OpenRaiser/Intern-Atlas huggingface dataset:
LiDAR-Anchored Collaborative Distillation for Robust 2D Representations
Venue: arXiv cs.CV 2026
Year: 2026

As deep learning continues to advance, self-supervised learning has made considerable strides. It allows 2D image encoders to extract useful features for various downstream tasks, including those related to vision-based systems. Nevertheless, pre-trained 2D image encoders fall short in conducting the task under noisy and adverse weather conditions beyond clear daytime scenes, which require for robust visual perception. To address these issues, we propose a novel self-supervised approach, \textbf{Collaborative Distillation}, which leverages 3D LiDAR as self-supervision to improve robustness to noisy and adverse weather conditions in 2D image encoders while retaining their original capabilities. Our method outperforms competing methods in various downstream tasks across diverse conditions and exhibits strong generalization ability. In addition, our method also improves 3D awareness stemming from LiDAR's characteristics. This advancement highlights our method's practicality and adaptability in real-world scenarios.
Constraint-Rectified Training for Efficient Chain-of-Thought
Venue: arXiv cs.LG 2026
Year: 2026

Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), especially when combined with reinforcement learning (RL) based post-training methods. While longer reasoning traces can improve answer quality and unlock abilities such as self-correction, they also incur high inference costs and often introduce redundant steps, known as overthinking. Recent research seeks to develop efficient reasoning strategies that balance reasoning length and accuracy, either through length-aware reward design or prompt-based calibration. However, these heuristic-based approaches may suffer from severe accuracy drop and be very sensitive to hyperparameters. To address these problems, we introduce CRT (Constraint-Rectified Training), a principled post-training framework based on reference-guarded constrained optimization, yielding a more stable and interpretable formulation for efficient reasoning. CRT alternates between minimizing reasoning length and rectifying accuracy only when performance falls below the reference, enabling stable and effective pruning of redundant reasoning. We further extend CRT with a two-stage training scheme that first discovers the shortest reliable reasoning patterns and then refines accuracy under a learnt length budget, preventing the re-emergence of verbose CoT. Our comprehensive evaluation shows that this framework consistently reduces token usage while maintaining answer quality at a robust and reliable level. Further analysis reveals that CRT improves reasoning efficiency not only by shortening responses but also by reducing internal language redundancy, leading to a new evaluation metric. Moreover, CRT-based training naturally yields a sequence of intermediate checkpoints that span a spectrum of explanation lengths while preserving correctness, enabling fine-grained control over reasoning verbosity without retraining.
Analytical Results for Two Exponential Family Distributions in Hierarchical Dirichlet Processes
Venue: arXiv cs.LG 2026
Year: 2026

The Hierarchical Dirichlet Process (HDP) provides a flexible Bayesian nonparametric framework for modeling grouped data with a shared yet unbounded collection of mixture components. While existing applications of the HDP predominantly focus on the Dirichlet-multinomial conjugate structure, the framework itself is considerably more general and, in principle, accommodates a broad class of conjugate prior-likelihood pairs. In particular, exponential family distributions offer a unified and analytically tractable modeling paradigm that encompasses many commonly used distributions. In this paper, we investigate analytic results for two important members of the exponential family within the HDP framework: the Poisson distribution and the normal distribution. We derive explicit closed-form expressions for the corresponding Gamma-Poisson and Normal-Gamma-Normal conjugate pairs under the hierarchical Dirichlet process construction. Detailed derivations and proofs are provided to clarify the underlying mathematical structure and to demonstrate how conjugacy can be systematically exploited in hierarchical nonparametric models. Our work extends the applicability of the HDP beyond the Dirichlet-multinomial setting and furnishes practical analytic results for researchers employing hierarchical Bayesian nonparametrics.
Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, model-specific implementations, and engineering complexity. We introduce Flow-Factory, a unified framework that decouples algorithms, models, and rewards through through a modular, registry-based architecture. This design enables seamless integration of new algorithms and architectures, as demonstrated by our support for GRPO, DiffusionNFT, and AWM across Flux, Qwen-Image, and WAN video models. By minimizing implementation overhead, Flow-Factory empowers researchers to rapidly prototype and scale future innovations with ease. Flow-Factory provides production-ready memory optimization, flexible multi-reward training, and seamless distributed training support. The codebase is available at https://github.com/X-GenGroup/Flow-Factory.
News Harvesting from Google News combining Web Scraping, LLM Metadata Extraction and SCImago Media Rankings enrichment: a case study of IFMIF-DONES
Venue: arXiv cs.DL 2026
Year: 2026

