OpenRaiser/Intern-Atlas huggingface dataset:
Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventions
Venue: arXiv cs.LG 2025
Year: 2025

Counterfactual explanations are increasingly proposed as interpretable mechanisms to achieve algorithmic recourse. However, current counterfactual techniques for time series classification are predominantly designed with static data assumptions and focus on generating minimal input perturbations to flip model predictions. This paper argues that such approaches are fundamentally insufficient in clinical recommendation settings, where interventions unfold over time and must be causally plausible and temporally coherent. We advocate for a shift towards counterfactuals that reflect sustained, goal-directed interventions aligned with clinical reasoning and patient-specific dynamics. We identify critical gaps in existing methods that limit their practical applicability, specifically, temporal blind spots and the lack of user-centered considerations in both method design and evaluation metrics. To support our position, we conduct a robustness analysis of several state-of-the-art methods for time series and show that the generated counterfactuals are highly sensitive to stochastic noise. This finding highlights their limited reliability in real-world clinical settings, where minor measurement variations are inevitable. We conclude by calling for methods and evaluation frameworks that go beyond mere prediction changes without considering feasibility or actionability. We emphasize the need for actionable, purpose-driven interventions that are feasible in real-world contexts for the users of such applications.
Residual GRU+MHSA: A Lightweight Hybrid Recurrent Attention Model for Cardiovascular Disease Detection
Venue: arXiv cs.LG 2025
Year: 2025

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, underscoring the need for reliable and efficient predictive tools that support early intervention. Traditional diagnostic approaches rely on handcrafted features and clinician expertise, while machine learning methods improve reproducibility but often struggle to generalize across noisy and heterogeneous clinical data. In this work, we propose Residual GRU with Multi-Head Self-Attention, a compact deep learning architecture designed for tabular clinical records. The model integrates residual bidirectional gated recurrent units for sequential modeling of feature columns, a channel reweighting block, and multi-head self-attention pooling with a learnable classification token to capture global context. We evaluate the model on the UCI Heart Disease dataset using 5-fold stratified cross-validation and compare it against classical methods such as Logistic Regression, Random Forest, and Support Vector Machines, as well as modern deep learning baselines including DeepMLP, convolutional networks, recurrent networks, and Transformers. The proposed model achieves an accuracy of 0.861, macro-F1 of 0.860, ROC-AUC of 0.908, and PR-AUC of 0.904, outperforming all baselines. Ablation studies confirm the individual contributions of residual recurrence, channel gating, and attention pooling. t-SNE visualizations further indicate that the learned embeddings exhibit clearer separation between disease and non-disease classes compared to raw features. These results demonstrate that lightweight hybrid recurrent and attention-based architectures provide a strong balance between accuracy and efficiency for clinical risk prediction, supporting deployment in resource-constrained healthcare settings.
Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes
Venue: arXiv cs.LG 2025
Year: 2025

Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties. Markovian Reinforcement Learning (RL) approaches are not suitable for such tasks, while RL with non-Markovian reward decision processes (NMRDPs) enables agents to tackle temporal-dependency tasks. This approach has long been known to lack formal guarantees on both (near-)optimality and sample efficiency. We contribute to solving both issues with QR-MAX, a novel model-based algorithm for discrete NMRDPs that factorizes Markovian transition learning from non-Markovian reward handling via reward machines. To the best of our knowledge, this is the first model-based RL algorithm for discrete-action NMRDPs that exploits this factorization to obtain PAC convergence to $\varepsilon$-optimal policies with polynomial sample complexity. We then extend QR-MAX to continuous state spaces with Bucket-QR-MAX, a SimHash-based discretiser that preserves the same factorized structure and achieves fast and stable learning without manual gridding or function approximation. We experimentally compare our method with modern state-of-the-art model-based RL approaches on environments of increasing complexity, showing a significant improvement in sample efficiency and increased robustness in finding optimal policies.
PruneX: A Hierarchical Communication-Efficient System for Distributed CNN Training with Structured Pruning
Venue: arXiv cs.DC 2025
Year: 2025

