OpenRaiser/Intern-Atlas huggingface dataset:
Capsule: Efficient Player Isolation for Datacenters
Venue: arXiv cs.DC 2025
Year: 2025

Cloud gaming is increasingly popular. A challenge for cloud provider is to keep datacenter utilization high: a non-trivial task due to application variety. These applications come in different shapes and sizes. So do cloud datacenter resources, e.g., CPUs, GPUs, NPUs. Part of the challenge stems from game engines being predominantly designed to run only one player. For example, one player in a lightweight game might utilize only a fraction of the cloud server GPU. The remaining GPU capacity will be left underutilized, an undesired outcome for the cloud provider. We introduce Capsule, a mechanism to seamlessly share one GPU, and other cloud servers resources, across multiple players. Sharing makes the cost of multiple players sublinear. We implemented Capsule in O3DE, a popular open source game engine. Our evaluations show that Capsule increases datacenter resource utilization by accommodating up to 2.25x more players, without degrading player gaming experience. This is the product of Capsule using up to 1.43x less GPU, 3.11x less VRAM, 3.7x less CPU, and 3.87x less RAM compared to the baseline. Capsule is also application agnostic. We ran four applications on Capsule-based O3DE with no application changes. Our experiences with four applications, three servers with different hardware specifications, including the one with four GPUs, and multi-server cluster show that Capsule design can be adopted by other game engines to increase datacenter utilization across cloud providers.
Lag-Relative Sparse Attention In Long Context Training
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have made significant strides in natural language processing and generation, yet their ability to handle long-context input remains constrained by the quadratic complexity of attention computation and linear-increasing key-value memory footprint. To reduce computational costs and memory, key-value cache compression techniques are commonly applied at inference time, but this often leads to severe performance degradation, as models are not trained to handle compressed context. Although there are more sophisticated compression methods, they are typically unsuitable for post-training because of their incompatibility with gradient-based optimization or high computation overhead. To fill this gap with no additional parameter and little computation overhead, we propose Lag-Relative Sparse Attention(LRSA) anchored by the LagKV compression method for long context post-training. Our method performs chunk-by-chunk prefilling, which selects the top K most relevant key-value pairs in a fixed-size lagging window, allowing the model to focus on salient historical context while maintaining efficiency. Experimental results show that our approach significantly enhances the robustness of the LLM with key-value compression and achieves better fine-tuned results in the question-answer tuning task.
Task-Driven Discrete Representation Learning
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, deep discrete representation learning (DRL) has achieved significant success across various domains. Most DRL frameworks (e.g., the widely used VQ-VAE and its variants) have primarily focused on generative settings, where the quality of a representation is implicitly gauged by the fidelity of its generation. In fact, the goodness of a discrete representation remain ambiguously defined across the literature. In this work, we adopt a practical approach that examines DRL from a task-driven perspective. We propose a unified framework that explores the usefulness of discrete features in relation to downstream tasks, with generation naturally viewed as one possible application. In this context, the properties of discrete representations as well as the way they benefit certain tasks are also relatively understudied. We therefore provide an additional theoretical analysis of the trade-off between representational capacity and sample complexity, shedding light on how discrete representation utilization impacts task performance. Finally, we demonstrate the flexibility and effectiveness of our framework across diverse applications.
Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning
Venue: arXiv cs.LG 2025
Year: 2025

In-context learning (ICL) allows large language models (LLMs) to solve novel tasks without weight updates. Despite its empirical success, the mechanism behind ICL remains poorly understood, limiting our ability to interpret, improve, and reliably apply it. In this paper, we propose a new theoretical perspective that interprets ICL as an implicit form of knowledge distillation (KD), where prompt demonstrations guide the model to form a task-specific reference model during inference. Under this view, we derive a Rademacher complexity-based generalization bound and prove that the bias of the distilled weights grows linearly with the Maximum Mean Discrepancy (MMD) between the prompt and target distributions. This theoretical framework explains several empirical phenomena and unifies prior gradient-based and distributional analyses. To the best of our knowledge, this is the first to formalize inference-time attention as a distillation process, which provides theoretical insights for future prompt engineering and automated demonstration selection.
Decidable Reversible Equivalences for Finite Petri Nets
Venue: arXiv cs.LO 2025
Year: 2025

