OpenRaiser/Intern-Atlas huggingface dataset:
Structured and Fast Optimization: The Kronecker SGD Algorithm
Venue: arXiv cs.LG 2026
Year: 2026

Stochastic gradient descent (SGD) now acts as a fundamental part of optimization in current machine learning. Meanwhile, deep learning architectures have shown outstanding performance in a wide range of fields, such as natural language processing, bioinformatics, and computer vision. Nevertheless, as the parameter size $d$ increases, these models encounter serious efficiency challenges. Previous studies show that the per step calculation expense scales linearly with the input size $d$. To mitigate this, our paper explores inherent patterns, such as Kronecker products within the training examples. We consider input data points that can be represented as tensor products of lower-dimensional vectors. We introduce a novel stochastic optimization method where the computational load for every update scales sublinearly with $d$, assuming moderate structural properties of the inputs. We believe our research is the first work achieving this result, representing a significant step forward for efficient deep learning optimization. Our theoretical findings are supported by a formal theorem, demonstrating that the proposed algorithm can train a two-layer fully connected neural network with a per-iteration cost independent of $d$.
AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide for Simultaneous Speech Translation
Venue: arXiv cs.CL 2026
Year: 2026

Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for machine translation-related tasks. Among these studies, attention resulted to be a useful source of information to get insights about word alignment also when the input text is substituted with audio segments, as in the case of the speech translation (ST) task. In this paper, we propose AlignAtt, a novel policy for simultaneous ST (SimulST) that exploits the attention information to generate source-target alignments that guide the model during inference. Through experiments on the 8 language pairs of MuST-C v1.0, we show that AlignAtt outperforms previous state-of-the-art SimulST policies applied to offline-trained models with gains in terms of BLEU of 2 points and latency reductions ranging from 0.5s to 0.8s across the 8 languages.
A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing
Venue: arXiv cs.LG 2026
Year: 2026

Industrial soft sensing is crucial for accurate process monitoring through reliable inference of dominant sensor variables. However, developing effective data-driven soft sensor models presents challenges, such as achieving domain adaptability, addressing incomplete sensor labels, and learning stochastic data variability. To overcome these challenges, we propose a Deep Variational Potential Flow (DVPF) framework for cross-domain soft sensor modeling, taking into account the lack of sensor labels in the target domain. Our framework introduces sequential variational Bayes with recurrent neural network (RNN) parameterization to address the maximum likelihood estimation problem that characterizes cross-domain soft sensing. Central to the framework is a potential flow that performs unsupervised Bayesian inference on the RNN-extracted features to obtain an exact representation of the intractable posterior distribution. Together, these DVPF components learn domain-adaptable features that effectively capture complex cross-domain process dynamics and data variability. We validate the proposed DVPF on a real industrial multiphase flow process across varying operating modes. The results show that the DVPF demonstrates superior performance in cross-domain soft sensing compared to existing deep feature-based domain adaptation methods.
Roll in the Tanks! Measuring Left-wing Extremism on Reddit at Scale
Venue: arXiv cs.SI 2026
Year: 2026

Social media's role in the spread and evolution of extremism is a focus of intense study. Online extremists have been involved in the dissemination of online hate, mis- and disinformation, and real-world violence. While the majority of research has focused on right-wing extremism, recent real-world incidents have highlighted the potential for far-left extremists to engage in violence and cause real-world harm as well. In this paper, we present the first large-scale measurement of left-wing extremism on social media. Analyzing 1.3 million posts from 53,000 authors from tankie subreddits, we focus on ``tankies,'' a left-wing community that first arose in the 1950s in support of hardline actions of the USSR and has evolved to support what they call ``Actually Existing Socialist'' countries, e.g., CCP-run China, the USSR, and North Korea. Among other things, our analysis reveals that these groups occupy the periphery of the broader far-left community on Reddit, and their discourse distinctively focus on state-level politics and support for authoritarian regimes, rather than on social justice issues. Finally, we show that tankies have high toxicity scores and use pejorative language, mirroring toxicity patterns reported for other online extremist communities. Our findings provide empirical evidence of the distinct positioning and discourse of left-wing extremist groups on social media.
Termination of Graph Transformation Systems via Generalized Weighted Type Graphs
Venue: arXiv cs.LO 2026
Year: 2026

