OpenRaiser/Intern-Atlas huggingface dataset:
Wall-Proximity Matters: Understanding the Effect of Device Placement with Respect to the Wall for Indoor Wi-Fi Sensing
Venue: arXiv cs.NI 2025
Year: 2025

Wi-Fi sensing has been extensively explored for various applications, including vital sign monitoring, human activity recognition, indoor localization, and tracking. However, practical implementation in real-world scenarios is hindered by unstable sensing performance and limited knowledge of wireless sensing coverage. While previous works have aimed to address these challenges, they have overlooked the impact of walls on dynamic sensing capabilities in indoor environments. To fill this gap, we present a theoretical model that accounts for the effect of wall-device distance on sensing coverage. By incorporating both the wall-reflected path and the line-of-sight (LoS) path for dynamic signals, we develop a comprehensive sensing coverage model tailored for indoor environments. This model demonstrates that strategically deploying the transmitter and receiver in proximity to the wall within a specific range can significantly expand sensing coverage. We assess the performance of our model through experiments in respiratory monitoring and stationary crowd counting applications, showcasing a notable 11.2% improvement in counting accuracy. These findings pave the way for optimized deployment strategies in Wi-Fi sensing, facilitating more effective and accurate sensing solutions across various applications.
DateLogicQA: Benchmarking Temporal Biases in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces DateLogicQA, a benchmark with 190 questions covering diverse date formats, temporal contexts, and reasoning types. We propose the Semantic Integrity Metric to assess tokenization quality and analyse two biases: Representation-Level Bias, affecting embeddings, and Logical-Level Bias, influencing reasoning outputs. Our findings provide a comprehensive evaluation of LLMs' capabilities and limitations in temporal reasoning, highlighting key challenges in handling temporal data accurately.
What Human-Horse Interactions may Teach us About Effective Human-AI Interactions
Venue: arXiv cs.HC 2025
Year: 2025

This article explores human-horse interactions as a metaphor for understanding and designing effective human-AI partnerships. Drawing on the long history of human collaboration with horses, we propose that AI, like horses, should complement rather than replace human capabilities. We move beyond traditional benchmarks such as the Turing test, which emphasize AI's ability to mimic human intelligence, and instead advocate for a symbiotic relationship where distinct intelligences enhance each other. We analyze key elements of human-horse relationships: trust, communication, and mutual adaptability, to highlight essential principles for human-AI collaboration. Trust is critical in both partnerships, built through predictability and shared understanding, while communication and feedback loops foster mutual adaptability. We further discuss the importance of taming and habituation in shaping these interactions, likening it to how humans train AI to perform reliably and ethically in real-world settings. The article also addresses the asymmetry of responsibility, where humans ultimately bear the greater burden of oversight and ethical judgment. Finally, we emphasize that long-term commitment and continuous learning are vital in both human-horse and human-AI relationships, as ongoing interaction refines the partnership and increases mutual adaptability. By drawing on these insights from human-horse interactions, we offer a vision for designing AI systems that are trustworthy, adaptable, and capable of fostering symbiotic human-AI partnerships.
Level-Set Parameters: Novel Representation for 3D Shape Analysis
Venue: arXiv cs.CV 2025
Year: 2025

3D shape analysis has been largely focused on traditional 3D representations of point clouds and meshes, but the discrete nature of these data makes the analysis susceptible to variations in input resolutions. Recent development of neural fields brings in level-set parameters from signed distance functions as a novel, continuous, and numerical representation of 3D shapes, where the shape surfaces are defined as zero-level-sets of those functions. This motivates us to extend shape analysis from the traditional 3D data to these novel parameter data. Since the level-set parameters are not Euclidean like point clouds, we establish correlations across different shapes by formulating them as a pseudo-normal distribution, and learn the distribution prior from the respective dataset. To further explore the level-set parameters with shape transformations, we propose to condition a subset of these parameters on rotations and translations, and generate them with a hypernetwork. This simplifies the pose-related shape analysis compared to using traditional data. We demonstrate the promise of the novel representations through applications in shape classification (arbitrary poses), retrieval, and 6D object pose estimation.
Quantum Codes from Group Ring Codes
Venue: arXiv cs.IT 2025
Year: 2025

