OpenRaiser/Intern-Atlas huggingface dataset:
Towards a Unified System of Representation for Continuity and Discontinuity in Natural Language
Venue: arXiv cs.CL 2025
Year: 2025

Syntactic discontinuity is a grammatical phenomenon in which a constituent is split into more than one part because of the insertion of an element which is not part of the constituent. This is observed in many languages across the world such as Turkish, Russian, Japanese, Warlpiri, Navajo, Hopi, Dyirbal, Yidiny etc. Different formalisms/frameworks in current linguistic theory approach the problem of discontinuous structures in different ways. Each framework/formalism has widely been viewed as an independent and non-converging system of analysis. In this paper, we propose a unified system of representation for both continuity and discontinuity in structures of natural languages by taking into account three formalisms, in particular, Phrase Structure Grammar (PSG) for its widely used notion of constituency, Dependency Grammar (DG) for its head-dependent relations, and Categorial Grammar (CG) for its focus on functor-argument relations. We attempt to show that discontinuous expressions as well as continuous structures can be analysed through a unified mathematical derivation incorporating the representations of linguistic structure in these three grammar formalisms.
Evaluating Sparse Autoencoders: From Shallow Design to Matching Pursuit
Venue: arXiv cs.LG 2025
Year: 2025

Sparse autoencoders (SAEs) have recently become central tools for interpretability, leveraging dictionary learning principles to extract sparse, interpretable features from neural representations whose underlying structure is typically unknown. This paper evaluates SAEs in a controlled setting using MNIST, which reveals that current shallow architectures implicitly rely on a quasi-orthogonality assumption that limits the ability to extract correlated features. To move beyond this, we compare them with an iterative SAE that unrolls Matching Pursuit (MP-SAE), enabling the residual-guided extraction of correlated features that arise in hierarchical settings such as handwritten digit generation while guaranteeing monotonic improvement of the reconstruction as more atoms are selected.
Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Venue: arXiv cs.HC 2025
Year: 2025

Effective teamwork is essential across diverse domains. During the team formation stage, a key challenge is forming teams that effectively balance user preferences with task objectives to enhance overall team satisfaction. In the team performing stage, maintaining cohesion and engagement is critical for sustaining high team performance. However, existing computational tools and algorithms for team optimization often rely on static data inputs, narrow algorithmic objectives, or solutions tailored for specific contexts, failing to account for the dynamic interplay of team members personalities, evolving goals, and changing individual preferences. Therefore, teams may encounter member dissatisfaction, as purely algorithmic assignments can reduce members commitment to team goals or experience suboptimal engagement due to the absence of timely, personalized guidance to help members adjust their behaviors and interactions as team dynamics evolve. Ultimately, these challenges can lead to reduced overall team performance. My Ph.D. dissertation aims to develop AI-augmented team optimization frameworks and practical systems that enhance team satisfaction, engagement, and performance. First, I propose a team formation framework that leverages a multi-armed bandit algorithm to iteratively refine team composition based on user preferences, ensuring alignment between individual needs and collective team goals to enhance team satisfaction. Second, I introduce tAIfa (Team AI Feedback Assistant), an AI-powered system that utilizes large language models (LLMs) to deliver immediate, personalized feedback to both teams and individual members, enhancing cohesion and engagement. Finally, I present PuppeteerLLM, an LLM-based simulation framework that simulates multi-agent teams to model complex team dynamics within realistic environments, incorporating task-driven collaboration and long-term coordination.
Kinetics: Rethinking Test-Time Scaling Laws
Venue: arXiv cs.LG 2025
Year: 2025

We rethink test-time scaling laws from a practical efficiency perspective, revealing that the effectiveness of smaller models is significantly overestimated. Prior work, grounded in compute-optimality, overlooks critical memory access bottlenecks introduced by inference-time strategies (e.g., Best-of-$N$, long CoTs). Our holistic analysis, spanning models from 0.6B to 32B parameters, reveals a new Kinetics Scaling Law that better guides resource allocation by incorporating both computation and memory access costs. Kinetics Scaling Law suggests that test-time compute is more effective when used on models above a threshold than smaller ones. A key reason is that in TTS, attention, rather than parameter count, emerges as the dominant cost factor. Motivated by this, we propose a new scaling paradigm centered on sparse attention, which lowers per-token cost and enables longer generations and more parallel samples within the same resource budget. Empirically, we show that sparse attention models consistently outperform dense counterparts, achieving over 60 points gains in low-cost regimes and over 5 points gains in high-cost regimes for problem-solving accuracy on AIME, encompassing evaluations on state-of-the-art MoEs. These results suggest that sparse attention is essential and increasingly important with more computing invested, for realizing the full potential of test-time scaling where, unlike training, accuracy has yet to saturate as a function of computation, and continues to improve through increased generation. The code is available at https://github.com/Infini-AI-Lab/Kinetics.
Defurnishing with X-Ray Vision: Joint Removal of Furniture from Panoramas and Mesh
Venue: arXiv cs.CV 2025
Year: 2025

