Article Title:
Large Language Models for Cyber Security: A Systematic Literature Review

Article Date: 8 May 2024

Article Description:
The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2405.04760v4.pdf

GitHub:
• https://github.com/hiyouga/llama-efficient-tuning

Datasets:
• 10,000 People - Human Pose Recognition Data
==================================

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🔹 Title:
PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

🔹 Publication Date: Published on Jun 12

🔹 Abstract:
PosterCraft improves aesthetic poster generation through a unified, modular pipeline with enhanced text rendering, region-aware fine-tuning, aesthetic reinforcement learning, and joint vision-language refinement. AI-generated summary Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster100K ; (iii) aesthetic-text-reinforcement learning via best-of-n preference optimization ; and (iv) joint vision-language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal-approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the Project page: https://ephemeral182.github.io/PosterCraft

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.10741
• PDF: https://arxiv.org/pdf/2506.10741
• Project Page: https://ephemeral182.github.io/PosterCraft/
• Github: https://ephemeral182.github.io/PosterCraft

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🔹 Title:
Scaling Reasoning without Attention

🔹 Publication Date: Published on May 28

🔹 Abstract:
A new attention-free language model using state space dual layers and a two-phase curriculum fine-tuning strategy outperforms Transformer models on complex reasoning tasks. AI-generated summary Large language models ( LLMs ) have made significant advances in complex reasoning tasks, yet they remain bottlenecked by two core challenges: architectural inefficiency due to reliance on Transformers , and a lack of structured fine-tuning for high-difficulty domains. We introduce \ourmodel, an attention-free language model that addresses both issues through architectural and data-centric innovations. Built on the state space dual (SSD) layers of Mamba-2 , our model eliminates the need for self-attention and key-value caching, enabling fixed-memory, constant-time inference . To train it for complex reasoning, we propose a two-phase curriculum fine-tuning strategy based on the PromptCoT synthesis paradigm , which generates pedagogically structured problems via abstract concept selection and rationale-guided generation. On benchmark evaluations, \ourmodel-7B outperforms strong Transformer and hybrid models of comparable scale, and even surpasses the much larger Gemma3-27B by 2.6\% on AIME 24 , 0.6\% on AIME 25 , and 3.0\% on Livecodebench . These results highlight the potential of state space models as efficient and scalable alternatives to attention-based architectures for high-capacity reasoning .

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2505.22425
• PDF: https://arxiv.org/pdf/2505.22425

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Article Title:
Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents

Article Date: 17 May 2025

Article Description:
Large Language Model (LLM)-based search agents have shown remarkable capabilities in solving complex tasks by dynamically decomposing problems and addressing them through interleaved reasoning and retrieval. However, this interleaved paradigm introduces substantial efficiency bottlenecks. First, we observe that both highly accurate and overly approximate retrieval methods degrade system efficiency: exact search incurs significant retrieval overhead, while coarse retrieval requires additional reasoning steps during generation. Second, we identify inefficiencies in system design, including improper scheduling and frequent retrieval stalls, which lead to cascading latency -- where even minor delays in retrieval amplify end-to-end inference time. To address these challenges, we introduce SearchAgent-X, a high-efficiency inference framework for LLM-based search agents. SearchAgent-X leverages high-recall approximate retrieval and incorporates two key techniques: priority-aware scheduling and non-stall retrieval. Extensive experiments demonstrate that SearchAgent-X consistently outperforms state-of-the-art systems such as vLLM and HNSW-based retrieval across diverse tasks, achieving up to 3.4$\times$ higher throughput and 5$\times$ lower latency, without compromising generation quality. SearchAgent-X is available at https://github.com/tiannuo-yang/SearchAgent-X.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2505.12065v1.pdf

GitHub:
• https://github.com/tiannuo-yang/searchagent-x

Datasets:
• No datasets information available
==================================

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🔹 Title:
Sekai: A Video Dataset towards World Exploration

