OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Class Imbalanced Learning with Support Vector Machines via Differential Evolution
Venue: arXiv cs.LG 2025
Year: 2025

Support vector machine (SVM) is a powerful machine learning algorithm to handle classification tasks. However, the classical SVM is developed for binary problems with the assumption of balanced datasets. Obviously, the multi-class imbalanced classification problems are more complex. In this paper, we propose an improved SVM via Differential Evolution (i-SVM-DE) method to deal with it. An improved SVM (i-SVM) model is proposed to handle the data imbalance by combining cost sensitive technique and separation margin modification in the constraints, which formalize a parameter optimization problem. By using one-versus-one (OVO) scheme, a multi-class problem is decomposed into a number of binary subproblems. A large optimization problem is formalized through concatenating the parameters in the binary subproblems. To find the optimal model effectively and learn the support vectors for each class simultaneously, an improved differential evolution (DE) algorithm is applied to solve this large optimization problem. Instead of the validation set, we propose the fitness functions to evaluate the learned model and obtain the optimal parameters in the search process of DE. A series of experiments are carried out to verify the benefits of our proposed method. The results indicate that i-SVM-DE is statistically superior by comparing with the other baseline methods.
Behavioral Analysis of Information Salience in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) excel at text summarization, a task that requires models to select content based on its importance. However, the exact notion of salience that LLMs have internalized remains unclear. To bridge this gap, we introduce an explainable framework to systematically derive and investigate information salience in LLMs through their summarization behavior. Using length-controlled summarization as a behavioral probe into the content selection process, and tracing the answerability of Questions Under Discussion throughout, we derive a proxy for how models prioritize information. Our experiments on 13 models across four datasets reveal that LLMs have a nuanced, hierarchical notion of salience, generally consistent across model families and sizes. While models show highly consistent behavior and hence salience patterns, this notion of salience cannot be accessed through introspection, and only weakly correlates with human perceptions of information salience.
Reward Models Identify Consistency, Not Causality
Venue: arXiv cs.LG 2025
Year: 2025

Reward models (RMs) play a crucial role in aligning large language models (LLMs) with human preferences and enhancing reasoning quality. Traditionally, RMs are trained to rank candidate outputs based on their correctness and coherence. However, in this work, we present several surprising findings that challenge common assumptions about RM behavior. Our analysis reveals that state-of-the-art reward models prioritize structural consistency over causal correctness. Specifically, removing the problem statement has minimal impact on reward scores, whereas altering numerical values or disrupting the reasoning flow significantly affects RM outputs. Furthermore, RMs exhibit a strong dependence on complete reasoning trajectories truncated or incomplete steps lead to significant variations in reward assignments, indicating that RMs primarily rely on learned reasoning patterns rather than explicit problem comprehension. These findings hold across multiple architectures, datasets, and tasks, leading to three key insights: (1) RMs primarily assess coherence rather than true reasoning quality; (2) The role of explicit problem comprehension in reward assignment is overstated; (3) Current RMs may be more effective at ranking responses than verifying logical validity. Our results suggest a fundamental limitation in existing reward modeling approaches, emphasizing the need for a shift toward causality-aware reward models that go beyond consistency-driven evaluation.
Augmenting Coaching with GenAI: Insights into Use, Effectiveness, and Future Potential
Venue: arXiv cs.HC 2025
Year: 2025

The integration of generative AI (GenAI) tools, particularly large language models (LLMs), is transforming professional coaching workflows. This study explores how coaches use GenAI, the perceived benefits and limitations of these tools, and broader attitudes toward AI-assisted coaching. A survey of 205 coaching professionals reveals widespread adoption of GenAI for research, content creation, and administrative support, while its role in relational and interpretative coaching remains limited. Findings indicate that AI literacy and perceived AI impact strongly predict GenAI adoption, with positive attitudes fostering greater use. Ethical considerations, particularly transparency and data privacy, are a key concern, with frequent AI users demonstrating greater ethical awareness. Regression analyses show that while perceived effectiveness drives GenAI adoption, concerns about AI replacing human coaches do not significantly influence usage. Coaches express interest in future AI capabilities that enhance personalization, real-time feedback, and administrative automation while maintaining human oversight. The study highlights that GenAI functions best as an augmentation tool rather than a replacement, emphasizing the need for AI literacy training, ethical guidelines, and human-centered AI integration. These findings contribute to the ongoing discourse on human-AI collaboration, advocating for responsible and effective AI adoption in professional coaching.
iAgent: LLM Agent as a Shield between User and Recommender Systems
Venue: arXiv cs.CL 2025
Year: 2025

Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, the defect of recommendation algorithms may put users in very vulnerable positions under this paradigm. First, many sophisticated models are often designed with commercial objectives in mind, focusing on the platform's benefits, which may hinder their ability to protect and capture users' true interests. Second, these models are typically optimized using data from all users, which may overlook individual user's preferences. Due to these shortcomings, users may experience several disadvantages under the traditional user-platform direct exposure paradigm, such as lack of control over the recommender system, potential manipulation by the platform, echo chamber effects, or lack of personalization for less active users due to the dominance of active users during collaborative learning. Therefore, there is an urgent need to develop a new paradigm to protect user interests and alleviate these issues. Recently, some researchers have introduced LLM agents to simulate user behaviors, these approaches primarily aim to optimize platform-side performance, leaving core issues in recommender systems unresolved. To address these limitations, we propose a new user-agent-platform paradigm, where agent serves as the protective shield between user and recommender system that enables indirect exposure.
The Restricted Isometry Property for Measurements from Group Orbits
Venue: arXiv cs.IT 2025
Year: 2025

It is known that sparse recovery by measurements from random circulant matrices provides good recovery bounds. We generalize this to measurements that arise as a random orbit of a group representation for some finite group G. We derive estimates for the number of measurements required to guarantee the restricted isometry property with high probability. Following this, we present several examples highlighting the role of appropriate representation-theoretic assumptions.
CDGS: Confidence-Aware Depth Regularization for 3D Gaussian Splatting
Venue: arXiv cs.GR 2025
Year: 2025

3D Gaussian Splatting (3DGS) has shown significant advantages in novel view synthesis (NVS), particularly in achieving high rendering speeds and high-quality results. However, its geometric accuracy in 3D reconstruction remains limited due to the lack of explicit geometric constraints during optimization. This paper introduces CDGS, a confidence-aware depth regularization approach developed to enhance 3DGS. We leverage multi-cue confidence maps of monocular depth estimation and sparse Structure-from-Motion depth to adaptively adjust depth supervision during the optimization process. Our method demonstrates improved geometric detail preservation in early training stages and achieves competitive performance in both NVS quality and geometric accuracy. Experiments on the publicly available Tanks and Temples benchmark dataset show that our method achieves more stable convergence behavior and more accurate geometric reconstruction results, with improvements of up to 2.31 dB in PSNR for NVS and consistently lower geometric errors in M3C2 distance metrics. Notably, our method reaches comparable F-scores to the original 3DGS with only 50% of the training iterations. We expect this work will facilitate the development of efficient and accurate 3D reconstruction systems for real-world applications such as digital twin creation, heritage preservation, or forestry applications.
Counter Pools: Counter Representation for Efficient Stream Processing
Venue: arXiv cs.NI 2025
Year: 2025

Due to the large data volume and number of distinct elements, space is often the bottleneck of many stream processing systems. The data structures used by these systems often consist of counters whose optimization yields significant memory savings. The challenge lies in balancing the size of the counters: too small, and they overflow; too large, and memory capacity limits their number. In this work, we suggest an efficient encoding scheme that sizes each counter according to its needs. Our approach uses fixed-sized pools of memory (e.g., a single memory word or 64 bits), where each pool manages a small number of counters. We pay special attention to performance and demonstrate considerable improvements for various streaming algorithms and workload characteristics.
Entity Framing and Role Portrayal in the News
Venue: arXiv cs.CL 2025
Year: 2025

We introduce a novel multilingual hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence.
Multi-dataset synergistic in supervised learning to pre-label structural components in point clouds from shell construction scenes
Venue: arXiv cs.CV 2025
Year: 2025

