OpenRaiser/Intern-Atlas huggingface dataset:
Superpixel-Based Image Segmentation Using Squared 2-Wasserstein Distances
Venue: arXiv cs.CV 2026
Year: 2026

We present an efficient method for image segmentation in the presence of strong inhomogeneities. The approach can be interpreted as a two-level clustering procedure: pixels are first grouped into superpixels via a linear least-squares assignment problem, which can be viewed as a special case of a discrete optimal transport (OT) problem, and these superpixels are subsequently greedily merged into object-level segments using the squared 2-Wasserstein distance between their empirical distributions. In contrast to conventional superpixel merging strategies based on mean-color distances, our framework employs a distributional OT distance, yielding a mathematically unified formulation across both clustering levels. Numerical experiments demonstrate that this perspective leads to improved segmentation accuracy on challenging images while retaining high computational efficiency.
GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing
Venue: arXiv cs.CV 2026
Year: 2026

In recent years, Multimodal Large Language Models (MLLMs) have made significant progress in visual question answering tasks. However, directly applying existing fine-tuning methods to remote sensing (RS) images often leads to issues such as overfitting on background noise or neglecting target details. This is primarily due to the large-scale variations, sparse target distributions, and complex regional semantic features inherent in RS images. These challenges limit the effectiveness of MLLMs in RS tasks. To address these challenges, we propose a parameter-efficient fine-tuning (PEFT) strategy called Guided Region-Aware Sparse Prompting (GRASP). GRASP introduces spatially structured soft prompts associated with spatial blocks extracted from a frozen visual token grid. Through a question-guided sparse fusion mechanism, GRASP dynamically aggregates task-specific context into a compact global prompt, enabling the model to focus on relevant regions while filtering out background noise. Extensive experiments on multiple RSVQA benchmarks show that GRASP achieves competitive performance compared to existing fine-tuning and prompt-based methods while maintaining high parameter efficiency.
The Triangle of Similarity: A Multi-Faceted Framework for Comparing Neural Network Representations
Venue: arXiv cs.LG 2026
Year: 2026

Comparing neural network representations is essential for understanding and validating models in scientific applications. Existing methods, however, often provide a limited view. We propose the Triangle of Similarity, a framework that combines three complementary perspectives: static representational similarity (CKA/Procrustes), functional similarity (Linear Mode Connectivity or Predictive Similarity), and sparsity similarity (robustness under pruning). Analyzing a range of CNNs, Vision Transformers, and Vision-Language Models using both in-distribution (ImageNetV2) and out-of-distribution (CIFAR-10) testbeds, our initial findings suggest that: (1) architectural family is a primary determinant of representational similarity, forming distinct clusters; (2) CKA self-similarity and task accuracy are strongly correlated during pruning, though accuracy often degrades more sharply; and (3) for some model pairs, pruning appears to regularize representations, exposing a shared computational core. This framework offers a more holistic approach for assessing whether models have converged on similar internal mechanisms, providing a useful tool for model selection and analysis in scientific research.
Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts
Venue: arXiv cs.LG 2026
Year: 2026

Mixture-of-Experts (MoE) models are typically pre-trained with explicit load-balancing constraints to ensure statistically balanced expert routing. Despite this, we observe that even well-trained MoE models exhibit significantly imbalanced routing. This behavior is arguably natural-and even desirable - as imbalanced routing allows models to concentrate domain-specific knowledge within a subset of experts. Expert parallelism (EP) is designed to scale MoE models by distributing experts across multiple devices, but with a less-discussed assumption of balanced routing. Under extreme imbalance, EP can funnel a disproportionate number of tokens to a small number of experts, leading to compute- and memory-bound failures on overloaded devices during post-training or inference, where explicit load balancing is often inapplicable. We propose Least-Loaded Expert Parallelism (LLEP), a novel EP algorithm that dynamically reroutes excess tokens and associated expert parameters from overloaded devices to underutilized ones. This ensures that all devices complete their workloads within the minimum collective latency while respecting memory constraints. Across different model scales, LLEP achieves up to 5x speedup and 4x reduction in peak memory usage compared to standard EP. This enables faster and higher-throughput post-training and inference, with ~1.9x faster for gpt-oss-120b. We support our method with extensive theoretical analysis and comprehensive empirical evaluations, including ablation studies. These results illuminate key trade-offs and enable a principled framework for hardware-specific hyper-parameter tuning to achieve optimal performance.
How does Graph Structure Modulate Membership-Inference Risk for Graph Neural Networks?
Venue: arXiv cs.LG 2026
Year: 2026

