OpenRaiser/Intern-Atlas huggingface dataset:
Co-occurrence is not Factual Association in Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Pretrained language models can encode a large amount of knowledge and utilize it for various reasoning tasks, yet they can still struggle to learn novel factual knowledge effectively from finetuning on limited textual demonstrations. In this work, we show that the reason for this deficiency is that language models are biased to learn word co-occurrence statistics instead of true factual associations. We identify the differences between two forms of knowledge representation in language models: knowledge in the form of co-occurrence statistics is encoded in the middle layers of the transformer model and does not generalize well to reasoning scenarios beyond simple question answering, while true factual associations are encoded in the lower layers and can be freely utilized in various reasoning tasks. Based on these observations, we propose two strategies to improve the learning of factual associations in language models. We show that training on text with implicit rather than explicit factual associations can force the model to learn factual associations instead of co-occurrence statistics, significantly improving the generalization of newly learned knowledge. We also propose a simple training method to actively forget the learned co-occurrence statistics, which unblocks and enhances the learning of factual associations when training on plain narrative text. On both synthetic and real-world corpora, the two proposed strategies improve the generalization of the knowledge learned during finetuning to reasoning scenarios such as indirect and multi-hop question answering.
NP-Completeness and Physical Zero-Knowledge Proofs for Zeiger
Venue: arXiv cs.CC 2025
Year: 2025

Zeiger is a pencil puzzle consisting of a rectangular grid, with each cell having an arrow pointing in horizontal or vertical direction. Some cells also contain a positive integer. The objective of this puzzle is to fill a positive integer into every unnumbered cell such that the integer in each cell is equal to the number of different integers in all cells along the direction an arrow in that cell points to. In this paper, we prove that deciding solvability of a given Zeiger puzzle is NP-complete via a reduction from the not-all-equal positive 3SAT (NAE3SAT+) problem. We also construct a card-based physical zero-knowledge proof protocol for Zeiger, which enables a prover to physically show a verifier the existence of the puzzle's solution without revealing it.
MobileViews: A Million-scale and Diverse Mobile GUI Dataset
Venue: arXiv cs.HC 2025
Year: 2025

Visual language models (VLMs) empower mobile GUI agents to interpret complex mobile screens and respond to user requests. Training such capable agents requires large-scale, high-quality mobile GUI data. However, existing mobile GUI datasets are limited in scale, data comprehensiveness, and fidelity. To overcome this, we utilize two mobile SoC clusters to provide over 200 native, high-fidelity mobile environments, along with a VLM-enhanced automatic application traversal framework for highly parallel, automated dataset collection with minimal human intervention. With this system, we propose MobileViews, a million-scale mobile GUI dataset comprising over 1.2 million unique screenshot-view hierarchy pairs from more than 30K modern Android applications. We assess the effectiveness of MobileViews by training four VLMs using the reinforcement learning-based GUI grounding task and evaluating them on two representative GUI grounding benchmarks. Results show that MobileViews significantly enhances grounding accuracy by up to 6.1%. Further analysis of data scale and quality underscores the critical role of large, high-quality datasets as reliable sources for training mobile GUI agents. The MobileViews dataset is publicly available at https://huggingface.co/datasets/mllmTeam/MobileViews.
Near-field Beam-focusing Pattern under Discrete Phase Shifters
Venue: arXiv cs.IT 2025
Year: 2025

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology for enabling near-field communications in future wireless systems. However, the huge number of antennas deployed pose demanding challenges on the hardware cost and power consumption, especially when the antennas employ high-resolution phase shifters (PSs). To address this issue, in this paper, we consider low-resolution discrete PSs at the XL-array which are practically more energy efficient, and investigate the impact of PS resolution on the near-field beam-focusing effect. To this end, we propose a new Fourier series expansion method to efficiently tackle the difficulty in characterizing the beam pattern properties under phase quantization. Interestingly, we analytically show, for the first time, that 1) discrete PSs introduce additional grating lobes; 2) the main lobe still exhibits the beam-focusing property with its beam power increasing with PS resolution; and 3) there are two types of grating lobes, featured by the beam-focusing and beam-steering properties, respectively. In addition, we provide intuitive understanding for the appearance of grating lobes under discrete PSs from an array-of-subarrays perspective. Finally, numerical results demonstrate that the grating lobes generally degrade communication rate performance. However, a low-resolution of 3-bit PSs can achieve similar beam pattern and rate performance with the continuous PS counterpart, while it attains much higher energy efficiency.
FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension
Venue: arXiv cs.CV 2025
Year: 2025

