OpenRaiser/Intern-Atlas huggingface dataset:
Approximately Counting and Sampling Hamiltonian Motifs in Sublinear Time
Venue: arXiv cs.DS 2025
Year: 2025

Counting small subgraphs, referred to as motifs, in large graphs is a fundamental task in graph analysis, extensively studied across various contexts and computational models. In the sublinear-time regime, the relaxed problem of approximate counting has been explored within two prominent query frameworks: the standard model, which permits degree, neighbor, and pair queries, and the strictly more powerful augmented model, which additionally allows for uniform edge sampling. Currently, in the standard model, (optimal) results have been established only for approximately counting edges, stars, and cliques, all of which have a radius of one. This contrasts sharply with the state of affairs in the augmented model, where algorithmic results (some of which are optimal) are known for any input motif, leading to a disparity which we term the ``scope gap" between the two models. In this work, we make significant progress in bridging this gap. Our approach draws inspiration from recent advancements in the augmented model and utilizes a framework centered on counting by uniform sampling, thus allowing us to establish new results in the standard model and simplify on previous results. In particular, our first, and main, contribution is a new algorithm in the standard model for approximately counting any Hamiltonian motif in sublinear time. Our second contribution is a variant of our algorithm that enables nearly uniform sampling of these motifs, a capability previously limited in the standard model to edges and cliques. Our third contribution is to introduce even simpler algorithms for stars and cliques by exploiting their radius-one property. As a result, we simplify all previously known algorithms in the standard model for stars (Gonen, Ron, Shavitt (SODA 2010)), triangles (Eden, Levi, Ron Seshadhri (FOCS 2015)) and cliques (Eden, Ron, Seshadri (STOC 2018)).
A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

The arrival of Machine Learning (ML) completely changed how we can unlock valuable information from data. Traditional methods, where everything was stored in one place, had big problems with keeping information private, handling large amounts of data, and avoiding unfair advantages. Machine Learning has become a powerful tool that uses Artificial Intelligence (AI) to overcome these challenges. We started by learning the basics of Machine Learning, including the different types like supervised, unsupervised, and reinforcement learning. We also explored the important steps involved, such as preparing the data, choosing the right model, training it, and then checking its performance. Next, we examined some key challenges in Machine Learning, such as models learning too much from specific examples (overfitting), not learning enough (underfitting), and reflecting biases in the data used. Moving beyond centralized systems, we looked at decentralized Machine Learning and its benefits, like keeping data private, getting answers faster, and using a wider variety of data sources. We then focused on a specific type called federated learning, where models are trained without directly sharing sensitive information. Real-world examples from healthcare and finance were used to show how collaborative Machine Learning can solve important problems while still protecting information security. Finally, we discussed challenges like communication efficiency, dealing with different types of data, and security. We also explored using a Zero Trust framework, which provides an extra layer of protection for collaborative Machine Learning systems. This approach is paving the way for a bright future for this groundbreaking technology.
An Asymmetric Independence Model for Causal Discovery on Path Spaces
Venue: arXiv cs.LG 2025
Year: 2025

We develop the theory linking 'E-separation' in directed mixed graphs (DMGs) with conditional independence relations among coordinate processes in stochastic differential equations (SDEs), where causal relationships are determined by "which variables enter the governing equation of which other variables". We prove a global Markov property for cyclic SDEs, which naturally extends to partially observed cyclic SDEs, because our asymmetric independence model is closed under marginalization. We then characterize the class of graphs that encode the same set of independence relations, yielding a result analogous to the seminal 'same skeleton and v-structures' result for directed acyclic graphs (DAGs). In the fully observed case, we show that each such equivalence class of graphs has a greatest element as a parsimonious representation and develop algorithms to identify this greatest element from data. We conjecture that a greatest element also exists under partial observations, which we verify computationally for graphs with up to four nodes.
Leveraging Semantic Attribute Binding for Free-Lunch Color Control in Diffusion Models
Venue: arXiv cs.GR 2025
Year: 2025

Recent advances in text-to-image (T2I) diffusion models have enabled remarkable control over various attributes, yet precise color specification remains a fundamental challenge. Existing approaches, such as ColorPeel, rely on model personalization, requiring additional optimization and limiting flexibility in specifying arbitrary colors. In this work, we introduce ColorWave, a novel training-free approach that achieves exact RGB-level color control in diffusion models without fine-tuning. By systematically analyzing the cross-attention mechanisms within IP-Adapter, we uncover an implicit binding between textual color descriptors and reference image features. Leveraging this insight, our method rewires these bindings to enforce precise color attribution while preserving the generative capabilities of pretrained models. Our approach maintains generation quality and diversity, outperforming prior methods in accuracy and applicability across diverse object categories. Through extensive evaluations, we demonstrate that ColorWave establishes a new paradigm for structured, color-consistent diffusion-based image synthesis.
FDCT: Frequency-Aware Decomposition and Cross-Modal Token-Alignment for Multi-Sensor Target Classification
Venue: arXiv cs.CV 2025
Year: 2025

