OpenRaiser/Intern-Atlas huggingface dataset:
Verifying First-Order Temporal Properties of Infinite-State Systems via Timers and Rankings
Venue: arXiv cs.LO 2026
Year: 2026

We present a unified deductive verification framework for first-order temporal properties based on well-founded rankings, where verification conditions are discharged using SMT solvers. To that end, we introduce a novel reduction from verification of arbitrary temporal properties to verification of termination. Our reduction augments the system with prophecy timer variables that predict the number of steps along a trace until the next time certain temporal formulas, including the negated property, hold. In contrast to standard tableaux-based reductions, which reduce the problem to fair termination, our reduction does not introduce fairness assumptions. To verify termination of the augmented system, we follow the traditional approach of assigning each state a rank from a well-founded set and showing that the rank decreases in every transition. We leverage the recently proposed formalism of implicit rankings to express and automatically verify the decrease of rank using SMT solvers, even when the rank is not expressible in first-order logic. We extend implicit rankings from finite to infinite domains, enabling verification of more general systems and making them applicable to the augmented systems generated by our reduction, which allows us to exploit the decrease of timers in termination proofs. We evaluate our technique on a range of temporal verification tasks from previous works, giving simple, intuitive proofs for them within our framework.
FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels
Venue: arXiv cs.SE 2026
Year: 2026

Artificial Intelligence (AI) applications, such as Large Language Models, are primarily driven and executed by Graphics Processing Units (GPUs). These GPU programs (kernels) consume substantial amounts of energy, yet software developers often lack the hardware expertise and ad hoc knowledge required to optimize for power efficiency. We propose FlipFlop, a framework using static code analysis to predict energy consumption and recommend Pareto-optimal thread block configurations considering both power consumption and execution time. Our framework requires no runtime execution and analyzes PTX code, a low-level instruction set for CUDA-enabled GPUs. It is validated across a diverse set of GPUs and kernels, including multi-head attention, convolution, and matrix multiplication. FlipFlop achieves 83% accuracy in identifying locally optimal energy-efficient configurations, while also minimizing developer effort by reducing the optimization search space by 93.4%. For multi-head attention kernels, it yields up to 79% energy savings and 106% throughput gains relative to NVIDIA's occupancy heuristic. By integrating static analysis with real-time monitoring and providing explainable optimization guidance, FlipFlop empowers developers to create sustainable, high-performance GPU software which minimizes environmental and computational costs.
Beyond Mapping : Domain-Invariant Representations via Spectral Embedding of Optimal Transport Plans
Venue: arXiv cs.LG 2026
Year: 2026

Distributional shifts between training and inference time data remain a central challenge in machine learning, often leading to poor performance. It motivated the study of principled approaches for domain alignment, such as optimal transport based unsupervised domain adaptation, that relies on approximating Monge map using transport plans, which is sensitive to the transport problem regularization strategy and hyperparameters, and might yield biased domains alignment. In this work, we propose to interpret smoothed transport plans as adjacency matrices of bipartite graphs connecting source to target domain and derive domain-invariant samples' representations through spectral embedding. We evaluate our approach on acoustic adaptation benchmarks for music genre recognition, music-speech discrimination, as well as electrical cable defect detection and classification tasks using time domain reflection in different diagnosis settings, achieving overall strong performances.
The Geometry of Thought: How Scale Restructures Reasoning In Large Language Models
Venue: arXiv cs.AI 2026
Year: 2026