The significant effort required to annotate data for new training datasets hinders computer vision research and machine learning in the construction industry. This work explores adapting standard datasets and the latest transformer model architectures for point cloud semantic segmentation in the context of shell construction sites. Unlike common approaches focused on object segmentation of building interiors and furniture, this study addressed the challenges of segmenting complex structural components in Architecture, Engineering, and Construction (AEC). We establish a baseline through supervised training and a custom validation dataset, evaluate the cross-domain inference with large-scale indoor datasets, and utilize transfer learning to maximize segmentation performance with minimal new data. The findings indicate that with minimal fine-tuning, pre-trained transformer architectures offer an effective strategy for building component segmentation. Our results are promising for automating the annotation of new, previously unseen data when creating larger training resources and for the segmentation of frequently recurring objects.
Pitch Imperfect: Detecting Audio Deepfakes Through Acoustic Prosodic Analysis
Venue: arXiv cs.SD 2025
Year: 2025

Audio deepfakes are increasingly in-differentiable from organic speech, often fooling both authentication systems and human listeners. While many techniques use low-level audio features or optimization black-box model training, focusing on the features that humans use to recognize speech will likely be a more long-term robust approach to detection. We explore the use of prosody, or the high-level linguistic features of human speech (e.g., pitch, intonation, jitter) as a more foundational means of detecting audio deepfakes. We develop a detector based on six classical prosodic features and demonstrate that our model performs as well as other baseline models used by the community to detect audio deepfakes with an accuracy of 93% and an EER of 24.7%. More importantly, we demonstrate the benefits of using a linguistic features-based approach over existing models by applying an adaptive adversary using an $L_{\infty}$ norm attack against the detectors and using attention mechanisms in our training for explainability. We show that we can explain the prosodic features that have highest impact on the model's decision (Jitter, Shimmer and Mean Fundamental Frequency) and that other models are extremely susceptible to simple $L_{\infty}$ norm attacks (99.3% relative degradation in accuracy). While overall performance may be similar, we illustrate the robustness and explainability benefits to a prosody feature approach to audio deepfake detection.
HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitoring Hidden States
Venue: arXiv cs.CL 2025
Year: 2025

The integration of additional modalities increases the susceptibility of large vision-language models (LVLMs) to safety risks, such as jailbreak attacks, compared to their language-only counterparts. While existing research primarily focuses on post-hoc alignment techniques, the underlying safety mechanisms within LVLMs remain largely unexplored. In this work , we investigate whether LVLMs inherently encode safety-relevant signals within their internal activations during inference. Our findings reveal that LVLMs exhibit distinct activation patterns when processing unsafe prompts, which can be leveraged to detect and mitigate adversarial inputs without requiring extensive fine-tuning. Building on this insight, we introduce HiddenDetect, a novel tuning-free framework that harnesses internal model activations to enhance safety. Experimental results show that {HiddenDetect} surpasses state-of-the-art methods in detecting jailbreak attacks against LVLMs. By utilizing intrinsic safety-aware patterns, our method provides an efficient and scalable solution for strengthening LVLM robustness against multimodal threats. Our code will be released publicly at https://github.com/leigest519/HiddenDetect.
SQL4NN: Validation and expressive querying of models as data
Venue: arXiv cs.DB 2025
Year: 2025

We consider machine learning models, learned from data, to be an important, intensional, kind of data in themselves. As such, various analysis tasks on models can be thought of as queries over this intensional data, often combined with extensional data such as data for training or validation. We demonstrate that relational database systems and SQL can actually be well suited for many such tasks.
Online Resource Management for the Uplink of Wideband Hybrid Beamforming System
Venue: arXiv cs.NI 2025
Year: 2025

This paper studies the radio resource management (RRM) for the uplink (UL) of a cellular system with codebook-based hybrid beamforming. We consider the often neglected but highly practical multi-channel case with fewer radio frequency chains in the base station than user equipment (UEs) in the cell, assuming one RF chain per UE. As for any UL RRM, a per-time slot solution is needed as the allocation of power to subchannels by a UE can only be done once it knows which subchannels it has been allocated. The RRM in this system comprises beam selection, user selection and power allocation, three steps that are intricately coupled and we will show that the order in which they are performed does impact performance and so does the amount of coupling that we take into account. Specifically, we propose 4 online sequential solutions with different orders in which the steps are called and of different complexities, i.e., different levels of coupling between the steps. Our extensive numerical campaign for a mmWave system shows how a well-designed heuristic that takes some level of couplings between the steps can make the performance exceedingly better than a benchmark.
Byzantine Game Theory: Sun Tzus Boxes
Venue: arXiv cs.GT 2025
Year: 2025

