OpenRaiser/Intern-Atlas huggingface dataset:
GUIDE: Enhancing Gradient Inversion Attacks in Federated Learning with Denoising Models
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) enables collaborative training of Machine Learning (ML) models across multiple clients while preserving their privacy. Rather than sharing raw data, federated clients transmit locally computed updates to train the global model. Although this paradigm should provide stronger privacy guarantees than centralized ML, client updates remain vulnerable to privacy leakage. Adversaries can exploit them to infer sensitive properties about the training data or even to reconstruct the original inputs via Gradient Inversion Attacks (GIAs). Under the honest-butcurious threat model, GIAs attempt to reconstruct training data by reversing intermediate updates using optimizationbased techniques. We observe that these approaches usually reconstruct noisy approximations of the original inputs, whose quality can be enhanced with specialized denoising models. This paper presents Gradient Update Inversion with DEnoising (GUIDE), a novel methodology that leverages diffusion models as denoising tools to improve image reconstruction attacks in FL. GUIDE can be integrated into any GIAs that exploits surrogate datasets, a widely adopted assumption in GIAs literature. We comprehensively evaluate our approach in two attack scenarios that use different FL algorithms, models, and datasets. Our results demonstrate that GUIDE integrates seamlessly with two state-ofthe- art GIAs, substantially improving reconstruction quality across multiple metrics. Specifically, GUIDE achieves up to 46% higher perceptual similarity, as measured by the DreamSim metric.
Space-Time Rate-Splitting Multiple Access for Multibeam LEO Satellite Networks
Venue: arXiv cs.IT 2025
Year: 2025

This paper proposes a novel space-time rate-splitting multiple access (ST-RSMA) framework for multibeam low Earth orbit (LEO) satellite communications (SATCOM) systems, where space-time coding is integrated into the common stream transmission. This design enables full diversity gain in the common stream transmission for all users, regardless of the uncertainty of the channel state information (CSI) and network load conditions, thereby overcoming the performance limitations of conventional RSMA that employs a single beamforming vector for all users. To further enhance performance, we develop a weighted minimum mean square error (WMMSE)-based algorithm tailored to ST-RSMA that jointly optimizes the power allocation for the common stream and the power/beamforming vectors for private streams, aiming to maximize the minimum user rate. Numerical results show that ST-RSMA significantly outperforms conventional RSMA and other multiple access techniques, offering a robust and scalable solution for LEO SATCOM.
LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena
Venue: arXiv cs.AI 2025
Year: 2025

Forecasting is not only a fundamental intellectual pursuit but also is of significant importance to societal systems such as finance and economics. With the rapid advances of large language models (LLMs) trained on Internet-scale data, it raises the promise of employing LLMs to forecast real-world future events, an emerging paradigm we call "LLM-as-a-Prophet". This paper systematically investigates such predictive intelligence of LLMs. To this end, we build Prophet Arena, a general evaluation benchmark that continuously collects live forecasting tasks and decomposes each task into distinct pipeline stages, in order to support our controlled and large-scale experimentation. Our comprehensive evaluation reveals that many LLMs already exhibit impressive forecasting capabilities, reflected in, e.g., their small calibration errors, consistent prediction confidence and promising market returns. However, we also uncover key bottlenecks towards achieving superior predictive intelligence via LLM-as-a-Prophet, such as LLMs' inaccurate event recalls, misunderstanding of data sources and slower information aggregation compared to markets when resolution nears.
Pointing-Error-Induced Fading in an Open-Loop THz Uplink with Hardware Impairments
Venue: arXiv cs.NI 2025
Year: 2025