We present a pipeline for generating defurnished replicas of indoor spaces represented as textured meshes and corresponding multi-view panoramic images. To achieve this, we first segment and remove furniture from the mesh representation, extend planes, and fill holes, obtaining a simplified defurnished mesh (SDM). This SDM acts as an ``X-ray'' of the scene's underlying structure, guiding the defurnishing process. We extract Canny edges from depth and normal images rendered from the SDM. We then use these as a guide to remove the furniture from panorama images via ControlNet inpainting. This control signal ensures the availability of global geometric information that may be hidden from a particular panoramic view by the furniture being removed. The inpainted panoramas are used to texture the mesh. We show that our approach produces higher quality assets than methods that rely on neural radiance fields, which tend to produce blurry low-resolution images, or RGB-D inpainting, which is highly susceptible to hallucinations.
Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
Venue: arXiv cs.CR 2025
Year: 2025

Recent advancements in large language models (LLMs) have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. However, existing mitigation strategies primarily focus on reactively addressing jailbreak incidents after safety guardrails have been compromised, removing harmful gradients during fine-tuning, or continuously reinforcing safety alignment throughout fine-tuning. As such, they tend to overlook a critical upstream factor: the role of the original safety-alignment data. This paper therefore investigates the degradation of safety guardrails through the lens of representation similarity between upstream alignment datasets and downstream fine-tuning tasks. Our experiments demonstrate that high similarity between these datasets significantly weakens safety guardrails, making models more susceptible to jailbreaks. Conversely, low similarity between these two types of datasets yields substantially more robust models and thus reduces harmfulness score by up to 10.33%. By highlighting the importance of upstream dataset design in the building of durable safety guardrails and reducing real-world vulnerability to jailbreak attacks, these findings offer actionable insights for fine-tuning service providers.
AI-Driven Dynamic Firewall Optimization Using Reinforcement Learning for Anomaly Detection and Prevention
Venue: arXiv cs.CR 2025
Year: 2025

The growing complexity of cyber threats has rendered static firewalls increasingly ineffective for dynamic, real-time intrusion prevention. This paper proposes a novel AI-driven dynamic firewall optimization framework that leverages deep reinforcement learning (DRL) to autonomously adapt and update firewall rules in response to evolving network threats. Our system employs a Markov Decision Process (MDP) formulation, where the RL agent observes network states, detects anomalies using a hybrid LSTM-CNN model, and dynamically modifies firewall configurations to mitigate risks. We train and evaluate our framework on the NSL-KDD and CIC-IDS2017 datasets using a simulated software-defined network environment. Results demonstrate significant improvements in detection accuracy, false positive reduction, and rule update latency when compared to traditional signature- and behavior-based firewalls. The proposed method provides a scalable, autonomous solution for enhancing network resilience against complex attack vectors in both enterprise and critical infrastructure settings.
Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review
Venue: arXiv cs.SE 2025
Year: 2025

This survey investigates how classical software design patterns can enhance the reliability and scalability of communication in Large Language Model (LLM)-driven agentic AI systems, focusing particularly on the Model Context Protocol (MCP). It examines the foundational architectures of LLM-based agents and their evolution from isolated operation to sophisticated, multi-agent collaboration, addressing key communication hurdles that arise in this transition. The study revisits well-established patterns, including Mediator, Observer, Publish-Subscribe, and Broker, and analyzes their relevance in structuring agent interactions within MCP-compliant frameworks. To clarify these dynamics, the article provides conceptual schematics and formal models that map out communication pathways and optimize data flow. It further explores architectural variations suited to different degrees of agent autonomy and system complexity. Real-world applications in domains such as real-time financial processing and investment banking are discussed, illustrating how these patterns and MCP can meet specific operational demands. The article concludes by outlining open challenges, potential security risks, and promising directions for advancing robust, interoperable, and scalable multi-agent LLM ecosystems.
Game Theory in Social Media: A Stackelberg Model of Collaboration, Conflict, and Algorithmic Incentives
Venue: arXiv cs.GT 2025
Year: 2025

