A new minimum-lap-time planning framework incorporates robustness to state disturbances and parameter uncertainty, validated on simulated FSAE car using MPC.

This paper proposes a barometer-aided attitude estimation architecture that uses barometric altitude measurements to provide complementary vertical motion information, enhancing attitude estimation within nonlinear observers on SO(3). Two observers are designed: a deterministic Riccati observer cascaded with a complementary filter achieving almost-global asymptotic stability, and a nonlinear observer on SO(3)×R2 guaranteeing local exponential stability. Simulations and real flight data validate the method's effectiveness under minimal-sensing configurations.

WANDA is a synthetic data engine that learns open-world mobile manipulation policies from a single demonstration. It reconstructs background and interaction trajectories, rearranges configurations, uses Corrective State Expansion for robustness, and synthesizes trajectories on diverse 3D worlds, achieving generalization and cross-embodiment support.

This paper introduces HRO, a hierarchical room-to-object framework for zero-shot object-goal navigation powered by large language models (LLMs). Unlike existing flat reasoning methods, HRO mimics human-like hierarchical spatial cognition, enabling the agent to explore from room-level to object-level in a coarse-to-fine manner. Experiments on Gibson and HM3D datasets demonstrate superior success rate and generalization over prior LLM-based approaches.

A multimodal teleoperation architecture for ROVs using 3D Gaussian Splatting to generate occlusion-free exocentric views and a vibrotactile suit for haptic cues. A human study with 30 participants showed the exocentric view significantly improves performance under high latency, with fNIRS indicating sustained executive control rather than cognitive overload.

Researchers propose a hierarchical Bayesian generative model that operationalizes uncanny valley guidelines as mathematical design variables. The model maps the effect onto four variables: deviation from predicted robot-category mean, inconsistency in human likeness across modalities, prediction uncertainty, and observational uncertainty. Experiments show that increased observational uncertainty attenuates familiarity dips at intermediate human likeness, while low prediction uncertainty boosts ratings for robot-like appearances. This framework provides a computational basis for algorithmically evaluating and optimizing humanoid robot appearance and behavior.

High-Voltage Electrostatic Actuators (HVEAs) are emerging as a compelling alternative for haptic interfaces requiring soft, thin, silent, and energy-efficient actuation. This survey reviews four major classes: electrostatic switchable adhesives, dielectric elastomer actuators, soft electrohydraulic actuators, and electrokinetic pumps. It analyzes their mechanisms, bandwidths, force densities, and scalability for rendering cutaneous and kinesthetic feedback, and outlines design constraints and future research directions.

HRIBench is a diagnostic benchmark for intent-aware human-robot collaboration, using structured scenario scripts to model agent roles, temporal dependencies, and coordination constraints. It defines three interaction roles—Instructor, Collaborator, and Intruder—across 13 tasks with over 650 evaluation episodes, introducing interpretable metrics like synchronization, responsiveness, protocol compliance, and safety. Evaluations show current foundation policies struggle in collaboration, but fine-tuning on HRIBench significantly improves performance.

This paper reports a systematic literature review on autonomous UAV route planning for coverage-oriented environmental monitoring. Following the PRISMA 2020 framework, it searches Scopus and Web of Science for studies from 2015 to 2026, focusing on path planning, coverage path planning, and informative path planning. Preliminary analysis of 247 retained studies reveals a concentration on coverage formulations, multi-UAV coordination, and energy-aware optimization, with fewer addressing weather, uncertainty, or obstacles. Most studies rely on simulation validation, indicating a simulation-to-reality gap, and recent work shows growing interest in reinforcement learning, hybrid optimization, and geometry-aware planning.

A robust polarization-aware differentiable path tracing method is proposed, using path replay and local caching to estimate unbiased gradients, overcoming rank deficiency in polarimetric operators for stable inverse rendering.

