OpenRaiser/Intern-Atlas huggingface dataset:
FireRedChat: A Pluggable, Full-Duplex Voice Interaction System with Cascaded and Semi-Cascaded Implementations
Venue: arXiv cs.SD 2025
Year: 2025

Full-duplex voice interaction allows users and agents to speak simultaneously with controllable barge-in, enabling lifelike assistants and customer service. Existing solutions are either end-to-end, difficult to design and hard to control, or modular pipelines governed by turn-taking controllers that ease upgrades and per-module optimization; however, prior modular frameworks depend on non-open components and external providers, limiting holistic optimization. In this work, we present a complete, practical full-duplex voice interaction system comprising a turn-taking controller, an interaction module, and a dialogue manager. The controller integrates streaming personalized VAD (pVAD) to suppress false barge-ins from noise and non-primary speakers, precisely timestamp primary-speaker segments, and explicitly enable primary-speaker barge-ins; a semantic end-of-turn detector improves stop decisions. It upgrades heterogeneous half-duplex pipelines, cascaded, semi-cascaded, and speech-to-speech, to full duplex. Using internal models, we implement cascaded and semi-cascaded variants; the semi-cascaded one captures emotional and paralinguistic cues, yields more coherent responses, lowers latency and error propagation, and improves robustness. A dialogue manager extends capabilities via tool invocation and context management. We also propose three system-level metrics, barge-in, end-of-turn detection accuracy, and end-to-end latency, to assess naturalness, control accuracy, and efficiency. Experiments show fewer false interruptions, more accurate semantic ends, and lower latency approaching industrial systems, enabling robust, natural, real-time full-duplex interaction. Demos: https://fireredteam.github.io/demos/firered_chat.
QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients
Venue: arXiv cs.LG 2025
Year: 2025

Photoplethysmogram (PPG) and electrocardiogram (ECG) are commonly recorded in intesive care unit (ICU) and operating room (OR). However, the high incidence of poor, incomplete, and inconsistent signal quality, can lead to false alarms or diagnostic inaccuracies. The methods explored so far suffer from limited generalizability, reliance on extensive labeled data, and poor cross-task transferability. To overcome these challenges, we introduce QualityFM, a novel multimodal foundation model for these physiological signals, designed to acquire a general-purpose understanding of signal quality. Our model is pre-trained on an large-scale dataset comprising over 21 million 30-second waveforms and 179,757 hours of data. Our approach involves a dual-track architecture that processes paired physiological signals of differing quality, leveraging a self-distillation strategy where an encoder for high-quality signals is used to guide the training of an encoder for low-quality signals. To efficiently handle long sequential signals and capture essential local quasi-periodic patterns, we integrate a windowed sparse attention mechanism within our Transformer-based model. Furthermore, a composite loss function, which combines direct distillation loss on encoder outputs with indirect reconstruction loss based on power and phase spectra, ensures the preservation of frequency-domain characteristics of the signals. We pre-train three models with varying parameter counts (9.6 M to 319 M) and demonstrate their efficacy and practical value through transfer learning on three distinct clinical tasks: false alarm of ventricular tachycardia detection, the identification of atrial fibrillation and the estimation of arterial blood pressure (ABP) from PPG and ECG signals.
Crown, Frame, Reverse: Layer-Wise Scaling Variants for LLM Pre-Training
Venue: arXiv cs.CL 2025
Year: 2025

Transformer-based language models traditionally use uniform (isotropic) layer sizes, yet they ignore the diverse functional roles that different depths can play and their computational capacity needs. Building on Layer-Wise Scaling (LWS) and pruning literature, we introduce three new LWS variants - Framed, Reverse, and Crown - that redistribute FFN widths and attention heads via two or three-point linear interpolation in the pre-training stage. We present the first systematic ablation of LWS and its variants, on a fixed budget of 180M parameters, trained on 5B tokens. All models converge to similar losses and achieve better performance compared to an equal-cost isotropic baseline, without a substantial decrease in training throughput. This work represents an initial step into the design space of layer-wise architectures for pre-training, but future work should scale experiments to orders of magnitude more tokens and parameters to fully assess their potential.
Modeling in the Design Multiverse
Venue: arXiv cs.SE 2025
Year: 2025