Social media platforms are ecosystems in which many decisions are constantly made for the benefit of the creators in order to maximize engagement, which leads to a maximization of income. The decisions, ranging from collaboration to public conflict or ``beefing,'' are heavily influenced by social media algorithms, viewer preferences, and sponsor risk. This paper models this interaction as a Stackelberg game in which the algorithm is the leader, setting exposure and reward rules, and the content creators are the followers, who optimize their content to maximize engagement. It focuses on two influencer strategies of collaborating and beefing. Viewer preferences are modeled indirectly through the algorithm's utility function, which rewards engagement metrics like click-through rate and watch time. Our simplified game-theoretic model demonstrates how different algorithmic priorities can shift creator strategies and provides insight into the equilibrium dynamics of social media influence.
State Estimation and Control of Dynamic Systems from High-Dimensional Image Data
Venue: arXiv cs.CV 2025
Year: 2025

Accurate state estimation is critical for optimal policy design in dynamic systems. However, obtaining true system states is often impractical or infeasible, complicating the policy learning process. This paper introduces a novel neural architecture that integrates spatial feature extraction using convolutional neural networks (CNNs) and temporal modeling through gated recurrent units (GRUs), enabling effective state representation from sequences of images and corresponding actions. These learned state representations are used to train a reinforcement learning agent with a Deep Q-Network (DQN). Experimental results demonstrate that our proposed approach enables real-time, accurate estimation and control without direct access to ground-truth states. Additionally, we provide a quantitative evaluation methodology for assessing the accuracy of the learned states, highlighting their impact on policy performance and control stability.
A Compendium of Autonomous Navigation using Object Detection and Tracking in Unmanned Aerial Vehicles
Venue: arXiv cs.CV 2025
Year: 2025

Unmanned Aerial Vehicles (UAVs) are one of the most revolutionary inventions of 21st century. At the core of a UAV lies the central processing system that uses wireless signals to control their movement. The most popular UAVs are quadcopters that use a set of four motors, arranged as two on either side with opposite spin. An autonomous UAV is called a drone. Drones have been in service in the US army since the 90's for covert missions critical to national security. It would not be wrong to claim that drones make up an integral part of the national security and provide the most valuable service during surveillance operations. While UAVs are controlled using wireless signals, there reside some challenges that disrupt the operation of such vehicles such as signal quality and range, real time processing, human expertise, robust hardware and data security. These challenges can be solved by programming UAVs to be autonomous, using object detection and tracking, through Computer Vision algorithms. Computer Vision is an interdisciplinary field that seeks the use of deep learning to gain a high-level understanding of digital images and videos for the purpose of automating the task of human visual system. Using computer vision, algorithms for detecting and tracking various objects can be developed suitable to the hardware so as to allow real time processing for immediate judgement. This paper attempts to review the various approaches several authors have proposed for the purpose of autonomous navigation of UAVs by through various algorithms of object detection and tracking in real time, for the purpose of applications in various fields such as disaster management, dense area exploration, traffic vehicle surveillance etc.
Talk2SAM: Text-Guided Semantic Enhancement for Complex-Shaped Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Segmenting objects with complex shapes, such as wires, bicycles, or structural grids, remains a significant challenge for current segmentation models, including the Segment Anything Model (SAM) and its high-quality variant SAM-HQ. These models often struggle with thin structures and fine boundaries, leading to poor segmentation quality. We propose Talk2SAM, a novel approach that integrates textual guidance to improve segmentation of such challenging objects. The method uses CLIP-based embeddings derived from user-provided text prompts to identify relevant semantic regions, which are then projected into the DINO feature space. These features serve as additional prompts for SAM-HQ, enhancing its ability to focus on the target object. Beyond improving segmentation accuracy, Talk2SAM allows user-controllable segmentation, enabling disambiguation of objects within a single bounding box based on textual input. We evaluate our approach on three benchmarks: BIG, ThinObject5K, and DIS5K. Talk2SAM consistently outperforms SAM-HQ, achieving up to +5.9\% IoU and +8.3\% boundary IoU improvements. Our results demonstrate that incorporating natural language guidance provides a flexible and effective means for precise object segmentation, particularly in cases where traditional prompt-based methods fail. The source code is available on GitHub: https://github.com/richlukich/Talk2SAM
IGSM: Improved Geometric and Sensitivity Matching for Finetuning Pruned Diffusion Models
Venue: arXiv cs.GR 2025
Year: 2025