We analyze the open-loop mechanical tracking performance of a sub-Terahertz (sub-THz) and Terahertz (THz) uplink communication system. These high-frequency bands enable multi-gigabit links through large bandwidths and narrow beams, but require precise pointing to overcome spreading loss. A tracking system can be used to orient horn antennas toward mobile targets. We develop a mathematical model that captures the mechanical dynamics of a real tracking system, which includes motion latency and acceleration and velocity limits, to quantify pointing errors during satellite passes and integrate these effects into the link budget. We evaluate the trade-offs between beam directionality and pointing tolerance across different Low Earth Orbit (LEO) satellite trajectories and control strategies. The results link the hardware limitations to the communications performance, providing design guidelines for high-frequency Non-Terrestrial Network (NTN) uplink under practical mechanical constraints.
Handling Extreme Class Imbalance: Using GANs in Data Augmentation for Suicide Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Suicide prediction is the key for prevention, but real data with sufficient positive samples is rare and causes extreme class imbalance. We utilized machine learning (ML) to build the model and deep learning (DL) techniques, like Generative Adversarial Networks (GAN), to generate synthetic data samples to enhance the dataset. The initial dataset contained 656 samples, with only four positive cases, prompting the need for data augmentation. A variety of machine learning models, ranging from interpretable data models to black box algorithmic models, were used. On real test data, Logistic Regression (LR) achieved a weighted precision of 0.99, a weighted recall of 0.85, and a weighted F1 score of 0.91; Random Forest (RF) showed 0.98, 0.99, and 0.99, respectively; and Support Vector Machine (SVM) achieved 0.99, 0.76, and 0.86. LR and SVM correctly identified one suicide attempt case (sensitivity:1.0) and misclassified LR(20) and SVM (31) non-attempts as attempts (specificity: 0.85 & 0.76, respectively). RF identified 0 suicide attempt cases (sensitivity: 0.0) with 0 false positives (specificity: 1.0). These results highlight the models' effectiveness, with GAN playing a key role in generating synthetic data to support suicide prevention modeling efforts.
DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Supervised Speech Foundational Model
Venue: arXiv cs.SD 2025
Year: 2025

Self-supervised speech models have achieved remarkable success on content-driven tasks, yet they remain limited in capturing speaker-discriminative features critical for verification, diarization, and profiling applications. We introduce DELULU, a speaker-aware self-supervised foundational model that addresses this limitation by integrating external supervision into the pseudo-label generation process. DELULU leverages frame-level embeddings from ReDimNet, a state-of-the-art speaker verification model, to guide the k-means clustering step during pre-training, introducing a strong speaker-discriminative inductive bias that aligns representation learning with speaker identity. The model is trained using a dual objective that combines masked prediction and denoising, further enhancing robustness and generalization. DELULU significantly outperforms prior self-supervised learning (SSL) models across a range of speaker-centric tasks, achieving up to 62% relative improvement in equal error rate (EER) for speaker verification and consistent gains on zero-shot profiling tasks such as gender, age, accent, and speaker counting. Our findings demonstrate that DELULU is a strong universal encoder for speaker-aware speech processing, enabling superior performance even without task-specific fine-tuning.
Multilingual Text-to-Image Person Retrieval via Bidirectional Relation Reasoning and Aligning
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image person retrieval (TIPR) aims to identify the target person using textual descriptions, facing challenge in modality heterogeneity. Prior works have attempted to address it by developing cross-modal global or local alignment strategies. However, global methods typically overlook fine-grained cross-modal differences, whereas local methods require prior information to explore explicit part alignments. Additionally, current methods are English-centric, restricting their application in multilingual contexts. To alleviate these issues, we pioneer a multilingual TIPR task by developing a multilingual TIPR benchmark, for which we leverage large language models for initial translations and refine them by integrating domain-specific knowledge. Correspondingly, we propose Bi-IRRA: a Bidirectional Implicit Relation Reasoning and Aligning framework to learn alignment across languages and modalities. Within Bi-IRRA, a bidirectional implicit relation reasoning module enables bidirectional prediction of masked image and text, implicitly enhancing the modeling of local relations across languages and modalities, a multi-dimensional global alignment module is integrated to bridge the modality heterogeneity. The proposed method achieves new state-of-the-art results on all multilingual TIPR datasets. Data and code are presented in https://github.com/Flame-Chasers/Bi-IRRA.
PANER: A Paraphrase-Augmented Framework for Low-Resource Named Entity Recognition
Venue: arXiv cs.CL 2025
Year: 2025

