OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation
Venue: arXiv cs.CV 2025
Year: 2025

Despite the progress in cross-domain few-shot learning, a model pre-trained with DINO combined with a prototypical classifier outperforms the latest SOTA methods. A crucial limitation that needs to be overcome is that updating too many parameters of the transformers leads to overfitting due to the scarcity of labeled samples. To address this challenge, we propose a new concept, coalescent projection, as an effective successor to soft prompts. Additionally, we propose a novel pseudo-class generation method, combined with self-supervised transformations, that relies solely on the base domain to prepare the network to encounter unseen samples from different domains. The proposed method exhibits its effectiveness in comprehensive experiments on the extreme domain-shift problem of the BSCD-FSL benchmark. Our code is published at \href{https://github.com/Naeem-Paeedeh/CPLSR}{https://github.com/Naeem-Paeedeh/CPLSR}.
SPAR: Scholar Paper Retrieval with LLM-based Agents for Enhanced Academic Search
Venue: arXiv cs.IR 2025
Year: 2025

Recent advances in large language models (LLMs) have opened new opportunities for academic literature retrieval. However, existing systems often rely on rigid pipelines and exhibit limited reasoning capabilities. We introduce SPAR, a multi-agent framework that incorporates RefChain-based query decomposition and query evolution to enable more flexible and effective search. To facilitate systematic evaluation, we also construct SPARBench, a challenging benchmark with expert-annotated relevance labels. Experimental results demonstrate that SPAR substantially outperforms strong baselines, achieving up to +56% F1 on AutoScholar and +23% F1 on SPARBench over the best-performing baseline. Together, SPAR and SPARBench provide a scalable, interpretable, and high-performing foundation for advancing research in scholarly retrieval. Code and data will be available at: https://github.com/xiaofengShi/SPAR
Disentangling Homophily and Heterophily in Multimodal Graph Clustering
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal graphs, which integrate unstructured heterogeneous data with structured interconnections, offer substantial real-world utility but remain insufficiently explored in unsupervised learning. In this work, we initiate the study of multimodal graph clustering, aiming to bridge this critical gap. Through empirical analysis, we observe that real-world multimodal graphs often exhibit hybrid neighborhood patterns, combining both homophilic and heterophilic relationships. To address this challenge, we propose a novel framework -- \textsc{Disentangled Multimodal Graph Clustering (DMGC)} -- which decomposes the original hybrid graph into two complementary views: (1) a homophily-enhanced graph that captures cross-modal class consistency, and (2) heterophily-aware graphs that preserve modality-specific inter-class distinctions. We introduce a \emph{Multimodal Dual-frequency Fusion} mechanism that jointly filters these disentangled graphs through a dual-pass strategy, enabling effective multimodal integration while mitigating category confusion. Our self-supervised alignment objectives further guide the learning process without requiring labels. Extensive experiments on both multimodal and multi-relational graph datasets demonstrate that DMGC achieves state-of-the-art performance, highlighting its effectiveness and generalizability across diverse settings. Our code is available at https://github.com/Uncnbb/DMGC.
GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou
Venue: arXiv cs.IR 2025
Year: 2025