Scale does not uniformly improve reasoning - it restructures it. Analyzing 25,000+ chain-of-thought trajectories across four domains (Law, Science, Code, Math) and two scales (8B, 70B parameters), we discover that neural scaling laws trigger domain-specific phase transitions rather than uniform capability gains. Legal reasoning undergoes Crystallization: 45% collapse in representational dimensionality (d95: 501 -> 274), 31% increase in trajectory alignment, and 10x manifold untangling. Scientific and mathematical reasoning remain Liquid - geometrically invariant despite 9x parameter increase. Code reasoning forms a discrete Lattice of strategic modes (silhouette: 0.13 -> 0.42). This geometry predicts learnability. We introduce Neural Reasoning Operators - learned mappings from initial to terminal hidden states. In crystalline legal reasoning, our operator achieves 63.6% accuracy on held-out tasks via probe decoding, predicting reasoning endpoints without traversing intermediate states. We further identify a universal oscillatory signature (coherence ~ -0.4) invariant across domains and scales, suggesting attention and feedforward layers drive reasoning through opposing dynamics. These findings establish that the cost of thought is determined not by task difficulty but by manifold geometry - offering a blueprint for inference acceleration where topology permits.
CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments
Venue: arXiv cs.RO 2026
Year: 2026

Long-horizon navigation in unstructured environments demands terrain abstractions that scale to tens of km$^2$ while preserving semantic and geometric structure, a combination existing methods fail to achieve. Grids scale poorly; quadtrees misalign with terrain boundaries; neither encodes landcover semantics essential for traversability-aware planning. This yields infeasible or unreliable paths for autonomous ground vehicles operating over 10+ km$^2$ under real-time constraints. CLEAR (Connected Landcover Elevation Abstract Representation) couples boundary-aware spatial decomposition with recursive plane fitting to produce convex, semantically aligned regions encoded as a terrain-aware graph. Evaluated on maps spanning 9-100~km$^2$ using a physics-based simulator, CLEAR achieves up to 10x faster planning than raw grids with only 6.7% cost overhead and delivers 6-9% shorter, more reliable paths than other abstraction baselines. These results highlight CLEAR's scalability and utility for long-range navigation in applications such as disaster response, defense, and planetary exploration.
CausationEntropy: Pythonic Optimal Causation Entropy
Venue: arXiv cs.LG 2026
Year: 2026

Optimal Causation Entropy (oCSE) is a robust causal network modeling technique that reveals causal networks from dynamical systems and coupled oscillators, distinguishing direct from indirect paths. CausationEntropy is a Python package that implements oCSE and several of its significant optimizations and methodological extensions. In this paper, we introduce the version 1.1 release of CausationEntropy, which includes new synthetic data generators, plotting tools, and several advanced information-theoretical causal network discovery algorithms with criteria for estimating Gaussian, k-nearest neighbors (kNN), geometric k-nearest neighbors (geometric-kNN), kernel density (KDE) and Poisson entropic estimators. The package is easy to install from the PyPi software repository, is thoroughly documented, supplemented with extensive code examples, and is modularly structured to support future additions. The entire codebase is released under the MIT license and is available on GitHub and through PyPi Repository. We expect this package to serve as a benchmark tool for causal discovery in complex dynamical systems.
Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

As reasoning modules, such as the chain-of-thought mechanism, are applied to large language models, they achieve strong performance on various tasks such as answering common-sense questions and solving math problems. The main challenge now is to assess the uncertainty of answers, which can help prevent misleading or serious hallucinations for users. Although current methods analyze long reasoning sequences by filtering unrelated tokens and examining potential connections between nearby tokens or sentences, the temporal spread of confidence is often overlooked. This oversight can lead to inflated overall confidence, even when earlier steps exhibit very low confidence. To address this issue, we propose a novel method that incorporates inter-step attention to analyze semantic correlations across steps. For handling long-horizon responses, we introduce a hidden confidence mechanism to retain historical confidence information, which is then combined with stepwise confidence to produce a more accurate overall estimate. We evaluate our method on the GAOKAO math benchmark and the CLadder causal reasoning dataset using mainstream open-source large language models. Our approach is shown to outperform state-of-the-art methods by achieving a superior balance between predictive quality and calibration, demonstrated by strong performance on both Negative Log-Likelihood and Expected Calibration Error.
A Lightweight Model-Driven 4D Radar Framework for Pervasive Human Detection in Harsh Conditions
Venue: arXiv cs.CV 2026
Year: 2026