Named Entity Recognition (NER) is a critical task that requires substantial annotated data, making it challenging in low-resource scenarios where label acquisition is expensive. While zero-shot and instruction-tuned approaches have made progress, they often fail to generalize to domain-specific entities and do not effectively utilize limited available data. We present a lightweight few-shot NER framework that addresses these challenges through two key innovations: (1) a new instruction tuning template with a simplified output format that combines principles from prior IT approaches to leverage the large context window of recent state-of-the-art LLMs; (2) introducing a strategic data augmentation technique that preserves entity information while paraphrasing the surrounding context, thereby expanding our training data without compromising semantic relationships. Experiments on benchmark datasets show that our method achieves performance comparable to state-of-the-art models on few-shot and zero-shot tasks, with our few-shot approach attaining an average F1 score of 80.1 on the CrossNER datasets. Models trained with our paraphrasing approach show consistent improvements in F1 scores of up to 17 points over baseline versions, offering a promising solution for groups with limited NER training data and compute power.
Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition
Venue: arXiv cs.CV 2025
Year: 2025

We address multi-reference visual place recognition (VPR), where reference sets captured under varying conditions are used to improve localisation performance. While deep learning with large-scale training improves robustness, increasing data diversity and model complexity incur extensive computational cost during training and deployment. Descriptor-level fusion via voting or aggregation avoids training, but often targets multi-sensor setups or relies on heuristics with limited gains under appearance and viewpoint change. We propose a training-free, descriptor-agnostic approach that jointly models places using multiple reference descriptors via matrix decomposition into basis representations, enabling projection-based residual matching. We also introduce SotonMV, a structured benchmark for multi-viewpoint VPR. On multi-appearance data, our method improves Recall@1 by up to ~18% over single-reference and outperforms multi-reference baselines across appearance and viewpoint changes, with gains of ~5% on unstructured data, demonstrating strong generalisation while remaining lightweight.
A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications
Venue: arXiv cs.NE 2025
Year: 2025

Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables neuromorphic applications running at the edge, meaning they process sensory input directly and without any up-link to or dependency on a cloud service. The kernel shows speed-up gains over single thread processing by a factor of four on moderately sized SNNs and 1.7X on a Synfire network. Furthermore, it load-balances all cores available on multi-core processors, such as ARM, which run today's mobile devices and is up to 70% more energy efficient compared to statical core assignment. The present work can enable the development of edge applications that have low Size, Weight, and Power (SWaP), and can prototype the integration of neuromorphic chips.
Human-AI Interactions: Cognitive, Behavioral, and Emotional Impacts
Venue: arXiv cs.HC 2025
Year: 2025

As stories of human-AI interactions continue to be highlighted in the news and research platforms, the challenges are becoming more pronounced, including potential risks of overreliance, cognitive offloading, social and emotional manipulation, and the nuanced degradation of human agency and judgment. This paper surveys recent research on these issues through the lens of the psychological triad: cognition, behavior, and emotion. Observations seem to suggest that while AI can substantially enhance memory, creativity, and engagement, it also introduces risks such as diminished critical thinking, skill erosion, and increased anxiety. Emotional outcomes are similarly mixed, with AI systems showing promise for support and stress reduction, but raising concerns about dependency, inappropriate attachments, and ethical oversight. This paper aims to underscore the need for responsible and context-aware AI design, highlighting gaps for longitudinal research and grounded evaluation frameworks to balance benefits with emerging human-centric risks.
Atlas-based Manifold Representations for Interpretable Riemannian Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