In the setting of Petri nets, we prove that {\em causal-net bisimilarity} \cite{G15,Gor22,Gor25a}, which is a refinement of history-preserving bisimilarity \cite{RT88,vGG89,DDM89}, and the novel {\em hereditary} causal-net bisimilarity, which is a refinement of hereditary history-preserving bisimilarity \cite{Bed91,JNW96}, do coincide. This means that causal-net bisimilarity is a {\em reversible behavioral equivalence}, as causal-net bisimilar markings not only are able to match each other's forward transitions, but also backward transitions by undoing performed events. Causal-net bisimilarity can be equivalently formulated as {\em structure-preserving bisimilarity} \cite{G15,Gor25a}, that is decidable on finite bounded Petri nets \cite{CG21a}. Moreover, place bisimilarity \cite{ABS91}, that we prove to be finer than causal-net bisimilarity, is also reversible and it was proved decidable for finite Petri nets in \cite{Gor21decid,Gor25a}. These results offer two decidable reversible behavioral equivalences in the true concurrency spectrum, which are alternative to the coarser hereditary history-preserving bisimilarity \cite{Bed91,JNW96}, that, unfortunately, is undecidable even for safe Petri nets \cite{JNS03}.
Dual-Perspective Disentangled Multi-Intent Alignment for Enhanced Collaborative Filtering
Venue: arXiv cs.IR 2025
Year: 2025

Disentangling user intents from implicit feedback has emerged as a promising strategy for enhancing both the accuracy and interpretability of recommendation systems. However, existing methods often model user and item intents independently and rely heavily on implicit structural signals, lacking explicit guidance to uncover the joint semantics that drive user-item interactions. To address these limitations, we propose DMICF, a dual-perspective collaborative filtering framework that unifies intent alignment, structural fusion, and discriminative training into a cohesive architecture. DMICF jointly encodes user-item graphs from both user and item views, leveraging cross-perspective structural signals to reinforce representation learning, especially under sparse or long-tail scenarios. A sub-intent alignment mechanism is introduced to uncover fine-grained semantic correspondences between users and items, enabling adaptive refinement of interaction representations. To enhance prediction quality, DMICF employs an intent-aware scoring module that aggregates compatibility signals across matched latent intents. Furthermore, a multi-negative softmax-based supervision strategy is incorporated to promote semantic disentanglement, encouraging alignment between relevant intents while suppressing spurious or entangled components. Extensive experiments confirm that DMICF consistently delivers robust performance across datasets with diverse interaction distributions. Qualitative analysis confirms that DMICF disentangles interaction intents and adaptively structures intent subspaces into semantically coherent clusters, enabling fine-grained personalization.
SecONNds: Secure Outsourced Neural Network Inference on ImageNet
Venue: arXiv cs.CR 2025
Year: 2025

