OpenRaiser/Intern-Atlas huggingface dataset:
KISS-IMU: Self-supervised Inertial Odometry with Motion-balanced Learning and Uncertainty-aware Inference
Venue: arXiv cs.RO 2026
Year: 2026

Inertial measurement units (IMUs), which provide high-frequency linear acceleration and angular velocity measurements, serve as fundamental sensing modalities in robotic systems. Recent advances in deep neural networks have led to remarkable progress in inertial odometry. However, the heavy reliance on ground truth data during training fundamentally limits scalability and generalization to unseen and diverse environments. We propose KISS-IMU, a novel self-supervised inertial odometry framework that eliminates ground truth dependency by leveraging simple LiDAR-based ICP registration and pose graph optimization as a supervisory signal. Our approach embodies two key principles: keeping the IMU stable through motion-aware balanced training and keeping the IMU strong through uncertainty-driven adaptive weighting during inference. To evaluate performance across diverse motion patterns and scenarios, we conducted comprehensive experiments on various real-world platforms, including quadruped robots. Importantly, we train only the IMU network in a self-supervised manner, with LiDAR serving solely as a lightweight supervisory signal rather than requiring additional learnable processes. This design enables the framework to ensure robustness without relying on joint multi-modal learning or ground truth supervision. The supplementary materials are available at https://sparolab.github.io/research/kiss_imu.
What are AI researchers worried about?
Venue: arXiv cs.CY 2026
Year: 2026

As AI attracts vast investment and attention, there are competing concerns about the technology's opportunities and uncertainties that blend technical and social questions. The public debate, dominated by a few powerful voices, tends to highlight extreme promises and threats. We wanted to know whether public discussions or technology companies' priorities were representative of AI researchers' opinions. Our survey of more than 4,000 AI researchers is, we think, the largest conducted to date. It was designed to understand attitudes to a variety of issues and include some comparisons with public attitudes derived from existing surveys. Most previous surveys of AI researchers have asked them for predictions of passing a technological threshold or the probabilities of some catastrophic event. These surveys mask the uncertainty and diversity that normally characterises scientific research. Our hypothesis was that the opinions of AI researchers would be markedly different from those of members of the public. While there are areas of divergence, particularly in attitudes to the technology's potential benefits, our survey shows some surprising convergence between researchers' and publics' opinions, particularly in the assessment and prioritisation of risk. Responses to an open text question 'What one thing most worries you about AI?' reveal that only 3% of AI researchers prioritise existential risks, despite the prominence given to these risks in media and policy. AI technologies and AI researchers seem to be much more 'normal' than public representations suggest. Our survey results suggest the possibility for new forms of public dialogue on AI's harms, risks and opportunities. Rather than speculating on future potential risks, policymakers and AI researchers should collaborate on understanding and mitigating the range of sociotechnical risks that are already of clear public concern.
Human, Algorithm, or Both? Gender Bias in Human-Augmented Recruiting
Venue: arXiv cs.CY 2026
Year: 2026

Recent years have seen rapid growth in the market for HR technology and AI-driven HR solutions in particular. This popularity has also resulted in increased attention to the negative aspects of using AI to support hiring practices, such as the risk of reinforcing existing biases against vulnerable groups based on gender or other sensitive attributes. Combining human experience with AI efficiency in making recruiting and selection decisions has the potential to help mitigate these biases, but despite a considerable amount of research on fairness in algorithmic hiring, actual empirical evaluations comparing the fairness of human, AI, and human-augmented decision-making remain scarce. In this study, we address this gap by presenting a quantitative analysis of gender bias across three scenarios of a real-world recruitment platform: (1) recruiters searching a CV database manually for relevant candidates, (2) AI-driven matching between candidates and jobs, and (3) a combination of human and AI-driven recruiting. We find that human recruiters produce lists of candidates that are fairer in terms of gender than the AI-only solution, with more deliberation by humans resulting in fairer outcomes. However, the combination of human and AI-driven is more than the sum of its parts and produces the fairest candidate lists: interacting with the slate of recommended candidates first before manually searching for additional candidates has a beneficial effect on the gender fairness of the set of candidates that are viewed, clicked, and contacted afterwards. Our work provides one of the first empirical comparisons of fairness across human, AI, and hybrid recruiting processes, offering evidence to inform the development of more equitable hiring practices and highlighting the importance of human oversight for mitigating bias in algorithmic hiring.
Exploring Socially Assistive Peer Mediation Robots for Teaching Conflict Resolution to Elementary School Students
Venue: arXiv cs.HC 2026
Year: 2026

