OpenRaiser/Intern-Atlas huggingface dataset:
Combinatorial Rising Bandits
Venue: arXiv cs.LG 2026
Year: 2026

Combinatorial online learning is a fundamental task for selecting the optimal action (or super arm) as a combination of base arms in sequential interactions with systems providing stochastic rewards. It is applicable to diverse domains such as robotics, social advertising, network routing, and recommendation systems. In many real-world scenarios, we often encounter rising rewards, where playing a base arm not only provides an instantaneous reward but also contributes to the enhancement of future rewards, e.g., robots improving through practice and social influence strengthening in the history of successful recommendations. Crucially, these enhancements may propagate to multiple super arms that share the same base arms, introducing dependencies beyond the scope of existing bandit models. To address this gap, we introduce the Combinatorial Rising Bandit (CRB) framework and propose a provably efficient and empirically effective algorithm, Combinatorial Rising Upper Confidence Bound (CRUCB). We empirically demonstrate the effectiveness of CRUCB in realistic deep reinforcement learning environments and synthetic settings, while our theoretical analysis establishes tight regret bounds. Together, they underscore the practical impact and theoretical rigor of our approach. Our code is available at https://github.com/ml-postech/Combinatorial-Rising-Bandits.
Smart Contract Vulnerabilities, Tools, and Benchmarks: an Updated Systematic Literature Review
Venue: arXiv cs.SE 2026
Year: 2026

Smart contracts are self-executing programs on blockchain platforms like Ethereum, which have revolutionized decentralized finance by enabling trustless transactions and the operation of decentralized applications. Despite their potential, the security of smart contracts remains a critical concern due to their immutability and transparency, which expose them to malicious actors. Numerous solutions for vulnerability detection have been proposed, but it is still unclear which one is the most effective. This paper presents a systematic literature review that explores vulnerabilities in Ethereum smart contracts, focusing on automated detection tools and benchmark evaluation. We reviewed 3,380 studies from five digital libraries and five major software engineering conferences, applying a structured selection process that resulted in 222 high-quality studies. The key results include a hierarchical taxonomy of 192 vulnerabilities grouped into 13 categories, a comprehensive list of 219 detection tools with corresponding functionalities, methods, and code transformation techniques, a mapping between our taxonomy and the list of tools, and a collection of 133 benchmarks used for tool evaluation. We conclude with a discussion about the insights into the current state of Ethereum smart contract security and directions for future research.
U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs
Venue: arXiv cs.CL 2026
Year: 2026

The current evaluation of mathematical skills in LLMs is limited, as existing benchmarks are either relatively small, primarily focus on elementary and high-school problems, or lack diversity in topics. Additionally, the inclusion of visual elements in tasks remains largely under-explored. To address these gaps, we introduce U-MATH, a novel benchmark of 1,100 unpublished open-ended university-level problems sourced from teaching materials. It is balanced across six core subjects, with 20% of multimodal problems. Given the open-ended nature of U-MATH problems, we employ an LLM to judge the correctness of generated solutions. To this end, we release $μ$-MATH, a dataset to evaluate the LLMs' capabilities in judging solutions. Benchmarking leading LLMs reveals marked limitations in multi-modal reasoning, with maximum accuracy reaching 93.1\% on textual tasks but only 58.5\% on visual ones. Furthermore, solution judgment proves challenging, requiring the most advanced models to achieve meaningfully high performance, even still peaking at an imperfect F1-score of 90.1\%.
JANUS: A Difference-Oriented Analyzer For Financial Centralization Risks in Smart Contracts
Venue: arXiv cs.LG 2026
Year: 2026

Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralization risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralization risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralization risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralization risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralization risks . Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