This article examines group ring codes over finite fields and finite groups. We also present a section on two-dimensional cyclic codes in the quotient ring $\mathbb{F}_q[x, y] / \langle x^{l} - 1, y^{m} - 1 \rangle$. These two-dimensional cyclic codes can be analyzed using the group ring $\mathbb{F}_q(C_{l} \times C_{m})$, where $C_{l}$ and $C_{m}$ represent cyclic groups of orders $l$ and $m$, respectively. The aim is to show that studying group ring codes provides a more compact approach compared to the quotient ring method. We further extend this group ring framework to study codes over other group structures, such as the dihedral group, direct products of cyclic and dihedral groups, direct products of two cyclic groups, and semidirect products of two groups. Additionally, we explore necessary and sufficient conditions for such group ring codes to be self-orthogonal under Euclidean, Hermitian, and symplectic inner products and propose a construction for quantum codes.
GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

3D open-vocabulary scene understanding, which accurately perceives complex semantic properties of objects in space, has gained significant attention in recent years. In this paper, we propose GAGS, a framework that distills 2D CLIP features into 3D Gaussian splatting, enabling open-vocabulary queries for renderings on arbitrary viewpoints. The main challenge of distilling 2D features for 3D fields lies in the multiview inconsistency of extracted 2D features, which provides unstable supervision for the 3D feature field. GAGS addresses this challenge with two novel strategies. First, GAGS associates the prompt point density of SAM with the camera distances, which significantly improves the multiview consistency of segmentation results. Second, GAGS further decodes a granularity factor to guide the distillation process and this granularity factor can be learned in a unsupervised manner to only select the multiview consistent 2D features in the distillation process. Experimental results on two datasets demonstrate significant performance and stability improvements of GAGS in visual grounding and semantic segmentation, with an inference speed 2$\times$ faster than baseline methods. The code and additional results are available at https://pz0826.github.io/GAGS-Webpage/ .
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Images captured in severe low-light circumstances often suffer from significant information absence. Existing singular modality image enhancement methods struggle to restore image regions lacking valid information. By leveraging light-impervious infrared images, visible and infrared image fusion methods have the potential to reveal information hidden in darkness. However, they primarily emphasize inter-modal complementation but neglect intra-modal enhancement, limiting the perceptual quality of output images. To address these limitations, we propose a novel task, dubbed visible and infrared information synthesis (VIIS), which aims to achieve both information enhancement and fusion of the two modalities. Given the difficulty in obtaining ground truth in the VIIS task, we design an information synthesis pretext task (ISPT) based on image augmentation. We employ a diffusion model as the framework and design a sparse attention-based dual-modalities residual (SADMR) conditioning mechanism to enhance information interaction between the two modalities. This mechanism enables features with prior knowledge from both modalities to adaptively and iteratively attend to each modality's information during the denoising process. Our extensive experiments demonstrate that our model qualitatively and quantitatively outperforms not only the state-of-the-art methods in relevant fields but also the newly designed baselines capable of both information enhancement and fusion. The code is available at https://github.com/Chenz418/VIIS.
Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation
Venue: arXiv cs.CL 2025
Year: 2025

The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined narratives. Their capabilities to generate personalized (in various aspects) content have also been evaluated and mostly found usable. However, a combination of personalization and disinformation abilities of LLMs has not been comprehensively studied yet. Such a dangerous combination should trigger integrated safety filters of the LLMs, if there are some. This study fills this gap by evaluating vulnerabilities of recent open and closed LLMs, and their willingness to generate personalized disinformation news articles in English. We further explore whether the LLMs can reliably meta-evaluate the personalization quality and whether the personalization affects the generated-texts detectability. Our results demonstrate the need for stronger safety-filters and disclaimers, as those are not properly functioning in most of the evaluated LLMs. Additionally, our study revealed that the personalization actually reduces the safety-filter activations; thus effectively functioning as a jailbreak. Such behavior must be urgently addressed by LLM developers and service providers.
Minimum Data Rate Maximization for Uplink Pinching-Antenna Systems
Venue: arXiv cs.IT 2025
Year: 2025