Pervasive sensing in industrial and underground environments is severely constrained by airborne dust, smoke, confined geometry, and metallic structures, which rapidly degrade optical and LiDAR based perception. Elevation resolved 4D mmWave radar offers strong resilience to such conditions, yet there remains a limited understanding of how to process its sparse and anisotropic point clouds for reliable human detection in enclosed, visibility degraded spaces. This paper presents a fully model-driven 4D radar perception framework designed for real-time execution on embedded edge hardware. The system uses radar as its sole perception modality and integrates domain aware multi threshold filtering, ego motion compensated temporal accumulation, KD tree Euclidean clustering with Doppler aware refinement, and a rule based 3D classifier. The framework is evaluated in a dust filled enclosed trailer and in real underground mining tunnels, and in the tested scenarios the radar based detector maintains stable pedestrian identification as camera and LiDAR modalities fail under severe visibility degradation. These results suggest that the proposed model-driven approach provides robust, interpretable, and computationally efficient perception for safety-critical applications in harsh industrial and subterranean environments.
Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility
Venue: arXiv cs.LG 2026
Year: 2026

LLMs demonstrate strong performance on code benchmarks, yet round-trip code execution reveals limitations in their ability to maintain consistent reasoning across forward and backward execution. We present RoundTripCodeEval (RTCE), a comprehensive benchmark consisting of four distinct code execution reasoning tasks designed to rigorously test round-trip consistency. RTCE provides an execution-free, exact-match evaluation of bijection fidelity, assessing whether models preserve a consistent one-to-one mapping between encoding and decoding operations across various algorithms and directions. We systematically evaluate state-of-the-art Code-LLMs using zero-shot prompting, supervised fine-tuning on execution traces, and self-reflection mechanisms. Each yields modest improvements, but none closes the gap, indicating that current LLMs struggle with true round-trip consistency, which demonstrates that they lack the internal coherence required for trustworthy code reasoning. RTCE surfaces several new and previously unmeasured insights that are not captured by existing I/O-prediction, execution-reasoning, or round-trip natural-language benchmarks. We will release the code and the dataset upon acceptance.
QERS: Quantum Encryption Resilience Score for Post-Quantum Cryptography in Computer, IoT, and IIoT Systems
Venue: arXiv cs.CR 2026
Year: 2026

Post-quantum cryptography (PQC) is becoming essential for securing Internet of Things (IoT) and Industrial IoT (IIoT) systems against quantum-enabled adversaries. However, existing evaluation approaches primarily focus on isolated performance metrics, offering limited support for holistic security and deployment decisions. This paper introduces QERS (Quantum Encryption Resilience Score), a universal measurement framework that integrates cryptographic performance, system constraints, and multi-criteria decision analysis to assess PQC readiness in computer, IoT, and IIoT environments. QERS combines normalized metrics, weighted aggregation, and machine learning-assisted analysis to produce interpretable resilience scores across heterogeneous devices and communication protocols. Experimental results demonstrate how the framework enables comparative evaluation of post-quantum schemes under realistic resource constraints, supporting informed security design and migration planning. This work is presented as a preprint, with extended statistical validation planned as part of ongoing graduate research.
Deep Image Prior with L0 Gradient Regularizer for Image Smoothing
Venue: arXiv cs.CV 2026
Year: 2026

Image smoothing is a fundamental image processing operation that preserves the underlying structure, such as strong edges and contours, and removes minor details and textures in an image. Many image smoothing algorithms rely on computing local window statistics or solving an optimization problem. Recent state-of-the-art methods leverage deep learning, but they require a carefully curated training dataset. Because constructing a proper training dataset for image smoothing is challenging, we propose DIP-$\ell_0$, a deep image prior framework that incorporates the $\ell_0$ gradient regularizer. This framework can perform high-quality image smoothing without any training data. To properly minimize the associated loss function that has the nonconvex, nonsmooth $\ell_0$ ``norm", we develop an alternating direction method of multipliers algorithm that utilizes an off-the-shelf $\ell_0$ gradient minimization solver. Numerical experiments demonstrate that the proposed DIP-$\ell_0$ outperforms many image smoothing algorithms in edge-preserving image smoothing and JPEG artifact removal.
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Venue: arXiv cs.LG 2026
Year: 2026

