efficient IGE would be a key enabler for the practical use of the existing scheduling algorithms assuming known interference graphs.
Hidden Minima in Two-Layer ReLU Networks
Venue: arXiv cs.LG 2026
Year: 2026

We consider the optimization problem associated with training two-layer ReLU networks with \(d\) inputs under the squared loss, where the labels are generated by a target network. Recent work has identified two distinct classes of infinite families of minima: one whose training loss vanishes in the high-dimensional limit, and another whose loss remains bounded away from zero. The latter family is empirically avoided by stochastic gradient descent, hence \emph{hidden}, motivating the search for analytic criteria that distinguish hidden from non-hidden minima. A key challenge is that prior analyses have shown the Hessian spectra at hidden and non-hidden minima to coincide up to terms of order \(O(d^{-1/2})\), seemingly limiting the discriminative power of spectral methods. We therefore take a different route, studying instead certain curves along which the loss is locally minimized. Our main result shows that arcs emanating from hidden minima exhibit distinctive structural and symmetry properties, arising precisely from \(Ω(d^{-1/2})\) eigenvalue contributions that are absent from earlier analyses.
Static Deadlock Detection for Rust Programs
Venue: arXiv cs.PL 2026
Year: 2026

Rust relies on its unique ownership mechanism to ensure thread and memory safety. However, numerous potential security vulnerabilities persist in practical applications. New language features in Rust pose new challenges for vulnerability detection. This paper proposes a static deadlock detection method tailored for Rust programs, aiming to identify various deadlock types, including double lock, conflict lock, and deadlock associated with conditional variables. With due consideration for Rust's ownership and lifetimes, we first complete the pointer analysis. Then, based on the obtained points-to information, we analyze dependencies among variables to identify potential deadlocks. We develop a tool and conduct experiments based on the proposed method. The experimental results demonstrate that our method outperforms existing deadlock detection methods in precision.
LogPTR: Variable-Aware Log Parsing with Pointer Network
Venue: arXiv cs.SE 2026
Year: 2026

Due to the sheer size of software logs, developers rely on automated log analysis. Log parsing, which parses semi-structured logs into a structured format, is a prerequisite of automated log analysis. However, existing log parsers are unsatisfactory when applied in practice because they 1) ignore categories of variables, and 2) need labor-intensive model tuning. To address these limitations, we propose LogPTR, a variable-aware log parser that can extract the static and dynamic parts in logs, and further identify categories of variables. The key of LogPTR is formulating log parsing as a text summarization problem and using a pointer mechanism to copy words from the log message and label tokens indicating categories of variables. The experimental results on widely-used benchmark datasets show that LogPTR outperforms state-of-the-art log parsers on both general log parsing that extracts log templates and variable-aware log parsing that further identifies categories of variables.
Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges
Venue: arXiv cs.CL 2026
Year: 2026

The growing prevalence and rapid evolution of offensive language in social media amplify the complexities of detection, particularly highlighting the challenges in identifying such content across diverse languages. This survey presents a systematic and comprehensive exploration of Cross-Lingual Transfer Learning (CLTL) techniques in offensive language detection in social media. Our study stands as the first holistic overview to focus exclusively on the cross-lingual scenario in this domain. We analyse 67 relevant papers and categorise these studies across various dimensions, including the characteristics of multilingual datasets used, the cross-lingual resources employed, and the specific CLTL strategies implemented. According to "what to transfer", we also summarise three main CLTL transfer approaches: instance, feature, and parameter transfer. Additionally, we shed light on the current challenges and future research opportunities in this field. Furthermore, we have made our survey resources available online, including two comprehensive tables that provide accessible references to the multilingual datasets and CLTL methods used in the reviewed literature.
Unlocking Past Information: Temporal Embeddings in Cooperative Bird's Eye View Prediction
Venue: arXiv cs.CV 2026
Year: 2026

Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2% and under communication failures by up to 19%. The code is available at https://github.com/cvims/TempCoBEV