OpenRaiser/Intern-Atlas huggingface dataset:
Hand Gesture Recognition from Doppler Radar Signals Using Echo State Networks
Venue: arXiv cs.LG 2026
Year: 2026

Hand gesture recognition (HGR) is a fundamental technology in human computer interaction (HCI).In particular, HGR based on Doppler radar signals is suited for in-vehicle interfaces and robotic systems, necessitating lightweight and computationally efficient recognition techniques. However, conventional deep learning-based methods still suffer from high computational costs. To address this issue, we propose an Echo State Network (ESN) approach for radar-based HGR, using frequency-modulated-continuous-wave (FMCW) radar signals. Raw radar data is first converted into feature maps, such as range-time and Doppler-time maps, which are then fed into one or more recurrent neural network-based reservoirs. The obtained reservoir states are processed by readout classifiers, including ridge regression, support vector machines, and random forests. Comparative experiments demonstrate that our method outperforms existing approaches on an 11-class HGR task using the Soli dataset and surpasses existing deep learning models on a 4-class HGR task using the Dop-NET dataset. The results indicate that parallel processing using multi-reservoir ESNs are effective for recognizing temporal patterns from the multiple different feature maps in the time-space and time-frequency domains. Our ESN approaches achieve high recognition performance with low computational cost in HGR, showing great potential for more advanced HCI technologies, especially in resource-constrained environments.
SynthVerse: A Large-Scale Diverse Synthetic Dataset for Point Tracking
Venue: arXiv cs.CV 2026
Year: 2026

Point tracking aims to follow visual points through complex motion, occlusion, and viewpoint changes, and has advanced rapidly with modern foundation models. Yet progress toward general point tracking remains constrained by limited high-quality data, as existing datasets often provide insufficient diversity and imperfect trajectory annotations. To this end, we introduce SynthVerse, a large-scale, diverse synthetic dataset specifically designed for point tracking. SynthVerse includes several new domains and object types missing from existing synthetic datasets, such as animated-film-style content, embodied manipulation, scene navigation, and articulated objects. SynthVerse substantially expands dataset diversity by covering a broader range of object categories and providing high-quality dynamic motions and interactions, enabling more robust training and evaluation for general point tracking. In addition, we establish a highly diverse point tracking benchmark to systematically evaluate state-of-the-art methods under broader domain shifts. Extensive experiments and analyses demonstrate that training with SynthVerse yields consistent improvements in generalization and reveal limitations of existing trackers under diverse settings.
What's in a Benchmark? The Case of SWE-Bench in Automated Program Repair
Venue: arXiv cs.SE 2026
Year: 2026

The rapid progress in Automated Program Repair (APR) has been fueled by advances in AI, particularly large language models (LLMs) and agent-based systems. SWE-Bench is a benchmark designed to evaluate repair systems using real issues mined from popular open-source Python repositories. Its public leaderboards-SWE-Bench Lite and Verified-have become central platforms for tracking progress and comparing solutions. In this paper, we present the first comprehensive study of these two leaderboards, examining who is submitting solutions, the products behind the submissions, the LLMs employed, and the openness of the approaches. We analyze 79 entries submitted to Lite leaderboard and 133 to Verified. Our results show that most entries on both leaderboards originate from industry, particularly small companies and large publicly traded companies. These submissions often achieve top results, although academic contributions-typically open source-also remain competitive. We also find a clear dominance of proprietary LLMs, especially Claude family, with state-of-the-art results on both leaderboards currently achieved by Claude 4 Sonnet. These findings offer insights into the SWE-Bench ecosystem that can guide greater transparency and diversity in future benchmark-driven research.
Can Theory-Informed Message Framing Drive Honest and Motivated Performance with Better Assessment Experiences in a Remote Assessment?
Venue: arXiv cs.HC 2026
Year: 2026

Remote unproctored assessments increasingly use messaging interventions to reduce cheating, but existing approaches lack theoretical grounding, focus narrowly on cheating suppression while overlooking performance and experience, and treat cheating as binary rather than continuous. This study examines whether messages based on 15 psychological concepts from self-determination, cognitive dissonance, social norms, and self-efficacy theories can reduce cheating while preserving performance and experience. Through an expert workshop (N=5), we developed 45 theory-informed messages and tested them with online participants (N=1232) who completed an incentivized anagram task. Participants were classified as non-cheaters (0% items cheated), partial-cheaters (1-99% cheated), or full-cheaters (100% cheated). Results show that concept-based messages reduced full-cheating occurrence by 42% (33% to 19%), increased non-cheating by 19% (53% to 63%), with no negative effects on performance or experience across integrity groups. Surprisingly, messages grounded in different theoretical concepts produced virtually identical effects. Analyses of self-rated psychological mechanisms revealed that messages influenced multiple mechanisms simultaneously rather than their intended targets, though these mechanisms predicted behavior, performance, and experience. These findings show that causal pathways are more complex than current theories predict. Practically, integrity interventions using supportive motivation rather than rule enforcement can reduce cheating without impairing performance or experience.
Growth First, Care Second? Tracing the Landscape of LLM Value Preferences in Everyday Dilemmas
Venue: arXiv cs.CY 2026
Year: 2026