We refine the weighted type graph technique for proving termination of double pushout (DPO) graph transformation systems. We increase the power of the approach for graphs, we generalize the technique to other categories, and we allow for variations of DPO that occur in the literature.
Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems
Venue: arXiv cs.CY 2026
Year: 2026

We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of potentially conflicting local regulations. Then, we discuss key components of a lightweight and effective AI transparency and/or accountability regulation. To prevent overregulation, the proposed approach encourages collaboration between regulators and industry to create a scalable and cost-efficient mutually beneficial solution. This includes using automated assessments and benchmarks with results transparently communicated through AI cards in an open AI register to facilitate meaningful public comparisons of competing AI systems. Such AI cards should report standardized measures tailored to the specific high-risk applications of AI systems and could be used for conformity assessments under AI transparency and accountability policies such as the European Union's AI Act.
LinkDID: A Privacy-Preserving, Sybil-Resistant and Key-Recoverable Decentralized Identity Scheme
Venue: arXiv cs.CR 2026
Year: 2026

Decentralized identity frameworks grant users full sovereignty over their digital assets in the Web3 ecosystem. However, allowing arbitrary creation of identifiers makes the system susceptible to Sybil attacks and puts assets at risk when keys are lost or compromised. Moreover, the lack of identification prevents anonymous credential schemes from deterring malicious transfers. While existing solutions attempt to address these issues by linking identifiers to entities through trusted intermediaries, these entities are not always accessible and require costly offline interactions. In this work, we introduce LinkDID, a decentralized identity scheme offering Sybil resistance, trustless key recovery, and nontransferable anonymous credentials. LinkDID creates blockchainbased bindings between identifiers and gradually combines identifiers belonging to the same holder into a unified associated identifier. As all identifiers within an association are presumed to belong to one individual, any fraudulent activity can be detected. The association grows larger as interactions increase, substantially reducing the likelihood of successful Sybil attacks. This mechanism allows holders to recover identifiers with lost or stolen keys by proving knowledge of specific association structures. Additionally, LinkDID prevents unauthorized transfers through blockchain-based identifier-key bindings and proofs of ownership for credentials. The evaluation shows that LinkDID effectively achieves progressive Sybil resistance while surpassing state-of-the-art anonymous credential schemes, achieving identifier association and credential presentation times of 2.41s and 3.31s on consumer-grade devices.
The Expansion Problem for Infinite Trees
Venue: arXiv cs.FL 2026
Year: 2026

We study Ramsey like theorems for infinite trees and similar combinatorial tools. As an application we consider the expansion problem for tree algebras.
Kernel Limit for a Class of Recurrent Neural Networks Trained on Ergodic Data Sequences
Venue: arXiv cs.LG 2026
Year: 2026

