OpenRaiser/Intern-Atlas huggingface dataset:
M-CIF: Multi-Scale Alignment For CIF-Based Non-Autoregressive ASR
Venue: arXiv cs.SD 2025
Year: 2025

The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanism creates a smooth and monotonic mapping from acoustic features to target tokens, achieving performance on Mandarin competitive with other NAR approaches. However, without finer-grained guidance, its stability degrades in some languages such as English and French. In this paper, we propose Multi-scale CIF (M-CIF), which performs multi-level alignment by integrating character and phoneme level supervision progressively distilled into subword representations, thereby enhancing robust acoustic-text alignment. Experiments show that M-CIF reduces WER compared to the Paraformer baseline, especially on CommonVoice by 4.21% in German and 3.05% in French. To further investigate these gains, we define phonetic confusion errors (PE) and space-related segmentation errors (SE) as evaluation metrics. Analysis of these metrics across different M-CIF settings reveals that the phoneme and character layers are essential for enhancing progressive CIF alignment.
Dopamine-driven synaptic credit assignment in neural networks
Venue: arXiv cs.AI 2025
Year: 2025

Solving the synaptic Credit Assignment Problem(CAP) is central to learning in both biological and artificial neural systems. Finding an optimal solution for synaptic CAP means setting the synaptic weights that assign credit to each neuron for influencing the final output and behavior of neural networks or animals. Gradient-based methods solve this problem in artificial neural networks using back-propagation, however, not in the most efficient way. For instance, back-propagation requires a chain of top-down gradient computations. This leads to an expensive optimization process in terms of computing power and memory linked with well-known weight transport and update locking problems. To address these shortcomings, we take a NeuroAI approach and draw inspiration from neural Reinforcement Learning to develop a derivative-free optimizer for training neural networks, Dopamine. Dopamine is developed for Weight Perturbation (WP) learning that exploits stochastic updating of weights towards optima. It achieves this by minimizing the regret, a form of Reward Prediction Error (RPE) between the expected outcome from the perturbed model and the actual outcome from the unperturbed model. We use this RPE to adjust the learning rate in the network (i.e., creating an adaptive learning rate strategy, similar to the role of dopamine in the brain). We tested the Dopamine optimizer for training multi-layered perceptrons for XOR tasks, and recurrent neural networks for chaotic time series forecasting. Dopamine-trained models demonstrate accelerated convergence and outperform standard WP, and give comparable performance to gradient-based algorithms, while consuming significantly less computation and memory. Overall, the Dopamine optimizer not only finds robust solutions and comparable performance to the state-of-the-art Machine Learning optimizers but is also neurobiologically more plausible.
MOGRAS: Human Motion with Grasping in 3D Scenes
Venue: arXiv cs.CV 2025
Year: 2025

Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can generate full-body motion within 3D scenes, they often lack the fidelity for fine-grained tasks like object grasping. Conversely, methods that generate precise grasping motions typically ignore the surrounding 3D scene. This gap, generating full-body grasping motions that are physically plausible within a 3D scene, remains a significant challenge. To address this, we introduce MOGRAS (Human MOtion with GRAsping in 3D Scenes), a large-scale dataset that bridges this gap. MOGRAS provides pre-grasping full-body walking motions and final grasping poses within richly annotated 3D indoor scenes. We leverage MOGRAS to benchmark existing full-body grasping methods and demonstrate their limitations in scene-aware generation. Furthermore, we propose a simple yet effective method to adapt existing approaches to work seamlessly within 3D scenes. Through extensive quantitative and qualitative experiments, we validate the effectiveness of our dataset and highlight the significant improvements our proposed method achieves, paving the way for more realistic human-scene interactions.
LongCat-Video Technical Report
Venue: arXiv cs.CV 2025
Year: 2025

