OpenRaiser/Intern-Atlas huggingface dataset:
Aligning Brain Signals with Multimodal Speech and Vision Embeddings
Venue: arXiv cs.LG 2025
Year: 2025

When we hear the word "house", we don't just process sound, we imagine walls, doors, memories. The brain builds meaning through layers, moving from raw acoustics to rich, multimodal associations. Inspired by this, we build on recent work from Meta that aligned EEG signals with averaged wav2vec2 speech embeddings, and ask a deeper question: which layers of pre-trained models best reflect this layered processing in the brain? We compare embeddings from two models: wav2vec2, which encodes sound into language, and CLIP, which maps words to images. Using EEG recorded during natural speech perception, we evaluate how these embeddings align with brain activity using ridge regression and contrastive decoding. We test three strategies: individual layers, progressive concatenation, and progressive summation. The findings suggest that combining multimodal, layer-aware representations may bring us closer to decoding how the brain understands language, not just as sound, but as experience.
Real-DRL: Teach and Learn in Reality
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces the Real-DRL framework for safety-critical autonomous systems, enabling runtime learning of a deep reinforcement learning (DRL) agent to develop safe and high-performance action policies in real plants (i.e., real physical systems to be controlled), while prioritizing safety! The Real-DRL consists of three interactive components: a DRL-Student, a PHY-Teacher, and a Trigger. The DRL-Student is a DRL agent that innovates in the dual self-learning and teaching-to-learn paradigm and the real-time safety-informed batch sampling. On the other hand, PHY-Teacher is a physics-model-based design of action policies that focuses solely on safety-critical functions. PHY-Teacher is novel in its real-time patch for two key missions: i) fostering the teaching-to-learn paradigm for DRL-Student and ii) backing up the safety of real plants. The Trigger manages the interaction between the DRL-Student and the PHY-Teacher. Powered by the three interactive components, the Real-DRL can effectively address safety challenges that arise from the unknown unknowns and the Sim2Real gap. Additionally, Real-DRL notably features i) assured safety, ii) automatic hierarchy learning (i.e., safety-first learning and then high-performance learning), and iii) safety-informed batch sampling to address the learning experience imbalance caused by corner cases. Experiments with a real quadruped robot, a quadruped robot in NVIDIA Isaac Gym, and a cart-pole system, along with comparisons and ablation studies, demonstrate the Real-DRL's effectiveness and unique features.
Analysis of Line Break prediction models for detecting defensive breakthrough in football
Venue: arXiv cs.LG 2025
Year: 2025

In football, attacking teams attempt to break through the opponent's defensive line to create scoring opportunities. This action, known as a Line Break, is a critical indicator of offensive effectiveness and tactical performance, yet previous studies have mainly focused on shots or goal opportunities rather than on how teams break the defensive line. In this study, we develop a machine learning model to predict Line Breaks using event and tracking data from the 2023 J1 League season. The model incorporates 189 features, including player positions, velocities, and spatial configurations, and employs an XGBoost classifier to estimate the probability of Line Breaks. The proposed model achieved high predictive accuracy, with an AUC of 0.982 and a Brier score of 0.015. Furthermore, SHAP analysis revealed that factors such as offensive player speed, gaps in the defensive line, and offensive players' spatial distributions significantly contribute to the occurrence of Line Breaks. Finally, we found a moderate positive correlation between the predicted probability of being Line-Broken and the number of shots and crosses conceded at the team level. These results suggest that Line Breaks are closely linked to the creation of scoring opportunities and provide a quantitative framework for understanding tactical dynamics in football.
ParaScopes: What do Language Models Activations Encode About Future Text?
Venue: arXiv cs.CL 2025
Year: 2025

Interpretability studies in language models often investigate forward-looking representations of activations. However, as language models become capable of doing ever longer time horizon tasks, methods for understanding activations often remain limited to testing specific concepts or tokens. We develop a framework of Residual Stream Decoders as a method of probing model activations for paragraph-scale and document-scale plans. We test several methods and find information can be decoded equivalent to 5+ tokens of future context in small models. These results lay the groundwork for better monitoring of language models and better understanding how they might encode longer-term planning information.
EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Membership inference attacks (MIA) aim to infer whether a particular data point is part of the training dataset of a model. In this paper, we propose a new task in the context of LLM privacy: entity-level discovery of membership risk focused on sensitive information (PII, credit card numbers, etc). Existing methods for MIA can detect the presence of entire prompts or documents in the LLM training data, but they fail to capture risks at a finer granularity. We propose the ``EL-MIA'' framework for auditing entity-level membership risks in LLMs. We construct a benchmark dataset for the evaluation of MIA methods on this task. Using this benchmark, we conduct a systematic comparison of existing MIA techniques as well as two newly proposed methods. We provide a comprehensive analysis of the results, trying to explain the relation of the entity level MIA susceptability with the model scale, training epochs, and other surface level factors. Our findings reveal that existing MIA methods are limited when it comes to entity-level membership inference of the sensitive attributes, while this susceptibility can be outlined with relatively straightforward methods, highlighting the need for stronger adversaries to stress test the provided threat model.
Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach
Venue: arXiv cs.RO 2025
Year: 2025