People increasingly seek advice online from both human peers and large language model (LLM)-based chatbots. Such advice rarely involves identifying a single correct answer; instead, it typically requires navigating trade-offs among competing values. We aim to characterize how LLMs navigate value trade-offs across different advice-seeking contexts. First, we examine the value trade-off structure underlying advice seeking using a curated dataset from four advice-oriented subreddits. Using a bottom-up approach, we inductively construct a hierarchical value framework by aggregating fine-grained values extracted from individual advice options into higher-level value categories. We construct value co-occurrence networks to characterize how values co-occur within dilemmas and find substantial heterogeneity in value trade-off structures across advice-seeking contexts: a women-focused subreddit exhibits the highest network density, indicating more complex value conflicts; women's, men's, and friendship-related subreddits exhibit highly correlated value-conflict patterns centered on security-related tensions (security vs. respect/connection/commitment); by contrast, career advice forms a distinct structure where security frequently clashes with self-actualization and growth. We then evaluate LLM value preferences against these dilemmas and find that, across models and contexts, LLMs consistently prioritize values related to Exploration & Growth over Benevolence & Connection. This systemically skewed value orientation highlights a potential risk of value homogenization in AI-mediated advice, raising concerns about how such systems may shape decision-making and normative outcomes at scale.
A Framework of Critical Success Factors for Agile Software Development
Venue: arXiv cs.SE 2026
Year: 2026

Despite the popularity of Agile software development, achieving consistent project success remains challenging. This systematic literature review identifies critical success factors (CSFs) in Agile projects by analyzing 53 primary studies. Employing thematic synthesis with content analysis, our analysis yielded 21 CSFs categorized into five themes: organizational, people, technical, process, and project. Team effectiveness and project management emerged as the most frequently cited CSFs, highlighting the importance of people and process factors. These interpreted themes and factors contributed to the development of a theoretical framework to identify how these factors contribute to project success. This study offers valuable insights for researchers and practitioners, guiding future research to validate these findings and test the proposed framework using quantitative methods.
SALAD-Pan: Sensor-Agnostic Latent Adaptive Diffusion for Pan-Sharpening
Venue: arXiv cs.CV 2026
Year: 2026

Recently, diffusion models bring novel insights for Pan-sharpening and notably boost fusion precision. However, most existing models perform diffusion in the pixel space and train distinct models for different multispectral (MS) imagery, suffering from high latency and sensor-specific limitations. In this paper, we present SALAD-Pan, a sensor-agnostic latent space diffusion method for efficient pansharpening. Specifically, SALAD-Pan trains a band-wise single-channel VAE to encode high-resolution multispectral (HRMS) into compact latent representations, supporting MS images with various channel counts and establishing a basis for acceleration. Then spectral physical properties, along with PAN and MS images, are injected into the diffusion backbone through unidirectional and bidirectional interactive control structures respectively, achieving high-precision fusion in the diffusion process. Finally, a lightweight cross-spectral attention module is added to the central layer of diffusion model, reinforcing spectral connections to boost spectral consistency and further elevate fusion precision. Experimental results on GaoFen-2, QuickBird, and WorldView-3 demonstrate that SALAD-Pan outperforms state-of-the-art diffusion-based methods across all three datasets, attains a 2-3x inference speedup, and exhibits robust zero-shot (cross-sensor) capability.
ProAgentBench: Evaluating LLM Agents for Proactive Assistance with Real-World Data
Venue: arXiv cs.HC 2026
Year: 2026

Proactive agents that anticipate user intentions without explicit prompts represent a significant evolution in human-AI interaction, promising to reduce cognitive load and streamline workflows. However, existing datasets suffer from two critical deficiencies: (1) reliance on LLM-synthesized data that fails to capture authentic human decision-making patterns, and (2) focus on isolated tasks rather than continuous workflows, missing the pre-assistance behavioral context essential for learning proactive intervention signals. To address these gaps, we introduce ProAgentBench, a rigorous benchmark for proactive agents in working scenarios. Our contributions include: (1) a hierarchical task framework that decomposes proactive assistance into timing prediction and assist content generation; (2) a privacy-compliant dataset with 28,000+ events from 500+ hours of real user sessions, preserving bursty interaction patterns (burstiness B=0.787) absent in synthetic data; and (3) extensive experiments that evaluates LLM- and VLM-based baselines. Numerically, we showed that long-term memory and historical context significantly enhance prediction accuracy, while real-world training data substantially outperforms synthetic alternatives. We release our dataset and code at https://anonymous.4open.science/r/ProAgentBench-6BC0.
The Supportiveness-Safety Tradeoff in LLM Well-Being Agents
Venue: arXiv cs.HC 2026
Year: 2026