Video generation is a critical pathway toward world models, with efficient long video inference as a key capability. Toward this end, we introduce LongCat-Video, a foundational video generation model with 13.6B parameters, delivering strong performance across multiple video generation tasks. It particularly excels in efficient and high-quality long video generation, representing our first step toward world models. Key features include: Unified architecture for multiple tasks: Built on the Diffusion Transformer (DiT) framework, LongCat-Video supports Text-to-Video, Image-to-Video, and Video-Continuation tasks with a single model; Long video generation: Pretraining on Video-Continuation tasks enables LongCat-Video to maintain high quality and temporal coherence in the generation of minutes-long videos; Efficient inference: LongCat-Video generates 720p, 30fps videos within minutes by employing a coarse-to-fine generation strategy along both the temporal and spatial axes. Block Sparse Attention further enhances efficiency, particularly at high resolutions; Strong performance with multi-reward RLHF: Multi-reward RLHF training enables LongCat-Video to achieve performance on par with the latest closed-source and leading open-source models. Code and model weights are publicly available to accelerate progress in the field.
Taming Silent Failures: A Framework for Verifiable AI Reliability
Venue: arXiv cs.SE 2025
Year: 2025

The integration of Artificial Intelligence (AI) into safety-critical systems introduces a new reliability paradigm: silent failures, where AI produces confident but incorrect outputs that can be dangerous. This paper introduces the Formal Assurance and Monitoring Environment (FAME), a novel framework that confronts this challenge. FAME synergizes the mathematical rigor of offline formal synthesis with the vigilance of online runtime monitoring to create a verifiable safety net around opaque AI components. We demonstrate its efficacy in an autonomous vehicle perception system, where FAME successfully detected 93.5% of critical safety violations that were otherwise silent. By contextualizing our framework within the ISO 26262 and ISO/PAS 8800 standards, we provide reliability engineers with a practical, certifiable pathway for deploying trustworthy AI. FAME represents a crucial shift from accepting probabilistic performance to enforcing provable safety in next-generation systems.
DiffusionLane: Diffusion Model for Lane Detection
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we present a novel diffusion-based model for lane detection, called DiffusionLane, which treats the lane detection task as a denoising diffusion process in the parameter space of the lane. Firstly, we add the Gaussian noise to the parameters (the starting point and the angle) of ground truth lanes to obtain noisy lane anchors, and the model learns to refine the noisy lane anchors in a progressive way to obtain the target lanes. Secondly, we propose a hybrid decoding strategy to address the poor feature representation of the encoder, resulting from the noisy lane anchors. Specifically, we design a hybrid diffusion decoder to combine global-level and local-level decoders for high-quality lane anchors. Then, to improve the feature representation of the encoder, we employ an auxiliary head in the training stage to adopt the learnable lane anchors for enriching the supervision on the encoder. Experimental results on four benchmarks, Carlane, Tusimple, CULane, and LLAMAS, show that DiffusionLane possesses a strong generalization ability and promising detection performance compared to the previous state-of-the-art methods. For example, DiffusionLane with ResNet18 surpasses the existing methods by at least 1\% accuracy on the domain adaptation dataset Carlane. Besides, DiffusionLane with MobileNetV4 gets 81.32\% F1 score on CULane, 96.89\% accuracy on Tusimple with ResNet34, and 97.59\% F1 score on LLAMAS with ResNet101. Code will be available at https://github.com/zkyntu/UnLanedet.
FOA Tokenizer: Low-bitrate Neural Codec for First Order Ambisonics with Spatial Consistency Loss
Venue: arXiv cs.SD 2025
Year: 2025