Currently, short video platforms have become the primary place for individuals to share experiences and obtain information. To better meet users' needs for acquiring information while browsing short videos, some apps have introduced a search entry at the bottom of videos, accompanied with recommended relevant queries. This scenario is known as query recommendation in video-related search, where core task is item-to-query (I2Q) recommendation. As this scenario has only emerged in recent years, there is a notable scarcity of academic research and publicly available datasets in this domain. To address this gap, we systematically examine the challenges associated with this scenario for the first time. Subsequently, we release a large-scale dataset derived from real-world data pertaining to the query recommendation in video-\textit{\textbf{r}}elated \textit{\textbf{s}}earch on the \textit{\textbf{Kuai}}shou app (\textbf{KuaiRS}). Presently, existing methods rely on embeddings to calculate similarity for matching short videos with queries, lacking deep interaction between the semantic content and the query. In this paper, we introduce a novel LLM-based framework named \textbf{GREAT}, which \textit{\textbf{g}}uides que\textit{\textbf{r}}y g\textit{\textbf{e}}ner\textit{\textbf{a}}tion with a \textit{\textbf{t}}rie to address I2Q recommendation in related search. Specifically, we initially gather high-quality queries with high exposure and click-through rate to construct a query-based trie. During training, we enhance the LLM's capability to generate high-quality queries using the query-based trie. In the inference phase, the query-based trie serves as a guide for the token generation. Finally, we further refine the relevance and literal quality between items and queries via a post-processing module. Extensive offline and online experiments demonstrate the effectiveness of our proposed method.
Idefix-Closed Languages and Their Application in Contextual Grammars
Venue: arXiv cs.FL 2025
Year: 2025

In this paper, we continue the research on the power of contextual grammars with selection languages from subfamilies of the family of regular languages. We investigate infix-, prefix-, and suffix-closed languages (referred to as idefix-closed languages) and compare such language families to some other subregular families of languages (finite, monoidal, nilpotent, combinational, (symmetric) definite, ordered, non-counting, power-separating, commutative, circular, union-free, star, and comet languages). Further, we compare the families of the hierarchies obtained for external and internal contextual grammars with the language families defined by these new types for the selection. In this way, we extend the existing hierarchies by new language families. Moreover, we solve an open problem regarding internal contextual grammars with suffix-closed selection languages.
On some Classes of Reversible 2-head Automata
Venue: arXiv cs.FL 2025
Year: 2025

Deterministic 2-head finite automata which are machines that process an input word from both ends are analyzed for their ability to perform reversible computations. This implies that the automata are backward deterministic, enabling unique forward and backward computation. We explore the computational power of such automata, discovering that, while some regular languages cannot be accepted by these machines, they are capable of accepting some characteristic linear languages, e.g., the language of palindromes. Additionally, we prove that restricted variants, i.e., both 1-limited reversible 2-head finite automata and complete reversible 2-head finite automata are less powerful and they form a proper hierarchy. In the former, in each computation step exactly one input letter is being processed, i.e., only one of the heads can read a letter. These automata are also characterized by putting their states to classes based on the head(s) used to reach and to leave the state. In the complete reversible 2-head finite automata, it is required that any input can be fully read by the automaton. The accepted families are also compared to the classes generated by left deterministic linear grammars.
On the Inevitability of Left-Leaning Political Bias in Aligned Language Models
Venue: arXiv cs.CL 2025
Year: 2025

The guiding principle of AI alignment is to train large language models (LLMs) to be harmless, helpful, and honest (HHH). At the same time, there are mounting concerns that LLMs exhibit a left-wing political bias. Yet, the commitment to AI alignment cannot be harmonized with the latter critique. In this article, I argue that intelligent systems that are trained to be harmless and honest must necessarily exhibit left-wing political bias. Normative assumptions underlying alignment objectives inherently concur with progressive moral frameworks and left-wing principles, emphasizing harm avoidance, inclusivity, fairness, and empirical truthfulness. Conversely, right-wing ideologies often conflict with alignment guidelines. Yet, research on political bias in LLMs is consistently framing its insights about left-leaning tendencies as a risk, as problematic, or concerning. This way, researchers are actively arguing against AI alignment, tacitly fostering the violation of HHH principles.
Low-Power and Accurate IoT Monitoring Under Radio Resource Constraint
Venue: arXiv cs.NI 2025
Year: 2025