This paper addresses, for the first time, the uplink performance optimization of multi-user pinching-antenna (PA) systems, recently developed for next-generation wireless networks. By leveraging the unique capabilities of PAs to dynamically configure wireless channels, we focus on maximizing the minimum achievable data rate between devices to achieve a balanced trade-off between throughput and fairness. An effective approach is proposed that separately optimizes the positions of the PAs and the resource allocation. The antenna positioning problem is reformulated into a convex one, while a closed-form solution is provided for the resource allocation. Simulation results demonstrate the superior performance of the investigated system using the proposed algorithm over corresponding counterparts, emphasizing the significant potential of PA systems for robust and efficient uplink communication in next-generation wireless networks.
Harvesting energy from turbulent winds with Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Airborne Wind Energy (AWE) is an emerging technology designed to harness the power of high-altitude winds, offering a solution to several limitations of conventional wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station and driven by the wind, convert its mechanical energy into electrical energy by means of a generator. Such systems are usually controlled by manoeuvering the kite so as to follow a predefined path prescribed by optimal control techniques, such as model-predictive control. These methods are strongly dependent on the specific model at use and difficult to generalize, especially in unpredictable conditions such as the turbulent atmospheric boundary layer. Our aim is to explore the possibility of replacing these techniques with an approach based on Reinforcement Learning (RL). Unlike traditional methods, RL does not require a predefined model, making it robust to variability and uncertainty. Our experimental results in complex simulated environments demonstrate that AWE agents trained with RL can effectively extract energy from turbulent flows, relying on minimal local information about the kite orientation and speed relative to the wind.
WATCHDOG: an ontology-aWare risk AssessmenT approaCH via object-oriented DisruptiOn Graphs
Venue: arXiv cs.AI 2025
Year: 2025

When considering risky events or actions, we must not downplay the role of involved objects: a charged battery in our phone averts the risk of being stranded in the desert after a flat tyre, and a functional firewall mitigates the risk of a hacker intruding the network. The Common Ontology of Value and Risk (COVER) highlights how the role of objects and their relationships remains pivotal to performing transparent, complete and accountable risk assessment. In this paper, we operationalize some of the notions proposed by COVER -- such as parthood between objects and participation of objects in events/actions -- by presenting a new framework for risk assessment: WATCHDOG. WATCHDOG enriches the expressivity of vetted formal models for risk -- i.e., fault trees and attack trees -- by bridging the disciplines of ontology and formal methods into an ontology-aware formal framework composed by a more expressive modelling formalism, Object-Oriented Disruption Graphs (DOGs), logic (DOGLog) and an intermediate query language (DOGLang). With these, WATCHDOG allows risk assessors to pose questions about disruption propagation, disruption likelihood and risk levels, keeping the fundamental role of objects at risk always in sight.
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
Venue: arXiv cs.CL 2025
Year: 2025

We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks to improvements in large language models (LLMs), there has also been a rapid development in AI agents that interact with and affect change in their surrounding environments. But how performant are AI agents at accelerating or even autonomously performing work-related tasks? The answer to this question has important implications both for industry looking to adopt AI into their workflows and for economic policy to understand the effects that adoption of AI may have on the labor market. To measure the progress of these LLM agents' performance on performing real-world professional tasks, in this paper we introduce TheAgentCompany, an extensible benchmark for evaluating AI agents that interact with the world in similar ways to those of a digital worker: by browsing the Web, writing code, running programs, and communicating with other coworkers. We build a self-contained environment with internal web sites and data that mimics a small software company environment, and create a variety of tasks that may be performed by workers in such a company. We test baseline agents powered by both closed API-based and open-weights language models (LMs), and find that the most competitive agent can complete 30% of tasks autonomously. This paints a nuanced picture on task automation with LM agents--in a setting simulating a real workplace, a good portion of simpler tasks could be solved autonomously, but more difficult long-horizon tasks are still beyond the reach of current systems. We release code, data, environment, and experiments on https://the-agent-company.com.
State Space Models are Strong Text Rerankers
Venue: arXiv cs.CL 2025
Year: 2025

Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state space models (SSMs) like Mamba offer promising advantages, particularly $O(1)$ time complexity in inference. Despite their potential, SSMs' effectiveness at text reranking -- a task requiring fine-grained query-document interaction and long-context understanding -- remains underexplored. This study benchmarks SSM-based architectures (specifically, Mamba-1 and Mamba-2) against transformer-based models across various scales, architectures, and pre-training objectives, focusing on performance and efficiency in text reranking tasks. We find that (1) Mamba architectures achieve competitive text ranking performance, comparable to transformer-based models of similar size; (2) they are less efficient in training and inference compared to transformers with flash attention; and (3) Mamba-2 outperforms Mamba-1 in both performance and efficiency. These results underscore the potential of state space models as a transformer alternative and highlight areas for improvement in future IR applications.
HA-RDet: Hybrid Anchor Rotation Detector for Oriented Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Oriented object detection in aerial images poses a significant challenge due to their varying sizes and orientations. Current state-of-the-art detectors typically rely on either two-stage or one-stage approaches, often employing Anchor-based strategies, which can result in computationally expensive operations due to the redundant number of generated anchors during training. In contrast, Anchor-free mechanisms offer faster processing but suffer from a reduction in the number of training samples, potentially impacting detection accuracy. To address these limitations, we propose the Hybrid-Anchor Rotation Detector (HA-RDet), which combines the advantages of both anchor-based and anchor-free schemes for oriented object detection. By utilizing only one preset anchor for each location on the feature maps and refining these anchors with our Orientation-Aware Convolution technique, HA-RDet achieves competitive accuracies, including 75.41 mAP on DOTA-v1, 65.3 mAP on DIOR-R, and 90.2 mAP on HRSC2016, against current anchor-based state-of-the-art methods, while significantly reducing computational resources.
Differentially Private Multi-objective Selection: Pareto and Aggregation Approaches
Venue: arXiv cs.CR 2025
Year: 2025

Differentially private selection mechanisms are fundamental building blocks for privacy-preserving data analysis. While numerous mechanisms exist for single-objective selection, many real-world applications require optimizing multiple competing objectives simultaneously. We present two novel mechanisms for differentially private multi-objective selection: PrivPareto and PrivAgg. PrivPareto uses a novel Pareto score to identify solutions near the Pareto frontier, while PrivAgg enables privacy-preserving weighted aggregation of multiple objectives. Both mechanisms support global and local sensitivity approaches, with comprehensive theoretical analysis showing how to compose sensitivities of multiple utility functions. We demonstrate the practical applicability through two real-world applications: cost-sensitive decision tree construction and multi-objective influential node selection in social networks. The experimental results showed that our local sensitivity-based approaches achieve significantly better utility compared to global sensitivity approaches across both applications and both Pareto and Aggregation approaches. Moreover, the local sensitivity-based approaches are able to perform well with typical privacy budget values $ε\in [0.01, 1]$ in most experiments.
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem
Venue: arXiv cs.AI 2025
Year: 2025

Mixed-integer programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems. Recently, learning-based approaches have shown a potential to speed up MIP solving via offline training that then guides important design decisions during the search. However, a significant drawback of these methods is their heavy reliance on offline training, which requires collecting training datasets and computationally costly training epochs yet offering only limited generalization to unseen (larger) instances. In this paper, we propose Balans, an adaptive meta-solver for MIPs with online learning capability that does not require any supervision or apriori training. At its core, Balans is based on adaptive large-neighborhood search, operating on top of an MIP solver by successive applications of destroy and repair neighborhood operators. During the search, the selection among different neighborhood definitions is guided on the fly for the instance at hand via multi-armed bandit algorithms. Our extensive experiments on hard optimization instances show that Balans offers significant performance gains over the default MIP solver, is better than committing to any single best neighborhood, and improves over the state-of-the-art large-neighborhood search for MIPs. Finally, we release Balans as a highly configurable, MIP solver agnostic, open-source software.
The One RING: a Robotic Indoor Navigation Generalist
Venue: arXiv cs.RO 2025
Year: 2025

Modern robots vary significantly in shape, size, and sensor configurations used to perceive and interact with their environments. However, most navigation policies are embodiment-specific--a policy trained on one robot typically fails to generalize to another, even with minor changes in body size or camera viewpoint. As custom hardware becomes increasingly common, there is a growing need for a single policy that generalizes across embodiments, eliminating the need to retrain for each specific robot. In this paper, we introduce RING (Robotic Indoor Navigation Generalist), an embodiment-agnostic policy that turns any mobile robot into an effective indoor semantic navigator. Trained entirely in simulation, RING leverages large-scale randomization over robot embodiments to enable robust generalization to many real-world platforms. To support this, we augment the AI2-THOR simulator to instantiate robots with controllable configurations, varying in body size, rotation pivot point, and camera parameters. On the visual object-goal navigation task, RING achieves strong cross-embodiment (XE) generalization--72.1% average success rate across five simulated embodiments (a 16.7% absolute improvement on the Chores-S benchmark) and 78.9% across four real-world platforms, including Stretch RE-1, LoCoBot, and Unitree Go1--matching or even surpassing embodiment-specific policies. We further deploy RING on the RB-Y1 wheeled humanoid in a real-world kitchen environment, showcasing its out-of-the-box potential for mobile manipulation platforms. (Project website: https://one-ring-policy.allen.ai)