Neural audio codecs have been widely studied for mono and stereo signals, but spatial audio remains largely unexplored. We present the first discrete neural spatial audio codec for first-order ambisonics (FOA). Building on the WavTokenizer architecture, we extend it to support four-channel FOA signals and introduce a novel spatial consistency loss to preserve directional cues in the reconstructed signals under a highly compressed representation. Our codec compresses 4-channel FOA audio at 24 kHz into 75 discrete tokens per second, corresponding to a bit rate of 0.9 kbps. Evaluations on simulated reverberant mixtures, non-reverberant clean speech, and FOA mixtures with real room impulse responses show accurate reconstruction, with mean angular errors of 13.76°, 3.96°, and 25.83°, respectively, across the three conditions. In addition, discrete latent representations derived from our codec provide useful features for downstream spatial audio tasks, as demonstrated on sound event localization and detection with STARSS23 real recordings.
SteerX: Disentangled Steering for LLM Personalization
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have shown remarkable success in recent years, enabling a wide range of applications, including intelligent assistants that support users' daily life and work. A critical factor in building such assistants is personalizing LLMs, as user preferences and needs vary widely. Activation steering, which directly leverages directions representing user preference in the LLM activation space to adjust its behavior, offers a cost-effective way to align the model's outputs with individual users. However, existing methods rely on all historical data to compute the steering vector, ignoring that not all content reflects true user preferences, which undermines the personalization signal. To address this, we propose SteerX, a disentangled steering method that isolates preference-driven components from preference-agnostic components. Grounded in causal inference theory, SteerX estimates token-level causal effects to identify preference-driven tokens, transforms these discrete signals into a coherent description, and then leverages them to steer personalized LLM generation. By focusing on the truly preference-driven information, SteerX produces more accurate activation steering vectors and enhances personalization. Experiments on two representative steering backbone methods across real-world datasets demonstrate that SteerX consistently enhances steering vector quality, offering a practical solution for more effective LLM personalization.
Infinitely many families of distance-optimal binary linear codes with respect to the sphere packing bound
Venue: arXiv cs.IT 2025
Year: 2025

R. W. Hamming published the Hamming codes and the sphere packing bound in 1950. In the past 75 years, infinite families of distance-optimal linear codes over finite fields with minimum distance at most 8 with respect to the sphere packing bound have been reported in the literature. However, it is a 75-year-old open problem in coding theory whether there is an infinite family of distance-optimal linear codes over finite fields with arbitrarily large minimum distance with respect to the sphere packing bound. This main objective of this paper is to settle this long-standing open problem in coding theory. As by-products, several infinite families of distance-optimal binary codes with small minimum distances are presented. Two infinite families of binary five-weight codes are reported. Some open problems are also proposed.
LacMaterial: Large Language Models as Analogical Chemists for Materials Discovery
Venue: arXiv cs.LG 2025
Year: 2025

Analogical reasoning, the transfer of relational structures across contexts (e.g., planet is to sun as electron is to nucleus), is fundamental to scientific discovery. Yet human insight is often constrained by domain expertise and surface-level biases, limiting access to deeper, structure-driven analogies both within and across disciplines. Large language models (LLMs), trained on vast cross-domain data, present a promising yet underexplored tool for analogical reasoning in science. Here, we demonstrate that LLMs can generate novel battery materials by (1) retrieving cross-domain analogs and analogy-guided exemplars to steer exploration beyond conventional dopant substitutions, and (2) constructing in-domain analogical templates from few labeled examples to guide targeted exploitation. These explicit analogical reasoning strategies yield candidates outside established compositional spaces and outperform standard prompting baselines. Our findings position LLMs as interpretable, expert-like hypothesis generators that leverage analogy-driven generalization for scientific innovation.
Monitoring State Transitions in Markovian Systems with Sampling Cost
Venue: arXiv cs.LG 2025
Year: 2025

We consider a node-monitor pair, where the node's state varies with time. The monitor needs to track the node's state at all times; however, there is a fixed cost for each state query. So the monitor may instead predict the state using time-series forecasting methods, including time-series foundation models (TSFMs), and query only when prediction uncertainty is high. Since query decisions influence prediction accuracy, determining when to query is nontrivial. A natural approach is a greedy policy that predicts when the expected prediction loss is below the query cost and queries otherwise. We analyze this policy in a Markovian setting, where the optimal (OPT) strategy is a state-dependent threshold policy minimizing the time-averaged sum of query cost and prediction losses. We show that, in general, the greedy policy is suboptimal and can have an unbounded competitive ratio, but under common conditions such as identically distributed transition probabilities, it performs close to OPT. For the case of unknown transition probabilities, we further propose a projected stochastic gradient descent (PSGD)-based learning variant of the greedy policy, which achieves a favorable predict-query tradeoff with improved computational efficiency compared to OPT.
ProGQL: A Provenance Graph Query System for Cyber Attack Investigation
Venue: arXiv cs.CR 2025
Year: 2025