Despite the popularity of the manifold hypothesis, current manifold-learning methods do not support machine learning directly on the latent $d$-dimensional data manifold, as they primarily aim to perform dimensionality reduction into $\mathbb{R}^D$, losing key manifold features when the embedding dimension $D$ approaches $d$. On the other hand, methods that directly learn the latent manifold as a differentiable atlas have been relatively underexplored. In this paper, we aim to give a proof of concept of the effectiveness and potential of atlas-based methods. To this end, we implement a generic data structure to maintain a differentiable atlas that enables Riemannian optimization over the manifold. We complement this with an unsupervised heuristic that learns a differentiable atlas from point cloud data. We experimentally demonstrate that this approach has advantages in terms of efficiency and accuracy in selected settings. Moreover, in a supervised classification task over the Klein bottle and in RNA velocity analysis of hematopoietic data, we showcase the improved interpretability and robustness of our approach.
SoftMimic: Learning Compliant Whole-body Control from Examples
Venue: arXiv cs.RO 2025
Year: 2025

We introduce SoftMimic, a framework for learning compliant whole-body control policies for humanoid robots from example motions. Imitating human motions with reinforcement learning allows humanoids to quickly learn new skills, but existing methods incentivize stiff control that aggressively corrects deviations from a reference motion, leading to brittle and unsafe behavior when the robot encounters unexpected contacts. In contrast, SoftMimic enables robots to respond compliantly to external forces while maintaining balance and posture. Our approach leverages an inverse kinematics solver to generate an augmented dataset of feasible compliant motions, which we use to train a reinforcement learning policy. By rewarding the policy for matching compliant responses rather than rigidly tracking the reference motion, SoftMimic learns to absorb disturbances and generalize to varied tasks from a single motion clip. We validate our method through simulations and real-world experiments, demonstrating safe and effective interaction with the environment.
Unbiased Gradient Low-Rank Projection
Venue: arXiv cs.LG 2025
Year: 2025

Memory-efficient optimization is critical for training increasingly large language models (LLMs). A popular strategy involves gradient low-rank projection, storing only the projected optimizer states, with GaLore being a representative example. However, a significant drawback of many such methods is their lack of convergence guarantees, as various low-rank projection approaches introduce inherent biases relative to the original optimization algorithms, which contribute to performance gaps compared to full-parameter training. Aiming to tackle this problem, this paper investigates the layerwise sampling technique for debiasing low-rank projection mechanisms. In particular, an instantiation of the paradigm gives rise to a novel and unbiased low-rank optimization method built upon GaLore's mechanism and the Muon algorithm, named GaLore Unbiased with Muon (GUM). We theoretically prove our method matches the convergence guarantees of the base Muon algorithm while preserving the memory efficiency of low-rank techniques. Empirical experiments on LLM fine-tuning and pretraining also demonstrate non-trivial improvements over GaLore and even better performance than full-parameter training. Further investigation shows that the improvement of this technique comes from a more uniform distribution of knowledge inside layers, leading to more efficient utilization of the model parameter space and better memorization.
A Homological Separation of $\mathbf{P}$ from $\mathbf{NP}$ via Computational Topology and Category Theory
Venue: arXiv cs.CC 2025
Year: 2025

This paper establishes the separation of complexity classes $\mathbf{P}$ and $\mathbf{NP}$ through a novel homological algebraic approach grounded in category theory. We construct the computational category $\mathbf{Comp}$, embedding computational problems and reductions into a unified categorical framework. By developing computational homology theory, we associate to each problem $L$ a chain complex $C_{\bullet}(L)$ whose homology groups $H_n(L)$ capture topological invariants of computational processes. Our main result demonstrates that problems in $\mathbf{P}$ exhibit trivial computational homology ($H_n(L) = 0$ for all $n > 0$), while $\mathbf{NP}$-complete problems such as SAT possess non-trivial homology ($H_1(\mathrm{SAT}) \neq 0$). This homological distinction provides the first rigorous proof of $\mathbf{P} \neq \mathbf{NP}$ using topological methods. Our work inaugurates computational topology as a new paradigm for complexity analysis, offering finer distinctions than traditional combinatorial approaches and establishing connections between structural complexity theory and homological invariants.
Evolve with Your Research -- Stepwise System Evolution from Document-driven to Fact-centric Research Data Management in Materials Science
Venue: arXiv cs.DL 2025
Year: 2025

