OpenRaiser/Intern-Atlas huggingface dataset:
Micro-splatting: Multistage Isotropy-informed Covariance Regularization Optimization for High-Fidelity 3D Gaussian Splatting
Venue: arXiv cs.GR 2025
Year: 2025

High-fidelity 3D Gaussian Splatting methods excel at capturing fine textures but often overlook model compactness, resulting in massive splat counts, bloated memory, long training, and complex post-processing. We present Micro-Splatting: Two-Stage Adaptive Growth and Refinement, a unified, in-training pipeline that preserves visual detail while drastically reducing model complexity without any post-processing or auxiliary neural modules. In Stage I (Growth), we introduce a trace-based covariance regularization to maintain near-isotropic Gaussians, mitigating low-pass filtering in high-frequency regions and improving spherical-harmonic color fitting. We then apply gradient-guided adaptive densification that subdivides splats only in visually complex regions, leaving smooth areas sparse. In Stage II (Refinement), we prune low-impact splats using a simple opacity-scale importance score and merge redundant neighbors via lightweight spatial and feature thresholds, producing a lean yet detail-rich model. On four object-centric benchmarks, Micro-Splatting reduces splat count and model size by up to 60% and shortens training by 20%, while matching or surpassing state-of-the-art PSNR, SSIM, and LPIPS in real-time rendering. These results demonstrate that Micro-Splatting delivers both compactness and high fidelity in a single, efficient, end-to-end framework.
Building Proactive and Instant-Reactive Safety Designs to Address Harassment in Social Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

Social Virtual Reality (VR) games offer immersive socialization experiences but pose significant challenges of harassment. Common solutions, such as reporting and moderation, address harassment after it happens but fail to prevent or stop harassment in the moment. In this study, we explore and design proactive and instant-reactive safety designs to mitigate harassment in social VR. Proactive designs prevent harassment from occurring, while instant-reactive designs minimize harm during incidents. We explore three directions for design: user-initiated personal bubbles, clarifying social norms, and encouraging bystander intervention. Through an iterative process, we first conducted a formative interview study to determine design goals for making these features effective, fit user needs, and robust to manipulation. We then implemented Puffer, an integrated safety system that includes a suite of proactive and instant-reactive features, as a social VR prototype. From an evaluation using simulated scenarios with participants, we find evidence that Puffer can help protect players during emergencies, foster prosocial norms, and create more positive social interactions. We conclude by discussing how system safety features can be designed to complement existing proactive and instant-reactive strategies, particularly for people with marginalized identities.
Video Flow as Time Series: Discovering Temporal Consistency and Variability for VideoQA
Venue: arXiv cs.CV 2025
Year: 2025

Video Question Answering (VideoQA) is a complex video-language task that demands a sophisticated understanding of both visual content and temporal dynamics. Traditional Transformer-style architectures, while effective in integrating multimodal data, often simplify temporal dynamics through positional encoding and fail to capture non-linear interactions within video sequences. In this paper, we introduce the Temporal Trio Transformer (T3T), a novel architecture that models time consistency and time variability. The T3T integrates three key components: Temporal Smoothing (TS), Temporal Difference (TD), and Temporal Fusion (TF). The TS module employs Brownian Bridge for capturing smooth, continuous temporal transitions, while the TD module identifies and encodes significant temporal variations and abrupt changes within the video content. Subsequently, the TF module synthesizes these temporal features with textual cues, facilitating a deeper contextual understanding and response accuracy. The efficacy of the T3T is demonstrated through extensive testing on multiple VideoQA benchmark datasets. Our results underscore the importance of a nuanced approach to temporal modeling in improving the accuracy and depth of video-based question answering.
PASE: Phoneme-Aware Speech Encoder to Improve Lip Sync Accuracy for Talking Head Synthesis
Venue: arXiv cs.GR 2025
Year: 2025

Recent talking head synthesis works typically adopt speech features extracted from large-scale pre-trained acoustic models. However, the intrinsic many-to-many relationship between speech and lip motion causes phoneme-viseme alignment ambiguity, leading to inaccurate and unstable lips. To further improve lip sync accuracy, we propose PASE (Phoneme-Aware Speech Encoder), a novel speech representation model that bridges the gap between phonemes and visemes. PASE explicitly introduces phoneme embeddings as alignment anchors and employs a contrastive alignment module to enhance the discriminability between corresponding audio-visual pairs. In addition, a prediction and reconstruction task is designed to improve robustness under noise and partial modality absence. Experimental results show PASE significantly improves lip sync accuracy and achieves state-of-the-art performance across both NeRF- and 3DGS-based rendering frameworks, outperforming conventional methods based on acoustic features by 13.7 % and 14.2 %, respectively. Importantly, PASE can be seamlessly integrated into diverse talking head pipelines to improve the lip sync accuracy without architectural modifications.
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-Gradients
Venue: arXiv cs.LG 2025
Year: 2025