Large language models (LLMs) are being integrated into socially assistive robots (SARs) and other conversational agents providing mental health and well-being support. These agents are often designed to sound empathic and supportive in order to maximize user's engagement, yet it remains unclear how increasing the level of supportive framing in system prompts influences safety relevant behavior. We evaluated 6 LLMs across 3 system prompts with varying levels of supportiveness on 80 synthetic queries spanning 4 well-being domains (1440 responses). An LLM judge framework, validated against human ratings, assessed safety and care quality. Moderately supportive prompts improved empathy and constructive support while maintaining safety. In contrast, strongly validating prompts significantly degraded safety and, in some cases, care across all domains, with substantial variation across models. We discuss implications for prompt design, model selection, and domain specific safeguards in SARs deployment.
Greedy-Gnorm: A Gradient Matrix Norm-Based Alternative to Attention Entropy for Head Pruning
Venue: arXiv cs.LG 2026
Year: 2026

Attention head pruning has emerged as an effective technique for transformer model compression, an increasingly important goal in the era of Green AI. However, existing pruning methods often rely on static importance scores, which fail to capture the evolving role of attention heads during iterative removal. We propose Greedy-Gradient norm (Greedy-Gnorm), a novel head pruning algorithm that dynamically recalculates head importance after each pruning step. Specifically, each head is scored by the elementwise product of the l2-norms of its Q/K/V gradient blocks, as estimated from a hold-out validation set and updated at every greedy iteration. This dynamic approach to scoring mitigates against stale rankings and better reflects gradient-informed importance as pruning progresses. Extensive experiments on BERT, ALBERT, RoBERTa, and XLM-RoBERTa demonstrate that Greedy-Gnorm consistently preserves accuracy under substantial head removal, outperforming attention entropy. By effectively reducing model size while maintaining task performance, Greedy-Gnorm offers a promising step toward more energy-efficient transformer model deployment.
EgoActor: Grounding Task Planning into Spatial-aware Egocentric Actions for Humanoid Robots via Visual-Language Models
Venue: arXiv cs.RO 2026
Year: 2026

Deploying humanoid robots in real-world settings is fundamentally challenging, as it demands tight integration of perception, locomotion, and manipulation under partial-information observations and dynamically changing environments. As well as transitioning robustly between sub-tasks of different types. Towards addressing these challenges, we propose a novel task - EgoActing, which requires directly grounding high-level instructions into various, precise, spatially aware humanoid actions. We further instantiate this task by introducing EgoActor, a unified and scalable vision-language model (VLM) that can predict locomotion primitives (e.g., walk, turn, move sideways, change height), head movements, manipulation commands, and human-robot interactions to coordinate perception and execution in real-time. We leverage broad supervision over egocentric RGB-only data from real-world demonstrations, spatial reasoning question-answering, and simulated environment demonstrations, enabling EgoActor to make robust, context-aware decisions and perform fluent action inference (under 1s) with both 8B and 4B parameter models. Extensive evaluations in both simulated and real-world environments demonstrate that EgoActor effectively bridges abstract task planning and concrete motor execution, while generalizing across diverse tasks and unseen environments.
Algebraic and Arithmetic Attributes of Hypergeometric Functions in SageMath
Venue: arXiv cs.SC 2026
Year: 2026

We report on implementations for algorithms treating algebraic and arithmetic properties of hypergeometric functions in the computer algebra system SageMath. We treat hypergeometric series over the rational numbers, over finite fields, and over the p-adics. Among other things, we provide implementations deciding algebraicity, computing valuations, and computing minimal polynomials in positive characteristic.
Forget to Generalize: Iterative Adaptation for Generalization in Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

The Web is naturally heterogeneous with user devices, geographic regions, browsing patterns, and contexts all leading to highly diverse, unique datasets. Federated Learning (FL) is an important paradigm for the Web because it enables privacy-preserving, collaborative machine learning across diverse user devices, web services and clients without needing to centralize sensitive data. However, its performance degrades severely under non-IID client distributions that is prevalent in real-world web systems. In this work, we propose a new training paradigm - Iterative Federated Adaptation (IFA) - that enhances generalization in heterogeneous federated settings through generation-wise forget and evolve strategy. Specifically, we divide training into multiple generations and, at the end of each, select a fraction of model parameters (a) randomly or (b) from the later layers of the model and reinitialize them. This iterative forget and evolve schedule allows the model to escape local minima and preserve globally relevant representations. Extensive experiments on CIFAR-10, MIT-Indoors, and Stanford Dogs datasets show that the proposed approach improves global accuracy, especially when the data cross clients are Non-IID. This method can be implemented on top any federated algorithm to improve its generalization performance. We observe an average of 21.5%improvement across datasets. This work advances the vision of scalable, privacy-preserving intelligence for real-world heterogeneous and distributed web systems.
Dual Mind World Model Inspired Network Digital Twin for Access Scheduling
Venue: arXiv cs.NI 2026
Year: 2026

