OpenRaiser/Intern-Atlas huggingface dataset:
Safe and Non-Conservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous vehicles must navigate dynamically uncertain environments while balancing the safety and driving efficiency. This challenge is exacerbated by the unpredictable nature of surrounding human-driven vehicles (HVs) and perception inaccuracies, which require planners to adapt to evolving uncertainties while maintaining safe trajectories. Overly conservative planners degrade driving efficiency, while deterministic approaches may encounter serious issues and risks of failure when faced with sudden and unexpected maneuvers. To address these issues, we propose a real-time contingency trajectory optimization framework in this paper. By employing event-triggered online learning of HV control-intent sets, our method dynamically quantifies multi-modal HV uncertainties and refines the forward reachable set (FRS) incrementally. Crucially, we enforce invariant safety through FRS-based barrier constraints that ensure safety without reliance on accurate trajectory prediction of HVs. These constraints are embedded in contingency trajectory optimization and solved efficiently through consensus alternative direction method of multipliers (ADMM). The system continuously adapts to the uncertainties in HV behaviors, preserving feasibility and safety without resorting to excessive conservatism. High-fidelity simulations on highway and urban scenarios, as well as a series of real-world experiments demonstrate significant improvements in driving efficiency and passenger comfort while maintaining safety under uncertainty. The project page is available at https://pathetiue.github.io/frscp.github.io/.
LINR Bridge: Vector Graphic Animation via Neural Implicits and Video Diffusion Priors
Venue: arXiv cs.CV 2025
Year: 2025

Vector graphics, known for their scalability and user-friendliness, provide a unique approach to visual content compared to traditional pixel-based images. Animation of these graphics, driven by the motion of their elements, offers enhanced comprehensibility and controllability but often requires substantial manual effort. To automate this process, we propose a novel method that integrates implicit neural representations with text-to-video diffusion models for vector graphic animation. Our approach employs layered implicit neural representations to reconstruct vector graphics, preserving their inherent properties such as infinite resolution and precise color and shape constraints, which effectively bridges the large domain gap between vector graphics and diffusion models. The neural representations are then optimized using video score distillation sampling, which leverages motion priors from pretrained text-to-video diffusion models. Finally, the vector graphics are warped to match the representations resulting in smooth animation. Experimental results validate the effectiveness of our method in generating vivid and natural vector graphic animations, demonstrating significant improvement over existing techniques that suffer from limitations in flexibility and animation quality.
Fine-Tuning Vision-Language Models for Visual Navigation Assistance
Venue: arXiv cs.CV 2025
Year: 2025

We address vision-language-driven indoor navigation to assist visually impaired individuals in reaching a target location using images and natural language guidance. Traditional navigation systems are ineffective indoors due to the lack of precise location data. Our approach integrates vision and language models to generate step-by-step navigational instructions, enhancing accessibility and independence. We fine-tune the BLIP-2 model with Low Rank Adaptation (LoRA) on a manually annotated indoor navigation dataset. We propose an evaluation metric that refines the BERT F1 score by emphasizing directional and sequential variables, providing a more comprehensive measure of navigational performance. After applying LoRA, the model significantly improved in generating directional instructions, overcoming limitations in the original BLIP-2 model.
Persuading Agents in Opinion Formation Games
Venue: arXiv cs.GT 2025
Year: 2025

Prominent opinion formation models such as the one by Friedkin and Johnsen (FJ) concentrate on the effects of peer pressure on public opinions. In practice, opinion formation is also based on information about the state of the world and persuasion efforts. In this paper, we analyze an approach of Bayesian persuasion in the FJ model. There is an unknown state of the world that influences the preconceptions of n agents. A sender S can (partially) reveal information about the state to all agents. The agents update their preconceptions, and an equilibrium of public opinions emerges. We propose algorithms for the sender to reveal information in order to optimize various aspects of the emerging equilibrium. For many natural sender objectives, we show that there are simple optimal strategies. We then focus on a general class of range-based objectives with desired opinion ranges for each agent. We provide efficient algorithms in several cases, e.g., when the matrix of preconceptions in all states has constant rank, or when there is only a polynomial number of range combinations that lead to positive value for S. This generalizes, e.g., instances with a constant number of states and/or agents, or instances with a logarithmic number of ranges. In general, we show that subadditive range-based objectives allow a simple n-approximation, and even for additive ones, obtaining an $n^{1-c}$-approximation is NP-hard, for any constant $c > 0$.
Neural Cone Radiosity for Interactive Global Illumination with Glossy Materials
Venue: arXiv cs.GR 2025
Year: 2025

