OpenRaiser/Intern-Atlas huggingface dataset:
Taking a Closer Look at Warnings Generated by PMD and SonarQube, their Rules and Compliance to Established Coding Standards
Venue: arXiv cs.SE 2026
Year: 2026

Context: Static code analysis (SCA) tools play a vital role in software development, reducing the cost and time required for code reviews. However, high false-positive and false-negative rates are reported for the best tools in the community. Accordingly, studies often aim to develop datasets for learning SCA warning patterns to reduce false results. These datasets are meant to possess high-quality and high-volume in covering the full range of faults/rules that typically result in false warnings and be compliant with established coding standards. However, existing studies have not utilised such datasets or identified the breadth of rules that are prone to false positives and their compliance to coding standards. Objectives: We analysed code from Stack Overflow and Apache Tomcat to capture variations in code length and style in detecting false-positive warnings from best-performing tools PMD and SonarQube, addressing this gap. Method: In deriving false-positive warnings, outcomes from the tools were labelled using established coding standards. Deeper analyses were then conducted to identify the rules that are prone to false-positives, reasons for these, and agreement/gaps between SCA rules and established standards. Results: Among our main outcomes, we observe that only a few SCA rules generate false positives, ranging from 4.64% to 18.45% across four datasets. Additionally, eliminating rules that contradict established standards significantly reduce the false-positive rate. Additionally, our findings reveal discrepancies between tools and established standards. Conclusion: Given the evidence established in this study, we recommend further investigations into gaps between tools and established standards, including the use of machine learning approaches to annotate larger datasets.
COMBAT: Conditional World Models for Behavioral Agent Training
Venue: arXiv cs.CV 2026
Year: 2026

Recent advances in video generation have spurred the development of world models capable of simulating 3D-consistent environments and interactions with static objects. However, a significant limitation remains in their ability to model dynamic, reactive agents that can intelligently influence and interact with the world. To address this gap, we introduce COMBAT, a real-time, action-controlled world model trained on the complex 1v1 fighting game Tekken 3. Our work demonstrates that diffusion models can successfully simulate a dynamic opponent that reacts to player actions, learning its behavior implicitly. Our approach utilizes a 1.2 billion parameter Diffusion Transformer, conditioned on latent representations from a deep compression autoencoder. We employ state-of-the-art techniques, including causal distillation and diffusion forcing, to achieve real-time inference. Crucially, we observe the emergence of sophisticated agent behavior by training the model solely on single-player inputs, without any explicit supervision for the opponent's policy. Unlike traditional imitation learning methods, which require complete action labels, COMBAT learns effectively from partially observed data to generate responsive behaviors for a controllable Player 1. We present an extensive study and introduce novel evaluation methods to benchmark this emergent agent behavior, establishing a strong foundation for training interactive agents within diffusion-based world models.
MME: Mixture of Mesh Experts with Random Walk Transformer Gating
Venue: arXiv cs.CV 2026
Year: 2026

In recent years, various methods have been proposed for mesh analysis, each offering distinct advantages and often excelling on different object classes. We present a novel Mixture of Experts (MoE) framework designed to harness the complementary strengths of these diverse approaches. We propose a new gate architecture that encourages each expert to specialise in the classes it excels in. Our design is guided by two key ideas: (1) random walks over the mesh surface effectively capture the regions that individual experts attend to, and (2) an attention mechanism that enables the gate to focus on the areas most informative for each expert's decision-making. To further enhance performance, we introduce a dynamic loss balancing scheme that adjusts a trade-off between diversity and similarity losses throughout the training, where diversity prompts expert specialization, and similarity enables knowledge sharing among the experts. Our framework achieves state-of-the-art results in mesh classification, retrieval, and semantic segmentation tasks. Our code is available at: https://github.com/amirbelder/MME-Mixture-of-Mesh-Experts.
Constitutional Black-Box Monitoring for Scheming in LLM Agents
Venue: arXiv cs.CL 2026
Year: 2026

Safe deployment of Large Language Model (LLM) agents in autonomous settings requires reliable oversight mechanisms. A central challenge is detecting scheming, where agents covertly pursue misaligned goals. One approach to mitigating such risks is LLM-based monitoring: using language models to examine agent behaviors for suspicious actions. We study constitutional black-box monitors: prompted classifiers that detect scheming using only externally observable inputs and outputs, optimized on synthetic data generated from natural-language behavior specifications. We introduce two pipelines for generating synthetic agent trajectories, STRIDE (iterative refinement) and Gloom (agent-environment simulation), from which we generate 1,000 samples each. We optimize frontier LLM monitors on these datasets via prompt sweeps, human refinement, and automated prompt optimization, and evaluate performance on 7,500 held-out trajectories from ControlArena, a suite of grounded environments where agents operate in more realistic contexts. Our results demonstrate that monitors selected purely on synthetic data can generalize to more realistic environments, capturing a meaningful scheming signal. However, we find that performance saturates quickly in our setting, with simple prompt sweeps matching the results of more extensive optimization. Pushing beyond this limit yields no further improvements and instead leads to overfitting.
MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine
Venue: arXiv cs.CL 2026
Year: 2026

Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are either closed-source or computationally prohibitive, precluding the on-premises deployment required for patient privacy and PHI compliance. We introduce MEDGPT-OSS, an open-weight, 20B-parameter generalist vision-language model designed to facilitate open research in clinical AI. Rather than relying on architectural complexity, MEDGPT-OSS pairs the GPT-oss language backbone with a visual front-end via a optimized, three-stage training curriculum. By progressively domain-adapting these modules through rigorous data curation and long-context multimodal alignment, we demonstrate that a 20B model can bridge the capacity gap. It successfully outperforms larger open medical models on out-of-distribution (OOD) multimodal reasoning and complex text-only clinical tasks. By unifying diverse modalities under a single instruction-following interface, MEDGPT-OSS maintains a parameter-efficient footprint fully compatible with commodity GPUs. We release the complete training recipe, open-weight checkpoints, and a rigorous evaluation harness to serve as a verifiable foundation for privacy-preserving, institution-specific clinical AI research.
PARCER as an Operational Contract to Reduce Variance, Cost, and Risk in LLM Systems
Venue: arXiv cs.SE 2026
Year: 2026

Systems based on Large Language Models (LLMs) have become formidable tools for automating research and software production. However, their governance remains a challenge when technical requirements demand absolute consistency, auditability, and predictable control over cost and latency. Recent literature highlights two phenomena that aggravate this scenario: the stochastic variance inherent in the model's judgment (often treated as "systemic noise") and the substantial degradation of context utilization in long inputs, with critical losses when decisive information is diluted in the middle of the prompt. This article proposes PARCER as an engineering response to these limitations. The framework acts as a declarative "operational contract" in YAML, transforming unstructured interactions into versioned and executable artifacts. PARCER imposes strict governance structured into seven operational phases, introducing decision hygiene practices inspired by legal judgments to mitigate noise, adaptive token budgeting, formalized recovery routes (fallbacks) for context preservation, and systemic observability via OpenTelemetry. The objective of this work is to present the conceptual and technical architecture of PARCER, positioning it as a necessary transition from simple "prompt engineering" to "context engineering with governable governance".
A Tree-Structured Two-Phase Commit Framework for OceanBase: Optimizing Scalability and Consistency
Venue: arXiv cs.DB 2026
Year: 2026

Modern distributed databases face challenges in achieving transactional consistency across distributed partitions. Traditional two-phase commit (2PC) protocols incur high coordination overhead and latency, and require complex recovery for dynamic partition transfers. This paper introduces a novel tree-shaped 2PC framework for OceanBase that leverages single-machine log streams to address these challenges through three innovations. First, we propose log streams as atomic participants, replacing partition-level coordination. By treating each log stream as the commit unit, a transaction spanning $N$ co-located partitions interacts with one participant, reducing coordination overhead by orders of magnitude (e.g., 99 percent reduction for $N=100$). Second, we design a tree-shaped 2PC protocol with coordinator-rooted DAG topology that dynamically handles partition transfers by recursively constructing commit trees. When a partition migrates during a transaction, the protocol embeds migration contexts as leaf nodes, eliminating explicit participant list updates, resolving circular dependencies, and ensuring linearizable commits under topology changes. Third, we introduce prepare-unknown and trans-unknown states to prevent consistency violations when participants lose context. These states signal uncertainty during retries, avoiding erroneous aborts from so-called lying participants while isolating users from ambiguity. Experimental evaluation demonstrates performance approaching that of single-machine transactions, with reduced latency and bandwidth consumption, validating the framework's effectiveness for modern distributed databases.
VEMamba: Efficient Isotropic Reconstruction of Volume Electron Microscopy with Axial-Lateral Consistent Mamba
Venue: arXiv cs.CV 2026
Year: 2026