The right to be forgotten is a fundamental principle of privacy-preserving regulations and extends to Machine Learning (ML) paradigms such as Federated Learning (FL). While FL enhances privacy by enabling collaborative model training without sharing private data, trained models still retain the influence of training data. Federated Unlearning (FU) methods recently proposed often rely on impractical assumptions for real-world FL deployments, such as storing client update histories or requiring access to a publicly available dataset. To address these constraints, this paper introduces a novel method that leverages negated Pseudo-gradients Updates for Federated Unlearning (PUF). Our approach only uses standard client model updates, which are employed during regular FL rounds, and interprets them as pseudo-gradients. When a client needs to be forgotten, we apply the negation of their pseudo-gradients, appropriately scaled, to the global model. Unlike state-of-the-art mechanisms, PUF seamlessly integrates with FL workflows, incurs no additional computational and communication overhead beyond standard FL rounds, and supports concurrent unlearning requests. We extensively evaluated the proposed method on two well-known benchmark image classification datasets (CIFAR-10 and CIFAR-100) and a real-world medical imaging dataset for segmentation (ProstateMRI), using three different neural architectures: two residual networks and a vision transformer. The experimental results across various settings demonstrate that PUF achieves state-of-the-art forgetting effectiveness and recovery time, without relying on any additional assumptions.
End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems
Venue: arXiv cs.CL 2025
Year: 2025

Large-scale coreference resolution presents a significant challenge in natural language processing, necessitating a balance between efficiency and accuracy. In response to this challenge, we introduce an End-to-End Neural Coreference Resolution system tailored for large-scale applications. Our system efficiently identifies and resolves coreference links in text, ensuring minimal computational overhead without compromising on performance. By utilizing advanced neural network architectures, we incorporate various contextual embeddings and attention mechanisms, which enhance the quality of predictions for coreference pairs. Furthermore, we apply optimization strategies to accelerate processing speeds, making the system suitable for real-world deployment. Extensive evaluations conducted on benchmark datasets demonstrate that our model achieves improved accuracy compared to existing approaches, while effectively maintaining rapid inference times. Rigorous testing confirms the ability of our system to deliver precise coreference resolutions efficiently, thereby establishing a benchmark for future advancements in this field.
Capacity Region for Covert Secret Key Generation over Multiple Access Channels
Venue: arXiv cs.IT 2025
Year: 2025

We study covert secret key generation over a binary-input two-user multiple access channel with one-way public discussion and derive bounds on the capacity region. Specifically, in this problem, there are three legitimate parties: Alice, Bob and Charlie. The goal is to allow Charlie to generate a secret key with Alice and another secret key with Bob, reliably, secretly and covertly. Reliability ensures that the key generated by Alice and Charlie is the same and the key generated by Bob and Charlie is the same. Secrecy ensures that the secret keys generated are only known to specific legitimate parties. Covertness ensures that the key generation process is undetectable by a warden Willie. As a corollary of our result, we establish bounds on the capacity region of wiretap secret key generation without the covertness constraint and discuss the impact of covertness. Our results generalize the point-to-point result of Tahmasbi and Bloch (TIFS 2020) to the setting of multiterminal communication.
Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Human Activity Recognition (HAR) primarily relied on traditional RGB cameras to achieve high-performance activity recognition. However, the challenging factors in real-world scenarios, such as insufficient lighting and rapid movements, inevitably degrade the performance of RGB cameras. To address these challenges, biologically inspired event cameras offer a promising solution to overcome the limitations of traditional RGB cameras. In this work, we rethink human activity recognition by combining the RGB and event cameras. The first contribution is the proposed large-scale multi-modal RGB-Event human activity recognition benchmark dataset, termed HARDVS 2.0, which bridges the dataset gaps. It contains 300 categories of everyday real-world actions with a total of 107,646 paired videos covering various challenging scenarios. Inspired by the physics-informed heat conduction model, we propose a novel multi-modal heat conduction operation framework for effective activity recognition, termed MMHCO-HAR. More in detail, given the RGB frames and event streams, we first extract the feature embeddings using a stem network. Then, multi-modal Heat Conduction blocks are designed to fuse the dual features, the key module of which is the multi-modal Heat Conduction Operation layer. We integrate RGB and event embeddings through a multi-modal DCT-IDCT layer while adaptively incorporating the thermal conductivity coefficient via FVEs into this module. After that, we propose an adaptive fusion module based on a policy routing strategy for high-performance classification. Comprehensive experiments demonstrate that our method consistently performs well, validating its effectiveness and robustness. The source code and benchmark dataset will be released on https://github.com/Event-AHU/HARDVS/tree/HARDVSv2
Adaptive Substructure-Aware Expert Model for Molecular Property Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Molecular property prediction is essential for applications such as drug discovery and toxicity assessment. While Graph Neural Networks (GNNs) have shown promising results by modeling molecules as molecular graphs, their reliance on data-driven learning limits their ability to generalize, particularly in the presence of data imbalance and diverse molecular substructures. Existing methods often overlook the varying contributions of different substructures to molecular properties, treating them uniformly. To address these challenges, we propose ASE-Mol, a novel GNN-based framework that leverages a Mixture-of-Experts (MoE) approach for molecular property prediction. ASE-Mol incorporates BRICS decomposition and significant substructure awareness to dynamically identify positive and negative substructures. By integrating a MoE architecture, it reduces the adverse impact of negative motifs while improving adaptability to positive motifs. Experimental results on eight benchmark datasets demonstrate that ASE-Mol achieves state-of-the-art performance, with significant improvements in both accuracy and interpretability.
Identifying and Replicating Code Patterns Driving Performance Regressions in Software Systems
Venue: arXiv cs.SE 2025
Year: 2025