Purpose: Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40-64 reaches, imposing time and fatigue burdens. We evaluate whether time-series foundation models can replace unrecorded trials from an early subset of reaches while preserving the reliability of standard Kinarm parameters. Methods: We analyzed VGR speed signals from 461 stroke and 599 control participants across 4- and 8-target reaching protocols. We withheld all but the first 8 or 16 reaching trials and used ARIMA, MOMENT, and Chronos models, fine-tuned on 70 percent of subjects, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, maximum speed) on combined recorded plus forecasted trials and compared them to full-length references using ICC(2,1). Results: Chronos forecasts restored ICC >= 0.90 for all parameters with only 8 recorded trials plus forecasts, matching the reliability of 24-28 recorded reaches (Delta ICC <= 0.07). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without materially compromising feature reliability. Conclusion: Foundation-model forecasting can greatly shorten Kinarm VGR assessment time. For the most impaired stroke survivors, sessions drop from 4-5 minutes to about 1 minute while preserving kinematic precision. This forecast-augmented paradigm promises efficient robotic evaluations for assessing motor impairments following stroke.
Incremental Selection of Most-Filtering Conjectures and Proofs of the Selected Conjectures
Venue: arXiv cs.AI 2025
Year: 2025

We present an improved incremental selection algorithm of the selection algorithm presented in [1] and prove all the selected conjectures.
Is Crowdsourcing a Puppet Show? Detecting a New Type of Fraud in Online Platforms
Venue: arXiv cs.HC 2025
Year: 2025

Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are important tools for researchers seeking to conduct studies with a broad, global participant base. Despite their popularity and demonstrated utility, we present evidence that suggests the integrity of data collected through Amazon MTurk is being threatened by the presence of puppeteers, apparently human workers controlling multiple puppet accounts that are capable of bypassing standard attention checks. If left undetected, puppeteers and their puppets can undermine the integrity of data collected on these platforms. This paper investigates data from two Amazon MTurk studies, finding that a substantial proportion of accounts (33% to 56.4%) are likely puppets. Our findings highlight the importance of adopting multifaceted strategies to ensure data integrity on crowdsourcing platforms. With the goal of detecting this type of fraud, we discuss a set of potential countermeasures for both puppets and bots with varying degrees of sophistication (e.g., employing AI). The problem of single entities (or puppeteers) manually controlling multiple accounts could exist on other crowdsourcing platforms; as such, their detection may be of broader application. While our findings suggest the need to re-evaluate the quality of crowdsourced data, many previous studies likely remain valid, particularly those with robust experimental designs. However, the presence of puppets may have contributed to false null results in some studies, suggesting that unpublished work may be worth revisiting with effective puppet detection strategies.
DocPrism: Local Categorization and External Filtering to Identify Relevant Code-Documentation Inconsistencies
Venue: arXiv cs.SE 2025
Year: 2025

Code-documentation inconsistencies are common and undesirable: they can lead to developer misunderstandings and software defects. This paper introduces DocPrism, a multi-language, code-documentation inconsistency detection tool. DocPrism uses a standard large language model (LLM) to analyze and explain inconsistencies. Plain use of LLMs for this task yield unacceptably high false positive rates: LLMs identify natural gaps between high-level documentation and detailed code implementations as inconsistencies. We introduce and apply the Local Categorization, External Filtering (LCEF) methodology to reduce false positives. LCEF relies on the LLM's local completion skills rather than its long-term reasoning skills. In our ablation study, LCEF reduces DocPrism's inconsistency flag rate from 98% to 14%, and increases accuracy from 14% to 94%. On a broad evaluation across Python, TypeScript, C++, and Java, DocPrism maintains a low flag rate of 15%, and achieves a precision of 0.62 without performing any fine-tuning.
Towards 1000-fold Electron Microscopy Image Compression for Connectomics via VQ-VAE with Transformer Prior
Venue: arXiv cs.CV 2025
Year: 2025