Federated Learning (FL) marks a transformative approach to distributed model training by combining locally optimized models from various clients into a unified global model. While FL preserves data privacy by eliminating centralized storage, it encounters significant challenges such as performance degradation, slower convergence, and reduced robustness of the global model due to the heterogeneity in client data distributions. Among the various forms of data heterogeneity, label skew emerges as a particularly formidable and prevalent issue, especially in domains such as image classification. To address these challenges, we begin with comprehensive experiments to pinpoint the underlying issues in the FL training process. Based on our findings, we then introduce an innovative dual-strategy approach designed to effectively resolve these issues. First, we introduce an adaptive loss function for client-side training, meticulously crafted to preserve previously acquired knowledge while maintaining an optimal equilibrium between local optimization and global model coherence. Secondly, we develop a dynamic aggregation strategy for aggregating client models at the server. This approach adapts to each client's unique learning patterns, effectively addressing the challenges of diverse data across the network. Our comprehensive evaluation, conducted across three diverse real-world datasets, coupled with theoretical convergence guarantees, demonstrates the superior efficacy of our method compared to several established state-of-the-art approaches.
Towards Fast Safe Online Reinforcement Learning via Policy Finetuning
Venue: arXiv cs.LG 2026
Year: 2026

The high costs and risks involved in extensive environment interactions hinder the practical application of current online safe reinforcement learning (RL) methods. While offline safe RL addresses this by learning policies from static datasets, the performance therein is usually limited due to reliance on data quality and challenges with out-of-distribution (OOD) actions. Inspired by recent successes in offline-to-online (O2O) RL, it is crucial to explore whether offline safe RL can be leveraged to facilitate faster and safer online policy learning, a direction that has yet to be fully investigated. To fill this gap, we first demonstrate that naively applying existing O2O algorithms from standard RL would not work well in the safe RL setting due to two unique challenges: \emph{erroneous Q-estimations}, resulted from offline-online objective mismatch and offline cost sparsity, and \emph{Lagrangian mismatch}, resulted from difficulties in aligning Lagrange multipliers between offline and online policies. To address these challenges, we introduce \textbf{Marvel}, a novel framework for O2O safe RL, comprising two key components that work in concert: \emph{Value Pre-Alignment} to align the Q-functions with the underlying truth before online learning, and \emph{Adaptive PID Control} to effectively adjust the Lagrange multipliers during online finetuning. Extensive experiments demonstrate that Marvel significantly outperforms existing baselines in both reward maximization and safety constraint satisfaction. By introducing the first policy-finetuning based framework for O2O safe RL, which is compatible with many offline and online safe RL methods, our work has the great potential to advance the field towards more efficient and practical safe RL solutions.
Phaedrus: Predicting Dynamic Application Behavior with Lightweight Generative Models and LLMs
Venue: arXiv cs.SE 2026
Year: 2026

Application profiling is essential for software optimization tasks such as code layout and memory placement, where optimization decisions depend on program behavior. However, modern applications exhibit significant input-dependent variability, limiting the effectiveness of conventional profiling approaches that rely on a single representative execution. We present Phaedrus, a compiler-assisted deep learning framework that predicts dynamic program behavior across diverse execution instances, with a focus on dynamic function call prediction. These predicted call sequences are used to guide input-specific compiler optimizations, enabling code specialization without requiring program execution. Phaedrus introduces two complementary techniques. Application Behavior Synthesis (Dynamis) is a profile-less approach in which large language models infer dynamic behavior directly from source code and static compiler analysis, bypassing traditional profiling. Application Profile Generalization (Morpheus) employs generative models trained on compressed and augmented Whole Program Path (WPP) function profiles to predict application behavior for unseen inputs. Experimental results show that Phaedrus accurately identifies frequently executed and runtime-dominated hotspot functions, covering up to 85-99% of total execution time. Using these predictions, Phaedrus enables superior profile-guided optimizations, achieving an average performance improvement of 6% (upto 25%) and a binary size reduction of 5.19% (upto 19%), without executing the target program. Additionally, Phaedrus reduces WPP function profile sizes by up to $10^{7} \times $.
Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning
Venue: arXiv cs.RO 2026
Year: 2026

In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving autonomous excavation using reinforcement learning (RL) through a rock excavation simulator. In the simulation, resolution can be defined by the particle size/number in the whole soil space. Fine-resolution simulations closely mimic real-world behavior but demand significant calculation time and challenging sample collection, while coarse-resolution simulations enable faster sample collection but deviate from real-world behavior. To combine the advantages of both resolutions, we explore using policies developed in coarse-resolution simulations for pre-training in fine-resolution simulations. To this end, we propose a novel policy learning framework called Progressive-Resolution Policy Distillation (PRPD), which progressively transfers policies through some middle-resolution simulations with conservative policy transfer to avoid domain gaps that could lead to policy transfer failure. Validation in a rock excavation simulator and nine real-world rock environments demonstrated that PRPD reduced sampling time to less than 1/7 while maintaining task success rates comparable to those achieved through policy learning in a fine-resolution simulation. Additional videos and supplementary results are available on our project page: https://yuki-kadokawa.github.io/prpd/
Debugging Defective Visualizations: Empirical Insights Informing a Human-AI Co-Debugging System
Venue: arXiv cs.HC 2026
Year: 2026

