OpenRaiser/Intern-Atlas huggingface dataset:
EDU-MATRIX: A Society-Centric Generative Cognitive Digital Twin Architecture for Secondary Education
Venue: arXiv cs.MA 2026
Year: 2026

Existing multi-agent simulations often suffer from the "Agent-Centric Paradox": rules are hard-coded into individual agents, making complex social dynamics rigid and difficult to align with educational values. This paper presents EDU-MATRIX, a society-centric generative cognitive digital twin architecture that shifts the paradigm from simulating "people" to simulating a "social space with a gravitational field." We introduce three architectural contributions: (1) An Environment Context Injection Engine (ECIE), which acts as a "social microkernel," dynamically injecting institutional rules (Gravity) into agents based on their spatial-temporal coordinates; (2) A Modular Logic Evolution Protocol (MLEP), where knowledge exists as "fluid" capsules that agents synthesize to generate new paradigms, ensuring high dialogue consistency (94.1%); and (3) Endogenous Alignment via Role-Topology, where safety constraints emerge from the agent's position in the social graph rather than external filters. Deployed as a digital twin of a secondary school with 2,400 agents, the system demonstrates how "social gravity" (rules) and "cognitive fluids" (knowledge) interact to produce emergent, value-aligned behaviors (Social Clustering Coefficient: 0.72).
Task-Aware Exploration via a Predictive Bisimulation Metric
Venue: arXiv cs.AI 2026
Year: 2026

Accelerating exploration in visual reinforcement learning under sparse rewards remains challenging due to the substantial task-irrelevant variations. Despite advances in intrinsic exploration, many methods either assume access to low-dimensional states or lack task-aware exploration strategies, thereby rendering them fragile in visual domains. To bridge this gap, we present TEB, a Task-aware Exploration approach that tightly couples task-relevant representations with exploration through a predictive Bisimulation metric. Specifically, TEB leverages the metric not only to learn behaviorally grounded task representations but also to measure behaviorally intrinsic novelty over the learned latent space. To realize this, we first theoretically mitigate the representation collapse of degenerate bisimulation metrics under sparse rewards by internally introducing a simple but effective predicted reward differential. Building on this robust metric, we design potential-based exploration bonuses, which measure the relative novelty of adjacent observations over the latent space. Extensive experiments on MetaWorld and Maze2D show that TEB achieves superior exploration ability and outperforms recent baselines.
LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency
Venue: arXiv cs.CV 2026
Year: 2026

This paper introduces LaS-Comp, a zero-shot and category-agnostic approach that leverages the rich geometric priors of 3D foundation models to enable 3D shape completion across diverse types of partial observations. Our contributions are threefold: First, \ourname{} harnesses these powerful generative priors for completion through a complementary two-stage design: (i) an explicit replacement stage that preserves the partial observation geometry to ensure faithful completion; and (ii) an implicit refinement stage ensures seamless boundaries between the observed and synthesized regions. Second, our framework is training-free and compatible with different 3D foundation models. Third, we introduce Omni-Comp, a comprehensive benchmark combining real-world and synthetic data with diverse and challenging partial patterns, enabling a more thorough and realistic evaluation. Both quantitative and qualitative experiments demonstrate that our approach outperforms previous state-of-the-art approaches. Our code and data will be available at \href{https://github.com/DavidYan2001/LaS-Comp}{LaS-Comp}.
Compact Hadamard Latent Codes for Efficient Spectral Rendering
Venue: arXiv cs.GR 2026
Year: 2026

Spectral rendering accurately reproduces wavelength-dependent appearance but is computationally expensive, as shading must be evaluated at many wavelength samples and scales roughly linearly with the number of samples. It also requires spectral textures and lights throughout the rendering pipeline. We propose Hadamard spectral codes, a compact latent representation that enables spectral rendering using standard RGB rendering operations. Spectral images are approximated with a small number of RGB rendering passes, followed by a decoding step. Our key requirement is latent linearity: scaling and addition in spectral space correspond to scaling and addition of codes, and the element-wise product of spectra (for example reflectance times illumination) is approximated by the element-wise product of their latent codes. We show that an exact low-dimensional algebra-preserving representation cannot exist for arbitrary spectra when the latent dimension k is smaller than the number of spectral samples n. We therefore introduce a learned non-negative linear encoder and decoder architecture that preserves scaling and addition exactly while encouraging approximate multiplicativity under the Hadamard product. With k = 6, we render k/3 = 2 RGB images per frame using an unmodified RGB renderer, reconstruct the latent image, and decode to high-resolution spectra or XYZ or RGB. Experiments on 3D scenes demonstrate that k = 6 significantly reduces color error compared to RGB baselines while being substantially faster than naive n-sample spectral rendering. Using k = 9 provides higher-quality reference results. We further introduce a lightweight neural upsampling network that maps RGB assets directly to latent codes, enabling integration of legacy RGB content into the spectral pipeline while maintaining perceptually accurate colors in rendered images.
Nazrin: Atomic Tactics for Graph Neural Networks for Theorem Proving in Lean 4
Venue: arXiv cs.LO 2026
Year: 2026