Energy usage prediction is important for various real-world applications, including grid management, infrastructure planning, and disaster response. Although a plethora of deep learning approaches have been proposed to perform this task, most of them either overlook the essential spatial correlations across households or fail to scale to individualized prediction, making them less effective for accurate fine-grained user-level prediction. In addition, due to the dynamic and uncertain nature of energy usage caused by various factors such as extreme weather events, quantifying uncertainty for reliable prediction is also significant, but it has not been fully explored in existing work. In this paper, we propose a unified framework called TrustEnergy for accurate and reliable user-level energy usage prediction. There are two key technical components in TrustEnergy, (i) a Hierarchical Spatiotemporal Representation module to efficiently capture both macro and micro energy usage patterns with a novel memory-augmented spatiotemporal graph neural network, and (ii) an innovative Sequential Conformalized Quantile Regression module to dynamically adjust uncertainty bounds to ensure valid prediction intervals over time, without making strong assumptions about the underlying data distribution. We implement and evaluate our TrustEnergy framework by working with an electricity provider in Florida, and the results show our TrustEnergy can achieve a 5.4% increase in prediction accuracy and 5.7% improvement in uncertainty quantification compared to state-of-the-art baselines.
Quantum Encryption Resilience Score (QERS) for MQTT, HTTP, and HTTPS under Post-Quantum Cryptography in Computer, IoT, and IIoT Systems
Venue: arXiv cs.CR 2026
Year: 2026

Post-quantum cryptography (PQC) introduces significant computational and communication overhead, which poses challenges for resource-constrained computer systems, Internet of Things (IoT), and Industrial IoT (IIoT) devices. This paper presents an experimental evaluation of the Quantum Encryption Resilience Score (QERS) applied to MQTT, HTTP, and HTTPS communication protocols operating under PQC. Using an ESP32-C6 client and an ARM-based Raspberry Pi CM4 server, latency, CPU utilization, RSSI, energy consumption, key size, and TLS handshake overhead are measured under realistic operating conditions. QERS integrates these heterogeneous metrics into normalized Basic, Tuned, and Fusion scores, enabling systematic comparison of protocol efficiency and security resilience. Experimental results show that MQTT provides the highest efficiency under PQC constraints, while HTTPS achieves the highest security-weighted resilience at the cost of increased latency and resource consumption. The proposed framework supports informed protocol selection and migration planning for PQC-enabled IoT and IIoT deployments.
Techniques of Modern Attacks
Venue: arXiv cs.CR 2026
Year: 2026

The techniques used in modern attacks have become an important factor for investigation. As we advance further into the digital age, cyber attackers are employing increasingly sophisticated and highly threatening methods. These attacks target not only organizations and governments but also extend to private and corporate sectors. Modern attack techniques, such as lateral movement and ransomware, are designed to infiltrate networks and steal sensitive data. Among these techniques, Advanced Persistent Threats (APTs) represent a complex method of attack aimed at specific targets to steal high-value sensitive information or damage the infrastructure of the targeted organization. In this paper, I will investigate Advanced Persistent Threats (APTs) as a modern attack technique, focusing on both the attack life cycle and cutting-edge detection and defense strategies proposed in recent academic research. I will analyze four representative papers to understand the evolution of APT detection mechanisms, including machine learning-driven behavioral analysis and network-level collaborative defense models. Through this comparative analysis, I aim to highlight the strengths and limitations of each approach and propose more adaptive APT mitigation strategies. The study seeks to analyze the key characteristics of APTs and provide a comprehensive high-level understanding of APTs along with potential solutions to the threats they pose.
MOSLD-Bench: Multilingual Open-Set Learning and Discovery Benchmark for Text Categorization
Venue: arXiv cs.CL 2026
Year: 2026