In peer mediation--an approach to conflict resolution used in many K-12 schools in the United States--students help other students to resolve conflicts. For schools without peer mediation programs, socially assistive robots (SARs) may be able to provide an accessible option to practice peer mediation. We investigate how elementary school students react to a peer mediator role-play activity through an exploratory study with SARs. We conducted a small single-session between-subjects study with 12 participants. The study had two conditions, one with two robots acting as disputants, and the other without the robots and just the tablet. We found that a majority of students had positive feedback on the activity, with many students saying the peer mediation practice helped them feel better about themselves. Some said that the activity taught them how to help friends during conflict, indicating that the use of SARs for peer mediation practice is promising. We observed that participants had varying reading levels that impacted their ability to read and dictate the turns in the role-play script, an important consideration for future study design. Additionally, we found that some participants were more expressive while reading the script and throughout the activity. Although we did not find statistical differences in pre-/post-session self-perception and quiz performance between the robot and tablet conditions, we found strong correlations (p<0.05) between certain trait-related measures and learning-related measures in the robot condition, which can inform future study design for SARs for this and related contexts.
Mind the Gap: Pitfalls of LLM Alignment with Asian Public Opinion
Venue: arXiv cs.CL 2026
Year: 2026

Large Language Models (LLMs) are increasingly being deployed in multilingual, multicultural settings, yet their reliance on predominantly English-centric training data risks misalignment with the diverse cultural values of different societies. In this paper, we present a comprehensive, multilingual audit of the cultural alignment of contemporary LLMs including GPT-4o-Mini, Gemini-2.5-Flash, Llama 3.2, Mistral and Gemma 3 across India, East Asia and Southeast Asia. Our study specifically focuses on the sensitive domain of religion as the prism for broader alignment. To facilitate this, we conduct a multi-faceted analysis of every LLM's internal representations, using log-probs/logits, to compare the model's opinion distributions against ground-truth public attitudes. We find that while the popular models generally align with public opinion on broad social issues, they consistently fail to accurately represent religious viewpoints, especially those of minority groups, often amplifying negative stereotypes. Lightweight interventions, such as demographic priming and native language prompting, partially mitigate but do not eliminate these cultural gaps. We further show that downstream evaluations on bias benchmarks (such as CrowS-Pairs, IndiBias, ThaiCLI, KoBBQ) reveal persistent harms and under-representation in sensitive contexts. Our findings underscore the urgent need for systematic, regionally grounded audits to ensure equitable global deployment of LLMs.
SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation
Venue: arXiv cs.RO 2026
Year: 2026