The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and communication costs, rendering them impractical for real-world deployment. We introduce SecONNds, a non-intrusive secure inference framework optimized for large ImageNet-scale Convolutional Neural Networks. SecONNds integrates a novel fully Boolean Goldreich-Micali-Wigderson (GMW) protocol for secure comparison -- addressing Yao's millionaires' problem -- using preprocessed Beaver's bit triples generated from Silent Random Oblivious Transfer. Our novel protocol achieves an online speedup of 17$\times$ in nonlinear operations compared to state-of-the-art solutions while reducing communication overhead. To further enhance performance, SecONNds employs Number Theoretic Transform (NTT) preprocessing and leverages GPU acceleration for homomorphic encryption operations, resulting in speedups of 1.6$\times$ on CPU and 2.2$\times$ on GPU for linear operations. We also present SecONNds-P, a bit-exact variant that ensures verifiable full-precision results in secure computation, matching the results of plaintext computations. Evaluated on a 37-bit quantized SqueezeNet model, SecONNds achieves an end-to-end inference time of 2.8 s on GPU and 3.6 s on CPU, with a total communication of just 420 MiB. SecONNds' efficiency and reduced computational load make it well-suited for deploying privacy-sensitive applications in resource-constrained environments. SecONNds is open source and can be accessed from: https://github.com/shashankballa/SecONNds.
VLM@school -- Evaluation of AI image understanding on German middle school knowledge
Venue: arXiv cs.AI 2025
Year: 2025

This paper introduces a novel benchmark dataset designed to evaluate the capabilities of Vision Language Models (VLMs) on tasks that combine visual reasoning with subject-specific background knowledge in the German language. In contrast to widely used English-language benchmarks that often rely on artificially difficult or decontextualized problems, this dataset draws from real middle school curricula across nine domains including mathematics, history, biology, and religion. The benchmark includes over 2,000 open-ended questions grounded in 486 images, ensuring that models must integrate visual interpretation with factual reasoning rather than rely on superficial textual cues. We evaluate thirteen state-of-the-art open-weight VLMs across multiple dimensions, including domain-specific accuracy and performance on adversarial crafted questions. Our findings reveal that even the strongest models achieve less than 45% overall accuracy, with particularly poor performance in music, mathematics, and adversarial settings. Furthermore, the results indicate significant discrepancies between success on popular benchmarks and real-world multimodal understanding. We conclude that middle school-level tasks offer a meaningful and underutilized avenue for stress-testing VLMs, especially in non-English contexts. The dataset and evaluation protocol serve as a rigorous testbed to better understand and improve the visual and linguistic reasoning capabilities of future AI systems.
Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
Venue: arXiv cs.SD 2025
Year: 2025

End-to-End Neural Diarization with Vector Clustering is a powerful and practical approach to perform Speaker Diarization. Multiple enhancements have been proposed for the segmentation model of these pipelines, but their synergy had not been thoroughly evaluated. In this work, we provide an in-depth analysis on the impact of major architecture choices on the performance of the pipeline. We investigate different encoders (SincNet, pretrained and finetuned WavLM), different decoders (LSTM, Mamba, and Conformer), different losses (multilabel and multiclass powerset), and different chunk sizes. Through in-depth experiments covering nine datasets, we found that the finetuned WavLM-based encoder always results in the best systems by a wide margin. The LSTM decoder is outclassed by Mamba- and Conformer-based decoders, and while we found Mamba more robust to other architecture choices, it is slightly inferior to our best architecture, which uses a Conformer encoder. We found that multilabel and multiclass powerset losses do not have the same distribution of errors. We confirmed that the multiclass loss helps almost all models attain superior performance, except when finetuning WavLM, in which case, multilabel is the superior choice. We also evaluated the impact of the chunk size on all aforementioned architecture choices and found that newer architectures tend to better handle long chunk sizes, which can greatly improve pipeline performance. Our best system achieved state-of-the-art results on five widely used speaker diarization datasets.
KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) have achieved impressive success across a wide range of graph-based tasks, yet they remain highly vulnerable to small, imperceptible perturbations and adversarial attacks. Although numerous defense methods have been proposed to address these vulnerabilities, many rely on heuristic metrics, overfit to specific attack patterns, and suffer from high computational complexity. In this paper, we propose Kernel Complexity-Based Edge Sanitization (KCES), a training-free, model-agnostic defense framework. KCES leverages Graph Kernel Complexity (GKC), a novel metric derived from the graph's Gram matrix that characterizes GNN generalization via its test error bound. Building on GKC, we define a KC score for each edge, measuring the change in GKC when the edge is removed. Edges with high KC scores, typically introduced by adversarial perturbations, are pruned to mitigate their harmful effects, thereby enhancing GNNs' robustness. KCES can also be seamlessly integrated with existing defense strategies as a plug-and-play module without requiring training. Theoretical analysis and extensive experiments demonstrate that KCES consistently enhances GNN robustness, outperforms state-of-the-art baselines, and amplifies the effectiveness of existing defenses, offering a principled and efficient solution for securing GNNs.
KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis
Venue: arXiv cs.CR 2025
Year: 2025