This study develops and evaluates a systematic methodology for constructing news datasets from Google News, combining automated web scraping, large language model (LLM)-based metadata extraction, and SCImago Media Rankings enrichment. Using the IFMIF-DONES fusion energy project as a case study, we implemented a five-stage data collection pipeline across 81 region-language combinations, yielding 1,482 validated records after a 56% noise reduction. Results are compared against two licensed press databases: MyNews (2,280 records) and ProQuest Newsstream Collection (148 records). Overlap analysis reveals high complementarity, with 76% of Google News records exclusive to this platform. The dataset captures content types absent from proprietary databases, including specialized outlets, institutional communications, and social media posts. However, significant methodological challenges emerge: temporal instability requiring synchronic collection, a 100-result cap per query demanding multi-stage strategies, and unexpected noise including academic PDFs, false positives, and pornographic content infiltrating results through black hat SEO techniques. LLM-assisted extraction proved effective for structured articles but exhibited systematic hallucination patterns requiring validation protocols. We conclude that Google News offers valuable complementary coverage for communication research but demands substantial methodological investment, multi-source triangulation, and robust filtering mechanisms to ensure dataset integrity.
The Constant Eye: Benchmarking and Bridging Appearance Robustness in Autonomous Driving
Venue: arXiv cs.CV 2026
Year: 2026

Despite rapid progress, autonomous driving algorithms remain notoriously fragile under Out-of-Distribution (OOD) conditions. We identify a critical decoupling failure in current research: the lack of distinction between appearance-based shifts, such as weather and lighting, and structural scene changes. This leaves a fundamental question unanswered: Is the planner failing because of complex road geometry, or simply because it is raining? To resolve this, we establish navdream, a high-fidelity robustness benchmark leveraging generative pixel-aligned style transfer. By creating a visual stress test with negligible geometric deviation, we isolate the impact of appearance on driving performance. Our evaluation reveals that existing planning algorithms often show significant degradation under OOD appearance conditions, even when the underlying scene structure remains consistent. To bridge this gap, we propose a universal perception interface leveraging a frozen visual foundation model (DINOv3). By extracting appearance-invariant features as a stable interface for the planner, we achieve exceptional zero-shot generalization across diverse planning paradigms, including regression-based, diffusion-based, and scoring-based models. Our plug-and-play solution maintains consistent performance across extreme appearance shifts without requiring further fine-tuning. The benchmark and code will be made available.
VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning, yet its reliance on external verifiers limits its scalability. Recent findings suggest that RLVR primarily functions by eliciting latent capabilities, motivating the development of verifier-free algorithms. However, in such settings, standard methods like Group Relative Policy Optimization face a critical challenge: destructive gradient variance that often leads to training collapse. To address this issue, we introduceVerifier-Independent Curriculum Reinforcement Learning (VI-CuRL), a framework that leverages the model's intrinsic confidence to construct a curriculum independent from external verifiers. By prioritizing high-confidence samples, VI-CuRL effectively manages the bias-variance trade-off, specifically targeting the reduction of action and problem variance. We provide a rigorous theoretical analysis, proving that our estimator guarantees asymptotic unbiasedness. Empirically, VI-CuRL promotes stability and consistently outperforms verifier-independent baselines across six challenging benchmarks with/without verifiers.
Hemispherical Angular Power Mapping of Installed mmWave Radar Modules Under Realistic Deployment Constraints
Venue: arXiv cs.RO 2026
Year: 2026

Characterizing the angular radiation behavior of installed millimeter-wave (mmWave) radar modules is increasingly important in practical sensing platforms, where packaging, mounting hardware, and nearby structures can significantly alter the effective emission profile. However, once a device is embedded in its host environment, conventional chamber- and turntable-based antenna measurements are often impractical. This paper presents a hemispherical angular received-power mapping methodology for in-situ EM validation of installed mmWave modules under realistic deployment constraints. The approach samples the accessible half-space around a stationary device-under-test by placing a calibrated receiving probe at prescribed (phi, theta, r) locations using geometry-consistent positioning and quasi-static acquisition. Amplitude-only received-power is recorded using standard RF instrumentation to generate hemispherical angular power maps that capture installation-dependent radiation characteristics. Proof-of-concept measurements on a 60-GHz radar module demonstrate repeatable hemi-spherical mapping with angular trends in good agreement with full-wave simulation, supporting practical on-site characterization of embedded mmWave transmitters.
Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
Venue: arXiv cs.LG 2026
Year: 2026