In automatic target recognition (ATR) systems, sensors may fail to capture discriminative, fine-grained detail features due to environmental conditions, noise created by CMOS chips, occlusion, parallaxes, and sensor misalignment. Therefore, multi-sensor image fusion is an effective choice to overcome these constraints. However, multi-modal image sensors are heterogeneous and have domain and granularity gaps. In addition, the multi-sensor images can be misaligned due to intricate background clutters, fluctuating illumination conditions, and uncontrolled sensor settings. In this paper, to overcome these issues, we decompose, align, and fuse multiple image sensor data for target classification. We extract the domain-specific and domain-invariant features from each sensor data. We propose to develop a shared unified discrete token (UDT) space between sensors to reduce the domain and granularity gaps. Additionally, we develop an alignment module to overcome the misalignment between multi-sensors and emphasize the discriminative representation of the UDT space. In the alignment module, we introduce sparsity constraints to provide a better cross-modal representation of the UDT space and robustness against various sensor settings. We achieve superior classification performance compared to single-modality classifiers and several state-of-the-art multi-modal fusion algorithms on four multi-sensor ATR datasets.
A Rule Based Solution to Co-reference Resolution in Clinical Text
Venue: arXiv cs.CL 2025
Year: 2025

Objective: The aim of this study was to build an effective co-reference resolution system tailored for the biomedical domain. Materials and Methods: Experiment materials used in this study is provided by the 2011 i2b2 Natural Language Processing Challenge. The 2011 i2b2 challenge involves coreference resolution in medical documents. Concept mentions have been annotated in clinical texts, and the mentions that co-refer in each document are to be linked by coreference chains. Normally, there are two ways of constructing a system to automatically discover co-referent links. One is to manually build rules for co-reference resolution, and the other category of approaches is to use machine learning systems to learn automatically from training datasets and then perform the resolution task on testing datasets. Results: Experiments show the existing co-reference resolution systems are able to find some of the co-referent links, and our rule based system performs well finding the majority of the co-referent links. Our system achieved 89.6% overall performance on multiple medical datasets. Conclusion: The experiment results show that manually crafted rules based on observation of training data is a valid way to accomplish high performance in this coreference resolution task for the critical biomedical domain.
AI Rivalry as a Craft: How Resisting and Embracing Generative AI Reshape Writing Professions
Venue: arXiv cs.HC 2025
Year: 2025

Generative AI (GAI) technologies are disrupting professional writing, challenging traditional practices. Recent studies explore GAI adoption experiences of creative practitioners, but we know little about how these experiences evolve into established practices and how GAI resistance alters these practices. To address this gap, we conducted 25 semi-structured interviews with writing professionals who adopted and/or resisted GAI. Using the theoretical lens of Job Crafting, we identify four strategies professionals employ to reshape their roles. Writing professionals employed GAI resisting strategies to maximize human potential, reinforce professional identity, carve out a professional niche, and preserve credibility within their networks. In contrast, GAI-enabled strategies allowed writers who embraced GAI to enhance desirable workflows, minimize mundane tasks, and engage in new AI-managerial labor. These strategies amplified their collaborations with GAI while reducing their reliance on other people. We conclude by discussing implications of GAI practices on writers' identity and practices as well as crafting theory.
Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets
Venue: arXiv cs.IR 2025
Year: 2025

The rise of personalized conversational search systems has been driven by advancements in Large Language Models (LLMs), enabling these systems to retrieve and generate answers for complex information needs. However, the automatic evaluation of responses generated by Retrieval Augmented Generation (RAG) systems remains an understudied challenge. In this paper, we introduce a new resource for assessing the retrieval effectiveness and relevance of response generated by RAG systems, using a nugget-based evaluation framework. Built upon the foundation of TREC iKAT 2023, our dataset extends to the TREC iKAT 2024 collection, which includes 17 conversations and 20,575 relevance passage assessments, together with 2,279 extracted gold nuggets, and 62 manually written gold answers from NIST assessors. While maintaining the core structure of its predecessor, this new collection enables a deeper exploration of generation tasks in conversational settings. Key improvements in iKAT 2024 include: (1) ``gold nuggets'' -- concise, essential pieces of information extracted from relevant passages of the collection -- which serve as a foundation for automatic response evaluation; (2) manually written answers to provide a gold standard for response evaluation; (3) unanswerable questions to evaluate model hallucination; (4) expanded user personas, providing richer contextual grounding; and (5) a transition from Personal Text Knowledge Base (PTKB) ranking to PTKB classification and selection. Built on this resource, we provide a framework for long-form answer generation evaluation, involving nuggets extraction and nuggets matching, linked to retrieval. This establishes a solid resource for advancing research in personalized conversational search and long-form answer generation. Our resources are publicly available at https://github.com/irlabamsterdam/CONE-RAG.
Parallel Batch-Dynamic Maximal Matching with Constant Work per Update
Venue: arXiv cs.DS 2025
Year: 2025