Petascale electron microscopy (EM) datasets push storage, transfer, and downstream analysis toward their current limits. We present a vector-quantized variational autoencoder-based (VQ-VAE) compression framework for EM that spans 16x to 1024x and enables pay-as-you-decode usage: top-only decoding for extreme compression, with an optional Transformer prior that predicts bottom tokens (without changing the compression ratio) to restore texture via feature-wise linear modulation (FiLM) and concatenation; we further introduce an ROI-driven workflow that performs selective high-resolution reconstruction from 1024x-compressed latents only where needed.
Hyperbolic Optimal Transport
Venue: arXiv cs.CV 2025
Year: 2025

The optimal transport (OT) problem aims to find the most efficient mapping between two probability distributions under a given cost function, and has diverse applications in many fields such as machine learning, computer vision and computer graphics. However, existing methods for computing optimal transport maps are primarily developed for Euclidean spaces and the sphere. In this paper, we explore the problem of computing the optimal transport map in hyperbolic space, which naturally arises in contexts involving hierarchical data, networks, and multi-genus Riemann surfaces. We propose a novel and efficient algorithm for computing the optimal transport map in hyperbolic space using a geometric variational technique by extending methods for Euclidean and spherical geometry to the hyperbolic setting. We also perform experiments on synthetic data and multi-genus surface models to validate the efficacy of the proposed method.
LongCat-Flash-Omni Technical Report
Venue: arXiv cs.MM 2025
Year: 2025

We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters, excelling at real-time audio-visual interaction. By adopting a curriculum-inspired progressive training strategy that transitions from simpler to increasingly complex modality sequence modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal capabilities while maintaining strong unimodal capability. Building upon LongCat-Flash, which adopts a high-performance Shortcut-connected Mixture-of-Experts (MoE) architecture with zero-computation experts, LongCat-Flash-Omni integrates efficient multimodal perception and speech reconstruction modules. Despite its immense size of 560B parameters (with 27B activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual interaction. For training infrastructure, we developed a modality-decoupled parallelism scheme specifically designed to manage the data and model heterogeneity inherent in large-scale multimodal training. This innovative approach demonstrates exceptional efficiency by sustaining over 90% of the throughput achieved by text-only training. Extensive evaluations show that LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal benchmarks among open-source models. Furthermore, it delivers highly competitive results across a wide range of modality-specific tasks, including text, image, and video understanding, as well as audio understanding and generation. We provide a comprehensive overview of the model architecture design, training procedures, and data strategies, and open-source the model to foster future research and development in the community.
A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis
Venue: arXiv cs.LG 2025
Year: 2025

In principle, deep learning models trained on medical time-series, including wearable photoplethysmography (PPG) sensor data, can provide a means to continuously monitor physiological parameters outside of clinical settings. However, there is considerable risk of poor performance when deployed in practical measurement scenarios leading to negative patient outcomes. Reliable uncertainties accompanying predictions can provide guidance to clinicians in their interpretation of the trustworthiness of model outputs. It is therefore of interest to compare the effectiveness of different approaches. Here we implement an unprecedented set of eight uncertainty quantification (UQ) techniques to models trained on two clinically relevant prediction tasks: Atrial Fibrillation (AF) detection (classification), and two variants of blood pressure regression. We formulate a comprehensive evaluation procedure to enable a rigorous comparison of these approaches. We observe a complex picture of uncertainty reliability across the different techniques, where the most optimal for a given task depends on the chosen expression of uncertainty, evaluation metric, and scale of reliability assessed. We find that assessing local calibration and adaptivity provides practically relevant insights about model behaviour that otherwise cannot be acquired using more commonly implemented global reliability metrics. We emphasise that criteria for evaluating UQ techniques should cater to the model's practical use case, where the use of a small number of measurements per patient places a premium on achieving small-scale reliability for the chosen expression of uncertainty, while preserving as much predictive performance as possible.
FGO MythBusters: Explaining how Kalman Filter variants achieve the same performance as FGO in navigation applications
Venue: arXiv cs.RO 2025
Year: 2025