Real-world design processes often involve the evolution and divergence of design paths (by branching, revising, merging, etc.), especially when multiple stakeholders or teams operate concurrently and/or explore different alternatives for complex and heterogeneous systems. Unfortunately, this variability in time and space can not be directly managed in current modeling spaces but requires resorting to external tools and methodologies. In order to tackle this problem, we introduce the Design Multiverse. The Design Multiverse aims to integrate in the modeling space a selection of revisions and variants, representing snapshots of a design state composed of multiple artifacts. This enables stakeholders to seamlessly trace, analyze, and manage design decisions, system variants, and their interdependencies. Concretely, in this paper we present a conceptual definition of the Design Multiverse, discuss usage scenarios such as model product lines and model/metamodel co-evolution, and propose an implementation leveraging the model federation paradigm.
Predicting Fetal Outcomes from Cardiotocography Signals Using a Supervised Variational Autoencoder
Venue: arXiv cs.LG 2025
Year: 2025

Objective: To develop and interpret a supervised variational autoencoder (VAE) model for classifying cardiotocography (CTG) signals based on pregnancy outcomes, addressing interpretability limits of current deep learning approaches. Methods: The OxMat CTG dataset was used to train a VAE on five-minute fetal heart rate (FHR) segments, labeled with postnatal outcomes. The model was optimised for signal reconstruction and outcome prediction, incorporating Kullback-Leibler divergence and total correlation (TC) constraints to structure the latent space. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and mean squared error (MSE). Interpretability was assessed using coefficient of determination, latent traversals and unsupervised component analyses. Results: The model achieved an AUROC of 0.752 at the segment level and 0.779 at the CTG level, where predicted scores were aggregated. Relaxing TC constraints improved both reconstruction and classification. Latent analysis showed that baseline-related features (e.g., FHR baseline, baseline shift) were well represented and aligned with model scores, while metrics like short- and long-term variability were less strongly encoded. Traversals revealed clear signal changes for baseline features, while other properties were entangled or subtle. Unsupervised decompositions corroborated these patterns. Findings: This work demonstrates that supervised VAEs can achieve competitive fetal outcome prediction while partially encoding clinically meaningful CTG features. The irregular, multi-timescale nature of FHR signals poses challenges for disentangling physiological components, distinguishing CTG from more periodic signals such as ECG. Although full interpretability was not achieved, the model supports clinically useful outcome prediction and provides a basis for future interpretable, generative models.
Mapping Community Appeals Systems: Lessons for Community-led Moderation in Multi-Level Governance
Venue: arXiv cs.HC 2025
Year: 2025

Platforms are increasingly adopting industrial models of moderation that prioritize scalability and consistency, frequently at the expense of context-sensitive and user-centered values. Building on the multi-level governance framework that examines the interdependent relationship between platforms and middle-level communities, we investigate community appeals systems on Discord as a model for successful community-led governance. We investigate how Discord servers operationalize appeal systems through a qualitative interview study with focus groups and individual interviews with 17 community moderators. Our findings reveal a structured appeals process that balances scalability, fairness, and accountability while upholding community-centered values of growth and rehabilitation. Communities design these processes to empower users, ensuring their voices are heard in moderation decisions and fostering a sense of belonging. This research provides insights into the practical implementation of community-led governance in a multi-level governance framework, illustrating how communities can maintain their core principles while integrating procedural fairness and tool-based design. We discuss how platforms can gain insights from community-led moderation work to motivate governance structures that effectively balance and align the interests of multiple stakeholders.
Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Existing open set recognition (OSR) methods are typically designed for static scenarios, where models aim to classify known classes and identify unknown ones within fixed scopes. This deviates from the expectation that the model should incrementally identify newly emerging unknown classes from continuous data streams and acquire corresponding knowledge. In such evolving scenarios, the discriminability of OSR decision boundaries is hard to maintain due to restricted access to former training data, causing severe inter-class confusion. To solve this problem, we propose retentive angular representation learning (RARL) for incremental open set recognition (IOSR). In RARL, unknown representations are encouraged to align around inactive prototypes within an angular space constructed under the equiangular tight frame, thereby mitigating excessive representation drift during knowledge updates. Specifically, we adopt a virtual-intrinsic interactive (VII) training strategy, which compacts known representations by enforcing clear inter-class margins through boundary-proximal virtual classes. Furthermore, a stratified rectification strategy is designed to refine decision boundaries, mitigating representation bias and feature space distortion caused by imbalances between old/new and positive/negative class samples. We conduct thorough evaluations on CIFAR100 and TinyImageNet datasets and establish a new benchmark for IOSR. Experimental results across various task setups demonstrate that the proposed method achieves state-of-the-art performance.
Approximating Condorcet Ordering for Vector-valued Mathematical Morphology
Venue: arXiv cs.CV 2025
Year: 2025

