OpenRaiser/Intern-Atlas huggingface dataset:
Attribute Filtering in Approximate Nearest Neighbor Search: An In-depth Experimental Study
Venue: arXiv cs.DB 2025
Year: 2025

With the growing integration of structured and unstructured data, new methods have emerged for performing similarity searches on vectors while honoring structured attribute constraints, i.e., a process known as Filtering Approximate Nearest Neighbor (Filtering ANN) search. Since many of these algorithms have only appeared in recent years and are designed to work with a variety of base indexing methods and filtering strategies, there is a pressing need for a unified analysis that identifies their core techniques and enables meaningful comparisons. In this work, we present a unified Filtering ANN search interface that encompasses the latest algorithms and evaluate them extensively from multiple perspectives. First, we propose a comprehensive taxonomy of existing Filtering ANN algorithms based on attribute types and filtering strategies. Next, we analyze their key components, i.e., index structures, pruning strategies, and entry point selection, to elucidate design differences and tradeoffs. We then conduct a broad experimental evaluation on 10 algorithms and 12 methods across 4 datasets (each with up to 10 million items), incorporating both synthetic and real attributes and covering selectivity levels from 0.1% to 100%. Finally, an in-depth component analysis reveals the influence of pruning, entry point selection, and edge filtering costs on overall performance. Based on our findings, we summarize the strengths and limitations of each approach, provide practical guidelines for selecting appropriate methods, and suggest promising directions for future research. Our code is available at: https://github.com/lmccccc/FANNBench.
From Confidence to Collapse in LLM Factual Robustness
Venue: arXiv cs.CL 2025
Year: 2025

Ensuring the robustness of factual knowledge in LLMs is critical for reliable applications in tasks such as question answering and reasoning. However, existing evaluation methods predominantly focus on performance-based metrics, often investigating from the perspective of prompt perturbations, which captures only the externally triggered side of knowledge robustness. To bridge this gap, we introduce a principled approach to measure factual robustness from the perspective of the generation process by analyzing token distribution entropy in combination with temperature scaling sensitivity. These two factors build the Factual Robustness Score (FRS), a novel metric which quantifies the stability of a fact against perturbations in decoding conditions, given its initial uncertainty. To validate our approach, we conduct extensive experiments on 5 LLMs across 3 closed-book QA datasets (SQuAD, TriviaQA, and HotpotQA). We show that factual robustness varies significantly -- smaller models report an FRS of $0.76$, larger ones $0.93$ -- with accuracy degrading by ~$60\%$ under increased uncertainty. These insights demonstrate how entropy and temperature scaling impact factual accuracy, and lay a foundation for developing more robust knowledge retention and retrieval in future models.
EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents
Venue: arXiv cs.CV 2025
Year: 2025

The widespread availability of tools for manipulating images and documents has made it increasingly easy to forge digital documents, posing a serious threat to Know Your Customer (KYC) processes and remote onboarding systems. Detecting such forgeries is essential to preserving the integrity and security of these services. In this work, we present EdgeDoc, a novel approach for the detection and localization of document forgeries. Our architecture combines a lightweight convolutional transformer with auxiliary noiseprint features extracted from the images, enhancing its ability to detect subtle manipulations. EdgeDoc achieved third place in the ICCV 2025 DeepID Challenge, demonstrating its competitiveness. Experimental results on the FantasyID dataset show that our method outperforms baseline approaches, highlighting its effectiveness in realworld scenarios. Project page : https://www.idiap. ch/paper/edgedoc/
Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Venue: arXiv cs.AI 2025
Year: 2025

Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role -- not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or conditions absent from the environment. We propose Instruct-Verify-and-Act (IVA), a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA improves false premise detection accuracy by 97.56% over baselines, while increasing successful responses in false-premise scenarios by 50.78%.
Edge-Native Digitization of Handwritten Marksheets: A Hybrid Heuristic-Deep Learning Framework
Venue: arXiv cs.CV 2025
Year: 2025