Context: Performance regressions negatively impact execution time and memory usage of software systems. Nevertheless, there is a lack of systematic methods to evaluate the effectiveness of performance test suites. Performance mutation testing, which introduces intentional defects (mutants) to measure and enhance fault-detection capabilities, is promising but underexplored. A key challenge is understanding if generated mutants accurately reflect real-world performance issues. Goal: This study evaluates and extends mutation operators for performance testing. Its objectives include (i) collecting existing performance mutation operators, (ii) introducing new operators from real-world code changes that impact performance, and (iii) evaluating these operators on real-world systems to see if they effectively degrade performance. Method: To this aim, we will (i) review the literature to identify performance mutation operators, (ii) conduct a mining study to extract patterns of code changes linked to performance regressions, (iii) propose new mutation operators based on these patterns, and (iv) apply and evaluate the operators to assess their effectiveness in exposing performance degradations. Expected Outcomes: We aim to provide an enriched set of mutation operators for performance testing, helping developers and researchers identify harmful coding practices and design better strategies to detect and prevent performance regressions.
Agent Guide: A Simple Agent Behavioral Watermarking Framework
Venue: arXiv cs.AI 2025
Year: 2025

The increasing deployment of intelligent agents in digital ecosystems, such as social media platforms, has raised significant concerns about traceability and accountability, particularly in cybersecurity and digital content protection. Traditional large language model (LLM) watermarking techniques, which rely on token-level manipulations, are ill-suited for agents due to the challenges of behavior tokenization and information loss during behavior-to-action translation. To address these issues, we propose Agent Guide, a novel behavioral watermarking framework that embeds watermarks by guiding the agent's high-level decisions (behavior) through probability biases, while preserving the naturalness of specific executions (action). Our approach decouples agent behavior into two levels, behavior (e.g., choosing to bookmark) and action (e.g., bookmarking with specific tags), and applies watermark-guided biases to the behavior probability distribution. We employ a z-statistic-based statistical analysis to detect the watermark, ensuring reliable extraction over multiple rounds. Experiments in a social media scenario with diverse agent profiles demonstrate that Agent Guide achieves effective watermark detection with a low false positive rate. Our framework provides a practical and robust solution for agent watermarking, with applications in identifying malicious agents and protecting proprietary agent systems.
Rethinking Review Citations: Impact on Scientific Integrity
Venue: arXiv cs.DL 2025
Year: 2025

The proliferation of surveys and review articles in academic journals has impacted citation metrics like impact factor and h-index, skewing evaluations of journal and researcher quality. This work investigates the implications of this trend, focusing on the field of Computer Science, where a notable increase in review publications has led to inflated citation counts and rankings. While reviews serve as valuable literature overviews, they should not overshadow the primary goal of research -to advance scientific knowledge through original contributions. We advocate for prioritizing citations of primary research in journal articles to uphold citation integrity and ensure fair recognition of substantive contributions. This approach preserves the reliability of citation-based metrics and supports genuine scientific advancement.
Balancing long- and short-term dynamics for the modeling of saliency in videos
Venue: arXiv cs.CV 2025
Year: 2025