Mathematical methods are developed to characterize the asymptotics of recurrent neural networks (RNN) as the number of hidden units, data samples in the sequence, hidden state updates, and training steps simultaneously grow to infinity. In the case of an RNN with a simplified weight matrix, we prove the convergence of the RNN to the solution of an infinite-dimensional ODE coupled with the fixed point of a random algebraic equation. The analysis requires addressing several challenges which are unique to RNNs. In typical mean-field applications (e.g., feedforward neural networks), discrete updates are of magnitude $\mathcal{O}(1/N)$ and the number of updates is $\mathcal{O}(N)$. Therefore, the system can be represented as an Euler approximation of an appropriate ODE/PDE, which it will converge to as $N \rightarrow \infty$. However, the RNN hidden layer updates are $\mathcal{O}(1)$. Therefore, RNNs cannot be represented as a discretization of an ODE/PDE and standard mean-field techniques cannot be applied. Instead, we develop a fixed point analysis for the evolution of the RNN memory states, with convergence estimates in terms of the number of update steps and the number of hidden units. The RNN hidden layer is studied as a function in a Sobolev space, whose evolution is governed by the data sequence (a Markov chain), the parameter updates, and its dependence on the RNN hidden layer at the previous time step. Due to the strong correlation between updates, a Poisson equation must be used to bound the fluctuations of the RNN around its limit equation. These mathematical methods give rise to the neural tangent kernel (NTK) limits for RNNs trained on data sequences as the number of data samples and size of the neural network grow to infinity.
Concurrent Deterministic Skiplist and Other Data Structures
Venue: arXiv cs.DC 2026
Year: 2026

Skiplists are used in a variety of applications for storing data subject to order criteria. In this article we discuss the design, analysis and performance of a concurrent deterministic skiplist on many-core NUMA nodes. We also evaluate the performance of concurrent lock-free unbounded queue implementation and two concurrent multi-reader,multi-writer(MWMR) hash table implementations and compare them with those from Intel's Thread Building Blocks(TBB) library. We introduce strategies for memory management that reduce page faults and cache misses for the memory access patterns in these data structures. This paper proposes hierarchical usage of concurrent data structures in programs to improve memory latencies by reducing memory accesses from remote NUMA nodes.
Towards Attributions of Input Variables in a Coalition
Venue: arXiv cs.LG 2026
Year: 2026

This paper focuses on the fundamental challenge of partitioning input variables in attribution methods for Explainable AI, particularly in Shapley value-based approaches. Previous methods always compute attributions given a predefined partition but lack theoretical guidance on how to form meaningful variable partitions. We identify that attribution conflicts arise when the attribution of a coalition differs from the sum of its individual variables' attributions. To address this, we analyze the numerical effects of AND-OR interactions in AI models and extend the Shapley value to a new attribution metric for variable coalitions. Our theoretical findings reveal that specific interactions cause attribution conflicts, and we propose three metrics to evaluate coalition faithfulness. Experiments on synthetic data, NLP, image classification, and the game of Go validate our approach, demonstrating consistency with human intuition and practical applicability.
Toward 6G Sidelink Reliability: MAC PRR Modeling for NR Mode 2 SPS and ns-3 Validation
Venue: arXiv cs.NI 2026
Year: 2026

5G New Radio (NR) Sidelink (SL) Mode 2 has enabled decentralized, infrastructure-less direct communications which is evolving to serve reliability-critical services in 6G SL. Particularly, the channel access in NR SL Mode 2 relies on the Sensing-based Semi-Persistent Scheduling (SPS) whose key features significantly influence the packet reception ratio (PRR). While SPS has been widely studied, existing analytical models typically abstract or omit several NR-specific SPS features that are standardized in the 3rd Generation Partnership Project (3GPP), limiting their ability to explain how SPS parameters shape MAC collision dynamics and PRR. This paper develops an analytical MAC-layer PRR model for broadcast NR SL mode 2 by explicitly modeling SPS-driven MAC collision events. The model captures (i) Collisions caused by simultaneous resource reselection and (ii) Persistent collisions induced by resource keeping across resource reservation intervals (RRIs). Based on the event-level characterization, we derive closed-form expressions for the steady-state MAC collision probability and PRR. We further extend the analysis to incorporate under-explored SPS features, including the duplicate transmissions per RRI and the minimum resource-availability requirement for reselection, and quantify their impact on PRR in under-saturated regimes. The analytical results are validated using ns-3 simulations based on the 5G-LENA framework, showing close agreement under under-saturation and revealing deviations as the system approaches saturation. The proposed model provides mechanistic insight and design guidance of tuning the SPS parameters to improve 6G SL reliability.
The Golden Path to Guarded Monotone Strict NP
Venue: arXiv cs.LO 2026
Year: 2026