We present AffectFlow-DINO, a multi-task learning system for the 11th ABAW challenge that extends a standard deterministic architecture with a conditional rectified-flow head to model the inherent ambiguity of in-the-wild facial behavior. The system jointly estimates continuous valence-arousal, classifies eight facial expressions, and detects twelve Action Units. Extensive ablation studies show that rectified-flow decoding consistently improves deterministic prediction, and post-hoc threshold calibration effectively recovers performance on rare classes. The final model achieves P_MTL=1.177, substantially outperforming the official baseline of 0.45.

This paper introduces JITOMA (Just-In-Time On-demand Memory Activation), a closed-loop framework that unifies task reasoning, perception, and memory to combat perceptual saturation in long-horizon robotics. It uses a task heatmap to filter observations and an LLM to dynamically activate relevant anchors, reducing computational overhead while maintaining stable performance. The authors also present JITOMA-Bench for evaluation.

A human-in-the-loop framework combining active learning and dual-loss optimization reduces annotation effort for laparoscopic video segmentation by 50%. It uses a foundation model to generate temporally consistent CAMs, with weak supervision on video-level labels and image-level mask loss on human-corrected annotations from active learning. Iterative pseudo-mask refinement eliminates the need for dense initial annotations.

Static deepfake detectors suffer drastic AUC drops of 45-50% on real-world content due to being trained once against a moving generative frontier. BitMind Forensics (BMF), trained via the Bittensor SN34 open adversarial competition, continuously refreshes its training distribution and achieves strong results across 19 public datasets, including robustness to JPEG compression and downscaling, and improvements over time on unseen generators.

A new study systematically compares pretrain-finetuning (PFT) and joint training (JT) paradigms for self-supervised learning, finding JT superior in data efficiency and low-label settings, while PFT is more reliable in specialized domains.

MGFace is a mask-gated face identification pipeline that predicts the mask status of a query face and conditionally routes similarity computation: global embedding matching for unmasked queries, and mask-aware patch-level re-ranking only for masked queries. On the extended LFW-Mask dataset, it achieves over 80% accuracy with FaceNet and over 90% with ArcFace, while reducing query time by approximately 20x compared to a prior EMD-based method.

A Transformer-based Masked Autoencoder learns representations for unsupervised steel surface defect recognition. Pretraining masks 75% of image patches, a lightweight decoder reconstructs them, and an auxiliary defect localization objective is jointly trained. Decoder achieves SSIM 0.92, MSE 0.47, and clustering yields 91.3% Hungarian matched accuracy on six defect categories.

Boogu-Image-0.1 is an open-source family of unified multimodal understanding and generation models including Base, Turbo, Edit, and Edit-Turbo variants. It delivers competitive performance in text-to-image generation, fast inference, instruction-based editing, and bilingual text rendering. Through targeted improvements in model understanding, data quality, and training pipelines, coupled with agentic inference-time scaling, it achieves results approaching leading closed-source systems with only 208.62 million unique images and a training cost of approximately $400K.

A new method called C-Norm addresses poor AI performance in cervical cancer screening by normalizing cell distribution in TCT images. It decouples abnormal and normal cells and re-synthesizes them for uniform distribution, then uses a hybrid YOLOv12-DINOv3 architecture for detection. Experiments show state-of-the-art results.

We propose Samba, a hybrid Mamba architecture for audio-visual navigation. It uses an adaptive selection-enabled Mamba State Encoder (M-SE) to replace conventional GRUs for temporal aggregation, and an Audio Mamba Encoder (AME) to address limitations of convolutional operators in capturing global time-frequency dependencies in spectrograms. Experiments show an 11.3% improvement in success rate on Matterport3D and even better performance on Replica, with lower computational cost. Accepted at IEEE SMC 2026.

Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing challenges for spatio-temporal forecasting. Existing approaches focus on graph, attention, and decomposition architectures but neglect the underlying nonlinear function approximator. STKAN introduces Taylor-polynomial Kolmogorov-Arnold Network modules into spatial and temporal token mixing. It constructs high-level spatial representations via a learnable soft node-group assignment, applies group-wise spatial mixing, models temporal dependencies, and uses self-attention for long-range interactions. Experiments on five traffic benchmarks show competitive performance, outperforming an MLP-based variant, suggesting that nonlinear function approximator design complements architectural inn…

Knowledge tracing (KT) predicts student performance by modeling evolving knowledge states. Existing methods treat interactions as a unified process, ignoring phase-specific learning. We propose Phase-Aware Knowledge Tracing (PAKT), which decomposes interactions into ability and proficiency phases. A multi-branch Transformer with type-aware readout captures phase-specific and holistic states. Causal analysis reveals confounding bias in phase-agnostic models. On six benchmarks, PAKT achieves up to 1.33% AUC improvement, averaging 0.82%.

Global station weather forecasting (GSWF) is crucial for localized and extreme weather prediction. Existing methods rely heavily on short-term patterns, struggling with chaotic dynamics and partial observations. We propose TSSM, a triaxial state space model with history-enhanced temporal-variable-historical paradigm. It incorporates period-aligned historical data to capture long-term, large-scale periodic patterns. TSSM achieves SOTA on Weather-5K with 10% accuracy gain and 61% extreme event improvement. It excels in long-horizon forecasting (37.5% gain at 240h) and iterative settings (103.5% gain at 48h×5). Under 80% missing observations, TSSM retains >90% performance, demonstrating robustness for real-world deployment.

A new framework treats the decision of when to invoke a large language model (LLM) in streaming inference as a risk-based sequential stopping problem. The authors prove six theoretical results covering minimum inter-event times, optimality of threshold policies, and regret bounds. Empirical tests on turbofan degradation data show that anomaly-score-driven risk functions outperform baseline methods by an order of magnitude in Pareto AUC.

Parameter decomposition (PD) decomposes neural networks into interpretable components but is computationally expensive for large models. The proposed targeted PD (tPD) introduces a high-rank catch-all component to handle non-target data, enabling efficient recovery of circuits for specific inputs. tPD extracts a CSS-only submodel from a 4-block transformer using 7% of the FLOPs of published decomposition, and surgically ablates memorized sequences in a 12-block transformer with negligible side effects. Accepted at the Mechanistic Interpretability Workshop, ICML 2026.

This paper presents a framework to analyze information discarded by machine learning models when inputs exhibit Lie group symmetries. It defines null fibers and stabilizers to measure the symmetry invisible to a model, and uses the Peter-Weyl theorem for a spectral characterization on compact groups. Efficient computation via Newton iteration is demonstrated. Applications to data masking, model fingerprinting, and privacy-preserving computation are experimentally validated on molecular property prediction under SO(3) and spherical image classification under the Möbius group PSL(2, C). The framework applies uniformly to classical neural networks and variational quantum circuits.

Federated Learning (FL) enables privacy-preserving collaborative training across distributed data, but lacks model transparency. Explainable AI (XAI) addresses opacity. Their intersection, Federated Explainable AI (FedXAI), is systematically reviewed in this survey, highlighting explainability's shift from post-hoc tool to integral FL component. A taxonomy classifies methods by role, model type, scope, integration level, FL settings, and data heterogeneity. Evaluation practices lack standardized benchmarks. Key challenges include non-IID data, security threats, communication efficiency, continual learning, and domain knowledge integration.

A new lightweight training strategy for deep learning models decouples feature extraction from classifier optimization, drastically reducing training time and energy consumption with minimal accuracy loss, as tested on multiple architectures and medical datasets.

This paper traces, with explicit numerical values, how PyTorch's automatic differentiation engine computes gradients for Physics-Informed Neural Network training, including physics derivative computation and parameter gradients. Using a 1-3-3-1 multilayer perceptron and a simple ODE, it details the computational graph, reverse-mode backward traversal, and the graph-on-graph mechanism for correct differentiation through physics-informed residuals.

A new system called Mycelium enables networked intelligence by connecting researchers and AI agents in a shared workspace, automatically routing observations and hypotheses to relevant team members. Tested in a biological multi-omics campaign, it turned local findings into cross-expert constraints and experimental designs.