Mixture-of-Experts (MoE) architectures are often considered a natural fit for continual learning because sparse routing should localize updates and reduce interference, yet MoE Transformers still forget substantially even with sparse, well-balanced expert utilization. We attribute this gap to a pre-routing bottleneck: multi-head attention concatenates head-specific signals into a single post-attention router input, forcing routing to act on co-occurring feature compositions rather than separable head channels. We show that this router input simultaneously encodes multiple separately decodable semantic and structural factors with uneven head support, and that different feature compositions induce weakly aligned parameter-gradient directions; as a result, routing maps many distinct compositions to the same route. We quantify this collision effect via a route-wise effective composition number $N_{eff}$ and find that higher $N_{eff}$ is associated with larger old-task loss increases after continual training. Motivated by these findings, we propose MH-MoE, which performs head-wise routing over sub-representations to increase routing granularity and reduce composition collisions. On TRACE with Qwen3-0.6B/8B, MH-MoE effectively mitigates forgetting, reducing BWT on Qwen3-0.6B from 11.2% (LoRAMoE) to 4.5%.
Vehicle behaviour estimation for abnormal event detection using distributed fiber optic sensing
Venue: arXiv cs.LG 2026
Year: 2026

The distributed fiber-optic sensing (DFOS) system is a cost-effective wide-area traffic monitoring technology that utilizes existing fiber infrastructure to effectively detect traffic congestions. However, detecting single-lane abnormalities, that lead to congestions, is still a challenge. These single-lane abnormalities can be detected by monitoring lane change behaviour of vehicles, performed to avoid congestion along the monitoring section of a road. This paper presents a method to detect single-lane abnormalities by tracking individual vehicle paths and detecting vehicle lane changes along a section of a road. We propose a method to estimate the vehicle position at all time instances and fit a path using clustering techniques. We detect vehicle lane change by monitoring any change in spectral centroid of vehicle vibrations by tracking a reference vehicle along a highway. The evaluation of our proposed method with real traffic data showed 80% accuracy for lane change detection events that represent presence of abnormalities.
RADAR: Exposing Unlogged NoSQL Operations
Venue: arXiv cs.CR 2026
Year: 2026

The widespread adoption of NoSQL databases has made digital forensics increasingly difficult as storage formats are diverse and often opaque, and audit logs cannot be assumed trustworthy when privileged insiders, such as DevOps or administrators, can disable, suppress, or manipulate logging to conceal activity. We present RADAR (Record & Artifact Detection, Alignment & Reporting), a log-adversary-aware framework that derives forensic ground truth by cross-referencing low-level storage artifacts against high-level application logs. RADAR analyzes artifacts reconstructed by the Automated NoSQL Carver (ANOC), which infers layouts and carves records directly from raw disk bytes, bypassing database APIs and the management system entirely, thereby treating physical storage as the independent evidence source. RADAR then reconciles carved artifacts with the audit log to identify delta artifacts such as unlogged insertions, silent deletions, and field-level updates that exist on disk but are absent from the logical history. We evaluate RADAR across ten NoSQL engines, including BerkeleyDB, LMDB, MDBX, etcd, ZODB, Durus, LiteDB, Realm, RavenDB, and NitriteDB, spanning key-value and document stores and multiple storage designs, e.g., copy-on-write/MVCC, B/B+ tree, and append-only. Under log-evasion scenarios, such as log suppression and post-maintenance attacks, including cases where historical bytes are pruned, RADAR consistently exposes unattributed operations while sustaining 31.7-397 MB/min processing throughput, demonstrating the feasibility of log-independent, trustworthy NoSQL forensics.
Channel Gain Map Reconstruction Based on Virtual Scatterer Model
Venue: arXiv cs.IT 2026
Year: 2026

This paper proposes an efficient method for modeling and reconstructing the channel gain map (CGM) based on virtual scatterers. Specifically, we develop a virtual scatterer model to characterize the channel power gain distribution in three-dimensional (3D) space, by capturing the multi-path propagation environment structure and exploiting the angular-domain spatial correlation of scatterer response. In this model, the CGM is represented as a function over a set of tunable parameters for virtual scatterers, including their number, positions, and scatterer response coefficients (SRCs), which can be estimated from a limited number of channel power gain measurements at a given set of locations within the region of interest. This new representation offers a flexible and scalable modeling framework for efficient and accurate CGM reconstruction. Furthermore, we propose a progressive estimation algorithm to acquire the scatterers' parameters. In this algorithm, we gradually increase the number of virtual scatterers to balance the computational complexity and estimation accuracy. In addition, by exploiting the spatial correlation of scatterer response, we propose a Gaussian process regression (GPR)-based inference method to predict the SRCs that cannot be directly estimated. Finally, ray-tracing-based simulation results under realistic physical environments validate the effectiveness of the proposed method, demonstrating that it achieves higher reconstruction accuracy compared to conventional CGM estimation approaches.
Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
Venue: arXiv cs.LG 2026
Year: 2026