This paper investigates how to achieve both low-power operations of sensor nodes and accurate state estimation using Kalman filter for internet of things (IoT) monitoring employing wireless sensor networks under radio resource constraint. We consider two policies used by the base station to collect observations from the sensor nodes: (i) an oblivious policy, based on statistics of the observations, and (ii) a decentralized policy, based on autonomous decision of each sensor based on its instantaneous observation. This work introduces a wake-up receiver and wake-up signaling to both policies to improve the energy efficiency of the sensor nodes. The decentralized policy designed with random access prioritizes transmissions of instantaneous observations that are highly likely to contribute to the improvement of state estimation. Our numerical results show that the decentralized policy improves the accuracy of the estimation in comparison to the oblivious policy under the constraint on the radio resource and consumed energy when the correlation between the processes observed by the sensor nodes is low. We also clarify the degree of correlation in which the superiority of two policies changes.
Probing Information Distribution in Transformer Architectures through Entropy Analysis
Venue: arXiv cs.CL 2025
Year: 2025

This work explores entropy analysis as a tool for probing information distribution within Transformer-based architectures. By quantifying token-level uncertainty and examining entropy patterns across different stages of processing, we aim to investigate how information is managed and transformed within these models. As a case study, we apply the methodology to a GPT-based large language model, illustrating its potential to reveal insights into model behavior and internal representations. This approach may offer insights into model behavior and contribute to the development of interpretability and evaluation frameworks for transformer-based models
Efficient Visual Appearance Optimization by Learning from Prior Preferences
Venue: arXiv cs.HC 2025
Year: 2025

Adjusting visual parameters such as brightness and contrast is common in our everyday experiences. Finding the optimal parameter setting is challenging due to the large search space and the lack of an explicit objective function, leaving users to rely solely on their implicit preferences. Prior work has explored Preferential Bayesian Optimization (PBO) to address this challenge, involving users to iteratively select preferred designs from candidate sets. However, PBO often requires many rounds of preference comparisons, making it more suitable for designers than everyday end-users. We propose Meta-PO, a novel method that integrates PBO with meta-learning to improve sample efficiency. Specifically, Meta-PO infers prior users' preferences and stores them as models, which are leveraged to intelligently suggest design candidates for the new users, enabling faster convergence and more personalized results. An experimental evaluation of our method for appearance design tasks on 2D and 3D content showed that participants achieved satisfactory appearance in 5.86 iterations using Meta-PO when participants shared similar goals with a population (e.g., tuning for a ``warm'' look) and in 8 iterations even generalizes across divergent goals (e.g., from ``vintage'', ``warm'', to ``holiday''). Meta-PO makes personalized visual optimization more applicable to end-users through a generalizable, more efficient optimization conditioned on preferences, with the potential to scale interface personalization more broadly.
Exploring the Use of Predictive Analytics by Austrian Tax Authorities: A Qualitative Study within the Task-Technology Fit Model
Venue: arXiv cs.CY 2025
Year: 2025

Taxes finance important government services that are now taken for granted in our society, such as infrastructure, health care, or retirement pensions. Tax authorities everywhere strive to ensure that all individuals and organizations comply with applicable tax laws. In this regard, tax authorities must prevent individuals and organizations from evading taxes in an illegal manner. To this end, Austrian tax authorities employ state-of-the-art predictive analytics technology for the selection of suspicious cases for tax audits, thus making efficient use of scarce resources for tax auditing. In this paper, we explore how Austrian tax authorities employ predictive analytics technology in tax auditing and how well the use of such technology fits the characteristics of the task at hand. We collaborated with the Austrian Federal Ministry of Finance's Predictive Analytics Competence Center to obtain insights into the application of predictive analytics technology by Austrian tax authorities. The thus obtained insights serve as the basis for a qualitative analysis in the context of the task-technology fit framework.
Stack Management for MPLS Network Actions: Integration of Nodes with Limited Hardware Capabilities
Venue: arXiv cs.NI 2025
Year: 2025

