OpenRaiser/Intern-Atlas huggingface dataset:
PLANSIEVE: Real-time Suboptimal Query Plan Detection Through Incremental Refinements
Venue: arXiv cs.DB 2025
Year: 2025

Cardinality estimation remains a fundamental challenge in query optimization, often resulting in sub-optimal execution plans and degraded performance. While errors in cardinality estimation are inevitable, existing methods for identifying sub-optimal plans -- such as metrics like Q-error, P-error, or L1-error -- are limited to post-execution analysis, requiring complete knowledge of true cardinalities and failing to prevent the execution of sub-optimal plans in real-time. This paper introduces PLANSIEVE, a novel framework that identifies sub-optimal plans during query optimization. PLANSIEVE operates by analyzing the relative order of sub-plans generated by the optimizer based on estimated and true cardinalities. It begins with surrogate cardinalities from any third-party estimator and incrementally refines these surrogates as the system processes more queries. Experimental results on the augmented JOB-LIGHT-SCALE and STATS-CEB-SCALE workloads demonstrate that PLANSIEVE achieves an accuracy of up to 88.7\% in predicting sub-optimal plans.
From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate
Venue: arXiv cs.CY 2025
Year: 2025

As the climate crisis deepens, artificial intelligence (AI) has emerged as a contested force: some champion its potential to advance renewable energy, materials discovery, and large-scale emissions monitoring, while others underscore its growing carbon footprint, water consumption, and material resource demands. Much of this debate has concentrated on direct impacts -- energy and water usage in data centers, e-waste from frequent hardware upgrades -- without addressing the significant indirect effects. This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption. We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses. Rebound effects undermine the assumption that improved technical efficiency alone will ensure net reductions in environmental harm. Instead, the trajectory of AI's impact also hinges on business incentives and market logics, governance and policymaking, and broader social and cultural norms. We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions. We conclude with recommendations that address rebound effects and challenge the market-driven imperatives fueling uncontrolled AI growth. By broadening the analysis to include both direct and indirect consequences, we aim to inform a more comprehensive, evidence-based dialogue on AI's role in the climate crisis.
Reconciling Predictive Multiplicity in Practice
Venue: arXiv cs.CY 2025
Year: 2025

Many machine learning applications predict individual probabilities, such as the likelihood that a person develops a particular illness. Since these probabilities are unknown, a key question is how to address situations in which different models trained on the same dataset produce varying predictions for certain individuals. This issue is exemplified by the model multiplicity (MM) phenomenon, where a set of comparable models yield inconsistent predictions. Roth, Tolbert, and Weinstein recently introduced a reconciliation procedure, the Reconcile algorithm, to address this problem. Given two disagreeing models, the algorithm leverages their disagreement to falsify and improve at least one of the models. In this paper, we empirically analyze the Reconcile algorithm using five widely-used fairness datasets: COMPAS, Communities and Crime, Adult, Statlog (German Credit Data), and the ACS Dataset. We examine how Reconcile fits within the model multiplicity literature and compare it to existing MM solutions, demonstrating its effectiveness. We also discuss potential improvements to the Reconcile algorithm theoretically and practically. Finally, we extend the Reconcile algorithm to the setting of causal inference, given that different competing estimators can again disagree on specific causal average treatment effect (CATE) values. We present the first extension of the Reconcile algorithm in causal inference, analyze its theoretical properties, and conduct empirical tests. Our results confirm the practical effectiveness of Reconcile and its applicability across various domains.
C-HDNet: A Fast Hyperdimensional Computing Based Method for Causal Effect Estimation from Networked Observational Data
Venue: arXiv cs.LG 2025
Year: 2025

We consider the problem of estimating causal effects from observational data in the presence of network confounding. In this context, an individual's treatment assignment and outcomes may be affected by their neighbors within the network. We propose a novel matching technique which leverages hyperdimensional computing to model network information and improve predictive performance. We present results of extensive experiments which show that the proposed method outperforms or is competitive with the state-of-the-art methods for causal effect estimation from network data, including advanced computationally demanding deep learning methods. Further, our technique benefits from simplicity and speed, with roughly an order of magnitude lower runtime compared to state-of-the-art methods, while offering similar causal effect estimation error rates.
Efficient Object Detection of Marine Debris using Pruned YOLO Model
Venue: arXiv cs.CV 2025
Year: 2025

