OpenRaiser/Intern-Atlas huggingface dataset:
The Window Dilemma: Why Concept Drift Detection is Ill-Posed
Venue: arXiv cs.LG 2026
Year: 2026

Non-stationarity of an underlying data generating process that leads to distributional changes over time is a key characteristic of Data Streams. This phenomenon, commonly referred to as Concept Drift, has been intensively studied, and Concept Drift Detectors have been established as a class of methods for detecting such changes (drifts). For the most part, Drift Detectors compare regions (windows) of the data stream and detect drift if those windows are sufficiently dissimilar. In this work, we introduce the Window Dilemma, an observation that perceived drift is a product of windowing and not necessarily the underlying data generating process. Additionally, we highlight that drift detection is ill-posed, primarily because verification of drift events are implausible in practice. We demonstrate these contributions first by an illustrative example, followed by empirical comparisons of drift detectors against a variety of alternative adaptation strategies. Our main finding is that traditional batch learning techniques often perform better than their drift-aware counterparts further bringing into question the purpose of detectors in Stream Classification.
Achieving Better Local Regret Bound for Online Non-Convex Bilevel Optimization
Venue: arXiv cs.LG 2026
Year: 2026

Online bilevel optimization (OBO) has emerged as a powerful framework for many machine learning problems. Prior works have developed several algorithms that minimize the standard bilevel local regret or the window-averaged bilevel local regret of the OBO problem, but the optimality of existing regret bounds remains unclear. In this work, we establish optimal regret bounds for both settings. For standard bilevel local regret, we propose an algorithm that achieves the optimal regret $Ω(1+V_T)$ with at most $O(T\log T)$ total inner-level gradient evaluations. We further develop a fully single-loop algorithm whose regret bound includes an additional gradient-variation terms. For the window-averaged bilevel local regret, we design an algorithm that captures sublinear environmental variation through a window-based analysis and achieves the optimal regret $Ω(T/W^2)$. Experiments validate our theoretical findings and demonstrate the practical effectiveness of the proposed methods.
User-Centric Object Navigation: A Benchmark with Integrated User Habits for Personalized Embodied Object Search
Venue: arXiv cs.RO 2026
Year: 2026

In the evolving field of robotics, the challenge of Object Navigation (ON) in household environments has attracted significant interest. Existing ON benchmarks typically place objects in locations guided by general scene priors, without accounting for the specific placement habits of individual users. This omission limits the adaptability of navigation agents in personalized household environments. To address this, we introduce User-centric Object Navigation (UcON), a new benchmark that incorporates user-specific object placement habits, referred to as user habits. This benchmark requires agents to leverage these user habits for more informed decision-making during navigation. UcON encompasses approximately 22,600 user habits across 489 object categories. UcON is, to our knowledge, the first benchmark that explicitly formalizes and evaluates habit-conditioned object navigation at scale and covers the widest range of target object categories. Additionally, we propose a habit retrieval module to extract and utilize habits related to target objects, enabling agents to infer their likely locations more effectively. Experimental results demonstrate that current SOTA methods exhibit substantial performance degradation under habit-driven object placement, while integrating user habits consistently improves success rates. Code is available at https://github.com/whcpumpkin/User-Centric-Object-Navigation.
Auditing Rust Crates Effectively
Venue: arXiv cs.PL 2026
Year: 2026

We introduce Cargo Scan, the first interactive program analysis tool designed to help developers audit third-party Rust code. Real systems written in Rust rely on thousands of transitive dependencies. These dependencies are as dangerous in Rust as they are in other languages (e.g., C or JavaScript) -- and auditing these dependencies today means manually inspecting every line of code. Unlike for most industrial languages, though, we can take advantage of Rust's type and module system to minimize the amount of code that developers need to inspect to the code that is potentially dangerous. Cargo Scan models such potentially dangerous code as effects and performs a side-effects analysis, tailored to Rust, to identify effects and track them across crate and module boundaries. In most cases (69.2%) developers can inspect flagged effects and decide whether the code is potentially dangerous locally. In some cases, however, the safety of an effect depends on the calling context -- how a function is called, potentially by a crate the developer imports later. Hence, Cargo Scan tracks context-dependent information using a call-graph, and collects audit results into composable and reusable audit files. In this paper, we describe our experience auditing Rust crates with Cargo Scan. In particular, we audit the popular client and server HTTP crate, hyper, and all of its dependencies; our experience shows that Cargo Scan can reduce the auditing burden of potentially dangerous code to a median of 0.2% of lines of code when compared to auditing whole crates. Looking at the Rust ecosystem more broadly, we find that Cargo Scan can automatically classify ~3.5K of the top 10K crates on crates.io as safe; of the crates that do require manual inspection, we find that most of the potentially dangerous side-effects are concentrated in roughly 3% of these crates.
Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning
Venue: arXiv cs.LG 2026
Year: 2026