Provenance analysis (PA) has recently emerged as an important solution for cyber attack investigation. PA leverages system monitoring to monitor system activities as a series of system audit events and organizes these events as a provenance graph to show the dependencies among system activities, which can reveal steps of cyber attacks. Despite their potential, existing PA techniques face two critical challenges: (1) they are inflexible and non-extensible, making it difficult to incorporate analyst expertise, and (2) they are memory inefficient, often requiring>100GB of RAM to hold entire event streams, which fundamentally limits scalability and deployment in real-world environments. To address these limitations, we propose the ProGQL framework, which provides a domain-specific graph search language with a well-engineered query engine, allowing PA over system audit events and expert knowledge to be jointly expressed as a graph search query and thereby facilitating the investigation of complex cyberattacks. In particular, to support dependency searches from a starting edge required in PA, ProGQL introduces new language constructs for constrained graph traversal, edge weight computation, value propagation along weighted edges, and graph merging to integrate multiple searches. Moreover, the ProGQL query engine is optimized for efficient incremental graph search across heterogeneous database backends, eliminating the need for full in-memory materialization and reducing memory overhead. Our evaluations on real attacks demonstrate the effectiveness of the ProGQL language in expressing a diverse set of complex attacks compared with the state-of-the-art graph query language Cypher, and the comparison with the SOTA PA technique DEPIMPACT further demonstrates the significant improvement of the scalability brought by our ProGQL framework's design.
Complementary Human-AI Clinical Reasoning in Ophthalmology
Venue: arXiv cs.HC 2025
Year: 2025

Vision impairment and blindness are a major global health challenge where gaps in the ophthalmology workforce limit access to specialist care. We evaluate AMIE, a medically fine-tuned conversational system based on Gemini with integrated web search and self-critique reasoning, using real-world clinical vignettes that reflect scenarios a general ophthalmologist would be expected to manage. We conducted two complementary evaluations: (1) a human-AI interactive diagnostic reasoning study in which ophthalmologists recorded initial differentials and plans, then reviewed AMIE's structured output and revised their answers; and (2) a masked preference and quality study comparing AMIE's narrative outputs with case author reference answers using a predefined rubric. AMIE showed standalone diagnostic performance comparable to clinicians at baseline. Crucially, after reviewing AMIE's responses, ophthalmologists tended to rank the correct diagnosis higher, reached greater agreement with one another, and enriched their investigation and management plans. Improvements were observed even when AMIE's top choice differed from or underperformed the clinician baseline, consistent with a complementary effect in which structured reasoning support helps clinicians re-rank rather than simply accept the model output. Preferences varied by clinical grade, suggesting opportunities to personalise responses by experience. Without ophthalmology-specific fine-tuning, AMIE matched clinician baseline and augmented clinical reasoning at the point of need, motivating multi-axis evaluation, domain adaptation, and prospective multimodal studies in real-world settings.
Stop the Nonconsensual Use of Nude Images in Research
Venue: arXiv cs.CY 2025
Year: 2025