🔹 Publication Date: Published on Jun 18

🔹 Abstract:
Sekai, a worldwide video dataset with comprehensive annotations, is introduced to support world exploration applications, enhancing video generation models. AI-generated summary Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning ``world'' in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5,000 hours of walking or drone view ( FPV and UVA ) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories . Experiments demonstrate the quality of the dataset. And, we use a subset to train an interactive video world exploration model , named YUME (meaning ``dream'' in Japanese). We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.15675
• PDF: https://arxiv.org/pdf/2506.15675
• Project Page: https://huggingface.co/datasets/Lixsp11/Sekai-Project
• Github: https://github.com/Lixsp11/sekai-codebase

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/Lixsp11/Sekai-Project

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Article Title:
MUSt3R: Multi-view Network for Stereo 3D Reconstruction

Article Date: Yohann Cabon

Article Description:
DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collections with no prior information about camera calibration nor viewpoint poses. Under the hood, however, DUSt3R processes image pairs, regressing local 3D reconstructions that need to be aligned in a global coordinate system. The number of pairs, growing quadratically, is an inherent limitation that becomes especially concerning for robust and fast optimization in the case of large image collections. In this paper, we propose an extension of DUSt3R from pairs to multiple views, that addresses all aforementioned concerns. Indeed, we propose a Multi-view Network for Stereo 3D Reconstruction, or MUSt3R, that modifies the DUSt3R architecture by making it symmetric and extending it to directly predict 3D structure for all views in a common coordinate frame. Second, we entail the model with a multi-layer memory mechanism which allows to reduce the computational complexity and to scale the reconstruction to large collections, inferring thousands of 3D pointmaps at high frame-rates with limited added complexity. The framework is designed to perform 3D reconstruction both offline and online, and hence can be seamlessly applied to SfM and visual SLAM scenarios showing state-of-the-art performance on various 3D downstream tasks, including uncalibrated Visual Odometry, relative camera pose, scale and focal estimation, 3D reconstruction and multi-view depth estimation.PDFAbstractCVPR 2025 PDFCVPR 2025 Abstract

PDF Download Link:
https://arxiv.org/pdf/2503.01661v1.pdf

GitHub:
• https://github.com/naver/must3r

Datasets:
• KITTI
• ScanNet
• TUM RGB-D
• MegaDepth
• ETH3D
• BlendedMVS
==================================

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Article Title:
Spiking Graph Convolutional Networks

Article Date: 5 May 2022

Article Description:
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing by utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g. citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2205.02767v2.pdf

GitHub:
• https://github.com/zulunzhu/spikinggcn

Datasets:
• MNIST
==================================

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🔹 Title:
Unified Vision-Language-Action Model

🔹 Publication Date: Published on Jun 24

🔹 Abstract:
UniVLA is a multimodal VLA model that autoregressively processes vision, language, and action as token sequences, incorporating world modeling for effective long-horizon policy learning and achieving state-of-the-art results across simulation and real-world benchmarks. AI-generated summary Vision-language-action models ( VLAs ) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on the general comprehension capabilities of vision-language models ( VLMs ) to generate action signals, often overlooking the rich temporal and causal structure embedded in visual observations. In this paper, we present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences . This formulation enables flexible multimodal tasks learning , particularly from large-scale video data. By incorporating world modeling during post-training, UniVLA captures causal dynamics from videos, facilitating effective transfer to downstream policy learning --especially for long-horizon tasks. Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN , LIBERO , and Simplenv-Bridge , significantly surpassing previous methods. For example, UniVLA achieves 95.5% average success rate on LIBERO benchmark, surpassing pi0-FAST's 85.5%. We further demonstrate its broad applicability on real-world ALOHA manipulation and autonomous driving.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.19850
• PDF: https://arxiv.org/pdf/2506.19850

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🔹 Title:
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content