The MPLS Network Actions (MNA) framework enhances MPLS forwarding with a generalized encoding for manifold extensions such as network slicing and in-situ OAM (IOAM). Network actions in MNA are encoded in Label Stack Entries (LSEs) and are added to the MPLS stack. Routers have a physical limit on the number of LSEs they can read, called the readable label depth (RLD). With MNA, routers must be able to process a minimum number of LSEs which requires a relatively large RLD. In this paper, we perform a hardware analysis of an MNA implementation and identify the reason for a large RLD requirement in the MNA protocol design. Based on this, we present a mechanism that reduces the required RLD for MNA nodes by restructuring the MPLS stack during forwarding. We then introduce the novel stack management network action that enables the proposed mechanism as well as its integration in networks with MNA-incapable nodes. The feasibility of the mechanism on programmable hardware is verified by providing a P4-based implementation. Further, the effects on the required RLD, ECMP, and packet overhead are discussed.
Rethinking Occlusion in FER: A Semantic-Aware Perspective and Go Beyond
Venue: arXiv cs.CV 2025
Year: 2025

Facial expression recognition (FER) is a challenging task due to pervasive occlusion and dataset biases. Especially when facial information is partially occluded, existing FER models struggle to extract effective facial features, leading to inaccurate classifications. In response, we present ORSANet, which introduces the following three key contributions: First, we introduce auxiliary multi-modal semantic guidance to disambiguate facial occlusion and learn high-level semantic knowledge, which is two-fold: 1) we introduce semantic segmentation maps as dense semantics prior to generate semantics-enhanced facial representations; 2) we introduce facial landmarks as sparse geometric prior to mitigate intrinsic noises in FER, such as identity and gender biases. Second, to facilitate the effective incorporation of these two multi-modal priors, we customize a Multi-scale Cross-interaction Module (MCM) to adaptively fuse the landmark feature and semantics-enhanced representations within different scales. Third, we design a Dynamic Adversarial Repulsion Enhancement Loss (DARELoss) that dynamically adjusts the margins of ambiguous classes, further enhancing the model's ability to distinguish similar expressions. We further construct the first occlusion-oriented FER dataset to facilitate specialized robustness analysis on various real-world occlusion conditions, dubbed Occlu-FER. Extensive experiments on both public benchmarks and Occlu-FER demonstrate that our proposed ORSANet achieves SOTA recognition performance. Code is publicly available at https://github.com/Wenyuzhy/ORSANet-master.
Quantum Programming in Polylogarithmic Time
Venue: arXiv cs.LO 2025
Year: 2025

Polylogarithmic time delineates a relevant notion of feasibility on several classical computational models such as Boolean circuits or parallel random access machines. As far as the quantum paradigm is concerned, this notion yields the complexity class FBQPOLYLOG of functions approximable in polylogarithmic time with a quantum random-access Turing machine. We introduce a quantum programming language with first-order recursive procedures, which provides the first programming-language-based characterization of FBQPOLYLOG. Each program computes a function in FBQPOLYLOG (soundness) and, conversely, each function of this complexity class is computed by a program (completeness). We also provide a compilation strategy from programs to uniform families of quantum circuits of polylogarithmic depth and polynomial size, whose set of computed functions is known as QNC, and recover the well-known separation result FBQPOLYLOG $\subsetneq$ QNC.
1.64-Approximation for Chromatic Correlation Clustering via Chromatic Cluster LP
Venue: arXiv cs.DS 2025
Year: 2025

Chromatic Correlation Clustering (CCC) generalizes Correlation Clustering by assigning multiple categorical relationships (colors) to edges and imposing chromatic constraints on the clusters. Unlike traditional Correlation Clustering, which only deals with binary $(+/-)$ relationships, CCC captures richer relational structures. Despite its importance, improving the approximation for CCC has been difficult due to the limitations of standard LP relaxations. We present a randomized $1.64$-approximation algorithm to the CCC problem, significantly improving the previous factor of $2.15$. Our approach extends the cluster LP framework to the chromatic setting by introducing a chromatic cluster LP relaxation and an rounding algorithm that utilizes both a cluster-based and a greedy pivot-based strategy. The analysis bypasses the integrality gap of $2$ for the CCC version of standard LP and highlights the potential of the cluster LP framework to address other variants of clustering problems.
PhishIntentionLLM: Uncovering Phishing Website Intentions through Multi-Agent Retrieval-Augmented Generation
Venue: arXiv cs.CR 2025
Year: 2025