Graph neural networks (GNNs) have become the standard tool for encoding data and their complex relationships into continuous representations, improving prediction accuracy in several machine learning tasks like node classification and link prediction. However, their use in sensitive applications has raised concerns about the potential leakage of training data. Research on privacy leakage in GNNs has largely been shaped by findings from non-graph domains, such as images and tabular data. We emphasize the need of graph specific analysis and investigate the impact of graph structure on node level membership inference. We formalize MI over node-neighbourhood tuples and investigate two important dimensions: (i) training graph construction and (ii) inference-time edge access. Empirically, snowball's coverage bias often harms generalisation relative to random sampling, while enabling inter-train-test edges at inference improves test accuracy, shrinks the train-test gap, and yields the lowest membership advantage across most of the models and datasets. We further show that the generalisation gap empirically measured as the performance difference between the train and test nodes is an incomplete proxy for MI risk: access to edges dominates-MI can rise or fall independent of gap changes. Finally, we examine the auditability of differentially private GNNs, adapting the definition of statistical exchangeability of train-test data points for graph based models. We show that for node level tasks the inductive splits (random or snowball sampled) break exchangeability, limiting the applicability of standard bounds for membership advantage of differential private models.
Deconstructing Taste: Toward a Human-Centered AI Framework for Modeling Consumer Aesthetic Perceptions
Venue: arXiv cs.HC 2026
Year: 2026

Understanding and modeling consumers' stylistic taste such as "sporty" is crucial for creating designs that truly connect with target audiences. However, capturing taste during the design process remains challenging because taste is abstract and subjective, and preference data alone provides limited guidance for concrete design decisions. This paper proposes an integrated human-centered computational framework that links subjective evaluations (e.g., perceived luxury of car wheels) with domain-specific features (e.g., spoke configuration) and computer vision-based measures (e.g., texture). By jointly modeling human-derived (consumer and designer) and machine-extracted features, our framework advances aesthetic assessment by explicitly linking model outcomes to interpretable design features. In particular, it demonstrates how perceptual features, domain-specific design patterns, and consumers' own interpretations of style contribute to aesthetic evaluations. This framework will enable product teams to better understand, communicate, and critique aesthetic decisions, supporting improved anticipation of consumer taste and more informed exploration of design alternatives at design time.
Bowling Online: Accounting for Civil Society Reshaped into Streamlined Photons within a Fiber Network
Venue: arXiv cs.CY 2026
Year: 2026

Civil society has been deemed by various scholars, such as Robert D. Putnam, to be a predictor and a cornerstone of a robust and consolidated democracy (Putnam et al., 1993). Putnam highlights in his book Bowling Alone (2000) that American civil society has become weaker: people organize less, and literally, they bowl alone. But what if there is yet another aspect to Putnam's story that has not been fully accounted for, namely the rise of Digital Civil Society (DCS)? Perhaps people in the third decade of the 21st century bowl online. They still organize, mobilize, and care for their civil liberties and democratic institutions; however, the public sphere in which this takes place has shifted online to cyberspace (Bernholz et al., 2013) or to what still needs to be conceptualized, the digital public sphere (DPS), which this article attempts to measure and demarcate.
Exploring EEG-driven brain-heart coupling across sleep stages in individuals with sleep disorders
Venue: arXiv cs.HC 2026
Year: 2026

The interactions between the brain and heart during sleep are responsible for regulating autonomic function. While brain-heart coupling has been studied in healthy populations, the relationships between neural and cardiac activity across sleep stages in the presence of sleep disorders are not clear. This study examines the influence of brain-driven cardiac activity across sleep stages for individuals with sleep disorders. Overnight recordings of C3 and C4 electroencephalogram (EEG) channels and electrocardiogram (ECG) signals from 146 individuals were preprocessed and analyzed in the frequency domain through a linear mixed-effect model. Our results show that parasympathetic activity is sensitive to changes in delta and beta powers during later stages of non-rapid eye movement (NREM) sleep, as both band powers exhibited strong negative effects on high-frequency heart rate variability (HF-HRV) power. These findings show that neural activity can drive vagal tone across sleep stages, suggesting that treatments on key EEG bands during NREM and REM stages may help restore regular cardiac behaviour.
Who Gets Which Message? Auditing Demographic Bias in LLM-Generated Targeted Text
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) are increasingly capable of generating personalized, persuasive text at scale, raising new questions about bias and fairness in automated communication. This paper presents the first systematic analysis of how LLMs behave when tasked with demographic-conditioned targeted messaging. We introduce a controlled evaluation framework using three leading models -- GPT-4o, Llama-3.3, and Mistral-Large 2.1 -- across two generation settings: Standalone Generation, which isolates intrinsic demographic effects, and Context-Rich Generation, which incorporates thematic and regional context to emulate realistic targeting. We evaluate generated messages along three dimensions: lexical content, language style, and persuasive framing. We instantiate this framework on climate communication and find consistent age- and gender-based asymmetries across models: male- and youth-targeted messages emphasize agency, innovation, and assertiveness, while female- and senior-targeted messages stress warmth, care, and tradition. Contextual prompts systematically amplify these disparities, with persuasion scores significantly higher for messages tailored to younger or male audiences. Our findings demonstrate how demographic stereotypes can surface and intensify in LLM-generated targeted communication, underscoring the need for bias-aware generation pipelines and transparent auditing frameworks that explicitly account for demographic conditioning in socially sensitive applications.
Rethinking Benchmarks for Differentially Private Image Classification
Venue: arXiv cs.LG 2026
Year: 2026