Emerging networked systems such as industrial IoT and real-time cyber-physical infrastructures demand intelligent scheduling strategies capable of adapting to dynamic traffic, deadlines, and interference constraints. In this work, we present a novel Digital Twin-enabled scheduling framework inspired by Dual Mind World Model (DMWM) architecture, for learning-informed and imagination-driven network control. Unlike conventional rule-based or purely data-driven policies, the proposed DMWM combines short-horizon predictive planning with symbolic model-based rollout, enabling the scheduler to anticipate future network states and adjust transmission decisions accordingly. We implement the framework in a configurable simulation testbed and benchmark its performance against traditional heuristics and reinforcement learning baselines under varied traffic conditions. Our results show that DMWM achieves superior performance in bursty, interference-limited, and deadline-sensitive environments, while maintaining interpretability and sample efficiency. The proposed design bridges the gap between network-level reasoning and low-overhead learning, marking a step toward scalable and adaptive NDT-based network optimization.
Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration
Venue: arXiv cs.AI 2026
Year: 2026

For the past decade, the trajectory of generative artificial intelligence (AI) has been dominated by a model-centric paradigm driven by scaling laws. Despite significant leaps in visual fidelity, this approach has encountered a ``usability ceiling'' manifested as the Intent-Execution Gap (i.e., the fundamental disparity between a creator's high-level intent and the stochastic, black-box nature of current single-shot models). In this paper, inspired by the Vibe Coding, we introduce the \textbf{Vibe AIGC}, a new paradigm for content generation via agentic orchestration, which represents the autonomous synthesis of hierarchical multi-agent workflows. Under this paradigm, the user's role transcends traditional prompt engineering, evolving into a Commander who provides a Vibe, a high-level representation encompassing aesthetic preferences, functional logic, and etc. A centralized Meta-Planner then functions as a system architect, deconstructing this ``Vibe'' into executable, verifiable, and adaptive agentic pipelines. By transitioning from stochastic inference to logical orchestration, Vibe AIGC bridges the gap between human imagination and machine execution. We contend that this shift will redefine the human-AI collaborative economy, transforming AI from a fragile inference engine into a robust system-level engineering partner that democratizes the creation of complex, long-horizon digital assets.
PEPR: Privileged Event-based Predictive Regularization for Domain Generalization
Venue: arXiv cs.CV 2026
Year: 2026

Deep neural networks for visual perception are highly susceptible to domain shift, which poses a critical challenge for real-world deployment under conditions that differ from the training data. To address this domain generalization challenge, we propose a cross-modal framework under the learning using privileged information (LUPI) paradigm for training a robust, single-modality RGB model. We leverage event cameras as a source of privileged information, available only during training. The two modalities exhibit complementary characteristics: the RGB stream is semantically dense but domain-dependent, whereas the event stream is sparse yet more domain-invariant. Direct feature alignment between them is therefore suboptimal, as it forces the RGB encoder to mimic the sparse event representation, thereby losing semantic detail. To overcome this, we introduce Privileged Event-based Predictive Regularization (PEPR), which reframes LUPI as a predictive problem in a shared latent space. Instead of enforcing direct cross-modal alignment, we train the RGB encoder with PEPR to predict event-based latent features, distilling robustness without sacrificing semantic richness. The resulting standalone RGB model consistently improves robustness to day-to-night and other domain shifts, outperforming alignment-based baselines across object detection and semantic segmentation.
Focus-LIME: Surgical Interpretation of Long-Context Large Language Models via Proxy-Based Neighborhood Selection
Venue: arXiv cs.CL 2026
Year: 2026

As Large Language Models (LLMs) scale to handle massive context windows, achieving surgical feature-level interpretation is essential for high-stakes tasks like legal auditing and code debugging. However, existing local model-agnostic explanation methods face a critical dilemma in these scenarios: feature-based methods suffer from attribution dilution due to high feature dimensionality, thus failing to provide faithful explanations. In this paper, we propose Focus-LIME, a coarse-to-fine framework designed to restore the tractability of surgical interpretation. Focus-LIME utilizes a proxy model to curate the perturbation neighborhood, allowing the target model to perform fine-grained attribution exclusively within the optimized context. Empirical evaluations on long-context benchmarks demonstrate that our method makes surgical explanations practicable and provides faithful explanations to users.