Open-set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalization of zero-shot learning, where the new classes are not known a priori, hence involving the active discovery of new classes. While zero-shot learning has been extensively studied in text classification, especially with the emergence of pre-trained language models, open-set learning and discovery is a comparatively new setup for the text domain. To this end, we introduce the first multilingual open-set learning and discovery (MOSLD) benchmark for text categorization by topic, comprising 960K data samples across 12 languages. To construct the benchmark, we (i) rearrange existing datasets and (ii) collect new data samples from the news domain. Moreover, we propose a novel framework for the OSLD task, which integrates multiple stages to continuously discover and learn new classes. We evaluate several language models, including our own, to obtain results that can be used as reference for future work. We release our benchmark at https://github.com/Adriana19Valentina/MOSLD-Bench.
Explicit Cognitive Allocation: A Principle for Governed and Auditable Inference in Large Language Models
Venue: arXiv cs.AI 2026
Year: 2026

The rapid adoption of large language models (LLMs) has enabled new forms of AI-assisted reasoning across scientific, technical, and organizational domains. However, prevailing modes of LLM use remain cognitively unstructured: problem framing, knowledge exploration, retrieval, methodological awareness, and explanation are typically collapsed into a single generative process. This cognitive collapse limits traceability, weakens epistemic control, and undermines reproducibility, particularly in high-responsibility settings. We introduce Explicit Cognitive Allocation, a general principle for structuring AI-assisted inference through the explicit separation and orchestration of epistemic functions. We instantiate this principle in the Cognitive Universal Agent (CUA), an architecture that organizes inference into distinct stages of exploration and framing, epistemic anchoring, instrumental and methodological mapping, and interpretive synthesis. Central to this framework is the notion of Universal Cognitive Instruments (UCIs), which formalize heterogeneous means, including computational, experimental, organizational, regulatory, and educational instruments, through which abstract inquiries become investigable. We evaluate the effects of explicit cognitive and instrumental allocation through controlled comparisons between CUA-orchestrated inference and baseline LLM inference under matched execution conditions. Across multiple prompts in the agricultural domain, CUA inference exhibits earlier and structurally governed epistemic convergence, higher epistemic alignment under semantic expansion, and systematic exposure of the instrumental landscape of inquiry. In contrast, baseline LLM inference shows greater variability in alignment and fails to explicitly surface instrumental structure.
Event-based Heterogeneous Information Processing for Online Vision-based Obstacle Detection and Localization
Venue: arXiv cs.RO 2026
Year: 2026

This paper introduces a novel framework for robotic vision-based navigation that integrates Hybrid Neural Networks (HNNs) with Spiking Neural Network (SNN)-based filtering to enhance situational awareness for unmodeled obstacle detection and localization. By leveraging the complementary strengths of Artificial Neural Networks (ANNs) and SNNs, the system achieves both accurate environmental understanding and fast, energy-efficient processing. The proposed architecture employs a dual-pathway approach: an ANN component processes static spatial features at low frequency, while an SNN component handles dynamic, event-based sensor data in real time. Unlike conventional hybrid architectures that rely on domain conversion mechanisms, our system incorporates a pre-developed SNN-based filter that directly utilizes spike-encoded inputs for localization and state estimation. Detected anomalies are validated using contextual information from the ANN pathway and continuously tracked to support anticipatory navigation strategies. Simulation results demonstrate that the proposed method offers acceptable detection accuracy while maintaining computational efficiency close to SNN-only implementations, which operate at a fraction of the resource cost. This framework represents a significant advancement in neuromorphic navigation systems for robots operating in unpredictable and dynamic environments.