In order to train, test, and evaluate nudity detection models, machine learning researchers typically rely on nude images scraped from the Internet. Our research finds that this content is collected and, in some cases, subsequently distributed by researchers without consent, leading to potential misuse and exacerbating harm against the subjects depicted. This position paper argues that the distribution of nonconsensually collected nude images by researchers perpetuates image-based sexual abuse and that the machine learning community should stop the nonconsensual use of nude images in research. To characterize the scope and nature of this problem, we conducted a systematic review of papers published in computing venues that collect and use nude images. Our results paint a grim reality: norms around the usage of nude images are sparse, leading to a litany of problematic practices like distributing and publishing nude images with uncensored faces, and intentionally collecting and sharing abusive content. We conclude with a call-to-action for publishing venues and a vision for research in nudity detection that balances user agency with concrete research objectives.
3D Roadway Scene Object Detection with LIDARs in Snowfall Conditions
Venue: arXiv cs.CV 2025
Year: 2025

Because 3D structure of a roadway environment can be characterized directly by a Light Detection and Ranging (LiDAR) sensors, they can be used to obtain exceptional situational awareness for assitive and autonomous driving systems. Although LiDARs demonstrate good performance in clean and clear weather conditions, their performance significantly deteriorates in adverse weather conditions such as those involving atmospheric precipitation. This may render perception capabilities of autonomous systems that use LiDAR data in learning based models to perform object detection and ranging ineffective. While efforts have been made to enhance the accuracy of these models, the extent of signal degradation under various weather conditions remains largely not quantified. In this study, we focus on the performance of an automotive grade LiDAR in snowy conditions in order to develop a physics-based model that examines failure modes of a LiDAR sensor. Specifically, we investigated how the LiDAR signal attenuates with different snowfall rates and how snow particles near the source serve as small but efficient reflectors. Utilizing our model, we transform data from clear conditions to simulate snowy scenarios, enabling a comparison of our synthetic data with actual snowy conditions. Furthermore, we employ this synthetic data, representative of different snowfall rates, to explore the impact on a pre-trained object detection model, assessing its performance under varying levels of snowfall
SignaApp a modern alternative to support signwriting notation for sign languages
Venue: arXiv cs.HC 2025
Year: 2025

The present work reports the development of an application called Signa App, which was designed following the philosophy of User-Centered De-sign. Signa App aims to provide a mobile platform for editing and creating texts in SignWriting notation. The proposal was based on the lack of a mo-bile application that is usable for Deaf individuals who use sign language. The application was tested with adults, children, and adolescents, and the results showed a high degree of acceptance and ease of use. The app has al-ready been introduced to the SignWriting user community, receiving posi-tive feedback. Likewise, the application is available on the Google Play Store
SmartMixed: A Two-Phase Training Strategy for Adaptive Activation Function Learning in Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

The choice of activation function plays a critical role in neural networks, yet most architectures still rely on fixed, uniform activation functions across all neurons. We introduce SmartMixed, a two-phase training strategy that allows networks to learn optimal per-neuron activation functions while preserving computational efficiency at inference. In the first phase, neurons adaptively select from a pool of candidate activation functions (ReLU, Sigmoid, Tanh, Leaky ReLU, ELU, SELU) using a differentiable hard-mixture mechanism. In the second phase, each neuron's activation function is fixed according to the learned selection, resulting in a computationally efficient network that supports continued training with optimized vectorized operations. We evaluate SmartMixed on the MNIST dataset using feedforward neural networks of varying depths. The analysis shows that neurons in different layers exhibit distinct preferences for activation functions, providing insights into the functional diversity within neural architectures.
Agent-GSPO: Communication-Efficient Multi-Agent Systems via Group Sequence Policy Optimization
Venue: arXiv cs.MA 2025
Year: 2025

To combat the prohibitive communication costs of ``free-for-all" multi-agent systems (MAS), we introduce \textbf{Agent-GSPO}, a framework that directly optimizes for token economy using sequence-level reinforcement learning. Agent-GSPO leverages the stable and memory-efficient Group Sequence Policy Optimization (GSPO) algorithm to train agents on a communication-aware reward that explicitly penalizes verbosity. Across seven reasoning benchmarks, Agent-GSPO not only achieves new state-of-the-art performance but does so with a fraction of the token consumption of existing methods. By fostering emergent strategies like ``strategic silence," our approach provides a practical blueprint for developing scalable and economically viable multi-agent systems.