We introduce the Byzantine Selection Problem, living at the intersection of game theory and fault-tolerant distributed computing. Here, an event organizer is presented with a group of $n$ agents, and wants to select $\ell < n$ of them to form a team. For these purposes, each agent $i$ self-reports a positive skill value $v_i$, and a team's value is the sum of its members' skill values. Ideally, the value of the team should be as large as possible, which can be easily achieved by selecting agents with the highest $\ell$ skill values. However, an unknown subset of at most $t < n$ agents are byzantine and hence not to be trusted, rendering their true skill values as $0$. In the spirit of the distributed computing literature, the identity of the byzantine agents is not random but instead chosen by an adversary aiming to minimize the value of the chosen team. Can we still select a team with good guarantees in this adversarial setting? As it turns out, deterministically, it remains optimal to select agents with the highest $\ell$ values. Yet, if $t \geq \ell$, the adversary can choose to make all selected agents byzantine, leading to a team of value zero. To provide meaningful guarantees, one hence needs to allow for randomization, in which case the expected value of the selected team needs to be maximized, assuming again that the adversary plays to minimize it. For this case, we provide linear-time randomized algorithms that maximize the expected value of the selected team.
VB-Com: Learning Vision-Blind Composite Humanoid Locomotion Against Deficient Perception
Venue: arXiv cs.RO 2025
Year: 2025

The performance of legged locomotion is closely tied to the accuracy and comprehensiveness of state observations. Blind policies, which rely solely on proprioception, are considered highly robust due to the reliability of proprioceptive observations. However, these policies significantly limit locomotion speed and often require collisions with the terrain to adapt. In contrast, Vision policies allows the robot to plan motions in advance and respond proactively to unstructured terrains with an online perception module. However, perception is often compromised by noisy real-world environments, potential sensor failures, and the limitations of current simulations in presenting dynamic or deformable terrains. Humanoid robots, with high degrees of freedom and inherently unstable morphology, are particularly susceptible to misguidance from deficient perception, which can result in falls or termination on challenging dynamic terrains. To leverage the advantages of both vision and blind policies, we propose VB-Com, a composite framework that enables humanoid robots to determine when to rely on the vision policy and when to switch to the blind policy under perceptual deficiency. We demonstrate that VB-Com effectively enables humanoid robots to traverse challenging terrains and obstacles despite perception deficiencies caused by dynamic terrains or perceptual noise.
A Survey of Model Architectures in Information Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

The period from 2019 to the present has represented one of the biggest paradigm shifts in information retrieval (IR) and natural language processing (NLP), culminating in the emergence of powerful large language models (LLMs) from 2022 onward. Methods leveraging pretrained encoder-only models (e.g., BERT) and LLMs have outperformed many previous approaches, particularly excelling in zero-shot scenarios and complex reasoning tasks. This work surveys the evolution of model architectures in IR, focusing on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. The review intentionally separates architectural considerations from training methodologies to provide a focused analysis of structural innovations in IR systems. We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs). We conclude with a forward-looking discussion of emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains such as autonomous search agents that is beyond traditional search paradigms.
Generating $π$-Functional Molecules Using STGG+ with Active Learning
Venue: arXiv cs.LG 2025
Year: 2025

Generating novel molecules with out-of-distribution properties is a major challenge in molecular discovery. While supervised learning methods generate high-quality molecules similar to those in a dataset, they struggle to generalize to out-of-distribution properties. Reinforcement learning can explore new chemical spaces but often conducts 'reward-hacking' and generates non-synthesizable molecules. In this work, we address this problem by integrating a state-of-the-art supervised learning method, STGG+, in an active learning loop. Our approach iteratively generates, evaluates, and fine-tunes STGG+ to continuously expand its knowledge. We denote this approach STGG+AL. We apply STGG+AL to the design of organic $π$-functional materials, specifically two challenging tasks: 1) generating highly absorptive molecules characterized by high oscillator strength and 2) designing absorptive molecules with reasonable oscillator strength in the near-infrared (NIR) range. The generated molecules are validated and rationalized in-silico with time-dependent density functional theory. Our results demonstrate that our method is highly effective in generating novel molecules with high oscillator strength, contrary to existing methods such as reinforcement learning (RL) methods. We open-source our active-learning code along with our Conjugated-xTB dataset containing 2.9 million $π$-conjugated molecules and the function for approximating the oscillator strength and absorption wavelength (based on sTDA-xTB).