Inter-node communication bandwidth increasingly constrains distributed training at scale on multi-node GPU clusters. While compact models are the ultimate deployment target, conventional pruning-aware distributed training systems typically fail to reduce communication overhead because unstructured sparsity cannot be efficiently exploited by highly optimized dense collective primitives. We present PruneX, a distributed data-parallel training system that co-designs pruning algorithms with cluster hierarchy to reduce inter-node bandwidth usage. PruneX introduces the Hierarchical Structured ADMM (H-SADMM) algorithm, which enforces node-level structured sparsity before inter-node synchronization, enabling dynamic buffer compaction that eliminates both zero-valued transmissions and indexing overhead. The system adopts a leader-follower execution model with separated intra-node and inter-node process groups, performing dense collectives on compacted tensors over bandwidth-limited links while confining full synchronization to high-bandwidth intra-node interconnects. Evaluation on ResNet architectures across 64 GPUs demonstrates that PruneX reduces inter-node communication volume by approximately 60% and achieves 6.75x strong scaling speedup, outperforming the dense baseline (5.81x) and Top-K gradient compression (3.71x) on the Puhti supercomputer at CSC - IT Center for Science (Finland).
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-guided Subtyping and Lesion-Wise Model Ensemble
Venue: arXiv cs.CV 2025
Year: 2025

Robust and generalizable segmentation of brain tumors on multi-parametric magnetic resonance imaging (MRI) remains difficult because tumor types differ widely. The BraTS 2025 Lighthouse Challenge benchmarks segmentation methods on diverse high-quality datasets of adult and pediatric tumors: multi-consortium international pediatric brain tumor segmentation (PED), preoperative meningioma tumor segmentation (MEN), meningioma radiotherapy segmentation (MEN-RT), and segmentation of pre- and post-treatment brain metastases (MET). We present a flexible, modular, and adaptable pipeline that improves segmentation performance by selecting and combining state-of-the-art models and applying tumor- and lesion-specific processing before and after training. Radiomic features extracted from MRI help detect tumor subtype, ensuring a more balanced training. Custom lesion-level performance metrics determine the influence of each model in the ensemble and optimize post-processing that further refines the predictions, enabling the workflow to tailor every step to each case. On the BraTS testing sets, our pipeline achieved performance comparable to top-ranked algorithms across multiple challenges. These findings confirm that custom lesion-aware processing and model selection yield robust segmentations yet without locking the method to a specific network architecture. Our method has the potential for quantitative tumor measurement in clinical practice, supporting diagnosis and prognosis.
Adapting Speech Language Model to Singing Voice Synthesis
Venue: arXiv cs.SD 2025
Year: 2025

Speech Language Models (SLMs) have recently emerged as a unified paradigm for addressing a wide range of speech-related tasks, including text-to-speech (TTS), speech enhancement (SE), and automatic speech recognition (ASR). However, the generalization capability of large-scale pre-trained SLMs remains underexplored. In this work, we adapt a 1.7B parameter TTS pretrained SLM for singing voice synthesis (SVS), using only a 135-hour synthetic singing corpus, ACE-Opencpop. Building upon the ESPNet-SpeechLM, our recipe involves the following procedure: (1) tokenization of music score conditions and singing waveforms, (2) multi-stream language model token prediction, (3) conditional flow matching-based mel-spectrogram generation. (4) a mel-to-wave vocoder. Experimental results demonstrate that our adapted SLM generalizes well to SVS and achieves performance comparable to leading discrete token-based SVS models.
Focus: A Streaming Concentration Architecture for Efficient Vision-Language Models
Venue: arXiv cs.AR 2025
Year: 2025

Vision-Language Models (VLMs) have demonstrated strong performance on tasks such as video captioning and visual question answering. However, their growing scale and video-level inputs lead to significant computational and memory overhead, posing challenges for real-time deployment on hardware accelerators. While prior work attempts to reduce redundancy via token pruning or merging, these methods typically operate at coarse granularity and incur high runtime overhead due to global token-level operations. In this study, we propose Focus, a Streaming Concentration Architecture that efficiently accelerates VLM inference through progressive, fine-grained redundancy elimination. Focus introduces a multilevel concentration paradigm that hierarchically compresses vision-language inputs at three levels: (1) semantic-guided token pruning based on textual prompts, (2) spatial-temporal block-level concentration using localized comparisons, and (3) vector-level redundancy removal via motion-aware matching. All concentration steps are tightly co-designed with the architecture to support streaming-friendly, on-chip execution. Focus leverages GEMM tiling, convolution-style layout, and cross-modal attention to minimize off-chip access while enabling high throughput. Implemented as a modular unit within a systolic-array accelerator, Focus achieves a 2.4x speedup and 3.3x reduction in energy, significantly outperforming state-of-the-art accelerators in both performance and energy efficiency. Full-stack implementation of Focus is open-sourced at https://github.com/dubcyfor3/Focus.
Enhancing Visual Sentiment Analysis via Semiotic Isotopy-Guided Dataset Construction
Venue: arXiv cs.CV 2025
Year: 2025