Mathematical morphology provides a nonlinear framework for image and spatial data processing and analysis. Although there have been many successful applications of mathematical morphology to vector-valued images, such as color and hyperspectral images, there is still no consensus on the most suitable vector ordering for constructing morphological operators. This paper addresses this issue by examining a reduced ordering approximating the Condorcet ranking derived from a set of vector orderings. Inspired by voting problems, the Condorcet ordering ranks elements from most to least voted, with voters representing different orderings. In this paper, we develop a machine learning approach that learns a reduced ordering that approximates the Condorcet ordering. Preliminary computational experiments confirm the effectiveness of learning the reduced mapping to define vector-valued morphological operators for color images.
A Secure Sequencer and Data Availability Committee for Rollups (Extended Version)
Venue: arXiv cs.CR 2025
Year: 2025

Blockchains face a scalability limitation, partly due to the throughput limitations of consensus protocols, especially when aiming to obtain a high degree of decentralization. Layer 2 Rollups (L2s) are a faster alternative to conventional blockchains. L2s perform most computations offchain using minimally blockchains (L1) under-the-hood to guarantee correctness. A sequencer is a service that receives offchain L2 transaction requests, batches these transactions, and commits compressed or hashed batches to L1. Using hashing needs less L1 space, which is beneficial for gas cost, but requires a data availability committee (DAC) service to translate hashes into their corresponding batches of transaction requests. The behavior of sequencers and DACs influence the evolution of the L2 blockchain, presenting a potential security threat and delaying L2 adoption. We propose in this paper fraud-proof mechanisms, arbitrated by L1 contracts, to detect and generate evidence of dishonest behavior of the sequencer and DAC. We study how these fraud-proofs limit the power of adversaries that control different number of sequencer and DACs members, and provide incentives for their honest behavior. We designed these fraud-proof mechanisms as two player games. Unlike the generic fraud-proofs in current L2s (designed to guarantee the correct execution of transactions), our fraud-proofs are over pred-etermined algorithms that verify the properties that determine the correctness of the DAC. Arbitrating over concrete algorithms makes our fraud-proofs more efficient, easier to understand, and simpler to prove correct. We provide as an artifact a mechanization in LEAN4 of our fraud-proof games, including (1) the verified strategies that honest players should play to win all games as well as (2) mechanisms to detect dishonest claims.
Reusable Surrogate Models for Distillation Columns
Venue: arXiv cs.CE 2025
Year: 2025

Surrogate modeling is a powerful methodology in chemical process engineering, frequently employed to accelerate optimization tasks where traditional flowsheet simulators are computationally prohibitive. However, the state-of-the-art is dominated by surrogate models trained for a narrow range of fixed chemical systems and operating conditions, limiting their reusability. This work introduces a paradigm shift towards reusable surrogates by developing a single model for distillation columns that generalizes across a vast design space. The key enabler is a novel ML-fueled modelfluid representation which allows for the generation of datasets of more than $1,000,000$ samples. This allows the surrogate to generalize not only over column specifications but also over the entire chemical space of homogeneous ternary vapor-liquid mixtures. We validate the model's accuracy and demonstrate its practical utility in a case study on entrainer distillation, where it successfully screens and ranks candidate entrainers, significantly reducing the computational effort compared to rigorous optimization.
AnalysisGNN: Unified Music Analysis with Graph Neural Networks
Venue: arXiv cs.SD 2025
Year: 2025

Recent years have seen a boom in computational approaches to music analysis, yet each one is typically tailored to a specific analytical domain. In this work, we introduce AnalysisGNN, a novel graph neural network framework that leverages a data-shuffling strategy with a custom weighted multi-task loss and logit fusion between task-specific classifiers to integrate heterogeneously annotated symbolic datasets for comprehensive score analysis. We further integrate a Non-Chord-Tone prediction module, which identifies and excludes passing and non-functional notes from all tasks, thereby improving the consistency of label signals. Experimental evaluations demonstrate that AnalysisGNN achieves performance comparable to traditional static-dataset approaches, while showing increased resilience to domain shifts and annotation inconsistencies across multiple heterogeneous corpora.
MRI-Based Brain Tumor Detection through an Explainable EfficientNetV2 and MLP-Mixer-Attention Architecture
Venue: arXiv cs.CV 2025
Year: 2025