Referring Expression Comprehension (REC) is a crucial cross-modal task that objectively evaluates the capabilities of language understanding, image comprehension, and language-to-image grounding. Consequently, it serves as an ideal testing ground for Multi-modal Large Language Models (MLLMs). In pursuit of this goal, we have established a new REC dataset characterized by two key features: Firstly, it is designed with controllable varying levels of difficulty, necessitating multi-level fine-grained reasoning across object categories, attributes, and multi-hop relationships. Secondly, it includes negative text and images created through fine-grained editing and generation based on existing data, thereby testing the model's ability to correctly reject scenarios where the target object is not visible in the image--an essential aspect often overlooked in existing datasets and approaches. Utilizing this high-quality dataset, we conducted comprehensive evaluations of both state-of-the-art specialist models and MLLMs. Our findings indicate that there remains a significant gap in achieving satisfactory grounding performance. We anticipate that our dataset will inspire new approaches to enhance visual reasoning and develop more advanced cross-modal interaction strategies, ultimately unlocking the full potential of MLLMs. Our code and the datasets are available at https://github.com/liujunzhuo/FineCops-Ref.
Automatic Geometric Decomposition for Analytical Inverse Kinematics
Venue: arXiv cs.RO 2025
Year: 2025

Calculating the inverse kinematics (IK) is a fundamental challenge in robotics. Compared to numerical or learning-based approaches, analytical IK provides higher efficiency and accuracy. However, existing analytical approaches are difficult to use in most applications, as they require human ingenuity in the derivation process, are numerically unstable, or rely on time-consuming symbolic manipulation. In contrast, we propose a method that, for the first time, enables an analytical IK derivation and computation in less than a millisecond in total. Our work is based on an automatic online decomposition of the IK into pre-solved, numerically stable subproblems via a kinematic classification of the respective manipulator. In numerical experiments, we demonstrate that our approach is orders of magnitude faster in deriving the IK than existing tools that employ symbolic manipulation. Following this one-time derivation, our method matches and often surpasses baselines, such as IKFast, in terms of speed and accuracy during the computation of explicit IK solutions. Finally, we provide an open-source C++ toolbox with Python wrappers that substantially reduces the entry barrier to using analytical IK in applications like rapid prototyping and kinematic robot design.
Unsourced Random Access: A Comprehensive Survey
Venue: arXiv cs.IT 2025
Year: 2025

Multiple access communication systems enable numerous users to share common communication resources, playing a crucial role in wireless networks. With the emergence of the sixth generation (6G) and beyond communication networks, supporting massive machine-type communications with sporadic activity patterns is expected to become a critical challenge. Unsourced random access (URA) has emerged as a promising paradigm to address this challenge by decoupling user identification from data transmission through the use of a common codebook. This survey offers a comprehensive overview of URA solutions, encompassing both theoretical foundations and practical applications. We present a systematic classification of URA solutions across three primary channel models: Gaussian multiple access channels (GMACs), single-antenna fading channels, and multiple-input multiple-output (MIMO) fading channels. For each category, we analyze and compare state-of-the-art solutions in terms of performance, complexity, and practical feasibility. Additionally, we discuss critical challenges such as interference management, computational complexity, and synchronization. The survey concludes with promising future research directions and potential methods to address existing limitations, providing a roadmap for researchers and practitioners in this rapidly evolving field.
Space-time process algebra with asynchronous communication
Venue: arXiv cs.LO 2025
Year: 2025

We introduce a process algebra that concerns the timed behaviour of distributed systems with a known spatial distribution. This process algebra provides a communication mechanism that deals with the fact that a datum sent at one point in space can only be received at another point in space at the point in time that the datum reaches that point in space. The variable-binding integration operator used in related process algebras to model such a communication mechanism is absent from this process algebra. This is considered an advantage because the variable-binding operator does not really fit in with an algebraic approach and a process algebra with this operator is not firmly founded in established metatheory.
Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval
Venue: arXiv cs.AI 2025
Year: 2025

This study leverages optimized context retrieval to enhance open-source Large Language Models (LLMs) for cost-effective, high performance healthcare AI. We demonstrate that this approach achieves state-of-the-art accuracy on medical question answering at a fraction of the cost of proprietary models, significantly improving the cost-accuracy Pareto frontier on the MedQA benchmark. Key contributions include: (1) OpenMedQA, a novel benchmark revealing a performance gap in open-ended medical QA compared to multiple-choice formats; (2) a practical, reproducible pipeline for context retrieval optimization; and (3) open-source resources (Prompt Engine, CoT/ToT/Thinking databases) to empower healthcare AI development. By advancing retrieval techniques and QA evaluation, we enable more affordable and reliable LLM solutions for healthcare.
MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure
Venue: arXiv cs.CL 2025
Year: 2025