We revisit benchmarks for differentially private image classification. We suggest a comprehensive set of benchmarks, allowing researchers to evaluate techniques for differentially private machine learning in a variety of settings, including with and without additional data, in convex settings, and on a variety of qualitatively different datasets. We further test established techniques on these benchmarks in order to see which ideas remain effective in different settings. Finally, we create a publicly available leader board for the community to track progress in differentially private machine learning.
Advancing Improvisation in Human-Robot Construction Collaboration: Taxonomy and Research Roadmap
Venue: arXiv cs.RO 2026
Year: 2026

The construction industry faces productivity stagnation, skilled labor shortages, and safety concerns. While robotic automation offers solutions, construction robots struggle to adapt to unstructured, dynamic sites. Central to this is improvisation, adapting to unexpected situations through creative problem-solving, which remains predominantly human. In construction's unpredictable environments, collaborative human-robot improvisation is essential for workflow continuity. This research develops a six-level taxonomy classifying human-robot collaboration (HRC) based on improvisation capabilities. Through systematic review of 214 articles (2010-2025), we categorize construction robotics across: Manual Work (Level 0), Human-Controlled Execution (Level 1), Adaptive Manipulation (Level 2), Imitation Learning (Level 3), Human-in-Loop BIM Workflow (Level 4), Cloud-Based Knowledge Integration (Level 5), and True Collaborative Improvisation (Level 6). Analysis reveals current research concentrates at lower levels, with critical gaps in experiential learning and limited progression toward collaborative improvisation. A five-dimensional radar framework illustrates progressive evolution of Planning, Cognitive Role, Physical Execution, Learning Capability, and Improvisation, demonstrating how complementary human-robot capabilities create team performance exceeding individual contributions. The research identifies three fundamental barriers: technical limitations in grounding and dialogic reasoning, conceptual gaps between human improvisation and robotics research, and methodological challenges. We recommend future research emphasizing improved human-robot communication via Augmented/Virtual Reality interfaces, large language model integration, and cloud-based knowledge systems to advance toward true collaborative improvisation.
Toward Risk Thresholds for AI-Enabled Cyber Threats: Enhancing Decision-Making Under Uncertainty with Bayesian Networks
Venue: arXiv cs.CR 2026
Year: 2026

Artificial intelligence (AI) is increasingly being used to augment and automate cyber operations, altering the scale, speed, and accessibility of malicious activity. These shifts raise urgent questions about when AI systems introduce unacceptable or intolerable cyber risk, and how risk thresholds should be identified before harms materialize at scale. In recent years, industry, government, and civil society actors have begun to articulate such thresholds for advanced AI systems, with the goal of signaling when models meaningfully amplify cyber threats, for example, by automating multi-stage intrusions, enabling zero-day discovery, or lowering the expertise required for sophisticated attacks. However, current approaches to determine these thresholds remain fragmented and limited. Many thresholds rely solely on capability benchmarks or narrow threat scenarios, and are weakly connected to empirical evidence. This paper proposes a structured approach to developing and evaluating AI cyber risk thresholds that is probabilistic, evidence-based, and operationalizable. In this paper we make three core contributions that build on our prior work that highlights the limitations of relying solely on capability assessments. First, we analyze existing industry cyber thresholds and identify common threshold elements as well as recurring methodological shortcomings. Second, we propose the use of Bayesian networks as a tool for modeling AI-enabled cyber risk, enabling the integration of heterogeneous evidence, explicit representation of uncertainty, and continuous updating as new information emerges. Third, we illustrate this approach through a focused case study on AI-augmented phishing, demonstrating how qualitative threat insights can be decomposed into measurable variables and recombined into structured risk estimates that better capture how AI changes attacker behavior and outcomes.
CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrieval
Venue: arXiv cs.CL 2026
Year: 2026