Modeling of high-frequency outgoing radiance distributions has long been a key challenge in rendering, particularly for glossy material. Such distributions concentrate radiative energy within a narrow lobe and are highly sensitive to changes in view direction. However, existing neural radiosity methods, which primarily rely on positional feature encoding, exhibit notable limitations in capturing these high-frequency, strongly view-dependent radiance distributions. To address this, we propose a highly-efficient approach by reflectance-aware ray cone encoding based on the neural radiosity framework, named neural cone radiosity. The core idea is to employ a pre-filtered multi-resolution hash grid to accurately approximate the glossy BSDF lobe, embedding view-dependent reflectance characteristics directly into the encoding process through continuous spatial aggregation. Our design not only significantly improves the network's ability to model high-frequency reflection distributions but also effectively handles surfaces with a wide range of glossiness levels, from highly glossy to low-gloss finishes. Meanwhile, our method reduces the network's burden in fitting complex radiance distributions, allowing the overall architecture to remain compact and efficient. Comprehensive experimental results demonstrate that our method consistently produces high-quality, noise-free renderings in real time under various glossiness conditions, and delivers superior fidelity and realism compared to baseline approaches.
RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
Venue: arXiv cs.LG 2025
Year: 2025

Identifying recurring patterns and rare events in large-scale signals is a fundamental challenge in fields such as astronomy, physical simulations, and biomedical science. Convolutional Dictionary Learning (CDL) offers a powerful framework for modeling local structures in signals, but its use for detecting rare or anomalous events remains largely unexplored. In particular, CDL faces two key challenges in this setting: high computational cost and sensitivity to artifacts and outliers. In this paper, we introduce RoseCDL, a scalable and robust CDL algorithm designed for unsupervised rare event detection in long signals. RoseCDL combines stochastic windowing for efficient training on large datasets with inline outlier detection to enhance robustness and isolate anomalous patterns. This reframes CDL as a practical tool for event discovery and characterization in real-world signals, extending its role beyond traditional tasks like compression or denoising.
FlexSAN: A Flexible Regenerative Satellite Access Network Architecture
Venue: arXiv cs.NI 2025
Year: 2025

The regenerative satellite access network (SAN) architecture deploys next-generation NodeB (gNBs) on satellites to enable enhanced network management capabilities. It supports two types of regenerative payload, on-board gNB and on-board gNB-Distributed Unit (gNB-DU). Measurement results based on our prototype implementation show that the on-board gNB offers lower latency, while the on-board gNB-DU is more cost-effective, and there is often a trade-off between Quality-of-Service (QoS) and operational expenditure (OPEX) when choosing between the two payload types. However, current SAN configurations are static and inflexible -- either deploying the full on-board gNB or only the on-board gNB-DU. This rigidity can lead to resource waste or poor user experiences. In this paper, we propose Flexible SAN (FlexSAN), an adaptive satellite access network architecture that dynamically configures the optimal regenerative payload based on real-time user demands. FlexSAN selects the lowest OPEX payload configuration when all user demands are satisfied, and otherwise maximizes the number of admitted users while ensuring QoS for connected users. To address the computational complexity of dynamic payload selection, we design an adaptive greedy heuristic algorithm. Extensive experiments validate FlexSAN's effectiveness, showing a 36.1% average improvement in user admission rates and a 15% OPEX reduction over static SANs.
VL Norm: Rethink Loss Aggregation in RLVR
Venue: arXiv cs.LG 2025
Year: 2025

We propose VL Norm (Variance-reduced Length-dependent Normalization), a simple yet effective loss aggregation method tailored to the characteristic of dynamic generation lengths in Reinforcement Learning with Verifiable Rewards (RLVR). Recently, RLVR has demonstrated strong potential in improving the reasoning capabilities of large language models (LLMs), but a major challenge lies in the large variability of response lengths during training, which leads to high gradient variance and unstable optimization. Although previous methods such as GRPO, DAPO, and Dr. GRPO introduce different loss normalization terms to address this issue, they either produce biased estimates or still suffer from high gradient variance. By analyzing the effect of varying lengths on policy loss both theoretically and empirically, we reformulate the problem as finding a minimum-variance unbiased estimator. Our proposed VL Norm not only provides an unbiased estimate of the true policy loss but also minimizes gradient variance in theory. Besides, VL Norm is easy to implement with less than 10 lines of code change. Extensive experiments show that it consistently achieves superior results across different model sizes, maximum lengths, and tasks. When integrated into the state-of-the-art RL algorithm DAPO, it achieves up to 2.67x faster convergence on the CountDown task. Our code is public at https://github.com/zerolllin/Delta-L-Normalization.
Temporal Image Forensics: A Review and Critical Evaluation
Venue: arXiv cs.CV 2025
Year: 2025