Temporal Graph Neural Networks (TGNNs) are pivotal in processing dynamic graphs. However, existing TGNNs primarily target one-time predictions for a given temporal span, whereas many practical applications require continuous predictions, that predictions are issued frequently over time. Directly adapting existing TGNNs to continuous-prediction scenarios introduces either significant computational overhead or prediction quality issues especially for large graphs. This paper revisits the challenge of { continuous predictions} in TGNNs, and introduces {\sc Coden}, a TGNN model designed for efficient and effective learning on dynamic graphs. {\sc Coden} innovatively overcomes the key complexity bottleneck in existing TGNNs while preserving comparable predictive accuracy. Moreover, we further provide theoretical analyses that substantiate the effectiveness and efficiency of {\sc Coden}, and clarify its duality relationship with both RNN-based and attention-based models. Our evaluations across five dynamic datasets show that {\sc Coden} surpasses existing performance benchmarks in both efficiency and effectiveness, establishing it as a superior solution for continuous prediction in evolving graph environments.
Secure Beamforming for ISAC Systems Under Communication Eavesdropper and Sensing Eavesdropper
Venue: arXiv cs.IT 2026
Year: 2026

Due to great efficiency improvement in resource and hardware space, integrated sensing and communication (ISAC) has gained much attention. In the paper, the physical layer security (PLS) of ISAC system under communication eavesdropper together with sensing eavesdropper is investigated. The system secrecy rate is maximized by transmit beamforming design of communication and sensing signals when taking sensing security, sensing performance and transmit power constraint into consideration. To deal with the formulated non-convex optimization problem, the successive convex approximation (SCA) together with the first-order Taylor expansion and semidefinite relaxation (SDR) is utilized. Additionally, it is theoretically validated that the SDR does not yield sub-optimality in the paper. Thereafter, an iterated joint secure beamforming algorithm against communication and sensing eavesdroppers is proposed. Simulation results validate the effectiveness and advance of the proposed scheme.
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
Venue: arXiv cs.CL 2026
Year: 2026

As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family of formats tailored for Ascend NPUs. Through rigorous comparison across weight-activation and KV-cache tasks, we provide three key insights: (1) INT8 suits narrow-range data, while floating-point formats excel with high-variance data; (2) in 4-bit regimes, HiF4's hierarchical scaling prevents the accuracy collapse seen in integer formats; and (3) HiFloat is fully compatible with state-of-the-art post-training quantization frameworks. Overall, HiFloat provides a solution for high-efficiency LLM inference on NPUs.
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
Venue: arXiv cs.LG 2026
Year: 2026

Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human judges or model fitting, but the sparsity severely limits RL sample efficiency. While recent methods have effectively improved RL via dense rewards, they rely heavily on high-quality human-annotated data or abundant expert supervision. To tackle these issues, this paper proposes Dual-granularity contrastive reward via generated Episodic Guidance (DEG), a novel framework to seek sample-efficient dense rewards without requiring human annotations or extensive supervision. Leveraging the prior knowledge of large video generation models, DEG only needs a small number of expert videos for domain adaptation to generate dedicated task guidance for each RL episode. Then, the proposed dual-granularity reward that balances coarse-grained exploration and fine-grained matching, will guide the agent to efficiently approximate the generated guidance video sequentially in the contrastive self-supervised latent space, and finally complete the target task. Extensive experiments on 18 diverse tasks across both simulation and real-world settings show that DEG can not only serve as an efficient exploration stimulus to help the agent quickly discover sparse success rewards, but also guide effective RL and stable policy convergence independently.
CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation
Venue: arXiv cs.CL 2026
Year: 2026

Legal text generated by large language models (LLMs) can usually achieve reasonable factual accuracy, but it frequently fails to adhere to the specialised stylistic norms and linguistic conventions of legal writing. In order to improve stylistic quality, a crucial first step is to establish a reliable evaluation method. However, having legal experts manually develop such a metric is impractical, as the implicit stylistic requirements in legal writing practice are difficult to formalise into explicit rubrics. Meanwhile, existing automatic evaluation methods also fall short: reference-based metrics conflate semantic accuracy with stylistic fidelity, and LLM-as-a-judge evaluations suffer from opacity and inconsistency. To address these challenges, we introduce CLASE (Chinese LegAlese Stylistic Evaluation), a hybrid evaluation method that focuses on the stylistic performance of legal text. The method incorporates a hybrid scoring mechanism that combines 1) linguistic feature-based scores and 2) experience-guided LLM-as-a-judge scores. Both the feature coefficients and the LLM scoring experiences are learned from contrastive pairs of authentic legal documents and their LLM-restored counterparts. This hybrid design captures both surface-level features and implicit stylistic norms in a transparent, reference-free manner. Experiments on 200 Chinese legal documents show that CLASE achieves substantially higher alignment with human judgments than traditional metrics and pure LLM-as-a-judge methods. Beyond improved alignment, CLASE provides interpretable score breakdowns and suggestions for improvements, offering a scalable and practical solution for professional stylistic evaluation in legal text generation (Code and data for CLASE is available at: https://github.com/rexera/CLASE).