🔹 Publication Date: Published on Jun 25

🔹 Abstract:
A biomedical text dataset, constructed from PubMed, uses a two-stage annotation process involving large and small language models to fine-tune and extract subsets for clinical NLP, improving pretraining efficiency and performance. AI-generated summary We introduce Biomed-Enriched , a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality . The educational quality score (rated 1 to 5) estimates how useful a paragraph is for college-level learning. These annotations are then used to fine-tune a small language model , which propagates the labels across the full PMC-OA corpus . The resulting metadata allows us to extract refined subsets, including 2M clinical case paragraphs with over 450K high-quality ones from articles with commercial-use licenses, and to construct several variants via quality filtering and domain upsampling. Clinical text is typically difficult to access due to privacy constraints, as hospital records cannot be publicly shared. Hence, our dataset provides an alternative large-scale, openly available collection of clinical cases from PubMed , making it a valuable resource for biomedical and clinical NLP. Preliminary continual-pretraining experiments with OLMo2 suggest these curated subsets enable targeted improvements, with clinical upsampling boosting performance by ~5% on MMLU ProfMed and educational quality filtering improving MedQA and MedMCQA by ~1%. Combinations of these techniques led to faster convergence, reaching same performance with a third of training tokens , indicating potential for more efficient and effective biomedical pretraining strategies.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.20331
• PDF: https://arxiv.org/pdf/2506.20331

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/almanach/Biomed-Enriched

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Article Title:
PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices

Article Date: 30 May 2024

Article Description:
Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query service that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different performance service levels (SLAs). The performance SLAs are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We demonstrate that the combination of a serverless paradigm, a natural-language-aided interface, and flexible SLAs and prices will substantially improve the usability of cloud data analytic systems.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2405.19784v2.pdf

GitHub:
• https://github.com/pixelsdb/pixels

Datasets:
• No datasets information available
==================================

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Article Title:
Optuna: A Next-generation Hyperparameter Optimization Framework

Article Date: 25 Jul 2019

Article Description:
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/1907.10902v1.pdf

GitHub:
• https://github.com/pfnet/optuna
• https://github.com/optuna/optuna
• https://github.com/Automunge/AutoMunge
• https://github.com/optuna/optuna-integration
• https://github.com/crcrpar/benchmark-runner-ci
• https://github.com/crcrpar/optuna-mirror
• https://github.com/himkt/optuna-test-rtds
• https://github.com/crcrpar/ci-example-execution
• https://github.com/yqian4/optuna
• https://github.com/brethvoice/optuna_demo_MNIST
• https://github.com/rickyHong/optuna-repl

Datasets:
• No datasets information available
==================================

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🔹 Title:
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling

🔹 Publication Date: Published on Jun 25

🔹 Abstract:
Investigating mid-training strategies reveals that high-quality mathematical corpora and well-formatted chain-of-thought reasoning examples enhance reinforcement learning performance in language models, leading to the development of OctoThinker. AI-generated summary Different base language model families, such as Llama and Qwen, exhibit divergent behaviors during post-training with reinforcement learning (RL), especially on reasoning-intensive tasks. What makes a base language model suitable for reinforcement learning ? Gaining deeper insight into this question is essential for developing RL-scalable foundation models of the next generation. In this work, we investigate how mid-training strategies shape RL dynamics, focusing on two representative model families: Qwen and Llama. Our study reveals that (1) high-quality mathematical corpora, such as MegaMath-Web-Pro , significantly improve both base model and RL performance, while existing alternatives (e.g., FineMath-4plus) fail to do so; (2) further adding QA-style data , particularly long chain-of-thought (CoT) reasoning examples, enhances RL outcomes, and instruction data further unlocks this effect; (3) while long-CoT improves reasoning depth, it can also induce verbosity of model responses and unstability of RL training, underscoring the importance of data formatting ; (4) scaling mid-training consistently leads to stronger downstream RL performance. Building on these insights, we introduce a two-stage mid-training strategy , Stable-then-Decay, in which base models are first trained on 200B tokens with a constant learning rate, followed by 20B tokens across three CoT-focused branches with learning rate decay . This yields OctoThinker , a family of models demonstrating strong RL compatibility and closing the performance gap with more RL-friendly model families, i.e., Qwen. We hope our work will help shape pre-training strategies for foundation models in the RL era. To support further research, we release our open-source models along with a curated math reasoning-intensive corpus of over 70 billion tokens (i.e., MegaMath-Web-Pro-Max ).