Sliding window-factor graph optimization (SW-FGO) has gained more and more attention in navigation research due to its robust approximation to non-Gaussian noises and nonlinearity of measuring models. There are lots of works focusing on its application performance compared to extended Kalman filter (EKF) but there is still a myth at the theoretical relationship between the SW-FGO and EKF. In this paper, we find the necessarily fair condition to connect SW-FGO and Kalman filter variants (KFV) (e.g., EKF, iterative EKF (IEKF), robust EKF (REKF) and robust iterative EKF (RIEKF)). Based on the conditions, we propose a recursive FGO (Re-FGO) framework to represent KFV under SW-FGO formulation. Under explicit conditions (Markov assumption, Gaussian noise with L2 loss, and a one-state window), Re-FGO regenerates exactly to EKF/IEKF/REKF/RIEKF, while SW-FGO shows measurable benefits in nonlinear, non-Gaussian regimes at a predictable compute cost. Finally, after clarifying the connection between them, we highlight the unique advantages of SW-FGO in practical phases, especially on numerical estimation and deep learning integration. The code and data used in this work is open sourced at https://github.com/Baoshan-Song/KFV-FGO-Comparison.
PEARL: Power- and Energy-Aware Multicore Intermittent Computing
Venue: arXiv cs.ET 2025
Year: 2025

Low-power multicore platforms are suitable for running data-intensive tasks in parallel, but they are highly inefficient for computing on intermittent power. In this work, we present PEARL (PowEr And eneRgy-aware MuLticore Intermittent Computing), a novel systems support that can make existing multicore microcontroller (MCU) platforms suitable for efficient intermittent computing. PEARL achieves this by leveraging only a three-threshold voltage tracking circuit and an external fast non-volatile memory, which multicore MCUs can smoothly interface. PEARL software runtime manages these components and performs energy- and power-aware adaptation of the multicore configuration to introduce minimal backup overheads and boost performance. Our evaluation shows that PEARL outperforms the state-of-the-art solutions by up to 30x and consumes up to 32x less energy.
Reversal Invariance in Autoregressive Language Models
Venue: arXiv cs.CL 2025
Year: 2025

We formalize a structural property of the causal (autoregressive) language modeling (CLM) objective: reversal invariance. Formally, the next-token prediction loss assigns identical likelihood to a corpus and its reversal, implying that standard CLM pretraining is direction-blind. This symmetry explains why models trained on reversed text can achieve comparable performance to those trained on forward text, despite the inherently time-asymmetric nature of human language and reasoning. We argue that this invariance represents a limitation of current pretraining objectives rather than a benign artifact. If natural language encodes directional dependencies - phonological, morphological, or causal - a symmetric objective may fail to capture them. We therefore propose viewing pretraining through the lens of temporal asymmetry, motivating future work on loss functions and architectures that explicitly model the arrow of language while retaining standard language modeling capacity.
VisionCAD: An Integration-Free Radiology Copilot Framework
Venue: arXiv cs.CV 2025
Year: 2025

Widespread clinical deployment of computer-aided diagnosis (CAD) systems is hindered by the challenge of integrating with existing hospital IT infrastructure. Here, we introduce VisionCAD, a vision-based radiological assistance framework that circumvents this barrier by capturing medical images directly from displays using a camera system. The framework operates through an automated pipeline that detects, restores, and analyzes on-screen medical images, transforming camera-captured visual data into diagnostic-quality images suitable for automated analysis and report generation. We validated VisionCAD across diverse medical imaging datasets, demonstrating that our modular architecture can flexibly utilize state-of-the-art diagnostic models for specific tasks. The system achieves diagnostic performance comparable to conventional CAD systems operating on original digital images, with an F1-score degradation typically less than 2\% across classification tasks, while natural language generation metrics for automated reports remain within 1\% of those derived from original images. By requiring only a camera device and standard computing resources, VisionCAD offers an accessible approach for AI-assisted diagnosis, enabling the deployment of diagnostic capabilities in diverse clinical settings without modifications to existing infrastructure.
Reducing students' misconceptions about video game development. A mixed-method study
Venue: arXiv cs.HC 2025
Year: 2025

This study examines students' naïve mindset (misconceptions) about video game development, idealized and inaccurate beliefs that shape an unrealistic understanding of the field. The research evaluated the effectiveness of a fifteen-hour-long lecture series delivered by industry professionals, designed to challenge this mindset and expose students to the complexities and realities of game production. A mixed-methods approach was employed, combining qualitative analysis with a prototype quantitative tool developed to measure levels of misconception. Participants included students (n = 91) from diverse academic backgrounds interested in game creation and professionals (n = 94) working in the video game industry. Findings show that the intervention significantly reduced students' naïve beliefs while enhancing their motivation to pursue careers in the industry. Exposure to professional perspectives fostered a more realistic and informed mindset, taking into account the understanding of the technical, collaborative, and business aspects of game development. The results suggest that incorporating similar expert-led interventions early in game development education can improve learning outcomes, support informed career choices, and mitigate future professional disappointment.