High-quality, long-horizon demonstrations are essential for embodied AI, yet acquiring such data for tightly coupled wheeled mobile manipulators remains a fundamental bottleneck. Unlike fixed-base systems, mobile manipulators require continuous coordination between $SE(2)$ locomotion and precise manipulation, exposing limitations in existing teleoperation and wearable interfaces. We present \textbf{SuperSuit}, a bimodal data acquisition framework that supports both robot-in-the-loop teleoperation and active demonstration under a shared kinematic interface. Both modalities produce structurally identical joint-space trajectories, enabling direct data mixing without modifying downstream policies. For locomotion, SuperSuit maps natural human stepping to continuous planar base velocities, eliminating discrete command switches. For manipulation, it employs a strictly isomorphic wearable arm in both modes, while policy training is formulated in a shift-invariant delta-joint representation to mitigate calibration offsets and structural compliance without inverse kinematics. Real-world experiments on long-horizon mobile manipulation tasks show 2.6$\times$ higher demonstration throughput in active mode compared to a teleoperation baseline, comparable policy performance when substituting teleoperation data with active demonstrations at fixed dataset size, and monotonic performance improvement as active data volume increases. These results indicate that consistent kinematic representations across collection modalities enable scalable data acquisition for long-horizon mobile manipulation.
Enhanced Protein Intrinsic Disorder Prediction Through Dual-View Multiscale Features and Multi-objective Evolutionary Algorithm
Venue: arXiv cs.NE 2026
Year: 2026

Intrinsically disordered regions of proteins play a crucial role in cell signaling and drug discovery. However, their high structural flexibility makes accurate residue-level prediction challenging. Existing methods often rely on single-view representations or rigid manual fusion strategies, which fail to effectively balance the complex interplay between local amino acid preferences and long-range sequence patterns. To address these limitations, we propose D2MOE, a Dual-View Multiscale Features and Multi-objective Evolutionary Algorithm, which consists of two stages. First, a dual-view multiscale feature extraction method is introduced. This method integrates evolutionary views with deep semantic views and employs multiscale extractors to capture structural information across diverse receptive fields. Second, a multi-objective evolutionary algorithm is designed to adaptively discover optimal fusion architectures. By co-evolving discrete feature selection and continuous fusion weights, the algorithm adaptively explores optimal cross-feature architectures to enhance predictive accuracy while maintaining model compactness. Experimental results across three benchmark datasets demonstrate that D2MOE consistently outperforms state-of-the-art methods. D2MOE combines the feature extraction capabilities of deep learning with the global search advantages of evolutionary algorithms, enabling efficient feature integration without manual design, and providing a robust computational tool for protein disorder prediction.
MoEless: Efficient MoE LLM Serving via Serverless Computing
Venue: arXiv cs.DC 2026
Year: 2026

Large Language Models (LLMs) have become a cornerstone of AI, driving progress across diverse domains such as content creation, search and recommendation systems, and AI-assisted workflows. To alleviate extreme training costs and advancing model scales, Mixture-of-Experts (MoE) has become a popular backbone for modern LLMs, which are commonly served in distributed deployment using expert parallelism (EP). However, MoE's sparse activation mechanism leads to severe expert load imbalance, where a few experts become overloaded while others remain idle, resulting in expert stragglers that inflate inference latency and serving cost. Existing expert load balancing solutions assume static resource configurations on serverful infrastructures, limiting expert scalability and elasticity, and resulting in either costly real-time expert swapping or degraded generation quality. We present MoEless, the first serverless MoE serving framework that mitigates expert load imbalance and accelerates inference via serverless experts. MoEless employs lightweight, layer-aware predictors to accurately estimate incoming expert load distributions and proactively identify stragglers. We design optimized expert scaling and placement strategies to maximize function locality, improve GPU utilization, and balance loads across experts and GPUs. MoEless is prototyped on top of Megatron-LM and deployed on an eight-GPU testbed. Experiments with open-source MoE models and real-world workloads show that MoEless reduces inference latency by 43% and inference cost by 84% compared to state-of-the-art solutions.
Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions
Venue: arXiv cs.RO 2026
Year: 2026