Automated Fact-Checking has largely focused on verifying general knowledge against static corpora, overlooking high-stakes domains like law where truth is evolving and technically complex. We introduce CaseFacts, a benchmark for verifying colloquial legal claims against U.S. Supreme Court precedents. Unlike existing resources that map formal texts to formal texts, CaseFacts challenges systems to bridge the semantic gap between layperson assertions and technical jurisprudence while accounting for temporal validity. The dataset consists of 6,294 claims categorized as Supported, Refuted, or Overruled. We construct this benchmark using a multi-stage pipeline that leverages Large Language Models (LLMs) to synthesize claims from expert case summaries, employing a novel semantic similarity heuristic to efficiently identify and verify complex legal overrulings. Experiments with state-of-the-art LLMs reveal that the task remains challenging; notably, augmenting models with unrestricted web search degrades performance compared to closed-book baselines due to the retrieval of noisy, non-authoritative precedents. We release CaseFacts to spur research into legal fact verification systems.
Quantifying Ergonomics in the Elevate Soft Robotic Suit
Venue: arXiv cs.RO 2026
Year: 2026

Soft robotic suits have the potential to rehabilitate, assist, and augment the human body. The low weight, cost, and minimal form-factor of these devices make them ideal for daily use by both healthy and impaired individuals. However, challenges associated with data-driven, user-specific, and comfort-first design of human-robot interfaces using soft materials limit their widespread translation and adoption. In this work, we present the quantitative evaluation of ergonomics and comfort of the Elevate suit - a cable driven soft robotic suit that assists shoulder elevation. Using a motion-capture system and force sensors, we measured the suit's ergonomics during assisted shoulder elevation up to 70 degrees. Two 4-hour sessions were conducted with one subject, involving transmitting cable tensions of up to 200N with no discomfort reported. We estimated that the pressure applied to the shoulder during assisted movements was within the range seen in a human grasp (approximately 69.1-85.1kPa), and estimated volumetric compression of <3% and <8% across the torso and upper arm, respectively. These results provide early validation of Elevate's ergonomic design in preparation for future studies with patient groups.
EMPM: Embodied MPM for Modeling and Simulation of Deformable Objects
Venue: arXiv cs.RO 2026
Year: 2026

Modeling deformable objects - especially continuum materials - in a way that is physically plausible, generalizable, and data-efficient remains challenging across 3D vision, graphics, and robotic manipulation. Many existing methods oversimplify the rich dynamics of deformable objects or require large training sets, which often limits generalization. We introduce embodied MPM (EMPM), a deformable object modeling and simulation framework built on a differentiable Material Point Method (MPM) simulator that captures the dynamics of challenging materials. From multi-view RGB-D videos, our approach reconstructs geometry and appearance, then uses an MPM physics engine to simulate object behavior by minimizing the mismatch between predicted and observed visual data. We further optimize MPM parameters online using sensory feedback, enabling adaptive, robust, and physics-aware object representations that open new possibilities for robotic manipulation of complex deformables. Experiments show that EMPM outperforms spring-mass baseline models. Project website: https://embodied-mpm.github.io.
Inference-Time Loss-Guided Colour Preservation in Diffusion Sampling
Venue: arXiv cs.CV 2026
Year: 2026

Precise color control remains a persistent failure mode in text-to-image diffusion systems, particularly in design-oriented workflows where outputs must satisfy explicit, user-specified color targets. We present an inference-time, region-constrained color preservation method that steers a pretrained diffusion model without any additional training. Our approach combines (i) ROI-based inpainting for spatial selectivity, (ii) background-latent re-imposition to prevent color drift outside the ROI, and (iii) latent nudging via gradient guidance using a composite loss defined in CIE Lab and linear RGB. The loss is constructed to control not only the mean ROI color but also the tail of the pixelwise error distribution through CVaR-style and soft-maximum penalties, with a late-start gate and a time-dependent schedule to stabilize guidance across denoising steps. We show that mean-only baselines can satisfy average color constraints while producing perceptually salient local failures, motivating our distribution-aware objective. The resulting method provides a practical, training-free mechanism for targeted color adherence that can be integrated into standard Stable Diffusion inpainting pipelines.
The Viscosity of Logic: Phase Transitions and Hysteresis in DPO Alignment
Venue: arXiv cs.LG 2026
Year: 2026

Direct Preference Optimization (DPO) is often tuned as if increasing alignment pressure (controlled by $β$) yields progressively "better" behavior. We instead treat $β$ as a control parameter and densely sweep it for three 7B open-weight families under a fixed DPO recipe. In Mistral, capability is sharply non-monotonic: aggregated logic-probe margins become positive only in a narrow band near $β\approx 10^{-2}$ and revert outside it, with boundary points that are seed-sensitive. Across architectures under the same sweep, we observe qualitatively different response modes: sharp reorganization in Mistral, selective changes in Llama, and smooth trade-offs in Qwen. Critically, the DPO preference margin can anticorrelate with reasoning capability (Pearson $r=-0.91$ for Llama logic), so margin-based selection can prefer capability-impaired models. Training path also matters: exposure to high $β$ induces capability losses that persist even after $β$ is reduced (hysteresis). These findings motivate capability-resolved evaluation across the $β$ landscape rather than reliance on margins or aggregate benchmarks.