Visualization authoring is an iterative process requiring users to adjust parameters to achieve desired aesthetics. Due to its complexity, users often create defective visualizations and struggle to fix them. Many seek help on forums (e.g., Stack Overflow), while others turn to AI, yet little is known about the strengths and limitations of these approaches, or how they can be effectively combined. We analyze Vega-Lite debugging cases from Stack Overflow, categorizing question types by askers, evaluating human responses, and assessing AI performance. Guided by these findings, we design a human-AI co-debugging system that combines LLM-generated suggestions with forum knowledge. We evaluated this system in a user study on 36 unresolved problems, comparing it with forum answers and LLM baselines. Our results show that while forum contributors provide accurate but slow solutions and LLMs offer immediate but sometimes misaligned guidance, the hybrid system resolves 86% of cases, higher than either alone.
Towards a Taxonomy of Software Log Smells
Venue: arXiv cs.SE 2026
Year: 2026

Context: Logging is an important part of modern software projects; logs are used in several tasks such as debugging and testing. Due to the complex nature of logging, it remains a difficult task with several pitfalls that could have serious consequences. Several other domains of software engineering have mitigated such threats by identifying the early signs of more serious issues, i.e., 'smells'. However, this concept is not yet properly defined for logging. Objective: The goal of this study is to create a taxonomy of log smells that can help developers write better logging code. To further help the developers and to identify issues that need more attention from the research community, we also map the identified smells to existing tools repairing them. Methods: We conducted a survey of the scientific literature to identify logging issues and related tools. After extracting relevant data from 51 articles, we used open coding to define logging issues and applied card sorting to derive log smells from these issues. Finally, we classified the tools based on their reported output. Results: We present a taxonomy of nine log smells and describe several facets for each of them. We also review existing tools repairing/removing some of these facets, highlighting the lack of tools addressing some log smells and identifying future research opportunities to close this gap. Conclusions: Logging is vulnerable to log smells throughout all phases of its life cycle, and these issues can affect both logging implementation and the resulting log files. Understanding these smells - along with their causes and consequences - can help reduce their occurrence and lead to higher-quality logging.
Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) face challenges in aligning with diverse cultural values despite their remarkable performance in generation, which stems from inherent monocultural biases and difficulties in capturing nuanced cultural semantics. Existing methods struggle to adapt to unknown culture after fine-tuning. Inspired by cultural geography across five continents, we propose Cultural Palette, a multi-agent framework that redefines cultural alignment as an adaptive "color-blending" process for country-specific adaptation. Our approach harnesses cultural geography across five continents through three key steps: First, we synthesize the Pentachromatic Cultural Palette Dataset using GPT-4o, refining continental-level dialogues with Hofstede's cultural dimensions to establish foundational cultural representations. Second, five continent-level alignment agents form specialized cultural communities that generate region-specific draft responses. Third, a Meta Agent employs Cultural MoErges to dynamically blend these cultural "colors" through attention-gated parameter merging, akin to mixing pigments on a palette, resolving conflicts while preserving cultural nuances to produce the final culturally-aligned response. Extensive experiments across various countries demonstrate that \textit{Cultural Palette} surpasses existing baselines in cultural alignment.
NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting
Venue: arXiv cs.CV 2026
Year: 2026

Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, subterranean robotics, and search & rescue in collapsed environments, require agents to operate with a co-located light and camera in the absence of external lighting. In such cases, dynamic near-field lighting introduces strong, view-dependent shading that significantly degrades SLAM performance. We introduce Near-Field Lighting Bundle Adjustment Loss (NFL-BA) which explicitly models near-field lighting as a part of Bundle Adjustment loss and enables better performance for scenes captured with dynamic lighting. NFL-BA can be integrated into neural rendering-based SLAM systems with implicit or explicit scene representations. Our evaluations mainly focus on endoscopy procedure where SLAM can enable autonomous navigation, guidance to unsurveyed regions, blindspot detections, and 3D visualizations, which can significantly improve patient outcomes and endoscopy experience for both physicians and patients. Replacing Photometric Bundle Adjustment loss of SLAM systems with NFL-BA leads to significant improvement in camera tracking, 37% for MonoGS and 14% for EndoGS, and leads to state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset. Further evaluation on indoor scenes captured with phone camera with flashlight turned on, also demonstrate significant improvement in SLAM performance due to NFL-BA. See results at https://asdunnbe.github.io/NFL-BA/
SAVGBench: Benchmarking Spatially Aligned Audio-Video Generation
Venue: arXiv cs.SD 2026
Year: 2026

This work addresses the lack of multimodal generative models capable of producing high-quality videos with spatially aligned audio. While recent advancements in generative models have been successful in video generation, they often overlook the spatial alignment between audio and visuals, which is essential for immersive experiences. To tackle this problem, we establish a new research direction in benchmarking the Spatially Aligned Audio-Video Generation (SAVG) task. We introduce a spatially aligned audio-visual dataset, whose audio and video data are curated based on whether sound events are onscreen or not. We also propose a new alignment metric that aims to evaluate the spatial alignment between audio and video. Then, using the dataset and metric, we benchmark two types of baseline methods: one is based on a joint audio-video generation model, and the other is a two-stage method that combines a video generation model and a video-to-audio generation model. Our experimental results demonstrate that gaps exist between the baseline methods and the ground truth in terms of video and audio quality, as well as spatial alignment between the two modalities.
RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications
Venue: arXiv cs.LG 2026
Year: 2026

In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python API for easy integration into neural network research, that we call RadFiled3D. Both developments are intended to be used to research alternative radiation simulation methods using deep learning.
Data-Efficient Inference of Neural Fluid Fields via SciML Foundation Model
Venue: arXiv cs.LG 2026
Year: 2026

Recent developments in 3D vision have enabled significant progress in inferring neural fluid fields and realistic rendering of fluid dynamics. However, these methods require dense captures of real-world flows, which demand specialized laboratory setups, making the process costly and challenging. Scientific machine learning (SciML) foundation models, pretrained on extensive simulations of partial differential equations (PDEs), encode rich multiphysics knowledge and thus provide promising sources of domain priors for fluid field inference. Nevertheless, the transferability of these foundation models to real-world vision problems remains largely underexplored. In this work, we demonstrate that SciML foundation models can significantly reduce the data requirements for inferring real-world 3D fluid dynamics while improving generalization. Our method leverages the strong forecasting capabilities and meaningful representations learned by SciML foundation models. We introduce a novel collaborative training strategy that equips neural fluid fields with augmented frames and fluid features extracted from the foundation model. Extensive experiments show substantial improvements in both quantitative metrics and visual quality over prior approaches. In particular, our method achieves a 9-36% improvement in peak signal-to-noise ratio (PSNR) for future prediction while reducing the number of required training frames by 25-50%. These results highlight the practical applicability of SciML foundation models for real-world fluid dynamics reconstruction. Our code is available at: https://github.com/delta-lab-ai/SciML-HY.
Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Vision-language models (VLMs) excel at extracting and reasoning about information from images. Yet, their capacity to leverage internal knowledge about specific entities remains underexplored. This work investigates the disparity in model performance when answering factual questions about an entity described in text versus depicted in an image. Our results reveal a significant accuracy drop - reaching 18% for some models - when the entity is presented visually instead of textually. To study this gap we present PopVQA, a dataset which allows separating entity recognition and question answering, and use it to benchmark several models. We hypothesize that this decline arises from limitations in how information flows from image tokens to query tokens. Thus, we use mechanistic interpretability tools to reveal that, although image tokens are preprocessed by the vision encoder, meaningful information flow from these tokens occurs only in the much deeper layers. Furthermore, critical image processing happens in the language model's middle layers, allowing few layers for consecutive reasoning, highlighting a potential inefficiency in how the model utilizes its layers for reasoning. These insights shed light on the internal mechanics of VLMs and offer pathways for enhancing their reasoning capabilities. PopVQA can be found at https://huggingface.co/datasets/idoco/PopVQA.