In Machine-Assisted Theorem Proving, a theorem proving agent searches for a sequence of expressions and tactics that can prove a conjecture in a proof assistant. In this work, we introduce several novel concepts and capabilities to address obstacles faced by machine-assisted theorem proving. We first present a set of \textbf{atomic tactics}, a small finite set of tactics capable of proving any provable statement in Lean. We then introduce a \textbf{transposing atomization} algorithm which turns arbitrary proof expressions into a series of atomic tactics. We next introduce the \textbf{ExprGraph} data structure, which provides a succinct representation for Lean expressions. Finally, we present the \textbf{Nazrin Prover}, a graph neural network-based theorem proving agent using atomic tactics and ExprGraph. Nazrin circumvents many challenges faced by existing proving agents by exclusively dispatching atomic tactics, and it is robust enough to both train and evaluate on consumer-grade hardware. We demonstrate the potential of tools like Nazrin using theorems from Lean's standard library and from Mathlib.
Efficient Dynamic Test Case Generation for Path-Based Coverage Criteria
Venue: arXiv cs.SE 2026
Year: 2026

We present a novel approach to test-case generation that satisfies four white-box, path-based coverage criteria: Prime Path, Simple Cycle, Simple Path, and Edge-Acyclic Path. Our method builds on a modified version of Johnson algorithm and enables test cases to be generated incrementally and on demand, rather than requiring the entire test suite to be computed upfront. This streaming capability represents a substantial advancement over existing approaches, as it allows testers to begin executing and refining tests immediately, thereby significantly improving the efficiency of test design. Our solution is inherently memory efficient, as it does not store all discovered coverage items; instead, it retains only the minimal set of paths required to generate subsequent coverage items on the fly. As a result, the approach scales to arbitrarily large graphs. In addition, the algorithm gives testers explicit control over the size of the generated test suite by allowing them to restrict the number of cycles permitted in a test path. The approach is grounded in new theoretical insights, most notably a novel characterization of prime paths in terms of the strongly connected components of control-flow graphs. We complement these theoretical contributions with a practical implementation and a comprehensive empirical evaluation. The results demonstrate that our method not only outperforms existing techniques in terms of execution time and memory consumption, but also provides testers with a more flexible and efficient tool for achieving high coverage while substantially reducing test design overhead.
LAMMI-Pathology: A Tool-Centric Bottom-Up LVLM-Agent Framework for Molecularly Informed Medical Intelligence in Pathology
Venue: arXiv cs.AI 2026
Year: 2026

The emergence of tool-calling-based agent systems introduces a more evidence-driven paradigm for pathology image analysis in contrast to the coarse-grained text-image diagnostic approaches. With the recent large-scale experimental adoption of spatial transcriptomics technologies, molecularly validated pathological diagnosis is becoming increasingly open and accessible. In this work, we propose LAMMI-Pathology (LVLM-Agent System for Molecularly Informed Medical Intelligence in Pathology), a scalable agent framework for domain-specific agent tool-calling. LAMMI-Pathology adopts a tool-centric, bottom-up architecture in which customized domain-adaptive tools serve as the foundation. These tools are clustered by domain style to form component agents, which are then coordinated through a top-level planner hierarchically, avoiding excessively long context lengths that could induce task drift. Based on that, we introduce a novel trajectory construction mechanism based on Atomic Execution Nodes (AENs), which serve as reliable and composable units for building semi-simulated reasoning trajectories that capture credible agent-tool interactions. Building on this foundation, we develop a trajectory-aware fine-tuning strategy that aligns the planner's decision-making process with these multi-step reasoning trajectories, thereby enhancing inference robustness in pathology understanding and its adaptive use of the customized toolset.
Should I Hide My Duck in the Lake?
Venue: arXiv cs.DB 2026
Year: 2026

Data lakes spend a significant fraction of query execution time on scanning data from remote storage. Decoding alone accounts for 46% of runtime when running TPC-H directly on Parquet files. To address this bottleneck, we propose a vision for a data processing SmartNIC for the cloud that sits on the network datapath of compute nodes to offload decoding and pushed-down operators, effectively hiding the cost of querying raw files. Our experimental estimations with DuckDB suggest that by operating directly on pre-filtered data as delivered by a SmartNIC, significantly smaller CPUs can still match query throughput of traditional setups.
ArabicNumBench: Evaluating Arabic Number Reading in Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

