OpenRaiser/Intern-Atlas huggingface dataset:
Learning Partitions with Optimal Query and Round Complexities
Venue: arXiv cs.DS 2025
Year: 2025

We consider the basic problem of learning an unknown partition of $n$ elements into at most $k$ sets using simple queries that reveal information about a small subset of elements. Our starting point is the well-studied pairwise same-set queries which ask if a pair of elements belong to the same class. It is known that non-adaptive algorithms require $Θ(n^2)$ queries, while adaptive algorithms require $Θ(nk)$ queries, and the best known algorithm uses $k-1$ rounds. This problem has been studied extensively over the last two decades in multiple communities due to its fundamental nature and relevance to clustering, active learning, and crowd sourcing. In many applications, it is of high interest to reduce adaptivity while minimizing query complexity. We give a complete characterization of the deterministic query complexity of this problem as a function of the number of rounds, $r$, interpolating between the non-adaptive and adaptive settings: for any constant $r$, the query complexity is $Θ(n^{1+\frac{1}{2^r-1}}k^{1-\frac{1}{2^r-1}})$. Our algorithm only needs $O(\log \log n)$ rounds to attain the optimal $O(nk)$ query complexity. Next, we consider two generalizations of pairwise queries to subsets $S$ of size at most $s$: (1) weak subset queries which return the number of classes intersected by $S$, and (2) strong subset queries which return the entire partition restricted on $S$. Once again in crowd sourcing applications, queries on large sets may be prohibitive. For non-adaptive algorithms, we show $Ω(n^2/s^2)$ strong queries are needed. Perhaps surprisingly, we show that there is a non-adaptive algorithm using weak queries that matches this bound up to log-factors for all $s \leq \sqrt{n}$. More generally, we obtain nearly matching upper and lower bounds for algorithms using subset queries in terms of both the number of rounds, $r$, and the query size bound, $s$.
SOAP: Style-Omniscient Animatable Portraits
Venue: arXiv cs.CV 2025
Year: 2025

Creating animatable 3D avatars from a single image remains challenging due to style limitations (realistic, cartoon, anime) and difficulties in handling accessories or hairstyles. While 3D diffusion models advance single-view reconstruction for general objects, outputs often lack animation controls or suffer from artifacts because of the domain gap. We propose SOAP, a style-omniscient framework to generate rigged, topology-consistent avatars from any portrait. Our method leverages a multiview diffusion model trained on 24K 3D heads with multiple styles and an adaptive optimization pipeline to deform the FLAME mesh while maintaining topology and rigging via differentiable rendering. The resulting textured avatars support FACS-based animation, integrate with eyeballs and teeth, and preserve details like braided hair or accessories. Extensive experiments demonstrate the superiority of our method over state-of-the-art techniques for both single-view head modeling and diffusion-based generation of Image-to-3D. Our code and data are publicly available for research purposes at https://github.com/TingtingLiao/soap.
Dequantified Diffusion-Schr{ö}dinger Bridge for Density Ratio Estimation
Venue: arXiv cs.LG 2025
Year: 2025

Density ratio estimation is fundamental to tasks involving $f$-divergences, yet existing methods often fail under significantly different distributions or inadequately overlapping supports -- the density-chasm and the support-chasm problems. Additionally, prior approaches yield divergent time scores near boundaries, leading to instability. We design $\textbf{D}^3\textbf{RE}$, a unified framework for \textbf{robust}, \textbf{stable} and \textbf{efficient} density ratio estimation. We propose the dequantified diffusion bridge interpolant (DDBI), which expands support coverage and stabilizes time scores via diffusion bridges and Gaussian dequantization. Building on DDBI, the proposed dequantified Schr{ö}dinger bridge interpolant (DSBI) incorporates optimal transport to solve the Schr{ö}dinger bridge problem, enhancing accuracy and efficiency. Our method offers uniform approximation and bounded time scores in theory, and outperforms baselines empirically in mutual information and density estimation tasks.
Pairing Real-Time Piano Transcription with Symbol-level Tracking for Precise and Robust Score Following
Venue: arXiv cs.SD 2025
Year: 2025

Real-time music tracking systems follow a musical performance and at any time report the current position in a corresponding score. Most existing methods approach this problem exclusively in the audio domain, typically using online time warping (OLTW) techniques on incoming audio and an audio representation of the score. Audio OLTW techniques have seen incremental improvements both in features and model heuristics which reached a performance plateau in the past ten years. We argue that converting and representing the performance in the symbolic domain -- thereby transforming music tracking into a symbolic task -- can be a more effective approach, even when the domain transformation is imperfect. Our music tracking system combines two real-time components: one handling audio-to-note transcription and the other a novel symbol-level tracker between transcribed input and score. We compare the performance of this mixed audio-symbolic approach with its equivalent audio-only counterpart, and demonstrate that our method outperforms the latter in terms of both precision, i.e., absolute tracking error, and robustness, i.e., tracking success.
SSH-Net: A Self-Supervised and Hybrid Network for Noisy Image Watermark Removal
Venue: arXiv cs.MM 2025
Year: 2025