Visual Sentiment Analysis (VSA) is a challenging task due to the vast diversity of emotionally salient images and the inherent difficulty of acquiring sufficient data to capture this variability comprehensively. Key obstacles include building large-scale VSA datasets and developing effective methodologies that enable algorithms to identify emotionally significant elements within an image. These challenges are reflected in the limited generalization performance of VSA algorithms and models when trained and tested across different datasets. Starting from a pool of existing data collections, our approach enables the creation of a new larger dataset that not only contains a wider variety of images than the original ones, but also permits training new models with improved capability to focus on emotionally relevant combinations of image elements. This is achieved through the integration of the semiotic isotopy concept within the dataset creation process, providing deeper insights into the emotional content of images. Empirical evaluations show that models trained on a dataset generated with our method consistently outperform those trained on the original data collections, achieving superior generalization across major VSA benchmarks
EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models
Venue: arXiv cs.RO 2025
Year: 2025

Achieving truly adaptive embodied intelligence requires agents that learn not just by imitating static demonstrations, but by continuously improving through environmental interaction, which is akin to how humans master skills through practice. Vision-Language-Action (VLA) models have advanced robotic manipulation by leveraging large language models, yet remain fundamentally limited by Supervised Finetuning (SFT): requiring hundreds of demonstrations per task, rigidly memorizing trajectories, and failing to adapt when deployment conditions deviate from training. We introduce EVOLVE-VLA, a test-time training framework enabling VLAs to continuously adapt through environment interaction with minimal or zero task-specific demonstrations. The key technical challenge is replacing oracle reward signals (unavailable at test time) with autonomous feedback. We address this through a learned progress estimator providing dense feedback, and critically, we design our framework to ``tame'' this inherently noisy signal via two mechanisms: (1) an accumulative progress estimation mechanism smoothing noisy point-wise estimates, and (2) a progressive horizon extension strategy enabling gradual policy evolution. EVOLVE-VLA achieves substantial gains: +8.6\% on long-horizon tasks, +22.0\% in 1-shot learning, and enables cross-task generalization -- achieving 20.8\% success on unseen tasks without task-specific demonstrations training (vs. 0\% for pure SFT). Qualitative analysis reveals emergent capabilities absent in demonstrations, including error recovery and novel strategies. This work represents a critical step toward VLAs that truly learn and adapt, moving beyond static imitation toward continuous self-improvements.
MMGR: Multi-Modal Generative Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Video foundation models generate visually realistic and temporally coherent content, but their reliability as world simulators depends on whether they capture physical, logical, and spatial constraints. Existing metrics such as Frechet Video Distance (FVD) emphasize perceptual quality and overlook reasoning failures, including violations of causality, physics, and global consistency. We introduce MMGR (Multi-Modal Generative Reasoning Evaluation and Benchmark), a principled evaluation framework based on five reasoning abilities: Physical, Logical, 3D Spatial, 2D Spatial, and Temporal. MMGR evaluates generative reasoning across three domains: Abstract Reasoning (ARC-AGI, Sudoku), Embodied Navigation (real-world 3D navigation and localization), and Physical Commonsense (sports and compositional interactions). MMGR applies fine-grained metrics that require holistic correctness across both video and image generation. We benchmark leading video models (Veo-3, Sora-2, Wan-2.2) and image models (Nano-banana, Nano-banana Pro, GPT-4o-image, Qwen-image), revealing strong performance gaps across domains. Models show moderate success on Physical Commonsense tasks but perform poorly on Abstract Reasoning (below 10 percent accuracy on ARC-AGI) and struggle with long-horizon spatial planning in embodied settings. Our analysis highlights key limitations in current models, including overreliance on perceptual data, weak global state consistency, and objectives that reward visual plausibility over causal correctness. MMGR offers a unified diagnostic benchmark and a path toward reasoning-aware generative world models.
Spherical Leech Quantization for Visual Tokenization and Generation
Venue: arXiv cs.CV 2025
Year: 2025

Non-parametric quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of different non-parametric quantization methods through the lens of lattice coding. The geometry of lattice codes explains the necessity of auxiliary loss terms when training auto-encoders with certain existing lookup-free quantization variants such as BSQ. As a step forward, we explore a few possible candidates, including random lattices, generalized Fibonacci lattices, and densest sphere packing lattices. Among all, we find the Leech lattice-based quantization method, which is dubbed as Spherical Leech Quantization ($Λ_{24}$-SQ), leads to both a simplified training recipe and an improved reconstruction-compression tradeoff thanks to its high symmetry and even distribution on the hypersphere. In image tokenization and compression tasks, this quantization approach achieves better reconstruction quality across all metrics than BSQ, the best prior art, while consuming slightly fewer bits. The improvement also extends to state-of-the-art auto-regressive image generation frameworks.
Boundaries in Hypernetwork Theory: Structure and Scope
Venue: arXiv cs.SI 2025
Year: 2025