Binary code similarity analysis (BCSA) is a crucial research area in many fields such as cybersecurity. Specifically, function-level diffing tools are the most widely used in BCSA: they perform function matching one by one for evaluating the similarity between binary programs. However, such methods need a high time complexity, making them unscalable in large-scale scenarios (e.g., 1/n-to-n search). Towards effective and efficient program-level BCSA, we propose KEENHash, a novel hashing approach that hashes binaries into program-level representations through large language model (LLM)-generated function embeddings. KEENHash condenses a binary into one compact and fixed-length program embedding using K-Means and Feature Hashing, allowing us to do effective and efficient large-scale program-level BCSA, surpassing the previous state-of-the-art methods. The experimental results show that KEENHash is at least 215 times faster than the state-of-the-art function matching tools while maintaining effectiveness. Furthermore, in a large-scale scenario with 5.3 billion similarity evaluations, KEENHash takes only 395.83 seconds while these tools will cost at least 56 days. We also evaluate KEENHash on the program clone search of large-scale BCSA across extensive datasets in 202,305 binaries. Compared with 4 state-of-the-art methods, KEENHash outperforms all of them by at least 23.16%, and displays remarkable superiority over them in the large-scale BCSA security scenario of malware detection.
Machine Unlearning for Robust DNNs: Attribution-Guided Partitioning and Neuron Pruning in Noisy Environments
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural networks (DNNs) have achieved remarkable success across diverse domains, but their performance can be severely degraded by noisy or corrupted training data. Conventional noise mitigation methods often rely on explicit assumptions about noise distributions or require extensive retraining, which can be impractical for large-scale models. Inspired by the principles of machine unlearning, we propose a novel framework that integrates attribution-guided data partitioning, discriminative neuron pruning, and targeted fine-tuning to mitigate the impact of noisy samples. Our approach employs gradient-based attribution to probabilistically distinguish high-quality examples from potentially corrupted ones without imposing restrictive assumptions on the noise. It then applies regression-based sensitivity analysis to identify and prune neurons that are most vulnerable to noise. Finally, the resulting network is fine-tuned on the high-quality data subset to efficiently recover and enhance its generalization performance. This integrated unlearning-inspired framework provides several advantages over conventional noise-robust learning approaches. Notably, it combines data-level unlearning with model-level adaptation, thereby avoiding the need for full model retraining or explicit noise modeling. We evaluate our method on representative tasks (e.g., CIFAR-10 image classification and speech recognition) under various noise levels and observe substantial gains in both accuracy and efficiency. For example, our framework achieves approximately a 10% absolute accuracy improvement over standard retraining on CIFAR-10 with injected label noise, while reducing retraining time by up to 47% in some settings. These results demonstrate the effectiveness and scalability of the proposed approach for achieving robust generalization in noisy environments.
SignAligner: Harmonizing Complementary Pose Modalities for Coherent Sign Language Generation
Venue: arXiv cs.CV 2025
Year: 2025