Visible watermark removal is challenging due to its inherent complexities and the noise carried within images. Existing methods primarily rely on supervised learning approaches that require paired datasets of watermarked and watermark-free images, which are often impractical to obtain in real-world scenarios. To address this challenge, we propose SSH-Net, a Self-Supervised and Hybrid Network specifically designed for noisy image watermark removal. SSH-Net synthesizes reference watermark-free images using the watermark distribution in a self-supervised manner and adopts a dual-network design to address the task. The upper network, focused on the simpler task of noise removal, employs a lightweight CNN-based architecture, while the lower network, designed to handle the more complex task of simultaneously removing watermarks and noise, incorporates Transformer blocks to model long-range dependencies and capture intricate image features. To enhance the model's effectiveness, a shared CNN-based feature encoder is introduced before dual networks to extract common features that both networks can leverage. Our code will be available at https://github.com/wenyang001/SSH-Net.
Integrating Communication, Sensing, and Security: Progress and Prospects of PLS in ISAC Systems
Venue: arXiv cs.ET 2025
Year: 2025

The sixth generation of wireless networks defined several key performance indicators (KPIs) for assessing its networks, mainly in terms of reliability, coverage, and sensing. In this regard, remarkable attention has been paid recently to the integrated sensing and communication (ISAC) paradigm as an enabler for efficiently and jointly performing communication and sensing using the same spectrum and hardware resources. On the other hand, ensuring communication and data security has been an imperative requirement for wireless networks throughout their evolution. The physical-layer security (PLS) concept paved the way to catering to the security needs in wireless networks in a sustainable way while guaranteeing theoretically secure transmissions, independently of the computational capacity of adversaries. Therefore, it is of paramount importance to consider a balanced trade-off between communication reliability, sensing, and security in future networks, such as the 5G and beyond, and the 6G. In this paper, we provide a comprehensive and system-wise review of designed secure ISAC systems from a PLS point of view. In particular, the impact of various physical-layer techniques, schemes, and wireless technologies to ensure the sensing-security trade-off is studied from the surveyed work. Furthermore, the amalgamation of PLS and ISAC is analyzed in a broader impact by considering attacks targeting data confidentiality, communication covertness, and sensing spoofing. The paper also serves as a tutorial by presenting several theoretical foundations on ISAC and PLS, which represent a practical guide for readers to develop novel secure ISAC network designs.
Enhancing Text2Cypher with Schema Filtering
Venue: arXiv cs.DB 2025
Year: 2025

Knowledge graphs represent complex data using nodes, relationships, and properties. Cypher, a powerful query language for graph databases, enables efficient modeling and querying. Recent advancements in large language models allow translation of natural language questions into Cypher queries - Text2Cypher. A common approach is incorporating database schema into prompts. However, complex schemas can introduce noise, increase hallucinations, and raise computational costs. Schema filtering addresses these challenges by including only relevant schema elements, improving query generation while reducing token costs. This work explores various schema filtering methods for Text2Cypher task and analyzes their impact on token length, performance, and cost. Results show that schema filtering effectively optimizes Text2Cypher, especially for smaller models. Consistent with prior research, we find that larger models benefit less from schema filtering due to their longer context capabilities. However, schema filtering remains valuable for both larger and smaller models in cost reduction.
Bandit Max-Min Fair Allocation
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we study a new decision-making problem called the bandit max-min fair allocation (BMMFA) problem. The goal of this problem is to maximize the minimum utility among agents with additive valuations by repeatedly assigning indivisible goods to them. One key feature of this problem is that each agent's valuation for each item can only be observed through the semi-bandit feedback, while existing work supposes that the item values are provided at the beginning of each round. Another key feature is that the algorithm's reward function is not additive with respect to rounds, unlike most bandit-setting problems. Our first contribution is to propose an algorithm that has an asymptotic regret bound of $O(m\sqrt{T}\ln T/n + m\sqrt{T \ln(mnT)})$, where $n$ is the number of agents, $m$ is the number of items, and $T$ is the time horizon. This is based on a novel combination of bandit techniques and a resource allocation algorithm studied in the literature on competitive analysis. Our second contribution is to provide the regret lower bound of $Ω(m\sqrt{T}/n)$. When $T$ is sufficiently larger than $n$, the gap between the upper and lower bounds is a logarithmic factor of $T$.
Biomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Prompt learning is one of the most effective paradigms for adapting pre-trained vision-language models (VLMs) to the biomedical image classification tasks in few shot scenarios. However, most of the current prompt learning methods only used the text prompts and ignored the particular structures (such as the complex anatomical structures and subtle pathological features) in the biomedical images. In this work, we propose Biomed-DPT, a knowledge-enhanced dual modality prompt tuning technique. In designing the text prompt, Biomed-DPT constructs a dual prompt including the template-driven clinical prompts and the large language model (LLM)-driven domain-adapted prompts, then extracts the clinical knowledge from the domain-adapted prompts through the knowledge distillation technique. In designing the vision prompt, Biomed-DPT introduces the zero vector as a soft prompt to leverage attention re-weighting so that the focus on non-diagnostic regions and the recognition of non-critical pathological features are avoided. Biomed-DPT achieves an average classification accuracy of 66.14\% across 11 biomedical image datasets covering 9 modalities and 10 organs, with performance reaching 78.06\% in base classes and 75.97\% in novel classes, surpassing the Context Optimization (CoOp) method by 6.20\%, 3.78\%, and 8.04\%, respectively. Our code are available at \underline{https://github.com/Kanyooo/Biomed-DPT}.
Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality
Venue: arXiv cs.GT 2025
Year: 2025