We present a work optimal algorithm for parallel fully batch-dynamic maximal matching against an oblivious adversary. It processes batches of updates (either insertions or deletions of edges) in constant expected amortized work per edge update, and in $O(\log^3 m)$ depth per batch whp, where $m$ is the maximum number of edges in the graph over time. This greatly improves on the recent result by Ghaffari and Trygub (2024) that requires $O(\log^9 m)$ amortized work per update and $O(\log^4 m )$ depth per batch, both whp. The algorithm can also be used for parallel batch-dynamic hyperedge maximal matching. For hypergraphs with rank $r$ (maximum cardinality of any edge) the algorithm supports batches of updates with $O(r^3)$ expected amortized work per edge update, and $O(\log^3 m)$ depth per batch whp. Ghaffari and Trygub's parallel batch-dynamic algorithm on hypergraphs requires $O(r^8 \log^9 m)$ amortized work per edge update whp. We leverage ideas from the prior algorithms but introduce substantial new ideas. Furthermore, our algorithm is relatively simple, perhaps even simpler than Assadi and Solomon's (2021) sequential dynamic hyperedge algorithm. We also present the first work-efficient algorithm for parallel static maximal matching on hypergraphs. For a hypergraph with total cardinality $m'$ (i.e., sum over the cardinality of each edge), the algorithm runs in $O(m')$ work in expectation and $O(\log^2 m)$ depth whp. The algorithm also has some properties that allow us to use it as a subroutine in the dynamic algorithm to select random edges in the graph to add to the matching. With a standard reduction from set cover to hyperedge maximal matching, we give state of the art $r$-approximate static and batch-dynamic parallel set cover algorithms, where $r$ is the maximum frequency of any element, and batch-dynamic updates consist of adding or removing batches of elements.
eXpLogic: Explaining Logic Types and Patterns in DiffLogic Networks
Venue: arXiv cs.LG 2025
Year: 2025

Constraining deep neural networks (DNNs) to learn individual logic types per node, as performed using the DiffLogic network architecture, opens the door to model-specific explanation techniques that quell the complexity inherent to DNNs. Inspired by principles of circuit analysis from computer engineering, this work presents an algorithm (eXpLogic) for producing saliency maps which explain input patterns that activate certain functions. The eXpLogic explanations: (1) show the exact set of inputs responsible for a decision, which helps interpret false negative and false positive predictions, (2) highlight common input patterns that activate certain outputs, and (3) help reduce the network size to improve class-specific inference. To evaluate the eXpLogic saliency map, we introduce a metric that quantifies how much an input changes before switching a model's class prediction (the SwitchDist) and use this metric to compare eXpLogic against the Vanilla Gradients (VG) and Integrated Gradient (IG) methods. Generally, we show that eXpLogic saliency maps are better at predicting which inputs will change the class score. These maps help reduce the network size and inference times by 87\% and 8\%, respectively, while having a limited impact (-3.8\%) on class-specific predictions. The broader value of this work to machine learning is in demonstrating how certain DNN architectures promote explainability, which is relevant to healthcare, defense, and law.
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes operational decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges, including performance disparity, robustness, uncertainty, interpretability, generalizability, and reproducibility. In this work, we present a multi-dimensional, quantitative evaluation of trustworthiness benchmarking three state-of-the-art deep learning architectures: recurrent (LSTM), operator-learning (DeepONet), and transformer-based (Informer), trained on 37 years of data from 482 U.S. basins to predict 20 water quality variables. Our investigation reveals systematic performance disparities tied to process complexity, data availability, and basin heterogeneity. Management-critical variables remain the least predictable and most uncertain. Robustness tests reveal pronounced sensitivity to outliers and corrupted targets; notably, the architecture with the strongest baseline performance (LSTM) proves most vulnerable under data corruption. Attribution analyses align for simple variables but diverge for nutrients, underscoring the need for multi-method interpretability. Spatial generalization to ungauged basins remains poor across all models. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (AI) responsibly in environmental management.
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL)-based large language models (LLMs), such as ChatGPT, DeepSeek, and Grok-3, have attracted widespread attention for their remarkable capabilities in multimodal data understanding. Meanwhile, the rapid expansion of information services has led to a growing demand for AI-enabled wireless networks. The open-source DeepSeek models are famous for their innovative designs, such as large-scale pure RL and cost-efficient training, which make them well-suited for practical deployment in wireless networks. By integrating DeepSeek-style LLMs with wireless infrastructures, a synergistic opportunity arises: the DeepSeek-style LLMs enhance network optimization with strong reasoning and decision-making abilities, while wireless infrastructure enables the broad deployment of these models. Motivated by this convergence, this survey presents a comprehensive DeepSeek-inspired exploration of RL-based LLMs in the context of wireless networks. We begin by reviewing key techniques behind network optimization to establish a foundation for understanding DeepSeek-style LLM integration. Next, we examine recent advancements in RL-based LLMs, using DeepSeek models as a representative example. Building on this, we explore the synergy between the two domains, highlighting motivations, challenges, and potential solutions. Finally, we highlight emerging directions for integrating LLMs with wireless networks, such as quantum, on-device, and neural-symbolic LLM models, as well as embodied AI agents. Overall, this survey offers a comprehensive examination of the interplay between DeepSeek-style LLMs and wireless networks, demonstrating how these domains can mutually enhance each other to drive innovation.
Faster Inference of LLMs using FP8 on the Intel Gaudi
Venue: arXiv cs.AR 2025
Year: 2025