Large language models (LLMs) excel at complex tasks with advances in reasoning capabilities. However, existing reward mechanisms remain tightly coupled to final correctness and pay little attention to the underlying reasoning process: trajectories with sound reasoning but wrong answers receive low credit, while lucky guesses with flawed logic may be highly rewarded, affecting reasoning generalization. From a causal perspective, we interpret multi-candidate reasoning for a fixed question as a family of counterfactual experiments with theoretical supports. Building on this, we propose Group Causal Counterfactual Policy Optimization to explicitly train LLMs to learn generalizable reasoning patterns. It proposes an episodic causal counterfactual reward that jointly captures (i) robustness, encouraging the answer distribution induced by a reasoning step to remain stable under counterfactual perturbations; and (ii) effectiveness, enforcing sufficient variability so that the learned reasoning strategy can transfer across questions. We then construct token-level advantages from this reward and optimize the policy, encouraging LLMs to favor reasoning patterns that are process-valid and counterfactually robust. Extensive experiments on diverse benchmarks demonstrate its advantages.
FDD CSI Feedback under Finite Downlink Training: A Rate-Distortion Perspective
Venue: arXiv cs.IT 2026
Year: 2026

This paper establishes the theoretical limits of channel state information (CSI) feedback in frequency-division duplexing (FDD) multi-antenna orthogonal frequency-division multiplexing (OFDM) systems under finite-length training with Gaussian pilots. The user employs minimum mean-squared error (MMSE) channel estimation followed by asymptotically optimal uplink feedback. Specifically, we derive a general rate-distortion function (RDF) of the overall CSI feedback system. We then provide both non-asymptotic bounds and asymptotic scaling for the RDF under arbitrary downlink signal-to-noise ratio (SNR) when the number of training symbols exceeds the antenna dimension. A key observation is that, with sufficient training, the overall RDF converges to the direct RDF corresponding to the case where the user has full access to the downlink CSI. More importantly, we demonstrate that even at a fixed downlink SNR, the convergence rate is inversely proportional to the training length. The simulation results show that our bounds are tight, and under very limited training, the deviation between the overall RDF and the direct RDF is substantial.
Implications of Russia's full-scale invasion of Ukraine for the international mobility of Ukrainian scholars
Venue: arXiv cs.DL 2026
Year: 2026

This study examines the implications of Russia's full-scale invasion of Ukraine for the international mobility of Ukrainian scholars. The dataset, drawn from the CWTS in-house Scopus database, includes Ukrainian scholars who were internationally mobile between 2020 and 2023. The analysis focuses on scholars affiliated with universities and the National Academy of Sciences of Ukraine (NASU) prior to moving abroad. The findings reveal an increase in the number of internationally mobile scholars in 2022-2023, driven primarily by rising mobility from universities. For NASU-affiliated scholars, Russia was the top destination country in 2020-2021 but fell to fourth place in 2022-2023, overtaken by Germany, China, and Poland. For university-affiliated scholars, Poland, Germany, and Russia consistently ranked as the top three destination countries across both periods. Statistical tests indicate no significant difference in mean Field-Weighted Citation Impact (FNCI) between scholars who were internationally mobile in 2020-2021 and those mobile in 2022-2023. However, the share of internationally mobile scholars with articles among the top 10% most cited globally increased among those previously affiliated with universities, while it declined among those affiliated with NASU. In both periods, the proportion of scholars with articles in the top 10% most cited globally, published during the five years prior to changing their country of affiliation, was higher among internationally mobile scholars than among those who remained affiliated with Ukrainian institutions. Whether this mobility constitutes a brain drain requires further research. If effectively leveraged, international mobility may strengthen Ukraine's integration into global scientific networks, support post-war recovery, and contribute to a more resilient, internationally connected, and competitive academic system.
AlertBERT: A noise-robust alert grouping framework for simultaneous cyber attacks
Venue: arXiv cs.CR 2026
Year: 2026

Automated detection of cyber attacks is a critical capability to counteract the growing volume and sophistication of cyber attacks. However, the high numbers of security alerts issued by intrusion detection systems lead to alert fatigue among analysts working in security operations centres (SOC), which in turn causes slow reaction time and incorrect decision making. Alert grouping, which refers to clustering of security alerts according to their underlying causes, can significantly reduce the number of distinct items analysts have to consider. Unfortunately, conventional time-based alert grouping solutions are unsuitable for large scale computer networks characterised by high levels of false positive alerts and simultaneously occurring attacks. To address these limitations, we propose AlertBERT, a self-supervised framework designed to group alerts from isolated or concurrent attacks in noisy environments. Thereby, our open-source implementation of AlertBERT leverages masked-language-models and density-based clustering to support both real-time or forensic operation. To evaluate our framework, we further introduce a novel data augmentation method that enables flexible control over noise levels and simulates concurrent attack occurrences. Based on the data sets generated through this method, we demonstrate that AlertBERT consistently outperforms conventional time-based grouping techniques, achieving superior accuracy in identifying correct alert groups.
MTQE.en-he: Machine Translation Quality Estimation for English-Hebrew
Venue: arXiv cs.CL 2026
Year: 2026