Volume Electron Microscopy (VEM) is crucial for 3D tissue imaging but often produces anisotropic data with poor axial resolution, hindering visualization and downstream analysis. Existing methods for isotropic reconstruction often suffer from neglecting abundant axial information and employing simple downsampling to simulate anisotropic data. To address these limitations, we propose VEMamba, an efficient framework for isotropic reconstruction. The core of VEMamba is a novel 3D Dependency Reordering paradigm, implemented via two key components: an Axial-Lateral Chunking Selective Scan Module (ALCSSM), which intelligently re-maps complex 3D spatial dependencies (both axial and lateral) into optimized 1D sequences for efficient Mamba-based modeling, explicitly enforcing axial-lateral consistency; and a Dynamic Weights Aggregation Module (DWAM) to adaptively aggregate these reordered sequence outputs for enhanced representational power. Furthermore, we introduce a realistic degradation simulation and then leverage Momentum Contrast (MoCo) to integrate this degradation-aware knowledge into the network for superior reconstruction. Extensive experiments on both simulated and real-world anisotropic VEM datasets demonstrate that VEMamba achieves highly competitive performance across various metrics while maintaining a lower computational footprint. The source code is available on GitHub: https://github.com/I2-Multimedia-Lab/VEMamba
A Novel Reconfigurable Dexterous Hand Based on Triple-Symmetric Bricard Parallel Mechanism
Venue: arXiv cs.RO 2026
Year: 2026

This paper introduces a novel design for a robotic hand based on parallel mechanisms. The proposed hand uses a triple-symmetric Bricard linkage as its reconfigurable palm, enhancing adaptability to objects of varying shapes and sizes. Through topological and dimensional synthesis, the mechanism achieves a well-balanced degree of freedom and link configuration suitable for reconfigurable palm motion, balancing dexterity, stability, and load capacity. Furthermore, kinematic analysis is performed using screw theory and closed-loop constraints, and performance is evaluated based on workspace, stiffness, and motion/force transmission efficiency. Finally, a prototype is developed and tested through a series of grasping experiments, demonstrating the ability to perform stable and efficient manipulation across a wide range of objects. The results validate the effectiveness of the design in improving grasping versatility and operational precision, offering a promising solution for advanced robotic manipulation tasks.
Capstone: Power-Capped Pipelining for Coarse-Grained Reconfigurable Array Compilers
Venue: arXiv cs.AR 2026
Year: 2026

Coarse-grained reconfigurable arrays (CGRAs) have attracted growing interest because they exhibit performance and energy efficiency competitive with ASICs while maintaining flexibility similar to FPGAs. These properties make CGRAs attractive in accelerator and other power-constrained system contexts. However, modern CGRA compilers aggressively pipeline for frequency and performance improvements, often violating hard power budgets. We empirically show that, in state-of-the-art CGRA compilers such as Cascade, post-place-and-route (post-PnR) pipelining increases power monotonically and ultimately exceeds fixed power caps across diverse workloads. In response, we introduce \emph{Capstone}, a power-aware extension of Cascade that integrates a fast, compiler-resident power model with a user-tunable controller that guides the bitstream selection process towards optimization targets. Capstone predicts per-iteration power directly inside the post-PnR compilation loop and selects one or a small set of PnR configurations such that at least one meets a user-specified power cap. Thus, we shift the objective from indiscriminately maximizing frequency to maximizing safe frequency under a discrete power cap. On a suite of kernels spanning fundamental dense and sparse applications, Capstone meets a power cap and minimizes remaining power headroom while preserving feasible performance. Our results indicate that cap-aware compilation is both necessary and practical, as the compiler can proactively land on cap-compliant points and expose predictable performance under power constraints.
Minimalist Compliance Control
Venue: arXiv cs.RO 2026
Year: 2026

Compliance control is essential for safe physical interaction, yet its adoption is limited by hardware requirements such as force torque sensors. While recent reinforcement learning approaches aim to bypass these constraints, they often suffer from sim-to-real gaps, lack safety guarantees, and add system complexity. We propose Minimalist Compliance Control, which enables compliant behavior using only motor current or voltage signals readily available in modern servos and quasi-direct-drive motors, without force sensors, current control, or learning. External wrenches are estimated from actuator signals and Jacobians and incorporated into a task-space admittance controller, preserving sufficient force measurement accuracy for stable and responsive compliance control. Our method is embodiment-agnostic and plug-and-play with diverse high-level planners. We validate our approach on a robot arm, a dexterous hand, and two humanoid robots across multiple contact-rich tasks, using vision-language models, imitation learning, and model-based planning. The results demonstrate robust, safe, and compliant interaction across embodiments and planning paradigms.
The Aftermath of DrawEduMath: Vision Language Models Underperform with Struggling Students and Misdiagnose Errors
Venue: arXiv cs.CL 2026
Year: 2026