The digitalisation of research requires data management systems capable of supporting a broad spectrum of usage scenarios, ranging from document-oriented repositories to fully factographic environments. This paper introduces a methodological approach for the stepwise development of such systems, illustrated by the MatInf Research Data Management System (RDMS). The proposed framework combines a graph-based STAR paradigm-emphasising Statefulness, Traceability, Aim, and Result-with the SET methodology, which enables systematic Standardisation, Extraction, and Testing of research data. Together, these principles provide a pathway towards FAIR-compliant data infrastructures, facilitating reproducibility, re-use, and integration of heterogeneous materials science data. By demonstrating the gradual consolidation of research outputs into unified datasets, this study highlights how adaptive RDMS design can support accelerated scientific discovery and enhance collaborative research in large-scale projects.
Deploying Atmospheric and Oceanic AI Models on Chinese Hardware and Framework: Migration Strategies, Performance Optimization and Analysis
Venue: arXiv cs.DC 2025
Year: 2025

With the growing role of artificial intelligence in climate and weather research, efficient model training and inference are in high demand. Current models like FourCastNet and AI-GOMS depend heavily on GPUs, limiting hardware independence, especially for Chinese domestic hardware and frameworks. To address this issue, we present a framework for migrating large-scale atmospheric and oceanic models from PyTorch to MindSpore and optimizing for Chinese chips, and evaluating their performance against GPUs. The framework focuses on software-hardware adaptation, memory optimization, and parallelism. Furthermore, the model's performance is evaluated across multiple metrics, including training speed, inference speed, model accuracy, and energy efficiency, with comparisons against GPU-based implementations. Experimental results demonstrate that the migration and optimization process preserves the models' original accuracy while significantly reducing system dependencies and improving operational efficiency by leveraging Chinese chips as a viable alternative for scientific computing. This work provides valuable insights and practical guidance for leveraging Chinese domestic chips and frameworks in atmospheric and oceanic AI model development, offering a pathway toward greater technological independence.
CMIS-Net: A Cascaded Multi-Scale Individual Standardization Network for Backchannel Agreement Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Backchannels are subtle listener responses, such as nods, smiles, or short verbal cues like "yes" or "uh-huh," which convey understanding and agreement in conversations. These signals provide feedback to speakers, improve the smoothness of interaction, and play a crucial role in developing human-like, responsive AI systems. However, the expression of backchannel behaviors is often significantly influenced by individual differences, operating across multiple scales: from instant dynamics such as response intensity (frame-level) to temporal patterns such as frequency and rhythm preferences (sequence-level). This presents a complex pattern recognition problem that contemporary emotion recognition methods have yet to fully address. Particularly, existing individualized methods in emotion recognition often operate at a single scale, overlooking the complementary nature of multi-scale behavioral cues. To address these challenges, we propose a novel Cascaded Multi-Scale Individual Standardization Network (CMIS-Net) that extracts individual-normalized backchannel features by removing person-specific neutral baselines from observed expressions. Operating at both frame and sequence levels, this normalization allows model to focus on relative changes from each person's baseline rather than absolute expression values. Furthermore, we introduce an implicit data augmentation module to address the observed training data distributional bias, improving model generalization. Comprehensive experiments and visualizations demonstrate that CMIS-Net effectively handles individual differences and data imbalance, achieving state-of-the-art performance in backchannel agreement detection.