Boundaries in Hypernetwork Theory (HT) are non-structural tags that restrict visibility without altering the underlying hypernetwork. They attach to hypersimplices as annotations and participate in no identity, typing, or alpha/beta semantics. Projection over a boundary, B(H, b) = pi_b(H), is filtering only: it selects exactly those hypersimplices carrying b and preserves all axioms of the structural kernel. The backcloth remains immutable, and no new structure is created, removed, or inferred. This paper formalises boundaries as a simple and conservative scoping mechanism. It clarifies their syntax, their interaction with projection, and their use in producing identity-preserving subsystem views that support modular modelling and overlapping perspectives. The account also makes explicit why conservative scoping matters: boundaries provide reproducible view extraction, stable subsystem isolation, and safe model exploration without altering the global structure. Scoped operator application is defined as ordinary structural-kernel composition applied to projected views, ensuring that view-level reasoning remains local and does not modify the global hypernetwork. This establishes a disciplined separation between immutable structure and scoped analysis while retaining full compatibility with the structural kernel. The paper includes a worked example demonstrating how boundaries yield coherent, identity-preserving subsystem views and how scoped reasoning supports refinement within these views. The result is a precise and minimal account of boundaries that complements - but does not extend - the structural kernel and completes the scoping mechanism required for practical multilevel modelling with HT.
A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour
Venue: arXiv cs.LG 2025
Year: 2025

Many travel decisions involve a degree of experience formation, where individuals learn their preferences over time. At the same time, there is extensive scope for heterogeneity across individual travellers, both in their underlying preferences and in how these evolve. The present paper puts forward a Latent Class Reinforcement Learning (LCRL) model that allows analysts to capture both of these phenomena. We apply the model to a driving simulator dataset and estimate the parameters through Variational Bayes. We identify three distinct classes of individuals that differ markedly in how they adapt their preferences: the first displays context-dependent preferences with context-specific exploitative tendencies; the second follows a persistent exploitative strategy regardless of context; and the third engages in an exploratory strategy combined with context-specific preferences.
Is GPT-OSS All You Need? Benchmarking Large Language Models for Financial Intelligence and the Surprising Efficiency Paradox
Venue: arXiv cs.LG 2025
Year: 2025

The rapid adoption of large language models in financial services necessitates rigorous evaluation frameworks to assess their performance, efficiency, and practical applicability. This paper conducts a comprehensive evaluation of the GPT-OSS model family alongside contemporary LLMs across ten diverse financial NLP tasks. Through extensive experimentation on 120B and 20B parameter variants of GPT-OSS, we reveal a counterintuitive finding: the smaller GPT-OSS-20B model achieves comparable accuracy (65.1% vs 66.5%) while demonstrating superior computational efficiency with 198.4 Token Efficiency Score and 159.80 tokens per second processing speed [1]. Our evaluation encompasses sentiment analysis, question answering, and entity recognition tasks using real-world financial datasets including Financial PhraseBank, FiQA-SA, and FLARE FINERORD. We introduce novel efficiency metrics that capture the trade-off between model performance and resource utilization, providing critical insights for deployment decisions in production environments. The benchmark reveals that GPT-OSS models consistently outperform larger competitors including Qwen3-235B, challenging the prevailing assumption that model scale directly correlates with task performance [2]. Our findings demonstrate that architectural innovations and training strategies in GPT-OSS enable smaller models to achieve competitive performance with significantly reduced computational overhead, offering a pathway toward sustainable and cost-effective deployment of LLMs in financial applications.
Generative Urban Flow Modeling: From Geometry to Airflow with Graph Diffusion
Venue: arXiv cs.LG 2025
Year: 2025

Urban wind flow modeling and simulation play an important role in air quality assessment and sustainable city planning. A key challenge for modeling and simulation is handling the complex geometries of the urban landscape. Low order models are limited in capturing the effects of geometry, while high-fidelity Computational Fluid Dynamics (CFD) simulations are prohibitively expensive, especially across multiple geometries or wind conditions. Here, we propose a generative diffusion framework for synthesizing steady-state urban wind fields over unstructured meshes that requires only geometry information. The framework combines a hierarchical graph neural network with score-based diffusion modeling to generate accurate and diverse velocity fields without requiring temporal rollouts or dense measurements. Trained across multiple mesh slices and wind angles, the model generalizes to unseen geometries, recovers key flow structures such as wakes and recirculation zones, and offers uncertainty-aware predictions. Ablation studies confirm robustness to mesh variation and performance under different inference regimes. This work develops is the first step towards foundation models for the built environment that can help urban planners rapidly evaluate design decisions under densification and climate uncertainty.