Sign language generation aims to produce diverse sign representations based on spoken language. However, achieving realistic and naturalistic generation remains a significant challenge due to the complexity of sign language, which encompasses intricate hand gestures, facial expressions, and body movements. In this work, we introduce PHOENIX14T+, an extended version of the widely-used RWTH-PHOENIX-Weather 2014T dataset, featuring three new sign representations: Pose, Hamer and Smplerx. We also propose a novel method, SignAligner, for realistic sign language generation, consisting of three stages: text-driven pose modalities co-generation, online collaborative correction of multimodality, and realistic sign video synthesis. First, by incorporating text semantics, we design a joint sign language generator to simultaneously produce posture coordinates, gesture actions, and body movements. The text encoder, based on a Transformer architecture, extracts semantic features, while a cross-modal attention mechanism integrates these features to generate diverse sign language representations, ensuring accurate mapping and controlling the diversity of modal features. Next, online collaborative correction is introduced to refine the generated pose modalities using a dynamic loss weighting strategy and cross-modal attention, facilitating the complementarity of information across modalities, eliminating spatiotemporal conflicts, and ensuring semantic coherence and action consistency. Finally, the corrected pose modalities are fed into a pre-trained video generation network to produce high-fidelity sign language videos. Extensive experiments demonstrate that SignAligner significantly improves both the accuracy and expressiveness of the generated sign videos.
Bounded Memory in Distributed Networks
Venue: arXiv cs.DC 2025
Year: 2025

The recent advent of programmable switches makes distributed algorithms readily deployable in real-world datacenter networks. However, there are still gaps between theory and practice that prevent the smooth adaptation of CONGEST algorithms to these environments. In this paper, we focus on the memory restrictions that arise in real-world deployments. We introduce the $μ$-CONGEST model where on top of the bandwidth restriction, the memory of nodes is also limited to $μ$ words, in line with real-world systems. We provide fast algorithms of two main flavors. First, we observe that many algorithms in the CONGEST model are memory-intensive and do not work in $μ$-CONGEST. A prime example of a family of algorithms that use large memory is clique-listing algorithms. We show that the memory issue that arises here cannot be resolved without incurring a cost in the round complexity, by establishing a lower bound on the round complexity of listing cliques in $μ$-CONGEST. We introduce novel techniques to overcome these issues and generalize the algorithms to work within a given memory bound. Combined with our lower bound, these provide tight tradeoffs between the running time and memory of nodes. Second, we show that it is possible to efficiently simulate various families of streaming algorithms in $μ$-CONGEST. These include fast simulations of $p$-pass algorithms, random order streams, and various types of mergeable streaming algorithms. Combining our contributions, we show that we can use streaming algorithms to efficiently generate statistics regarding combinatorial structures in the network. An example of an end result of this type is that we can efficiently identify and provide the per-color frequencies of the frequent monochromatic triangles in $μ$-CONGEST.
Expert Insight-Based Modeling of Non-Kinetic Strategic Deterrence of Rare Earth Supply Disruption:A Simulation-Driven Systematic Framework
Venue: arXiv cs.CY 2025
Year: 2025

This study constructs a quantifiable modelling framework to simulate non-kinetic strategic deterrence pathways in rare earth supply disruption scenarios, based on structured responses from expert interviews led by Dr. Daniel O'Connor, CEO of the Rare Earth Exchange (REE). Focusing on disruption impacts on national security systems, the study proposes four core modelling components: Security Critical Zones (SCZ), Strategic Signal Injection Function (SSIF), System-Capability Migration Function (SCIF), and Policy-Capability Transfer Function (PCTF). The framework integrates parametric ODEs, segmented function modelling, path-overlapping covariance matrices, and LSTM networks to simulate nonlinear suppression trajectories triggered by regime signals. Data is derived from expert interviews and scenario analyses centered on U.S.-China dynamics in ISR, electronic warfare, and rare earth control. Results show institutional signals have strong tempo and path-coupling effects, capable of causing rapid degradation of strategic capabilities. The model is adaptable across national resource frameworks and extendable to AI sandbox engines for situational simulation and counterfactual reasoning. This research introduces the first unified system for modelling, visualizing, and forecasting non-kinetic deterrence, offering methodological support to policymakers and analysts navigating institutionalized strategic competition.