We present ArabicNumBench, a comprehensive benchmark for evaluating large language models on Arabic number reading tasks across Eastern Arabic-Indic numerals (0-9 in Arabic script) and Western Arabic numerals (0-9). We evaluate 71 models from 10 providers using four prompting strategies (zero-shot, zero-shot CoT, few-shot, few-shot CoT) on 210 number reading tasks spanning six contextual categories: pure numerals, addresses, dates, quantities, and prices. Our evaluation comprises 59,010 individual test cases and tracks extraction methods to measure structured output generation. Evaluation reveals substantial performance variation, with accuracy ranging from 14.29\% to 99.05\% across models and strategies. Few-shot Chain-of-Thought prompting achieves 2.8x higher accuracy than zero-shot approaches (80.06\% vs 28.76\%). A striking finding emerges: models achieving elite accuracy (98-99\%) often produce predominantly unstructured output, with most responses lacking Arabic CoT markers. Only 6 models consistently generate structured output across all test cases, while the majority require fallback extraction methods despite high numerical accuracy. Comprehensive evaluation of 281 model-strategy combinations demonstrates that numerical accuracy and instruction-following represent distinct capabilities, establishing baselines for Arabic number comprehension and providing actionable guidance for model selection in production Arabic NLP systems.
MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations
Venue: arXiv cs.CV 2026
Year: 2026

Visual counterfactual explanations aim to reveal the minimal semantic modifications that can alter a model's prediction, providing causal and interpretable insights into deep neural networks. However, existing diffusion-based counterfactual generation methods are often computationally expensive, slow to sample, and imprecise in localizing the modified regions. To address these limitations, we propose MaskDiME, a simple, fast, and effective diffusion framework that unifies semantic consistency and spatial precision through localized sampling. Our approach adaptively focuses on decision-relevant regions to achieve localized and semantically consistent counterfactual generation while preserving high image fidelity. Our training-free framework, MaskDiME, achieves over 30x faster inference than the baseline method and achieves comparable or state-of-the-art performance across five benchmark datasets spanning diverse visual domains, establishing a practical and generalizable solution for efficient counterfactual explanation.
See What I See: An Attention-Guiding eHMI Approach for Autonomous Vehicles
Venue: arXiv cs.HC 2026
Year: 2026

As autonomous vehicles are gradually being deployed in the real world, external Human-Machine Interfaces (eHMIs) are expected to serve as a critical solution for enhancing vehicle-pedestrian communication. However, existing eHMI designs typically focus solely on the ego vehicle's status, which can inadvertently capture pedestrians' attention or encourage misguided reliance on the AV's signals, leading them to neglect scanning for other surrounding hazards. To address this, we propose the Attention-Guiding eHMI (AGeHMI), a projection-based visualization that employs directional cues and risk-based color coding to actively guide pedestrians' attention toward potential environmental dangers. Evaluation through a virtual reality user study (N = 20) suggests that AGeHMI effectively influences participants' visual attention distribution and significantly reduces potential collision risks with surrounding vehicles, while simultaneously improving subjective confidence and reducing cognitive workload.
Learning to Localize Reference Trajectories in Image-Space for Visual Navigation
Venue: arXiv cs.RO 2026
Year: 2026

We present LoTIS, a model for visual navigation that provides robot-agnostic image-space guidance by localizing a reference RGB trajectory in the robot's current view, without requiring camera calibration, poses, or robot-specific training. Instead of predicting actions tied to specific robots, we predict the image-space coordinates of the reference trajectory as they would appear in the robot's current view. This creates robot-agnostic visual guidance that easily integrates with local planning. Consequently, our model's predictions provide guidance zero-shot across diverse embodiments. By decoupling perception from action and learning to localize trajectory points rather than imitate behavioral priors, we enable a cross-trajectory training strategy for robustness to viewpoint and camera changes. We outperform state-of-the-art methods by 20-50 percentage points in success rate on conventional forward navigation, achieving 94-98% success rate across diverse sim and real environments. Furthermore, we achieve over 5x improvements on challenging tasks where baselines fail, such as backward traversal. The system is straightforward to use: we show how even a video from a phone camera directly enables different robots to navigate to any point on the trajectory. Videos, demo, and code are available at https://finnbusch.com/lotis.
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Large Language Models (LLMs) demonstrate strong reasoning performance, yet their ability to reliably monitor, diagnose, and correct their own errors remains limited. We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle (Planning, Monitoring, and Evaluation) as a structured prompting architecture, and study its integration within a lightweight dual-process MetaController for adaptive effort allocation. Across diverse reasoning and diagnostic benchmarks (GSM8K, CRUXEval, MBPP, AIME, CorrectBench, and TruthfulQA) using Llama-3 and Qwen-3 (8B), explicit regulatory structuring substantially improves error diagnosis and yields a threefold increase in successful self-correction. Blinded human evaluations over 580 query pairs show an 84% aggregate preference for trustworthiness and metacognitive self-awareness over standard and Chain-of-Thought baselines. Grounding LLM reasoning in established cognitive theory offers a principled path toward more transparent and diagnostically robust AI systems.
GenPlanner: From Noise to Plans -- Emergent Reasoning in Flow Matching and Diffusion Models
Venue: arXiv cs.AI 2026
Year: 2026