Temporal image forensics is the science of estimating the age of a digital image. Usually, time-dependent traces (age traces) introduced by the image acquisition pipeline are exploited for this purpose. In this review, a comprehensive overview of the field of temporal image forensics based on time-dependent traces from the image acquisition pipeline is given. This includes a detailed insight into the properties of known age traces (i.e., in-field sensor defects and sensor dust) and temporal image forensics techniques. Another key aspect of this work is to highlight the problem of content bias and to illustrate how important eXplainable Artificial Intelligence methods are to verify the reliability of temporal image forensics techniques. Apart from reviewing material presented in previous works, in this review: (i) a new (probably more realistic) forensic setting is proposed; (ii) the main properties (growth rate and spatial distribution) of in-field sensor defects are verified; (iii) it is shown that a method proposed to utilize in-field sensor defects for image age approximation actually exploits other traces (most likely content bias); (iv) the features learned by a neural network dating palmprint images are further investigated; (v) it is shown how easily a neural network can be distracted from learning age traces. For this purpose, previous work is analyzed, re-implemented if required and experiments are conducted.
Tight Bounds for Low-Error Frequency Moment Estimation and the Power of Multiple Passes
Venue: arXiv cs.DS 2025
Year: 2025

Estimating the second frequency moment $F_2$ of a data stream up to a $(1 \pm \varepsilon)$ factor is a central problem in the streaming literature. For errors $\varepsilon > Ω(1/\sqrt{n})$, the tight bound $Θ\left(\log(\varepsilon^2 n)/\varepsilon^2\right)$ was recently established by Braverman and Zamir. In this work, we complete the picture by resolving the remaining regime of small error, $\varepsilon < 1/\sqrt{n}$, showing that the optimal space complexity is $Θ\left( \min\left(n, \frac{1}{\varepsilon^2} \right) \cdot \left(1 + \left| \log(\varepsilon^2 n) \right| \right) \right)$ bits for all $\varepsilon \geq 1/n^2$, assuming a sufficiently large universe. This closes the gap between the best known $Ω(n)$ lower bound and the straightforward $O(n \log n)$ upper bound in that range, and shows that essentially storing the entire stream is necessary for high-precision estimation. To derive this bound, we fully characterize the two-party communication complexity of estimating the size of a set intersection up to an arbitrary additive error $\varepsilon n$. In particular, we prove a tight $Ω(n \log n)$ lower bound for one-way communication protocols when $\varepsilon < n^{-1/2-Ω(1)}$, in contrast to classical $O(n)$-bit protocols that use two-way communication. Motivated by this separation, we present a two-pass streaming algorithm that computes the exact histogram of a stream with high probability using only $O(n \log \log n)$ bits of space, in contrast to the $Θ(n \log n)$ bits required in one pass even to approximate $F_2$ with small error. This yields the first asymptotic separation between one-pass and $O(1)$-passes space complexity for small frequency moment estimation.
K2-Think: A Parameter-Efficient Reasoning System
Venue: arXiv cs.LG 2025
Year: 2025

K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1. Built on the Qwen2.5 base model, our system shows that smaller models can compete at the highest levels by combining advanced post-training and test-time computation techniques. The approach is based on six key technical pillars: Long Chain-of-thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewards (RLVR), Agentic planning prior to reasoning, Test-time Scaling, Speculative Decoding, and Inference-optimized Hardware, all using publicly available open-source datasets. K2-Think excels in mathematical reasoning, achieving state-of-the-art scores on public benchmarks for open-source models, while also performing strongly in other areas such as Code and Science. Our results confirm that a more parameter-efficient model like K2-Think 32B can compete with state-of-the-art systems through an integrated post-training recipe that includes long chain-of-thought training and strategic inference-time enhancements, making open-source reasoning systems more accessible and affordable. K2-Think is freely available at k2think.ai, offering best-in-class inference speeds of over 2,000 tokens per second per request via the Cerebras Wafer-Scale Engine.
Enhanced cast-128 with adaptive s-box optimization via neural networks for image protection
Venue: arXiv cs.CR 2025
Year: 2025