Phishing websites remain a major cybersecurity threat, yet existing methods primarily focus on detection, while the recognition of underlying malicious intentions remains largely unexplored. To address this gap, we propose PhishIntentionLLM, a multi-agent retrieval-augmented generation (RAG) framework that uncovers phishing intentions from website screenshots. Leveraging the visual-language capabilities of large language models (LLMs), our framework identifies four key phishing objectives: Credential Theft, Financial Fraud, Malware Distribution, and Personal Information Harvesting. We construct and release the first phishing intention ground truth dataset (~2K samples) and evaluate the framework using four commercial LLMs. Experimental results show that PhishIntentionLLM achieves a micro-precision of 0.7895 with GPT-4o and significantly outperforms the single-agent baseline with a ~95% improvement in micro-precision. Compared to the previous work, it achieves 0.8545 precision for credential theft, marking a ~4% improvement. Additionally, we generate a larger dataset of ~9K samples for large-scale phishing intention profiling across sectors. This work provides a scalable and interpretable solution for intention-aware phishing analysis.
Assessing the Benefits of Ground Vehicles as Moving Urban Base Stations
Venue: arXiv cs.NI 2025
Year: 2025

In the evolution towards 6G user-centric networking, the moving network (MN) paradigm can play an important role. In a MN, some small cell base stations (BS) are installed on top of vehicles, and enable a more dynamic, flexible and sustainable, network operation. By "following" the users movements and adapting dynamically to their requests, the MN paradigm enables a more efficient utilization of network resources, mitigating the need for dense small cell BS deployments at the cost of an increase in resource utilization due to wireless backhauling. This aspect is at least partly compensated by the shorter distance between users and BS, which allows for lower power and Line-of-Sight communications. While the MN paradigm has been investigated for some time, to date, it is still unclear in which conditions the advantages of MN outweigh the additional resource costs. In this paper, we propose a stochastic geometry framework for the characterization of the potential benefits of the MN paradigm as part of an HetNet in urban settings. Our approach allows the estimation of user-perceived performance, accounting for wireless backhaul connectivity as well as base station resource scheduling. We formulate an optimization problem for determining the resource-optimal network configurations and BS scheduling which minimize the overall amount of deployed BSs in a QoS-aware manner, and the minimum vehicular flow between different urban districts required to support them, and we propose an efficient stochastic heuristic to solve it. Our numerical assessment suggests that the MN paradigm, coupled with appropriate dynamic network management strategies, significantly reduces the amount of deployed network infrastructure while guaranteeing the target QoS perceived by users.
Designing at 1:1 Scale on Wall-Sized Displays Using Existing UI Design Tools
Venue: arXiv cs.HC 2025
Year: 2025

Wall-Sized Displays have spatial characteristics that are difficult to address during user interface design. The design at scale 1:1 could be part of the solution. In this paper, we present the results of two user studies and one technology review, exploring the usability of popular, desktop-optimized prototyping tools, for designing at scale on Wall-Sized Displays. We considered two wall-sized display setups, and three different interaction methods: touch, a keyboard equipped with a touchpad, and a tablet. We observed that designing at scale 1:1 was appreciated. Tablet-based interaction proved to be the most comfortable interaction method, and a mix of interaction modalities is promising. In addition, care must be given to the surrounding environment, such as furniture. We propose twelve design guidelines for a design tool dedicated to this specific context. Overall, existing user interface design tools do not yet fully support design on and for wall-sized displays and require further considerations in terms of placement of user interface elements and the provision of additional features.