We release MTQE.en-he: to our knowledge, the first publicly available English-Hebrew benchmark for Machine Translation Quality Estimation. MTQE.en-he contains 959 English segments from WMT24++, each paired with a machine translation into Hebrew, and Direct Assessment scores of the translation quality annotated by three human experts. We benchmark ChatGPT prompting, TransQuest, and CometKiwi and show that ensembling the three models outperforms the best single model (CometKiwi) by 6.4 percentage points Pearson and 5.6 percentage points Spearman. Fine-tuning experiments with TransQuest and CometKiwi reveal that full-model updates are sensitive to overfitting and distribution collapse, yet parameter-efficient methods (LoRA, BitFit, and FTHead, i.e., fine-tuning only the classification head) train stably and yield improvements of 2-3 percentage points. MTQE.en-he and our experimental results enable future research on this under-resourced language pair.
Which Graph Shift Operator? A Spectral Answer to an Empirical Question
Venue: arXiv cs.LG 2026
Year: 2026

Graph Neural Networks (GNNs) have established themselves as the leading models for learning on graph-structured data, generally categorized into spatial and spectral approaches. Central to these architectures is the Graph Shift Operator (GSO), a matrix representation of the graph structure used to filter node signals. However, selecting the optimal GSO, whether fixed or learnable, remains largely empirical. In this paper, we introduce a novel alignment gain metric that quantifies the geometric distortion between the input signal and label subspaces. Crucially, our theoretical analysis connects this alignment directly to generalization bounds via a spectral proxy for the Lipschitz constant. This yields a principled, computation-efficient criterion to rank and select the optimal GSO for any prediction task prior to training, eliminating the need for extensive search.
The Law of Task-Achieving Body Motion: Axiomatizing Success of Robot Manipulation Actions
Venue: arXiv cs.RO 2026
Year: 2026

Autonomous agents that perform everyday manipulation actions need to ensure that their body motions are semantically correct with respect to a task request, causally effective within their environment, and feasible for their embodiment. In order to enable robots to verify these properties, we introduce the Law of Task-Achieving Body Motion as an axiomatic correctness specification for body motions. To that end we introduce scoped Task-Environment-Embodiment (TEE) classes that represent world states as Semantic Digital Twins (SDTs) and define applicable physics models to decompose task achievement into three predicates: SatisfiesRequest for semantic request satisfaction over SDT state evolution; Causes for causal sufficiency under the scoped physics model; and CanPerform for safety and feasibility verification at the embodiment level. This decomposition yields a reusable, implementation-independent interface that supports motion synthesis and the verification of given body motions. It also supports typed failure diagnosis (semantic, causal, embodiment and out-of-scope), feasibility across robots and environments, and counterfactual reasoning about robot body motions. We demonstrate the usability of the law in practice by instantiating it for articulated container manipulation in kitchen environments on three contrasting mobile manipulation platforms
Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis
Venue: arXiv cs.LG 2026
Year: 2026

Chemical exchange saturation transfer (CEST) MRI is a non-invasive imaging modality for detecting metabolites. It offers higher resolution and sensitivity compared to conventional magnetic resonance spectroscopy (MRS). However, quantification of CEST data is challenging because the measured signal results from a complex interplay of many physiological variables. Here, we introduce a transformer-based neural network to fit parameters such as metabolite concentrations, exchange and relaxation rates of a physical model derived from Bloch-McConnell equations to in-vitro CEST spectra. We show that our self-supervised trained neural network clearly outperforms the solution of classical gradient-based solver.
Think Proprioceptively: Embodied Visual Reasoning for VLA Manipulation
Venue: arXiv cs.RO 2026
Year: 2026

Vision-language-action (VLA) models typically inject proprioception only as a late conditioning signal, which prevents robot state from shaping instruction understanding and from influencing which visual tokens are attended throughout the policy. We introduce ThinkProprio, which converts proprioception into a sequence of text tokens in the VLM embedding space and fuses them with the task instruction at the input. This early fusion lets embodied state participate in subsequent visual reasoning and token selection, biasing computation toward action-critical evidence while suppressing redundant visual tokens. In a systematic ablation over proprioception encoding, state entry point, and action-head conditioning, we find that text tokenization is more effective than learned projectors, and that retaining roughly 15% of visual tokens can match the performance of using the full token set. Across CALVIN, LIBERO, and real-world manipulation, ThinkProprio matches or improves over strong baselines while reducing end-to-end inference latency over 50%.
Perturbing the Phase: Analyzing Adversarial Robustness of Complex-Valued Neural Networks
Venue: arXiv cs.LG 2026
Year: 2026