Marine debris poses significant harm to marine life due to substances like microplastics, polychlorinated biphenyls, and pesticides, which damage habitats and poison organisms. Human-based solutions, such as diving, are increasingly ineffective in addressing this issue. Autonomous underwater vehicles (AUVs) are being developed for efficient sea garbage collection, with the choice of object detection architecture being critical. This research employs the YOLOv4 model for real-time detection of marine debris using the Trash-ICRA 19 dataset, consisting of 7683 images at 480x320 pixels. Various modifications-pretrained models, training from scratch, mosaic augmentation, layer freezing, YOLOv4-tiny, and channel pruning-are compared to enhance architecture efficiency. Channel pruning significantly improves detection speed, increasing the base YOLOv4 frame rate from 15.19 FPS to 19.4 FPS, with only a 1.2% drop in mean Average Precision, from 97.6% to 96.4%.
Sharing and Linear Logic with Restricted Access (Extended Version)
Venue: arXiv cs.LO 2025
Year: 2025

The two Girard translations provide two different means of obtaining embeddings of Intuitionistic Logic into Linear Logic, corresponding to different lambda-calculus calling mechanisms. The translations, mapping A -> B respectively to !A -o B and !(A -o B), have been shown to correspond respectively to call-by-name and call-by-value. In this work, we split the of-course modality of linear logic into two modalities, written "!" and "$\bullet$". Intuitively, the modality "!" specifies a subproof that can be duplicated and erased, but may not necessarily be "accessed", i.e. interacted with, while the combined modality "$!\bullet$" specifies a subproof that can moreover be accessed. The resulting system, called MSCLL, enjoys cut-elimination and is conservative over MELL. We study how restricting access to subproofs provides ways to control sharing in evaluation strategies. For this, we introduce a term-assignment for an intuitionistic fragment of MSCLL, called the $λ!\bullet$-calculus, which we show to enjoy subject reduction, confluence, and strong normalization of the simply typed fragment. We propose three sound and complete translations that respectively simulate call-by-name, call-by-value, and a variant of call-by-name that shares the evaluation of its arguments (similarly as in call-by-need). The translations are extended to simulate the Bang-calculus, as well as weak reduction strategies.
Generative AI Uses and Risks for Knowledge Workers in a Science Organization
Venue: arXiv cs.HC 2025
Year: 2025

Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative AI use in these organizations are uncertain. In this paper, we report on a collaborative study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools. We surveyed 66 employees, interviewed a subset (N=22), and measured early adoption of an internal generative AI interface called Argo lab-wide. We have four findings: (1) Argo usage data shows small but increasing use by Science and Operations employees; Common current and envisioned use cases for generative AI in this context conceptually fall into either a (2) copilot or (3) workflow agent modality; and (4) Concerns include sensitive data security, academic publishing, and job impacts. Based on our findings, we make recommendations for generative AI use in science and other organizations.
Non-Western Perspectives on Web Inclusivity: A Study of Accessibility Practices in the Global South
Venue: arXiv cs.SE 2025
Year: 2025

The Global South faces unique challenges in achieving digital inclusion due to a heavy reliance on mobile devices for internet access and the prevalence of slow or unreliable networks. While numerous studies have investigated web accessibility within specific sectors such as education, healthcare, and government services, these efforts have been largely constrained to individual countries or narrow contexts, leaving a critical gap in cross-regional, large-scale analysis. This paper addresses this gap by conducting the first large-scale comparative study of mobile web accessibility across the Global South. In this work, we evaluate 100,000 websites from 10 countries in the Global South to provide a comprehensive understanding of accessibility practices in these regions. Our findings reveal that websites from countries with strict accessibility regulations and enforcement tend to adhere better to Web Content Accessibility Guidelines (WCAG) guidelines. However, accessibility violations impact different disability groups in varying ways. Blind and low-vision individuals in the Global South are disproportionately affected, as only 40% of the evaluated websites meet critical accessibility guidelines. This significant shortfall is largely due to developers frequently neglecting to implement valid alt text for images and ARIA descriptions, which are essential specification mechanisms in the HTML standard for the effective operation of screen readers.
The Impact of Modern AI in Metadata Management
Venue: arXiv cs.DB 2025
Year: 2025

Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets.
Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Gait recognition is an emerging identification technology that distinguishes individuals at long distances by analyzing individual walking patterns. Traditional techniques rely heavily on large-scale labeled datasets, which incurs high costs and significant labeling challenges. Recently, researchers have explored unsupervised gait recognition with clustering-based unsupervised domain adaptation methods and achieved notable success. However, these methods directly use pseudo-label generated by clustering and neglect pseudolabel noise caused by domain differences, which affects the effect of the model training process. To mitigate these issues, we proposed a novel model called GaitDCCR, which aims to reduce the influence of noisy pseudo labels on clustering and model training. Our approach can be divided into two main stages: clustering and training stage. In the clustering stage, we propose Dynamic Cluster Parameters (DCP) and Dynamic Weight Centroids (DWC) to improve the efficiency of clustering and obtain reliable cluster centroids. In the training stage, we employ the classical teacher-student structure and propose Confidence-based Pseudo-label Refinement (CPR) and Contrastive Teacher Module (CTM) to encourage noisy samples to converge towards clusters containing their true identities. Extensive experiments on public gait datasets have demonstrated that our simple and effective method significantly enhances the performance of unsupervised gait recognition, laying the foundation for its application in the real-world. We will release the code at https://github.com/YanSun-github/GaitDCCR upon acceptance.
Embracing Reconfigurable Antennas in the Tri-hybrid MIMO Architecture for 6G and Beyond
Venue: arXiv cs.IT 2025
Year: 2025

Multiple-input multiple-output (MIMO) communication has led to immense enhancements in data rates and efficient spectrum management. The evolution of MIMO, though, has been accompanied by increased hardware complexity and array sizes, causing the system power consumption to increase. Despite past advances in power-efficient hybrid architectures, new solutions are needed to enable extremely large-scale MIMO deployments for 6G and beyond. In this paper, we introduce a novel architecture that integrates low-power reconfigurable antennas with both digital and analog precoding. This \emph{tri-hybrid} approach addresses key limitations in traditional and hybrid MIMO systems by improving power consumption and adds a new layer for signal processing. We provide an analysis of the proposed architecture and compare its performance with existing solutions, including fully-digital and hybrid MIMO systems. The results demonstrate significant improvements in energy efficiency, highlighting the potential of the tri-hybrid system to meet the growing demands of future wireless networks. We conclude the paper with a summary of design and implementation challenges, including the need for technological advancements in reconfigurable array hardware and tunable antenna parameters.
Predicting 3D representations for Dynamic Scenes
Venue: arXiv cs.CV 2025
Year: 2025

We present a novel framework for dynamic radiance field prediction given monocular video streams. Unlike previous methods that primarily focus on predicting future frames, our method goes a step further by generating explicit 3D representations of the dynamic scene. The framework builds on two core designs. First, we adopt an ego-centric unbounded triplane to explicitly represent the dynamic physical world. Second, we develop a 4D-aware transformer to aggregate features from monocular videos to update the triplane. Coupling these two designs enables us to train the proposed model with large-scale monocular videos in a self-supervised manner. Our model achieves top results in dynamic radiance field prediction on NVIDIA dynamic scenes, demonstrating its strong performance on 4D physical world modeling. Besides, our model shows a superior generalizability to unseen scenarios. Notably, we find that our approach emerges capabilities for geometry and semantic learning.
Jupybara: Operationalizing a Design Space for Actionable Data Analysis and Storytelling with LLMs
Venue: arXiv cs.HC 2025
Year: 2025

Mining and conveying actionable insights from complex data is a key challenge of exploratory data analysis (EDA) and storytelling. To address this challenge, we present a design space for actionable EDA and storytelling. Synthesizing theory and expert interviews, we highlight how semantic precision, rhetorical persuasion, and pragmatic relevance underpin effective EDA and storytelling. We also show how this design space subsumes common challenges in actionable EDA and storytelling, such as identifying appropriate analytical strategies and leveraging relevant domain knowledge. Building on the potential of LLMs to generate coherent narratives with commonsense reasoning, we contribute Jupybara, an AI-enabled assistant for actionable EDA and storytelling implemented as a Jupyter Notebook extension. Jupybara employs two strategies -- design-space-aware prompting and multi-agent architectures -- to operationalize our design space. An expert evaluation confirms Jupybara's usability, steerability, explainability, and reparability, as well as the effectiveness of our strategies in operationalizing the design space framework with LLMs.
LLM-AutoDiff: Auto-Differentiate Any LLM Workflow
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have reshaped natural language processing, powering applications from multi-hop retrieval and question answering to autonomous agent workflows. Yet, prompt engineering -- the task of crafting textual inputs to effectively direct LLMs -- remains difficult and labor-intensive, particularly for complex pipelines that combine multiple LLM calls with functional operations like retrieval and data formatting. We introduce LLM-AutoDiff: a novel framework for Automatic Prompt Engineering (APE) that extends textual gradient-based methods (such as Text-Grad) to multi-component, potentially cyclic LLM architectures. Implemented within the AdalFlow library, LLM-AutoDiff treats each textual input as a trainable parameter and uses a frozen backward engine LLM to generate feedback-akin to textual gradients -- that guide iterative prompt updates. Unlike prior single-node approaches, LLM-AutoDiff inherently accommodates functional nodes, preserves time-sequential behavior in repeated calls (e.g., multi-hop loops), and combats the "lost-in-the-middle" problem by isolating distinct sub-prompts (instructions, formats, or few-shot examples). It further boosts training efficiency by focusing on error-prone samples through selective gradient computation. Across diverse tasks, including single-step classification, multi-hop retrieval-based QA, and agent-driven pipelines, LLM-AutoDiff consistently outperforms existing textual gradient baselines in both accuracy and training cost. By unifying prompt optimization through a graph-centric lens, LLM-AutoDiff offers a powerful new paradigm for scaling and automating LLM workflows - mirroring the transformative role that automatic differentiation libraries have long played in neural network research.
Blockchain Address Poisoning
Venue: arXiv cs.CR 2025
Year: 2025