Guarded Monotone Strict NP (GMSNP) extends Monotone Monadic Strict NP (MMSNP) by guarded existentially quantified predicates of arbitrary arities. We prove that the containment and the FO-rewritability problems for GMSNP are decidable, thereby settling an open question of Bienvenu, ten Cate, Lutz, and Wolter, later restated by Bourhis and Lutz. Our proof also comes with a 2NEXPTIME upper bound on the complexity of the two problems, which matches the lower bounds for MMSNP due to Bourhis and Lutz. To obtain these results, we significantly improve the state of knowledge of the model-theoretic properties of GMSNP. Bodirsky, Knäuer, and Starke previously showed that every GMSNP sentence defines a finite union of CSPs of $ω$-categorical structures. We show that these structures can be used to obtain a reduction from the containment problem for GMSNP to the much simpler problem of testing the existence of a recolouring; a careful analysis of this yields said upper bound for containment. The upper bound for FO-rewritability is subsequently obtained by an application of several standard techniques from the theory of infinite-domain CSPs. As our secondary contribution, we refine the construction of Bodirsky, Knäuer, and Starke by adding a restricted form of homogeneity to the properties of these structures, making the logic amenable to future complexity classifications for query evaluation using techniques developed for infinite-domain CSPs.
A simple connection from loss flatness to compressed neural representations
Venue: arXiv cs.LG 2026
Year: 2026

Despite extensive study, the significance of sharpness -- the trace of the loss Hessian at local minima -- remains unclear. We investigate an alternative perspective: how sharpness relates to the geometric structure of neural representations, specifically representation compression, defined as how strongly neural activations concentrate under local input perturbations. We introduce three measures -- Local Volumetric Ratio (LVR), Maximum Local Sensitivity (MLS), and Local Dimensionality -- and derive upper bounds showing these are mathematically constrained by sharpness: flatter minima necessarily limit compression. We extend these bounds to reparametrization-invariant sharpness and introduce network-wide variants (NMLS, NVR) that provide tighter, more stable bounds than prior single-layer analyses. Empirically, we validate consistent positive correlations across feedforward, convolutional, and transformer architectures. Our results suggest that sharpness fundamentally quantifies representation compression, offering a principled resolution to contradictory findings on the sharpness-generalization relationship.
Arena-Independent Memory Bounds for Nash Equilibria in Reachability Games
Venue: arXiv cs.GT 2026
Year: 2026

We study the memory requirements of Nash equilibria in turn-based multiplayer games on possibly infinite graphs with reachability, safety, shortest-path, Büchi and co-Büchi objectives. We present constructions for finite-memory Nash equilibria in these games that apply to arbitrary game graphs, bypassing the finite-arena requirement that is central in existing approaches. We show that, for these five types of games, from any Nash equilibrium, we can derive another Nash equilibrium where all strategies are finite-memory such that all objectives satisfied by the outcome of the original equilibrium also are by the outcome of the derived equilibrium, without increasing costs for shortest-path games. Furthermore, we provide memory bounds that are independent of the size of the game graph for reachability, safety and shortest-path games. These bounds depend only on the number of players. To the best of our knowledge, we provide the first results pertaining to finite-memory constrained Nash equilibria in infinite arenas and the first arena-independent memory bounds for Nash equilibria.
Within-Dataset Disclosure Risk for Differential Privacy
Venue: arXiv cs.DB 2026
Year: 2026