Effective mathematics education requires identifying and responding to students' mistakes. For AI to support pedagogical applications, models must perform well across different levels of student proficiency. Our work provides an extensive, year-long snapshot of how 11 vision-language models (VLMs) perform on DrawEduMath, a QA benchmark involving real students' handwritten, hand-drawn responses to math problems. We find that models' weaknesses concentrate on a core component of math education: student error. All evaluated VLMs underperform when describing work from students who require more pedagogical help, and across all QA, they struggle the most on questions related to assessing student error. Thus, while VLMs may be optimized to be math problem solving experts, our results suggest that they require alternative development incentives to adequately support educational use cases.
Accelerating Multi-Scale Deformable Attention Using Near-Memory-Processing Architecture
Venue: arXiv cs.AR 2026
Year: 2026

Multi Scale Deformable Attention (MSDAttn) has become a fundamental component in various vision tasks due to its effective multi scale grid sampling (MSGS). However, its reliance on random sampling results in highly irregular memory access patterns, making it a memory intensive operation inefficient for GPUs. Near memory processing (NMP) offers a promising solution for accelerating memory bound kernels, yet existing NMP based attention accelerators remain suboptimal for MSDAttn due to incompatible load balancing and data reuse strategies. Specifically, current NMP solutions uniformly distribute processing elements (PEs) across all banks, leading to significant PE underutilization and excessive cross bank data transfers. Moreover, most rely on locality based reuse, which fails under MSDAttn's unpredictable sampling patterns. To address these challenges, this paper presents DANMP, a hardware software co designed NMP based MSDAttn accelerator. On the hardware side, DANMP adopts non uniform NMP integration to handle unbalanced workloads, allocating PEs only in select banks for hot entries, while cold data are processed at the bank group level reducing PE idleness and cross bank transfers. On the software side, it introduces a clustering and packing (CAP) method that leverages clustering to improve temporal locality in query processing, enhancing data reuse. Finally, we implement host NMP co optimization techniques, including an optimized programming model, customized instructions, and a tailored dataflow. Experiments on object detection inference show that DANMP achieves 97.43x speedup and 208.47x energy efficiency improvement over NVIDIA A6000 GPU.
SoberDSE: Sample-Efficient Design Space Exploration via Learning-Based Algorithm Selection
Venue: arXiv cs.AR 2026
Year: 2026

High-Level Synthesis (HLS) is a pivotal electronic design automation (EDA) technology that enables the generation of hardware circuits from high-level language descriptions. A critical step in HLS is Design Space Exploration (DSE), which seeks to identify high-quality hardware architectures under given constraints. However, the enormous size of the design space makes DSE computationally prohibitive. Although numerous algorithms have been proposed to accelerate DSE, our extensive experimental studies reveal that no single algorithm consistently achieves Pareto dominance across all problem instances. Consequently, the inability of any single algorithm to dominate all benchmarks necessitates an automated selection mechanism to identify the best-performing DSE algorithm for each specific case. To address this challenge, we propose the SoberDSE framework, which recommends suitable algorithm based on benchmark characteristics. Experimental results demonstrate that our SoberDSE framework significantly outperforms state-of-the-art heuristic-based DSE algorithms by up to 5.7 $\times$ and state-of-the-art learning-based DSE methods by up to 4.2 $\times$. Furthermore, compared to conventional classification models, SoberDSE delivers superior accuracy in small-sample learning scenarios, with an average enhancement of 35.57\%. Code and models are available at https://anonymous.4open.science/r/Sober-4377.
VizQStudio: Iterative Visualization Literacy MCQs Design with Simulated Students
Venue: arXiv cs.HC 2026
Year: 2026

Multiple-choice questions (MCQs) are a widely used educational tool, particularly in domains such as visualization literacy that require broad conceptual coverage and support diverse real-world applications. However, designing high-quality visualization literacy MCQs remains challenging, as instructors must coordinate multimodal elements (e.g., charts, question stems, and distractors), address diverse visualization tasks, and accommodate learners with heterogeneous backgrounds. Existing visualization literacy assessments primarily rely on standardized, fixed item banks, offering limited support for iterative question design that adapts to differences in learners' abilities, backgrounds, and reasoning strategies. To address these challenges, we present VizQStudio, a visual analytics system that supports instructors in iteratively designing and refining visualization literacy MCQs using MLLM-powered simulated students. Instructors can specify diverse student profiles spanning demographics, knowledge levels, and learning-related traits. The system then visualizes how simulated students reason about and respond to different question components, helping instructors explore potential misconceptions, difficulty calibration, and design trade-offs prior to classroom deployment. We investigate VizQStudio through a mixed-method evaluation, including expert interviews, case studies, a classroom deployment, and a large-scale online study. Overall, this work reframes MLLM-based student simulation in assessment authoring as a design-time, exploratory aid. By examining both its value and limitations in realistic instructional settings, we surface design insights that inform how future systems can support instructor-centered, iterative, and responsible uses of AI for multimodal assessment design in visualization literacy and related domains.