Low-Rank Adaptation (LoRA) is a widely adopted technique for parameter-efficient fine-tuning, but its slow convergence has spurred the development of numerous variants. Nevertheless, existing methods often fail to improve performance, memory footprint, and computational efficiency simultaneously. To address this challenge, we revisit the causes of LoRA's slow convergence. Building on these insights, we propose Matrix Shard Sharing (MiSS), which updates shards of the original weight matrix using a single shared trainable matrix $\boldsymbol{D}$, initialized to zeros. To simultaneously ensure computational efficiency, low memory footprint, and scalable serving, we introduce MiSS$^e$. Both theoretical analysis and empirical results demonstrate that our method reduces optimization complexity without compromising performance, thereby achieving a more favorable trade-off among performance, memory, and efficiency. Furthermore, we conduct a comprehensive comparative analysis of various PEFT methods, evaluating their memory usage, initialization overhead, and computational efficiency. By mapping the Pareto frontier across these dimensions, we show that MiSS occupies a favorable position, effectively capturing the advantages of prior approaches.
A Modular Robotic System for Autonomous Exploration and Semantic Updating in Large-Scale Indoor Environments
Venue: arXiv cs.RO 2025
Year: 2025

We present a modular robotic system for autonomous exploration and semantic updating of large-scale unknown environments. Our approach enables a mobile robot to build, revisit, and update a hybrid semantic map that integrates a 2D occupancy grid for geometry with a topological graph for object semantics. Unlike prior methods that rely on manual teleoperation or precollected datasets, our two-phase approach achieves end-to-end autonomy: first, a modified frontier-based exploration algorithm with dynamic search windows constructs a geometric map; second, using a greedy trajectory planner, environments are revisited, and object semantics are updated using open-vocabulary object detection and segmentation. This modular system, compatible with any metric SLAM framework, supports continuous operation by efficiently updating the semantic graph to reflect short-term and long-term changes such as object relocation, removal, or addition. We validate the approach on a Fetch robot in real-world indoor environments of approximately $8,500$m$^2$ and $117$m$^2$, demonstrating robust and scalable semantic mapping and continuous adaptation, marking a fully autonomous integration of exploration, mapping, and semantic updating on a physical robot.
ModCube: Modular, Self-Assembling Cubic Underwater Robot
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a low-cost, centralized modular underwater robot platform, ModCube, which can be used to study swarm coordination for a wide range of tasks in underwater environments. A ModCube structure consists of multiple ModCube robots. Each robot can move in six DoF with eight thrusters and can be rigidly connected to other ModCube robots with an electromagnet controlled by onboard computer. In this paper, we present a novel method for characterizing and visualizing dynamic behavior, along with four benchmarks to evaluate the morphological performance of the robot. Analysis shows that our ModCube design is desirable for omnidirectional tasks, compared with the configurations widely used by commercial underwater robots. We run real robot experiments in two water tanks to demonstrate the robust control and self-assemble of the proposed system, We also open-source the design and code to facilitate future research.
PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberation
Venue: arXiv cs.HC 2025
Year: 2025

Community and organizational policies are typically designed in a top-down, centralized fashion, with limited input from impacted stakeholders. This can result in policies that are misaligned with community needs or perceived as illegitimate. How can we support more collaborative, participatory approaches to policy design? In this paper, we present PolicyCraft, a system that structures collaborative policy design through case-grounded deliberation. Building on past research that highlights the value of concrete cases in establishing common ground, PolicyCraft supports users in collaboratively proposing, critiquing, and revising policies through discussion and voting on cases. A field study across two university courses showed that students using PolicyCraft reached greater consensus and developed better-supported course policies, compared with those using a baseline system that did not scaffold their use of concrete cases. Reflecting on our findings, we discuss opportunities for future HCI systems to help groups more effectively bridge between abstract policies and concrete cases.
Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint
Venue: arXiv cs.CL 2025
Year: 2025