The digitization of structured handwritten documents, such as academic marksheets, remains a significant challenge due to the dual complexity of irregular table structures and diverse handwriting styles. While recent Transformer-based approaches like TableNet and TrOCR achieve state-of-the-art accuracy, their high computational cost renders them unsuitable for resource-constrained edge deployments. This paper introduces a resource-efficient hybrid framework that integrates a heuristic OpenCV-based pipeline for rapid table structure detection with a modified lightweight YOLOv8 architecture for handwritten character recognition. By strategically removing the SPPF and deep C2f layers from the standard YOLOv8 backbone, we reduce computational overhead while maintaining high recognition fidelity. Experimental results on the EMNIST digit benchmark demonstrate that our Modified YOLOv8 model achieves 97.5% accuracy. Furthermore, we provide a comprehensive efficiency analysis showing that our framework offers a 95 times inference speedup over standard OCR pipelines and massive efficiency gains over emerging Large Multimodal Models (LMMs) like Qwen2.5-VL, achieving real-time performance 29 FPS on standard CPU hardware. A qualitative and quantitative evaluation on the AMES dataset, a challenging subset of real-world marksheets, confirms the system's robustness in handling mixed alphanumeric content, bridging the gap between high-performance deep learning and practical, scalable document automation.
Implicit and Explicit Formulas of the Joint RDF for a Tuple of Multivariate Gaussian Sources with Individual Square-Error Distortions
Venue: arXiv cs.IT 2025
Year: 2025

This paper analyzes the joint Rate Distortion Function (RDF) of correlated multivariate Gaussian sources with individual square-error distortions. Leveraging Hotelling's canonical variable form, presented is a closed-form characterization of the joint RDF, that involves {a system of nonlinear equations. Furthermore, for the special case of symmetric distortions (i.e., equal distortions), the joint RDF is explicitly expressed in terms of} two water-filling variables. The results greatly improve our understanding and advance the development of closed-form solutions of the joint RDF for multivariate Gaussian sources with individual square-error distortions.
Vision encoders should be image size agnostic and task driven
Venue: arXiv cs.CV 2025
Year: 2025

This position paper argues that the next generation of vision encoders should be image size agnostic and task driven. The source of our inspiration is biological. Not a structural aspect of biological vision, but a behavioral trait -- efficiency. We focus on a couple of ways in which vision in nature is efficient, but modern vision encoders not. We -- humans and animals -- deal with vast quantities of visual data, and need to be smart where we focus our limited energy -- it depends on the task. It is our belief that vision encoders should be dynamic and the computational complexity should depend on the task at hand rather than the size of the image. We, also, provide concrete first steps towards our vision -- a proof-of-concept solution for image classification. Despite classification being not very representative for what we are trying to achieve, it shows that our approach is feasible and promising.
ConceptGuard: Neuro-Symbolic Safety Guardrails via Sparse Interpretable Jailbreak Concepts
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models have found success in a variety of applications. However, their safety remains a concern due to the existence of various jailbreaking methods. Despite significant efforts, alignment and safety fine-tuning only provide a certain degree of robustness against jailbreak attacks that covertly mislead LLMs towards the generation of harmful content. This leaves them prone to a range of vulnerabilities, including targeted misuse and accidental user profiling. This work introduces \textbf{ConceptGuard}, a novel framework that leverages Sparse Autoencoders (SAEs) to identify interpretable concepts within LLM internals associated with different jailbreak themes. By extracting semantically meaningful internal representations, ConceptGuard enables building robust safety guardrails -- offering fully explainable and generalizable defenses without sacrificing model capabilities or requiring further fine-tuning. Leveraging advances in the mechanistic interpretability of LLMs, our approach provides evidence for a shared activation geometry for jailbreak attacks in the representation space, a potential foundation for designing more interpretable and generalizable safeguards against attackers.
The (C)omprehensive (A)rchitecture (P)attern (I)ntegration method: Navigating the sea of technology
Venue: arXiv cs.SE 2025
Year: 2025

The technological landscape changes daily, making it nearly impossible for a single person to be aware of all trends or available tools that may or may not be suitable for their software project. This makes tool selection and architectural design decisions a complex problem, especially for large-scale software systems. To tackle this issue, we introduce CAPI, the Comprehensive Architecture Pattern Integration method that uses a diagnostic decision tree to suggest architectural patterns depending on user needs. By suggesting patterns instead of tools, the overall complexity for further decisions is lower as there are fewer architectural patterns than tools due to the abstract nature of patterns. Moreover, since tools implement patterns, each non-proposed pattern reduces the number of tools to choose from, reducing complexity. We iteratively developed CAPI, evaluating its understandability and usability in small studies with academic participants. When satisfied with the outcome, we performed a user-study with industry representatives to investigate the state-of-the-art in technology selection and the effectiveness of our proposed method. We find that technology selection is largely performed via trial and error, that CAPI is uniformly perceived as helpful, and that CAPI is able to reproduce the productive architectural environments of our participants.
RotaTouille: Rotation Equivariant Deep Learning for Contours
Venue: arXiv cs.LG 2025
Year: 2025

Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice of starting point is arbitrary. It is therefore also desirable for deep learning methods to be equivariant under cyclic shifts. We present RotaTouille, a deep learning framework for learning from contour data that achieves both rotation and cyclic shift equivariance through complex-valued circular convolution. We further introduce and characterize equivariant non-linearities, coarsening layers, and global pooling layers to obtain invariant representations for downstream tasks. Finally, we demonstrate the effectiveness of RotaTouille through experiments in shape classification, reconstruction, and contour regression.
Agentic AI Empowered Multi-UAV Trajectory Optimization in Low-Altitude Economy Networks
Venue: arXiv cs.IT 2025
Year: 2025

This paper proposes a novel Agentic Retrieval-augmented generation with Mamba-Attention Integrated Transformer (ARMAIT) framework for multi-Unmanned Aerial Vehicle (UAV) trajectory optimization. The framework is built upon Large Language Models (LLMs), incorporating Retrieval-Augmented Generation (RAG) empowered by Agentic AI and integrated with a UAV-specific knowledge base. Through the Agentic RAG, the LLM autonomously interprets high-level task requirements and identifies the key components necessary for trajectory optimization, including model inputs and outputs, network architecture, reward functions, and task constraints. To support efficient modeling across different system scales, we introduce the Mamba-Attention Integrated Transformer (MAIT), a hybrid neural architecture that combines the long-range dependency modeling capability of attention mechanisms with the efficient temporal dynamic representation of Mamba. Furthermore, a Trajectory-Group Relative Policy Optimization (T-GRPO) method is proposed to achieve unified policy gradient optimization in both discrete and continuous trajectory spaces for MAIT training. Extensive experimental results validate the feasibility and effectiveness of the proposed ARMAIT framework.
Sequential Cohort Selection
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of fair cohort selection from an unknown population, with a focus on university admissions. We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent, before observing the actual applicant pool. In contrast, the sequential setting allows the policy to be updated across stages as new applicant data becomes available. This is achieved by optimizing admission policies using a population model, trained on data from previous admission cycles. We also study the fairness properties of the resulting policies in the one-shot setting, including meritocracy and group parity.
Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models
Venue: arXiv cs.MM 2025
Year: 2025

Over the past decade, adaptive video streaming technology has witnessed significant advancements, particularly driven by the rapid evolution of deep learning techniques. However, the black-box nature of deep learning algorithms presents challenges for developers in understanding decision-making processes and optimizing for specific application scenarios. Although existing research has enhanced algorithm interpretability through decision tree conversion, interpretability does not directly equate to developers' subjective comprehensibility. To address this challenge, we introduce \texttt{ComTree}, the first bitrate adaptation algorithm generation framework that considers comprehensibility. The framework initially generates the complete set of decision trees that meet performance requirements, then leverages large language models to evaluate these trees for developer comprehensibility, ultimately selecting solutions that best facilitate human understanding and enhancement. Experimental results demonstrate that \texttt{ComTree} significantly improves comprehensibility while maintaining competitive performance, showing potential for further advancement. The source code is available at https://github.com/thu-media/ComTree.
GreenLLM: SLO-Aware Dynamic Frequency Scaling for Energy-Efficient LLM Serving
Venue: arXiv cs.PF 2025
Year: 2025

Large Language Models (LLMs) are becoming the backbone of modern cloud services, yet their inference costs are dominated by GPU energy. Unlike traditional GPU workloads, LLM inference has two stages with different characteristics: the prefill phase, which is latency sensitive and scales quadratically with prompt length, and the decode phase, which progresses token by token with unpredictable length. Current GPU power governors (for example, NVIDIA's default) overlook this asymmetry and treat both stages uniformly. The result is mismatched voltage and frequency settings, head-of-line blocking, and excessive energy use. We introduce GreenLLM, an SLO-aware serving framework that minimizes GPU energy by explicitly separating prefill and decode control. At ingress, requests are routed into length-based queues so short prompts avoid head-of-line blocking and TTFT improves. For prefill, GreenLLM collects short traces on a GPU node, fits compact latency-power models over SM frequency, and solves a queueing-aware optimization to select energy-minimal clocks per class. During decode, a lightweight dual-loop controller tracks throughput (tokens per second) and adjusts frequency with hysteretic, fine-grained steps to hold tail TBT within target bounds. Across Alibaba and Azure trace replays, GreenLLM reduces total energy by up to 34 percent versus the default DVFS baseline, with no loss of throughput and with less than 3.5 percent additional SLO violations.
MuST2-Learn: Multi-view Spatial-Temporal-Type Learning for Heterogeneous Municipal Service Time Estimation
Venue: arXiv cs.LG 2025
Year: 2025