Low-precision data types are essential in modern neural networks during both training and inference as they enhance throughput and computational capacity by better exploiting available hardware resources. Despite the incorporation of FP8 in commercially available neural network accelerators, a comprehensive exposition of its underlying mechanisms, along with rigorous performance and accuracy evaluations, is still lacking. In this work, we contribute in three significant ways. First, we analyze the implementation details and quantization options associated with FP8 for inference on the Intel Gaudi AI accelerator. Second, we empirically quantify the throughput improvements afforded by the use of FP8 at both the operator level and in end-to-end scenarios. Third, we assess the accuracy impact of various FP8 quantization methods. Our experimental results indicate that the Intel Gaudi 2 accelerator consistently achieves high computational unit utilization, frequently exceeding 90% MFU, while incurring an accuracy degradation of less than 1%.
Beyond Human: Cognitive and Physical Augmentation through AI, Robotics, and XR -- Opportunities and Risks
Venue: arXiv cs.HC 2025
Year: 2025

As human augmentation technologies evolve, the convergence of AI, robotics, and extended reality (XR) is redefining human potential -- enhancing cognition, perception, and physical abilities. However, these advancements also introduce ethical dilemmas, security risks, and concerns over loss of control. This workshop explores both the transformative potential and the unintended consequences of augmentation technologies. Bringing together experts from HCI, neuroscience, robotics, and ethics, we will examine real-world applications, emerging risks, and governance strategies for responsible augmentation. The session will feature keynote talks and interactive discussions, addressing topics such as AI-enhanced cognition, wearable robotics, neural interfaces, and XR-driven augmentation. By fostering multidisciplinary dialogue, this workshop aims to generate actionable insights for responsible innovation, proposing ethical frameworks to balance human empowerment with risk mitigation. We invite researchers, practitioners, and industry leaders to contribute their perspectives and help shape the future of human augmentation.
Finite Field Multiple Access II:from Symbol-wise to Codeword-wise
Venue: arXiv cs.IT 2025
Year: 2025

A finite-field multiple-access (FFMA) system separates users within a finite field by utilizing different element-pairs (EPs) as virtual resources. The Cartesian product of distinct EPs forms an EP code, which serves as the input to a finite-field multiplexing module (FF-MUX). This allows the FFMA technique to reorder the channel coding and multiplexing modules, enabling the superimposed signals to function as codewords that can be decoded by a channel code. This flexibility allows the FFMA system to efficiently support a large number of users with short packet traffic, addressing the finite blocklength (FBL) challenge in multiuser reliable transmission. Designing EP codes is a central challenge in FFMA systems. In this paper, we construct EP codes based on a bit(s)-to-codeword transformation approach and define the corresponding EP code as a codeword-wise EP (CWEP) code. We then investigate the encoding process of EP codes, and propose unique sum-pattern mapping (USPM) structural property constraints to design uniquely decodable CWEP codes. Next, we present the $κ$-fold ternary orthogonal matrix ${\bf T}_{\rm o}(2^κ, 2^κ)$ over GF$(3^m)$, where $m = 2^κ$, and the ternary non-orthogonal matrix ${\bf T}_{\rm no}(M,m)$ over GF$(3^m)$, for constructing specific CWEP codes. Based on the proposed CWEP codes, we introduce three FFMA modes: channel codeword multiple access (FF-CCMA), code division multiple access (FF-CDMA), and non-orthogonal multiple access (FF-NOMA). Simulation results demonstrate that all three modes effectively support massive user transmissions with well-behaved error performance.