Cooperative transport and manipulation of heavy or bulky payloads by multiple manipulators requires coordinated formation tracking, while simultaneously enforcing strict safety constraints in varying environments with limited communication and real-time computation budgets. This paper presents a distributed control framework that achieves consensus coordination with safety guarantees via hierarchical event-triggered control barrier functions (CBFs). We first develop a consensus-based protocol that relies solely on local neighbor information to enforce both translational and rotational consistency in task space. Building on this coordination layer, we propose a three-level hierarchical event-triggered safety architecture with CBFs, which is integrated with a risk-aware leader selection and smooth switching strategy to reduce online computation. The proposed approach is validated through real-world hardware experiments using two Franka manipulators operating with static obstacles, as well as comprehensive simulations demonstrating scalable multi-arm cooperation with dynamic obstacles. Results demonstrate higher precision cooperation under strict safety constraints, achieving substantially reduced computational cost and communication frequency compared to baseline methods.
LATO: 3D Mesh Flow Matching with Structured TOpology Preserving LAtents
Venue: arXiv cs.CV 2026
Year: 2026

In this paper, we introduce LATO, a novel topology-preserving latent representation that enables scalable, flow matching-based synthesis of explicit 3D meshes. LATO represents a mesh as a Vertex Displacement Field (VDF) anchored on surface, incorporating a sparse voxel Variational Autoencoder (VAE) to compress this explicit signal into a structured, topology-aware voxel latent. To decapsulate the mesh, the VAE decoder progressively subdivides and prunes latent voxels to instantiate precise vertex locations. In the end, a dedicated connection head queries the voxel latent to predict edge connectivity between vertex pairs directly, allowing mesh topology to be recovered without isosurface extraction or heuristic meshing. For generative modeling, LATO adopts a two-stage flow matching process, first synthesizing the structure voxels and subsequently refining the voxel-wise topology features. Compared to prior isosurface/triangle-based diffusion models and autoregressive generation approaches, LATO generates meshes with complex geometry, well-formed topology while being highly efficient in inference.
Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows
Venue: arXiv cs.AI 2026
Year: 2026

Large language models (LLMs) can now translate a researcher's plain-language goal into executable computation, yet scientific workflows demand determinism, provenance, and governance that are difficult to guarantee when an LLM decides what runs. Semi-structured interviews with 18 experts across 10 industrial R&D stakeholders surface 2 competing requirements--deterministic, constrained execution and conversational flexibility without workflow rigidity--together with boundary properties (human-in-the-loop control and transparency) that any resolution must satisfy. We propose schema-gated orchestration as the resolving principle: the schema becomes a mandatory execution boundary at the composed-workflow level, so that nothing runs unless the complete action--including cross-step dependencies--validates against a machine-checkable specification. We operationalize the 2 requirements as execution determinism (ED) and conversational flexibility (CF), and use these axes to review 20 systems spanning 5 architectural groups along a validation-scope spectrum. Scores are assigned via a multi-model protocol--15 independent sessions across 3 LLM families--yielding substantial-to-near-perfect inter-model agreement (Krippendorff a=0.80 for ED and a=0.98 for CF), demonstrating that multi-model LLM scoring can serve as a reusable alternative to human expert panels for architectural assessment. The resulting landscape reveals an empirical Pareto front--no reviewed system achieves both high flexibility and high determinism--but a convergence zone emerges between the generative and workflow-centric extremes. We argue that a schema-gated architecture, separating conversational from execution authority, is positioned to decouple this trade-off, and distill 3 operational principles--clarification-before-execution, constrained plan-act orchestration, and tool-to-workflow-level gating--to guide adoption.
Physical Simulator In-the-Loop Video Generation
Venue: arXiv cs.CV 2026
Year: 2026