Non-emergency municipal services such as city 311 systems have been widely implemented across cities in Canada and the United States to enhance residents' quality of life. These systems enable residents to report issues, e.g., noise complaints, missed garbage collection, and potholes, via phone calls, mobile applications, or webpages. However, residents are often given limited information about when their service requests will be addressed, which can reduce transparency, lower resident satisfaction, and increase the number of follow-up inquiries. Predicting the service time for municipal service requests is challenging due to several complex factors: dynamic spatial-temporal correlations, underlying interactions among heterogeneous service request types, and high variation in service duration even within the same request category. In this work, we propose MuST2-Learn: a Multi-view Spatial-Temporal-Type Learning framework designed to address the aforementioned challenges by jointly modeling spatial, temporal, and service type dimensions. In detail, it incorporates an inter-type encoder to capture relationships among heterogeneous service request types and an intra-type variation encoder to model service time variation within homogeneous types. In addition, a spatiotemporal encoder is integrated to capture spatial and temporal correlations in each request type. The proposed framework is evaluated with extensive experiments using two real-world datasets. The results show that MuST2-Learn reduces mean absolute error by at least 32.5%, which outperforms state-of-the-art methods.
Comparative Analysis of UAV Path Planning Algorithms for Efficient Navigation in Urban 3D Environments
Venue: arXiv cs.RO 2025
Year: 2025

The most crucial challenges for UAVs are planning paths and avoiding obstacles in their way. In recent years, a wide variety of path-planning algorithms have been developed. These algorithms have successfully solved path-planning problems; however, they suffer from multiple challenges and limitations. To test the effectiveness and efficiency of three widely used algorithms, namely A*, RRT*, and Particle Swarm Optimization (PSO), this paper conducts extensive experiments in 3D urban city environments cluttered with obstacles. Three experiments were designed with two scenarios each to test the aforementioned algorithms. These experiments consider different city map sizes, different altitudes, and varying obstacle densities and sizes in the environment. According to the experimental results, the A* algorithm outperforms the others in both computation efficiency and path quality. PSO is especially suitable for tight turns and dense environments, and RRT* offers a balance and works well across all experiments due to its randomized approach to finding solutions.
A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering
Venue: arXiv cs.IR 2025
Year: 2025

In the realm of collaborative filtering recommendation systems, Graph Neural Networks (GNNs) have demonstrated remarkable performance but face significant challenges in deployment on resource-constrained edge devices due to their high embedding parameter requirements and computational costs. Using common quantization method directly on node embeddings may overlooks their graph based structure, causing error accumulation during message passing and degrading the quality of quantized embeddings.To address this, we propose Graph based Node-Aware Dynamic Quantization training for collaborative filtering (GNAQ), a novel quantization approach that leverages graph structural information to enhance the balance between efficiency and accuracy of GNNs for Top-K recommendation. GNAQ introduces a node-aware dynamic quantization strategy that adapts quantization scales to individual node embeddings by incorporating graph interaction relationships. Specifically, it initializes quantization intervals based on node-wise feature distributions and dynamically refines them through message passing in GNN layers. This approach mitigates information loss caused by fixed quantization scales and captures hierarchical semantic features in user-item interaction graphs. Additionally, GNAQ employs graph relation-aware gradient estimation to replace traditional straight-through estimators, ensuring more accurate gradient propagation during training. Extensive experiments on four real-world datasets demonstrate that GNAQ outperforms state-of-the-art quantization methods, including BiGeaR and N2UQ, by achieving average improvement in 27.8\% Recall@10 and 17.6\% NDCG@10 under 2-bit quantization. In particular, GNAQ is capable of maintaining the performance of full-precision models while reducing their model sizes by 8 to 12 times; in addition, the training time is twice as fast compared to quantization baseline methods.