Complex-valued neural networks (CVNNs) are rising in popularity for all kinds of applications. To safely use CVNNs in practice, analyzing their robustness against outliers is crucial. One well known technique to understand the behavior of deep neural networks is to investigate their behavior under adversarial attacks, which can be seen as worst case minimal perturbations. We design Phase Attacks, a kind of attack specifically targeting the phase information of complex-valued inputs. Additionally, we derive complex-valued versions of commonly used adversarial attacks. We show that in some scenarios CVNNs are more robust than RVNNs and that both are very susceptible to phase changes with the Phase Attacks decreasing the model performance more, than equally strong regular attacks, which can attack both phase and magnitude.
The Impossibility of Strategyproof Rank Aggregation
Venue: arXiv cs.GT 2026
Year: 2026

In rank aggregation, the goal is to combine multiple input rankings into a single output ranking. In this paper, we analyze rank aggregation methods, so-called social welfare functions (SWFs), with respect to strategyproofness, which requires that no agent can misreport his ranking to obtain an output ranking that is closer to his true ranking in terms of the Kemeny distance. As our main result, we show that no anonymous SWF satisfies unanimity and strategyproofness when there are at least four alternatives. This result is proven by SAT solving, a computer-aided theorem proving technique, and verified by Isabelle, a highly trustworthy interactive proof assistant. Further, we prove by hand that strategyproofness is incompatible with majority consistency, a variant of Condorcet-consistency for SWFs. Lastly, we show that all SWFs in two natural classes have a large incentive ratio and are thus highly manipulable.
Inference-Time Rethinking with Latent Thought Vectors for Math Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

Standard chain-of-thought reasoning generates a solution in a single forward pass, committing irrevocably to each token and lacking a mechanism to recover from early errors. We introduce Inference-Time Rethinking, a generative framework that enables iterative self-correction by decoupling declarative latent thought vectors from procedural generation. We factorize reasoning into a continuous latent thought vector (what to reason about) and a decoder that verbalizes the trace conditioned on this vector (how to reason). Beyond serving as a declarative buffer, latent thought vectors compress the reasoning structure into a continuous representation that abstracts away surface-level token variability, making gradient-based optimization over reasoning strategies well-posed. Our prior model maps unstructured noise to a learned manifold of valid reasoning patterns, and at test time we employ a Gibbs-style procedure that alternates between generating a candidate trace and optimizing the latent vector to better explain that trace, effectively navigating the latent manifold to refine the reasoning strategy. Training a 0.2B-parameter model from scratch on GSM8K, our method with 30 rethinking iterations surpasses baselines with 10 to 15 times more parameters, including a 3B counterpart. This result demonstrates that effective mathematical reasoning can emerge from sophisticated inference-time computation rather than solely from massive parameter counts.
Machine Learning Practitioners' Views on Data Quality in Light of EU Regulatory Requirements: A European Online Survey
Venue: arXiv cs.DB 2026
Year: 2026

Understanding how data quality aligns with regulatory requirements in machine learning (ML) systems presents a critical challenge for practitioners navigating the evolving EU regulatory landscape. To address this, we first propose a practical framework aligning established data quality dimensions with specific EU regulatory requirements. Second, we conducted a comprehensive online survey with over 180 EU-based data practitioners, investigating their approaches, key challenges, and unmet needs when ensuring data quality in ML systems that align with regulatory requirements. Our findings highlight crucial gaps between current practices and regulatory expectations, underscoring practitioners' need for more integrated data quality tools and better collaboration between technical and legal practitioners. These insights inform recommendations for bridging technical expertise and regulatory compliance, ultimately fostering responsible and trustworthy ML deployments.
Energy-Aware Metaheuristics
Venue: arXiv cs.NE 2026
Year: 2026

This paper presents a principled framework for designing energy-aware metaheuristics that operate under fixed energy budgets. We introduce a unified operator-level model that quantifies both numerical gain and energy usage, and define a robust Expected Improvement per Joule (EI/J) score that guides adaptive selection among operator variants during the search. The resulting energy-aware solvers dynamically choose between operators to self-control exploration and exploitation, aiming to maximize fitness gain under limited energy. We instantiate this framework with three representative metaheuristics - steady-state GA, PSO, and ILS - each equipped with both lightweight and heavy operator variants. Experiments on three heterogeneous combinatorial problems (Knapsack, NK-landscapes, and Error-Correcting Codes) show that the energy-aware variants consistently reach comparable fitness while requiring substantially less energy than their non-energy-aware baselines. EI/J values stabilize early and yield clear operator-selection patterns, with each solver reliably self-identifying the most improvement-per-Joule - efficient operator across problems.