In many blockchains, e.g., Ethereum, Binance Smart Chain (BSC), the primary representation used for wallet addresses is a hardly memorable 40-digit hexadecimal string. As a result, users often select addresses from their recent transaction history, which enables blockchain address poisoning. The adversary first generates lookalike addresses similar to one with which the victim has previously interacted, and then engages with the victim to ``poison'' their transaction history. The goal is to have the victim mistakenly send tokens to the lookalike address, as opposed to the intended recipient. Compared to contemporary studies, this paper provides four notable contributions. First, we develop a detection system and perform measurements over two years on both Ethereum and BSC. We identify 13~times more attack attempts than reported previously -- totaling 270M on-chain attacks targeting 17M victims. 6,633 incidents have caused at least 83.8M USD in losses, which makes blockchain address poisoning one of the largest cryptocurrency phishing schemes observed in the wild. Second, we analyze a few large attack entities using improved clustering techniques, and model attacker profitability and competition. Third, we reveal attack strategies -- targeted populations, success conditions (address similarity, timing), and cross-chain attacks. Fourth, we mathematically define and simulate the lookalike address generation process across various software- and hardware-based implementations, and identify a large-scale attacker group that appears to use GPUs. We also discuss defensive countermeasures.
MACI: Multi-Agent Collaborative Intelligence for Adaptive Reasoning and Temporal Planning
Venue: arXiv cs.AI 2025
Year: 2025

Artificial intelligence requires deliberate reasoning, temporal awareness, and effective constraint management, capabilities traditional LLMs often lack due to their reliance on pattern matching, limited self-verification, and inconsistent constraint handling. We introduce Multi-Agent Collaborative Intelligence (MACI), a framework comprising three key components: 1) a meta-planner (MP) that identifies, formulates, and refines all roles and constraints of a task (e.g., wedding planning) while generating a dependency graph, with common-sense augmentation to ensure realistic and practical constraints; 2) a collection of agents to facilitate planning and address task-specific requirements; and 3) a run-time monitor that manages plan adjustments as needed. By decoupling planning from validation, maintaining minimal agent context, and integrating common-sense reasoning, MACI overcomes the aforementioned limitations and demonstrates robust performance in two scheduling problems.
Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Motion customization aims to adapt the diffusion model (DM) to generate videos with the motion specified by a set of video clips with the same motion concept. To realize this goal, the adaptation of DM should be possible to model the specified motion concept, without compromising the ability to generate diverse appearances. Thus, the key to solving this problem lies in how to separate the motion concept from the appearance in the adaptation process of DM. Typical previous works explore different ways to represent and insert a motion concept into large-scale pretrained text-to-video diffusion models, e.g., learning a motion LoRA, using latent noise residuals, etc. While those methods can encode the motion concept, they also inevitably encode the appearance in the reference videos, resulting in weakened appearance generation capability. In this paper, we follow the typical way to learn a motion LoRA to encode the motion concept, but propose two novel strategies to enhance motion-appearance separation, including temporal attention purification (TAP) and appearance highway (AH). Specifically, we assume that in the temporal attention module, the pretrained Value embeddings are sufficient to serve as basic components needed by producing a new motion. Thus, in TAP, we choose only to reshape the temporal attention with motion LoRAs so that Value embeddings can be reorganized to produce a new motion. Further, in AH, we alter the starting point of each skip connection in U-Net from the output of each temporal attention module to the output of each spatial attention module. Extensive experiments demonstrate that compared to previous works, our method can generate videos with appearance more aligned with the text descriptions and motion more consistent with the reference videos.