Path planning in complex environments is one of the key problems of artificial intelligence because it requires simultaneous understanding of the geometry of space and the global structure of the problem. In this paper, we explore the potential of using generative models as planning and reasoning mechanisms. We propose GenPlanner, an approach based on diffusion models and flow matching, along with two variants: DiffPlanner and FlowPlanner. We demonstrate the application of generative models to find and generate correct paths in mazes. A multi-channel condition describing the structure of the environment, including an obstacle map and information about the starting and destination points, is used to condition trajectory generation. Unlike standard methods, our models generate trajectories iteratively, starting with random noise and gradually transforming it into a correct solution. Experiments conducted show that the proposed approach significantly outperforms the baseline CNN model. In particular, FlowPlanner demonstrates high performance even with a limited number of generation steps.
Habilis-$β$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model
Venue: arXiv cs.RO 2026
Year: 2026

We introduce Habilis-$β$, a fast-motion and long-lasting on-device vision-language-action (VLA) model designed for real-world deployment. Current VLA evaluation remains largely confined to single-trial success rates under curated resets, which fails to capture the fast-motion and long-lasting capabilities essential for practical operation. To address this, we introduce the Productivity-Reliability Plane (PRP), which evaluates performance through Tasks per Hour (TPH) and Mean Time Between Intervention (MTBI) under a continuous-run protocol that demands both high-speed execution and sustained robustness. Habilis-$β$ achieves high performance by integrating language-free pre-training on large-scale play data for robust interaction priors with post-training on cyclic task demonstrations that capture state drift across consecutive task iterations. The system further employs ESPADA for phase-adaptive motion shaping to accelerate free-space transit, utilizes rectified-flow distillation to enable high-frequency control on edge devices, and incorporates classifier-free guidance (CFG) as a deployment-time knob to dynamically balance instruction adherence and learned interaction priors. In 1-hour continuous-run evaluations, Habilis-$β$ achieves strong performance under the PRP metrics, compared to $π_{0.5}$ in both simulation and real-world environments. In simulation, Habilis-$β$ achieves 572.6 TPH and 39.2 s MTBI (vs. 120.5 TPH and 30.5 s for $π_{0.5}$), while in a real-world humanoid logistics workflow it achieves 124 TPH and 137.4 s MTBI (vs. 19 TPH and 46.1 s for $π_{0.5}$). Finally, Habilis-$β$ achieves the highest reported performance on the standard RoboTwin 2.0 leaderboard across representative tasks, validating its effectiveness in complex manipulation scenarios.
RotorSuite: A MATLAB/Simulink Toolbox for Tilt Multi-Rotor UAV Modeling
Venue: arXiv cs.RO 2026
Year: 2026

In recent years, aerial platforms have evolved from passive flying sensors into versatile, contact-aware robotic systems, leading to rapid advances in platform design. Standard coplanar and collinear quadrotors have been complemented by modern tilted and tilting multi-rotor platforms with enhanced maneuverability. To properly analyze, control, and validate the performance of these emerging platforms, an accurate modeling step is required; however, this can be time-consuming, user-dependent and error-prone. To address this issue, we propose a MATLAB/Simulink toolbox for modeling and simulating the dynamics of a broad class of multi-rotor platforms through both an analytical and physics-based approaches. The toolbox, named RotorSuite, is provided with comprehensive documentation and example use cases, representing a valuable tool for didactic, research, and industrial development purposes.
Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations
Venue: arXiv cs.CV 2026
Year: 2026

Cross-modal super-resolution (SR) on real-world misaligned data is challenging, as only unlabeled low-resolution (LR) source and high-resolution (HR) guide images with complex spatial misalignment are available. Previous methods either rely on fully simulated training data or adopt suboptimal alignment strategies that overlook cross-modal dependencies, limiting their performance in practice. To address these issues, we propose RobSelf, a self-supervised model that jointly optimizes a misalignment-aware feature translator and a content-aware reference filter online. The translator resolves unsupervised cross-modal and cross-resolution alignment via weakly-supervised, misalignment-aware translation, yielding an aligned guide feature. Guided by this feature, the filter performs reference-based discriminative self-enhancement on the source, enabling SR prediction with high resolution and high fidelity. Experiments on synthesized data and our collected real-world data demonstrate that RobSelf achieves state-of-the-art performance, outperforming existing self-supervised and supervised methods. Moreover, it achieves superior efficiency, up to 15.3$\times$ faster than prior self-supervised methods.