The article explores the emerging domain of incentive-aware machine learning (ML), which focuses on algorithmic decision-making in contexts where individuals can strategically modify their inputs to influence outcomes. It categorizes the research into three perspectives: robustness, aiming to design models resilient to "gaming"; fairness, analyzing the societal impacts of such systems; and improvement/causality, recognizing situations where strategic actions lead to genuine personal or societal improvement. The paper introduces a unified framework encapsulating models for these perspectives, including offline, online, and causal settings, and highlights key challenges such as differentiating between gaming and improvement and addressing heterogeneity among agents. By synthesizing findings from diverse works, we outline theoretical advancements and practical solutions for robust, fair, and causally-informed incentive-aware ML systems.
Overlapping Biclustering
Venue: arXiv cs.DS 2025
Year: 2025

We study the problem of transforming bipartite graphs into bicluster graphs. Abu-Khzam, Isenmann, and Merchad [IWOCA '25] introduced two variants of this problem. In both problems, the goal is to transform a bipartite graph into a bicluster graph with at most $k$ operations, where the allowed operations are inserting an edge, deleting an edge, and splitting a vertex. Splitting a vertex $v$ refers to replacing $v$ by two new vertices whose combined neighborhood equals the neighborhood of $v$. The latter models overlapping clusters, that is, vertices belonging to multiple clusters, and is motivated by several real-world applications. The versions differ in that one variant allows splitting any vertex, while the second variant only allows vertex splits on one side of the bipartition. Regarding computational complexity, they showed APX-hardness for both variants and a polynomial kernel (with $O(k^5)$ vertices) for the one-sided variant. They asked as open problems whether the polynomial kernel can be improved and whether it can also be extended for the other variant. We answer both questions in the affirmative and give kernels with $O(k^2)$ vertices for both variants. We also show that both problems can be solved in $O(k^{11k} + n + m)$ time, where $n$ and $m$ denote the number of vertices and edges in the input graph, respectively.
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation
Venue: arXiv cs.CL 2025
Year: 2025

The rapid advancement of Chinese LLMs underscores the need for vertical-domain evaluations to ensure reliable applications. However, existing benchmarks often lack domain coverage and provide limited insights into the Chinese working context. Leveraging qualification exams as a unified framework for expertise evaluation, we introduce QualBench, the first multi-domain Chinese QA benchmark dedicated to localized assessment of Chinese LLMs. The dataset includes over 17,000 questions across six vertical domains, drawn from 24 Chinese qualifications to align with national policies and professional standards. Results reveal an interesting pattern of Chinese LLMs consistently surpassing non-Chinese models, with the Qwen2.5 model outperforming the more advanced GPT-4o, emphasizing the value of localized domain knowledge in meeting qualification requirements. The average accuracy of 53.98% reveals the current gaps in domain coverage within model capabilities. Furthermore, we identify performance degradation caused by LLM crowdsourcing, assess data contamination, and illustrate the effectiveness of prompt engineering and model fine-tuning, suggesting opportunities for future improvements through multi-domain RAG and Federated Learning.
Advancing Neural Network Verification through Hierarchical Safety Abstract Interpretation
Venue: arXiv cs.AI 2025
Year: 2025