Recent advances in diffusion-based video generation have achieved remarkable visual realism but still struggle to obey basic physical laws such as gravity, inertia, and collision. Generated objects often move inconsistently across frames, exhibit implausible dynamics, or violate physical constraints, limiting the realism and reliability of AI-generated videos. We address this gap by introducing Physical Simulator In-the-loop Video Generation (PSIVG), a novel framework that integrates a physical simulator into the video diffusion process. Starting from a template video generated by a pre-trained diffusion model, PSIVG reconstructs the 4D scene and foreground object meshes, initializes them within a physical simulator, and generates physically consistent trajectories. These simulated trajectories are then used to guide the video generator toward spatio-temporally physically coherent motion. To further improve texture consistency during object movement, we propose a Test-Time Texture Consistency Optimization (TTCO) technique that adapts text and feature embeddings based on pixel correspondences from the simulator. Comprehensive experiments demonstrate that PSIVG produces videos that better adhere to real-world physics while preserving visual quality and diversity. Project Page: https://vcai.mpi-inf.mpg.de/projects/PSIVG/
Abductive Reasoning with Syllogistic Forms in Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Research in AI using Large-Language Models (LLMs) is rapidly evolving, and the comparison of their performance with human reasoning has become a key concern. Prior studies have indicated that LLMs and humans share similar biases, such as dismissing logically valid inferences that contradict common beliefs. However, criticizing LLMs for these biases might be unfair, considering our reasoning not only involves formal deduction but also abduction, which draws tentative conclusions from limited information. Abduction can be regarded as the inverse form of syllogism in its basic structure, that is, a process of drawing a minor premise from a major premise and conclusion. This paper explores the accuracy of LLMs in abductive reasoning by converting a syllogistic dataset into one suitable for abduction. It aims to investigate whether the state-of-the-art LLMs exhibit biases in abduction and to identify potential areas for improvement, emphasizing the importance of contextualized reasoning beyond formal deduction. This investigation is vital for advancing the understanding and application of LLMs in complex reasoning tasks, offering insights into bridging the gap between machine and human cognition.
Prosodic Boundary-Aware Streaming Generation for LLM-Based TTS with Streaming Text Input
Venue: arXiv cs.SD 2026
Year: 2026

Streaming TTS that receives streaming text is essential for interactive systems, yet this scheme faces two major challenges: unnatural prosody due to missing lookahead and long-form collapse due to unbounded context. We propose a prosodic-boundary-aware post-training strategy, adapting a pretrained LLM-based TTS model using weakly time-aligned data. Specifically, the model is adapted to learn early stopping at specified content boundaries when provided with limited future text. During inference, a sliding-window prompt carries forward previous text and speech tokens, ensuring bounded context and seamless concatenation. Evaluations show our method outperforms CosyVoice-Style interleaved baseline in both short and long-form scenarios. In long-text synthesis, especially, it achieves a 66.2% absolute reduction in word error rate (from 71.0% to 4.8%) and increases speaker and emotion similarity by 16.1% and 1.5% relatively, offering a robust solution for streaming TTS with incremental text.
Match4Annotate: Propagating Sparse Video Annotations via Implicit Neural Feature Matching
Venue: arXiv cs.CV 2026
Year: 2026

Acquiring per-frame video annotations remains a primary bottleneck for deploying computer vision in specialized domains such as medical imaging, where expert labeling is slow and costly. Label propagation offers a natural solution, yet existing approaches face fundamental limitations. Video trackers and segmentation models can propagate labels within a single sequence but require per-video initialization and cannot generalize across videos. Classic correspondence pipelines operate on detector-chosen keypoints and struggle in low-texture scenes, while dense feature matching and one-shot segmentation methods enable cross-video propagation but lack spatiotemporal smoothness and unified support for both point and mask annotations. We present Match4Annotate, a lightweight framework for both intra-video and inter-video propagation of point and mask annotations. Our method fits a SIREN-based implicit neural representation to DINOv3 features at test time, producing a continuous, high-resolution spatiotemporal feature field, and learns a smooth implicit deformation field between frame pairs to guide correspondence matching. We evaluate on three challenging clinical ultrasound datasets. Match4Annotate achieves state-of-the-art inter-video propagation, outperforming feature matching and one-shot segmentation baselines, while remaining competitive with specialized trackers for intra-video propagation. Our results show that lightweight, test-time-optimized feature matching pipelines have the potential to offer an efficient and accessible solution for scalable annotation workflows.