An improved CAST-128 encryption algorithm, which is done by implementing chaos-based adaptive S-box generation using Logistic sine Map (LSM), has been provided in this paper because of the increasing requirements of efficient and smart image encryption mechanisms. The study aims to address the drawbacks of static S-box models commonly used in traditional cryptographic systems, which are susceptible to linear and differential attacks. In the proposed scheme, the dynamic, non-linear, invertible, and highly cryptographic strength S-boxes are generated through a hybrid chaotic system that may have high non-linearity, strong and rigorous avalanche characteristics, and low differential uniformity. The process here is that the LSM is used to produce S-boxes having key-dependent parameters that are stuffed into the CAST-128 structure to encrypt the image in a block-wise manner. The performance of the encryption is assessed utilizing a set of standard grayscale images. The metrics that are used to evaluate the security are entropy, NPCR, UACI, PSNR, and histogram analysis. Outcomes indicate that randomness, resistance to statistical attacks, and country of encryption are significantly improved compared to the original CAST-128. The study is theoretically and practically relevant since it presents a lightweight S-box generation approach driven by chaos, which can increase the level of robustness of the image encryptions without enlisting machine learning. The system may be applied to secure communications, surveillance systems, and medical image protection on a real-time basis.
Getting In Contract with Large Language Models -- An Agency Theory Perspective On Large Language Model Alignment
Venue: arXiv cs.AI 2025
Year: 2025

Adopting Large language models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, discriminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM's black-box nature. While various research disciplines investigated AI alignment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor consider organizational AI adoption processes. Therefore, we propose LLM ATLAS (LLM Agency Theory-Led Alignment Strategy) a conceptual framework grounded in agency (contract) theory, to mitigate alignment problems during organizational LLM adoption. We conduct a conceptual literature analysis using the organizational LLM adoption phases and the agency theory as concepts. Our approach results in (1) providing an extended literature analysis process specific to AI alignment methods during organizational LLM adoption and (2) providing a first LLM alignment problem-solution space.
BDPM: A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis
Venue: arXiv cs.AI 2025
Year: 2025

Background: Parkinson's disease remains a major neurodegenerative disorder with high misdiagnosis rates, primarily due to reliance on clinical rating scales. Recent studies have demonstrated a strong association between gut microbiota and Parkinson's disease, suggesting that microbial composition may serve as a promising biomarker. Although deep learning models based ongut microbiota show potential for early prediction, most approaches rely on single classifiers and often overlook inter-strain correlations or temporal dynamics. Therefore, there is an urgent need for more robust feature extraction methods tailored to microbiome data. Methods: We proposed BDPM (A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis). First, we collected gut microbiota profiles from 39 Parkinson's patients and their healthy spouses to identify differentially abundant taxa. Second, we developed an innovative feature selection framework named RFRE (Random Forest combined with Recursive Feature Elimination), integrating ecological knowledge to enhance biological interpretability. Finally, we designed a hybrid classification model to capture temporal and spatial patterns in microbiome data.
The Carbon Footprint Wizard: A Knowledge-Augmented AI Interface for Streamlining Food Carbon Footprint Analysis
Venue: arXiv cs.AI 2025
Year: 2025

Environmental sustainability, particularly in relation to climate change, is a key concern for consumers, producers, and policymakers. The carbon footprint, based on greenhouse gas emissions, is a standard metric for quantifying the contribution to climate change of activities and is often assessed using life cycle assessment (LCA). However, conducting LCA is complex due to opaque and global supply chains, as well as fragmented data. This paper presents a methodology that combines advances in LCA and publicly available databases with knowledge-augmented AI techniques, including retrieval-augmented generation, to estimate cradle-to-gate carbon footprints of food products. We introduce a chatbot interface that allows users to interactively explore the carbon impact of composite meals and relate the results to familiar activities. A live web demonstration showcases our proof-of-concept system with arbitrary food items and follow-up questions, highlighting both the potential and limitations - such as database uncertainties and AI misinterpretations - of delivering LCA insights in an accessible format.
Faster, Self-Supervised Super-Resolution for Anisotropic Multi-View MRI Using a Sparse Coordinate Loss
Venue: arXiv cs.CV 2025
Year: 2025

Acquiring images in high resolution is often a challenging task. Especially in the medical sector, image quality has to be balanced with acquisition time and patient comfort. To strike a compromise between scan time and quality for Magnetic Resonance (MR) imaging, two anisotropic scans with different low-resolution (LR) orientations can be acquired. Typically, LR scans are analyzed individually by radiologists, which is time consuming and can lead to inaccurate interpretation. To tackle this, we propose a novel approach for fusing two orthogonal anisotropic LR MR images to reconstruct anatomical details in a unified representation. Our multi-view neural network is trained in a self-supervised manner, without requiring corresponding high-resolution (HR) data. To optimize the model, we introduce a sparse coordinate-based loss, enabling the integration of LR images with arbitrary scaling. We evaluate our method on MR images from two independent cohorts. Our results demonstrate comparable or even improved super-resolution (SR) performance compared to state-of-the-art (SOTA) self-supervised SR methods for different upsampling scales. By combining a patient-agnostic offline and a patient-specific online phase, we achieve a substantial speed-up of up to ten times for patient-specific reconstruction while achieving similar or better SR quality. Code is available at https://github.com/MajaSchle/tripleSR.