Differential privacy (DP) enables private data analysis. In a typical DP deployment, controllers manage individuals' sensitive data and are responsible for answering analysts' queries while protecting individuals' privacy. They do so by choosing the privacy parameter $ε$, which controls the degree of privacy for all individuals in all possible datasets. However, it is challenging for controllers to choose $ε$ because of the difficulty of interpreting the privacy implications of such a choice on the within-dataset individuals. To address this challenge, we first derive a relative disclosure risk indicator (RDR) that indicates the impact of choosing $ε$ on the within-dataset individuals' disclosure risk. We then design an algorithm to find $ε$ based on controllers' privacy preferences expressed as a function of the within-dataset individuals' RDRs, and an alternative algorithm that finds and releases $ε$ while satisfying DP. Lastly, we propose a solution that bounds the total privacy leakage when using the algorithm to answer multiple queries without requiring controllers to set the total privacy budget. We evaluate our contributions through an IRB-approved user study that shows the RDR is useful for helping controllers choose $ε$, and experimental evaluations showing our algorithms are efficient and scalable.
Budgeted Embedding Table For Recommender Systems
Venue: arXiv cs.IR 2026
Year: 2026

At the heart of contemporary recommender systems (RSs) are latent factor models that provide quality recommendation experience to users. These models use embedding vectors, which are typically of a uniform and fixed size, to represent users and items. As the number of users and items continues to grow, this design becomes inefficient and hard to scale. Recent lightweight embedding methods have enabled different users and items to have diverse embedding sizes, but are commonly subject to two major drawbacks. Firstly, they limit the embedding size search to optimizing a heuristic balancing the recommendation quality and the memory complexity, where the trade-off coefficient needs to be manually tuned for every memory budget requested. The implicitly enforced memory complexity term can even fail to cap the parameter usage, making the resultant embedding table fail to meet the memory budget strictly. Secondly, most solutions, especially reinforcement learning based ones derive and optimize the embedding size for each each user/item on an instance-by-instance basis, which impedes the search efficiency. In this paper, we propose Budgeted Embedding Table (BET), a novel method that generates table-level actions (i.e., embedding sizes for all users and items) that is guaranteed to meet pre-specified memory budgets. Furthermore, by leveraging a set-based action formulation and engaging set representation learning, we present an innovative action search strategy powered by an action fitness predictor that efficiently evaluates each table-level action. Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
Venue: arXiv cs.CV 2026
Year: 2026

This work aims to adapt large-scale pre-trained vision-language models, such as contrastive language-image pretraining (CLIP), to enhance the performance of object reidentification (Re-ID) across various supervision settings. Although prompt learning has enabled a recent work named CLIP-ReID to achieve promising performance, the underlying mechanisms and the necessity of prompt learning remain unclear due to the absence of semantic labels in ReID tasks. In this work, we first analyze the role prompt learning in CLIP-ReID and identify its limitations. Based on our investigations, we propose a simple yet effective approach to adapt CLIP for supervised object Re-ID. Our approach directly fine-tunes the image encoder of CLIP using a prototypical contrastive learning (PCL) loss, eliminating the need for prompt learning. Experimental results on both person and vehicle Re-ID datasets demonstrate the competitiveness of our method compared to CLIP-ReID. Furthermore, we extend our PCL-based CLIP fine-tuning approach to unsupervised scenarios, where we achieve state-of-the art performance. Code is available at https://github.com/RikoLi/PCL-CLIP.
MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonstrated the impressive performance of large generative language models on downstream tasks. However, existing generative language models generally neglect an inherent challenge in text corpus during training, i.e., the imbalance between frequent tokens and infrequent ones. It can lead a language model to be dominated by common and easy-to-learn tokens, thereby overlooking the infrequent and difficult-to-learn ones. To alleviate that, we propose a MiLe Loss function for mitigating the bias of learning difficulties with tokens. During training, it can dynamically assess the learning difficulty of a to-be-learned token, according to the information entropy of the corresponding predicted probability distribution over the vocabulary. Then it scales the training loss adaptively, trying to lead the model to focus more on the difficult-to-learn tokens. On the Pile dataset, we train generative language models at different scales of 468M, 1.2B, and 6.7B parameters. Experiments reveal that models incorporating the proposed MiLe Loss can gain consistent performance improvement on downstream benchmarks.