Diffusion models achieve realistic outcomes across a wide range of generative tasks, but their high computational cost remains a major barrier to deployment. Model pruning has emerged as a promising strategy to reduce inference cost and enable lightweight diffusion models. While effective, pruned diffusion models are proned to quality reduction due to limited capacity. A key limitation of current pruning approaches is that pruned models are finetuned using the same objective as the dense model, typically denoising score matching (DSM). Since the dense model is accessible during finetuning, it warrants a more effective approach for knowledge transfer from the dense to the pruned model. Motivated by this aim, we revisit the finetuning stage and propose IGSM (\textbf{I}mproved \textbf{G}eometric and \textbf{S}ensitivity \textbf{M}atching), a general-purpose finetuning framework that introduces a second-order Jacobian projection loss inspired by Finite-Time Lyapunov Exponents (FTLE). IGSM efficiently captures and aligns the geometric and the temporal dynamics of pruned models with their dense teachers using scalable second-order projections. Our approach is architecture-agnostic and applies to both U-Net- and Transformer-based diffusion models. Experiments on CIFAR-10, CelebA, LSUN-Church, and LSUN-Bedroom show that IGSM consistently narrows the performance gap between pruned and dense models, substantially improving sample quality. Code is available on GitHub: https://github.com/FATE4869/IGSM-Official
Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning
Venue: arXiv cs.CV 2025
Year: 2025

Vision-based reinforcement learning requires efficient and robust representations of image-based observations, especially when the images contain distracting (task-irrelevant) elements such as shadows, clouds, and light. It becomes more important if those distractions are not exposed during training. We design a Self-Predictive Dynamics (SPD) method to extract task-relevant features efficiently, even in unseen observations after training. SPD uses weak and strong augmentations in parallel, and learns representations by predicting inverse and forward transitions across the two-way augmented versions. In a set of MuJoCo visual control tasks and an autonomous driving task (CARLA), SPD outperforms previous studies in complex observations, and significantly improves the generalization performance for unseen observations. Our code is available at https://github.com/unigary/SPD.
TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy
Venue: arXiv cs.CR 2025
Year: 2025

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, character-level noise, and entity obfuscation for differential privacy enforcement, and combines these techniques into a unified defense mechanism. Using a binary propaganda classification dataset, baseline transformer models (BERT, GPT-2) we achieved F1 scores of 0.89 and 0.90. Applying TRIDENT's third-tier defense yields a reduced but effective cumulative F1 of 0.83, demonstrating strong privacy protection across mobile ML deployments with minimal degradation.
PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) face significant challenges in edge deployment due to their massive parameter scale. Vector Quantization (VQ), a clustering-based quantization method, serves as a prevalent solution to this issue for its extremely low-bit (even at 2-bit) and considerable accuracy. Since a vector is a quantity in mathematics and physics that has both direction and magnitude, existing VQ works typically quantize them in a coupled manner. However, we find that direction exhibits significantly greater sensitivity to quantization compared to the magnitude. For instance, when separately clustering the directions and magnitudes of weight vectors in LLaMA-2-7B, the accuracy drop of zero-shot tasks are 46.5\% and 2.3\%, respectively. This gap even increases with the reduction of clustering centers. Further, Euclidean distance, a common metric to access vector similarities in current VQ works, places greater emphasis on reducing the magnitude error. This property is contrary to the above finding, unavoidably leading to larger quantization errors. To these ends, this paper proposes Polar Coordinate Decoupled Vector Quantization (PCDVQ), an effective and efficient VQ framework consisting of two key modules: 1) Polar Coordinate Decoupling (PCD), which transforms vectors into their polar coordinate representations and perform independent quantization of the direction and magnitude parameters.2) Distribution Aligned Codebook Construction (DACC), which optimizes the direction and magnitude codebooks in accordance with the source distribution. Experimental results show that PCDVQ outperforms baseline methods at 2-bit level by at least 1.5\% zero-shot accuracy, establishing a novel paradigm for accurate and highly compressed LLMs.
Event Classification of Accelerometer Data for Industrial Package Monitoring with Embedded Deep Learning
Venue: arXiv cs.LG 2025
Year: 2025

Package monitoring is an important topic in industrial applications, with significant implications for operational efficiency and ecological sustainability. In this study, we propose an approach that employs an embedded system, placed on reusable packages, to detect their state (on a Forklift, in a Truck, or in an undetermined location). We aim to design a system with a lifespan of several years, corresponding to the lifespan of reusable packages. Our analysis demonstrates that maximizing device lifespan requires minimizing wake time. We propose a pipeline that includes data processing, training, and evaluation of the deep learning model designed for imbalanced, multiclass time series data collected from an embedded sensor. The method uses a one-dimensional Convolutional Neural Network architecture to classify accelerometer data from the IoT device. Before training, two data augmentation techniques are tested to solve the imbalance problem of the dataset: the Synthetic Minority Oversampling TEchnique and the ADAptive SYNthetic sampling approach. After training, compression techniques are implemented to have a small model size. On the considered twoclass problem, the methodology yields a precision of 94.54% for the first class and 95.83% for the second class, while compression techniques reduce the model size by a factor of four. The trained model is deployed on the IoT device, where it operates with a power consumption of 316 mW during inference.