Accurately aligning contextual representations in cross-lingual sentence embeddings is key for effective parallel data mining. A common strategy for achieving this alignment involves disentangling semantics and language in sentence embeddings derived from multilingual pre-trained models. However, we discover that current disentangled representation learning methods suffer from semantic leakage - a term we introduce to describe when a substantial amount of language-specific information is unintentionally leaked into semantic representations. This hinders the effective disentanglement of semantic and language representations, making it difficult to retrieve embeddings that distinctively represent the meaning of the sentence. To address this challenge, we propose a novel training objective, ORthogonAlity Constraint LEarning (ORACLE), tailored to enforce orthogonality between semantic and language embeddings. ORACLE builds upon two components: intra-class clustering and inter-class separation. Through experiments on cross-lingual retrieval and semantic textual similarity tasks, we demonstrate that training with the ORACLE objective effectively reduces semantic leakage and enhances semantic alignment within the embedding space.
XTRUST: On the Multilingual Trustworthiness of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have demonstrated remarkable capabilities across a range of natural language processing (NLP) tasks, capturing the attention of both practitioners and the broader public. A key question that now preoccupies the AI community concerns the capabilities and limitations of these models, with trustworthiness emerging as a central issue, particularly as LLMs are increasingly applied in sensitive fields like healthcare and finance, where errors can have serious consequences. However, most previous studies on the trustworthiness of LLMs have been limited to a single language, typically the predominant one in the dataset, such as English. In response to the growing global deployment of LLMs, we introduce XTRUST, the first comprehensive multilingual trustworthiness benchmark. XTRUST encompasses a diverse range of topics, including illegal activities, hallucination, out-of-distribution (OOD) robustness, physical and mental health, toxicity, fairness, misinformation, privacy, and machine ethics, across 10 different languages. Using XTRUST, we conduct an empirical evaluation of the multilingual trustworthiness of five widely used LLMs, offering an in-depth analysis of their performance across languages and tasks. Our results indicate that many LLMs struggle with certain low-resource languages, such as Arabic and Russian, highlighting the considerable room for improvement in the multilingual trustworthiness of current language models. The code is available at https://github.com/LluckyYH/XTRUST.
Digital Discrimination in Dating Apps and the Dutch Breeze case
Venue: arXiv cs.CY 2025
Year: 2025

In 2023, the Netherlands Institute for Human Rights, the Dutch non-discrimination authority, decided that Breeze, a Dutch dating app, was justified in suspecting that their algorithm discriminated against dark-skinned users. Consequently, the Institute decided that Breeze must prevent this discrimination based on ethnicity. This paper analyses the decision and explores three questions.What are the main points of the Breeze decision? Is the discrimination based on ethnicity in Breeze's matching algorithm illegal? We also explore a more general question: how can dating apps mitigate or stop discrimination in their matching algorithms? We illustrate the legal and technical difficulties dating apps face in tackling discrimination and highlight some promising solutions. We analyse the Breeze decision in-depth, combining insights from computer science and law. We discuss the implications of this judgment for scholarship and practice in the field of fair and non-discriminatory machine learning.
Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification
Venue: arXiv cs.CV 2025
Year: 2025

We study behavior change-based visual risk object identification (Visual-ROI), a critical framework designed to detect potential hazards for intelligent driving systems. Existing methods often show significant limitations in spatial accuracy and temporal consistency, stemming from an incomplete understanding of scene affordance. For example, these methods frequently misidentify vehicles that do not impact the ego vehicle as risk objects. Furthermore, existing behavior change-based methods are inefficient because they implement causal inference in the perspective image space. We propose a new framework with a Bird's Eye View (BEV) representation to overcome the above challenges. Specifically, we utilize potential fields as scene affordance, involving repulsive forces derived from road infrastructure and traffic participants, along with attractive forces sourced from target destinations. In this work, we compute potential fields by assigning different energy levels according to the semantic labels obtained from BEV semantic segmentation. We conduct thorough experiments and ablation studies, comparing the proposed method with various state-of-the-art algorithms on both synthetic and real-world datasets. Our results show a notable increase in spatial and temporal consistency, with enhancements of 20.3% and 11.6% on the RiskBench dataset, respectively. Additionally, we can improve computational efficiency by 88%. We achieve improvements of 5.4% in spatial accuracy and 7.2% in temporal consistency on the nuScenes dataset.
The Dark Side of Rich Rewards: Understanding and Mitigating Noise in VLM Rewards
Venue: arXiv cs.LG 2025
Year: 2025

While Vision-Language Models (VLMs) are increasingly used to generate reward signals for training embodied agents to follow instructions, our research reveals that agents guided by VLM rewards often underperform compared to those employing only intrinsic (exploration-driven) rewards, contradicting expectations set by recent work. We hypothesize that false positive rewards -- instances where unintended trajectories are incorrectly rewarded -- are more detrimental than false negatives. Our analysis confirms this hypothesis, revealing that the widely used cosine similarity metric is prone to false positive reward estimates. To address this, we introduce BiMI ({Bi}nary {M}utual {I}nformation), a novel reward function designed to mitigate noise. BiMI significantly enhances learning efficiency across diverse and challenging embodied navigation environments. Our findings offer a nuanced understanding of how different types of reward noise impact agent learning and highlight the importance of addressing multimodal reward signal noise when training embodied agents