Brain tumors are serious health problems that require early diagnosis due to their high mortality rates. Diagnosing tumors by examining Magnetic Resonance Imaging (MRI) images is a process that requires expertise and is prone to error. Therefore, the need for automated diagnosis systems is increasing day by day. In this context, a robust and explainable Deep Learning (DL) model for the classification of brain tumors is proposed. In this study, a publicly available Figshare dataset containing 3,064 T1-weighted contrast-enhanced brain MRI images of three tumor types was used. First, the classification performance of nine well-known CNN architectures was evaluated to determine the most effective backbone. Among these, EfficientNetV2 demonstrated the best performance and was selected as the backbone for further development. Subsequently, an attention-based MLP-Mixer architecture was integrated into EfficientNetV2 to enhance its classification capability. The performance of the final model was comprehensively compared with basic CNNs and the methods in the literature. Additionally, Grad-CAM visualization was used to interpret and validate the decision-making process of the proposed model. The proposed model's performance was evaluated using the five-fold cross-validation method. The proposed model demonstrated superior performance with 99.50% accuracy, 99.47% precision, 99.52% recall and 99.49% F1 score. The results obtained show that the model outperforms the studies in the literature. Moreover, Grad-CAM visualizations demonstrate that the model effectively focuses on relevant regions of MRI images, thus improving interpretability and clinical reliability. A robust deep learning model for clinical decision support systems has been obtained by combining EfficientNetV2 and attention-based MLP-Mixer, providing high accuracy and interpretability in brain tumor classification.
Efficiently Ranking Software Variants with Minimal Benchmarks
Venue: arXiv cs.SE 2025
Year: 2025

Benchmarking is a common practice in software engineering to assess the qualities and performance of software variants, coming from multiple competing systems or from configurations of the same system. Benchmarks are used notably to compare and understand variant performance, fine-tune software, detect regressions, or design new software systems. The execution of benchmarks to get a complete picture of software variants is highly costly in terms of computational resources and time. In this paper, we propose a novel approach for reducing benchmarks while maintaining stable rankings, using test suite optimization techniques. That is, we remove instances from the benchmarks while trying to keep the same rankings of the variants on all tests. Our method, BISection Sampling, BISS, strategically retains the most critical tests and applies a novel divide-and-conquer approach to efficiently sample among relevant remaining tests. We experiment with datasets and use cases from LLM leaderboards, SAT competitions, and configurable systems for performance modeling. Our results show that our method outperforms baselines even when operating on a subset of variants. Using BISS, we reduce the computational cost of the benchmarks on average to 44% and on more than half the benchmarks by up to 99% without loss in ranking stability.
Pacing Types: Safe Monitoring of Asynchronous Streams
Venue: arXiv cs.PL 2025
Year: 2025

Stream-based monitoring is a real-time safety assurance mechanism for complex cyber-physical systems such as unmanned aerial vehicles. In this context, a monitor aggregates streams of input data from sensors and other sources to give real-time statistics and assessments of the system's health. Since monitors are safety-critical components, it is crucial to ensure that they are free of potential runtime errors. One of the central challenges in designing reliable stream-based monitors is to deal with the asynchronous nature of data streams: in concrete applications, the different sensors being monitored produce values at different speeds, and it is the monitor's responsibility to correctly react to the asynchronous arrival of different streams of values. To ease this process, modern frameworks for stream-based monitoring such as RTLola feature an expressive specification language that allows to finely specify data synchronization policies. While this feature dramatically simplifies the design of monitors, it can also lead to subtle runtime errors. To mitigate this issue, this paper presents pacing types, a novel type system implemented in RTLola to ensure that monitors for asynchronous streams are well-behaved at runtime. We formalize the essence of pacing types for a core fragment of RTLola, and present a soundness proof of the pacing type system using a new logical relation.
Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint
Venue: arXiv cs.CL 2025
Year: 2025

Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance various abilities of Large Language Models (LLMs). However, prior studies have shown that IFT can significantly compromise LLMs' safety, particularly their ability to refuse malicious instructions, raising significant concerns. Recent research into the internal mechanisms of LLMs has identified the refusal direction (r-direction) in the hidden states, which plays a pivotal role in governing refusal behavior. Building on this insight, our study reveals that the r-direction tends to drift during training, which we identify as one of the causes of the associated safety risks. To mitigate such drift, our proposed ProCon method introduces a projection-constrained loss term that regularizes the projection magnitude of each training sample's hidden state onto the r-direction. Our initial analysis shows that applying an appropriate constraint can effectively mitigate the refusal direction drift and associated safety risks, but remains limited by overall performance barriers. To overcome this barrier, informed by our observation of early-stage sharp drift and a data-driven perspective, we introduce a warm-up strategy that emphasizes early-stage strong constraints and broaden the data distribution to strengthen constraint signals, leading to an enhanced ProCon method. Experimental results under various datasets, scenarios, and LLMs demonstrate that our method can significantly mitigate safety risks posed by IFT while preserving task performance gains. Even compared with strong baselines, our method consistently delivers superior overall performance. Crucially, our analysis indicates that ProCon can contribute to stabilizing the r-direction during training, while such an interpretability-driven exploration of LLMs' internal mechanisms lays a solid foundation for future safety research.