Traditional methods for formal verification (FV) of deep neural networks (DNNs) are constrained by a binary encoding of safety properties, where a model is classified as either safe or unsafe (robust or not robust). This binary encoding fails to capture the nuanced safety levels within a model, often resulting in either overly restrictive or too permissive requirements. In this paper, we introduce a novel problem formulation called Abstract DNN-Verification, which verifies a hierarchical structure of unsafe outputs, providing a more granular analysis of the safety aspect for a given DNN. Crucially, by leveraging abstract interpretation and reasoning about output reachable sets, our approach enables assessing multiple safety levels during the FV process, requiring the same (in the worst case) or even potentially less computational effort than the traditional binary verification approach. Specifically, we demonstrate how this formulation allows rank adversarial inputs according to their abstract safety level violation, offering a more detailed evaluation of the model's safety and robustness. Our contributions include a theoretical exploration of the relationship between our novel abstract safety formulation and existing approaches that employ abstract interpretation for robustness verification, complexity analysis of the novel problem introduced, and an empirical evaluation considering both a complex deep reinforcement learning task (based on Habitat 3.0) and standard DNN-Verification benchmarks.
Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Humans naturally exhibit bilateral symmetry in their gross manipulation skills, effortlessly mirroring simple actions between left and right hands. Bimanual robots-which also feature bilateral symmetry-should similarly exploit this property to perform tasks with either hand. Unlike humans, who often favor a dominant hand for fine dexterous skills, robots should ideally execute ambidextrous manipulation with equal proficiency. To this end, we introduce SYMDEX (SYMmetric DEXterity), a reinforcement learning framework for ambidextrous bi-manipulation that leverages the robot's inherent bilateral symmetry as an inductive bias. SYMDEX decomposes complex bimanual manipulation tasks into per-hand subtasks and trains dedicated policies for each. By exploiting bilateral symmetry via equivariant neural networks, experience from one arm is inherently leveraged by the opposite arm. We then distill the subtask policies into a global ambidextrous policy that is independent of the hand-task assignment. We evaluate SYMDEX on six challenging simulated manipulation tasks and demonstrate successful real-world deployment on two of them. Our approach strongly outperforms baselines on complex task in which the left and right hands perform different roles. We further demonstrate SYMDEX's scalability by extending it to a four-arm manipulation setup, where our symmetry-aware policies enable effective multi-arm collaboration and coordination. Our results highlight how structural symmetry as inductive bias in policy learning enhances sample efficiency, robustness, and generalization across diverse dexterous manipulation tasks.
Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
Venue: arXiv cs.CL 2025
Year: 2025

In this position paper, we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, at present, large language models (LLMs) are inadequate for this purpose, and we propose an ideal technology design aimed at enhancing argumentative skills. This involves re-framing LLMs as tools to exercise our critical thinking skills rather than replacing them. We introduce the concept of \textit{reasonable parrots} that embody the fundamental principles of relevance, responsibility, and freedom, and that interact through argumentative dialogical moves. These principles and moves arise out of millennia of work in argumentation theory and should serve as the starting point for LLM-based technology that incorporates basic principles of argumentation.
Mapping User Trust in Vision Language Models: Research Landscape, Challenges, and Prospects
Venue: arXiv cs.CV 2025
Year: 2025

The rapid adoption of Vision Language Models (VLMs), pre-trained on large image-text and video-text datasets, calls for protecting and informing users about when to trust these systems. This survey reviews studies on trust dynamics in user-VLM interactions, through a multi-disciplinary taxonomy encompassing different cognitive science capabilities, collaboration modes, and agent behaviours. Literature insights and findings from a workshop with prospective VLM users inform preliminary requirements for future VLM trust studies.
Aesthetics Without Semantics
Venue: arXiv cs.CV 2025
Year: 2025

While it is easy for human observers to judge an image as beautiful or ugly, aesthetic decisions result from a combination of entangled perceptual and cognitive (semantic) factors, making the understanding of aesthetic judgements particularly challenging from a scientific point of view. Furthermore, our research shows a prevailing bias in current databases, which include mostly beautiful images, further complicating the study and prediction of aesthetic responses. We address these limitations by creating a database of images with minimal semantic content and devising, and next exploiting, a method to generate images on the ugly side of aesthetic valuations. The resulting Minimum Semantic Content (MSC) database consists of a large and balanced collection of 10,426 images, each evaluated by 100 observers. We next use established image metrics to demonstrate how augmenting an image set biased towards beautiful images with ugly images can modify, or even invert, an observed relationship between image features and aesthetics valuation. Taken together, our study reveals that works in empirical aesthetics attempting to link image content and aesthetic judgements may magnify, underestimate, or simply miss interesting effects due to a limitation of the range of aesthetic values they consider.