The role of long- and short-term dynamics towards salient object detection in videos is under-researched. We present a Transformer-based approach to learn a joint representation of video frames and past saliency information. Our model embeds long- and short-term information to detect dynamically shifting saliency in video. We provide our model with a stream of video frames and past saliency maps, which acts as a prior for the next prediction, and extract spatiotemporal tokens from both modalities. The decomposition of the frame sequence into tokens lets the model incorporate short-term information from within the token, while being able to make long-term connections between tokens throughout the sequence. The core of the system consists of a dual-stream Transformer architecture to process the extracted sequences independently before fusing the two modalities. Additionally, we apply a saliency-based masking scheme to the input frames to learn an embedding that facilitates the recognition of deviations from previous outputs. We observe that the additional prior information aids in the first detection of the salient location. Our findings indicate that the ratio of spatiotemporal long- and short-term features directly impacts the model's performance. While increasing the short-term context is beneficial up to a certain threshold, the model's performance greatly benefits from an expansion of the long-term context.
Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment
Venue: arXiv cs.RO 2025
Year: 2025

The Autonomy of Unmanned Aerial Vehicles (UAVs) in indoor environments poses significant challenges due to the lack of reliable GPS signals in enclosed spaces such as warehouses, factories, and indoor facilities. Micro Aerial Vehicles (MAVs) are preferred for navigating in these complex, GPS-denied scenarios because of their agility, low power consumption, and limited computational capabilities. In this paper, we propose a Reinforcement Learning based Deep-Proximal Policy Optimization (D-PPO) algorithm to enhance realtime navigation through improving the computation efficiency. The end-to-end network is trained in 3D realistic meta-environments created using the Unreal Engine. With these trained meta-weights, the MAV system underwent extensive experimental trials in real-world indoor environments. The results indicate that the proposed method reduces computational latency by 91\% during training period without significant degradation in performance. The algorithm was tested on a DJI Tello drone, yielding similar results.
Learning Verified Monitors for Hidden Markov Models
Venue: arXiv cs.FL 2025
Year: 2025

Runtime monitors assess whether a system is in an unsafe state based on a stream of observations. We study the problem where the system is subject to probabilistic uncertainty and described by a hidden Markov model. A stream of observations is then unsafe if the probability of being in an unsafe state is above a threshold. A correct monitor recognizes the set of unsafe observations. The key contribution of this paper is the first correct-by-construction synthesis method for such monitors, represented as finite automata. The contribution combines four ingredients: First, we establish the coNP-hardness of checking whether an automaton is a correct monitor, i.e., a monitor without misclassifications. Second, we provide a reduction that reformulates the search for misclassifications into a standard probabilistic system synthesis problem. Third, we integrate the verification routine into an active automata learning routine to synthesize correct monitors. Fourth, we provide a prototypical implementation that shows the feasibility and limitations of the approach on a series of benchmarks.
A Corrector-aided Look-ahead Distance-based Guidance for Online Reference Path Following with an Efficient Mid-course Guidance Strategy
Venue: arXiv cs.RO 2025
Year: 2025

Efficient path-following is crucial in most of the applications of autonomous vehicles (UxV). Among various guidance strategies presented in literature, the look-ahead distance ($L_1$)-based nonlinear guidance has received significant attention due to its ease in implementation and ability to maintain a low cross-track error while following simpler reference paths and generating bounded lateral acceleration commands. However, the constant value of $L_1$ becomes problematic when the UxV is far away from the reference path and also produces higher cross-track error while following complex reference paths having high variation in radius of curvature. To address these challenges, the notion of look-ahead distance is leveraged in a novel way to develop a two-phase guidance strategy. Initially, when the UxV is far from the reference path, an optimized $L_1$ selection strategy is developed to guide the UxV towards the vicinity of the start point of the reference path, while maintaining minimal lateral acceleration command. Once the vehicle reaches a close neighborhood of the reference path, a novel notion of corrector point is incorporated in the constant $L_1$-based guidance scheme to generate the guidance command that effectively reduces the root mean square of the cross-track error and lateral acceleration requirement thereafter. Simulation results validate satisfactory performance of this proposed corrector point and look-ahead point pair-based guidance strategy, along with the developed mid-course guidance scheme. Also, its superiority over the conventional constant $L_1$ guidance scheme is established by simulation studies over different initial condition scenarios.