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.20512
• PDF: https://arxiv.org/pdf/2506.20512
• Github: https://github.com/GAIR-NLP/OctoThinker

🔹 Datasets citing this paper:
• https://huggingface.co/datasets/OctoThinker/MegaMath-Web-Pro-Max

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Article Title:
pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM

Article Date: 17 Feb 2025

Article Description:
pySLAM is an open-source Python framework for Visual SLAM, supporting monocular, stereo, and RGB-D cameras. It provides a flexible interface for integrating both classical and modern local features, making it adaptable to various SLAM tasks. The framework includes different loop closure methods, a volumetric reconstruction pipeline, and support for depth prediction models. Additionally, it offers a suite of tools for visual odometry and SLAM applications. Designed for both beginners and experienced researchers, pySLAM encourages community contributions, fostering collaborative development in the field of Visual SLAM.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2502.11955v2.pdf

GitHub:
• https://github.com/luigifreda/pyslam

Datasets:
• KITTI
• Replica
• TUM RGB-D
• EuRoC MAV
==================================

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🔹 Title:
PlayerOne: Egocentric World Simulator

🔹 Publication Date: Published on Jun 11

🔹 Abstract:
PlayerOne is an egocentric realistic world simulator that constructs and generates videos from user-captured images, using a coarse-to-fine training pipeline and advanced motion injection and reconstruction frameworks. AI-generated summary We introduce PlayerOne, the first egocentric realistic world simulator , facilitating immersive and unrestricted exploration within vividly dynamic environments. Given an egocentric scene image from the user, PlayerOne can accurately construct the corresponding world and generate egocentric videos that are strictly aligned with the real scene human motion of the user captured by an exocentric camera. PlayerOne is trained in a coarse-to-fine pipeline that first performs pretraining on large-scale egocentric text-video pairs for coarse-level egocentric understanding, followed by finetuning on synchronous motion-video data extracted from egocentric-exocentric video datasets with our automatic construction pipeline . Besides, considering the varying importance of different components, we design a part-disentangled motion injection scheme, enabling precise control of part-level movements. In addition, we devise a joint reconstruction framework that progressively models both the 4D scene and video frames , ensuring scene consistency in the long-form video generation . Experimental results demonstrate its great generalization ability in precise control of varying human movements and worldconsistent modeling of diverse scenarios. It marks the first endeavor into egocentric real-world simulation and can pave the way for the community to delve into fresh frontiers of world modeling and its diverse applications.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.09995
• PDF: https://arxiv.org/pdf/2506.09995
• Project Page: https://playerone-hku.github.io/
• Github: https://playerone-hku.github.io/

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🔹 Title:
When Less is Enough: Adaptive Token Reduction for Efficient Image Representation

🔹 Publication Date: Published on Mar 20

🔹 Abstract:
A method using autoencoder and Gumbel-Softmax selection identifies and retains only the most informative visual tokens, enabling efficient multimodal pruning with minimal performance loss. AI-generated summary Vision encoders typically generate a large number of visual tokens , providing information-rich representations but significantly increasing computational demands. This raises the question of whether all generated tokens are equally valuable or if some of them can be discarded to reduce computational costs without compromising quality. In this paper, we introduce a new method for determining feature utility based on the idea that less valuable features can be reconstructed from more valuable ones. We implement this concept by integrating an autoencoder with a Gumbel-Softmax selection mechanism , that allows identifying and retaining only the most informative visual tokens . To validate our approach, we compared the performance of the LLaVA-NeXT model, using features selected by our method with randomly selected features. We found that on OCR-based tasks , more than 50% of the visual context can be removed with minimal performance loss, whereas randomly discarding the same proportion of features significantly affects the model capabilities. Furthermore, in general-domain tasks , even randomly retaining only 30% of tokens achieves performance comparable to using the full set of visual tokens . Our results highlight a promising direction towards adaptive and efficient multimodal pruning that facilitates scalable and low-overhead inference without compromising performance.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2503.16660
• PDF: https://arxiv.org/pdf/2503.16660

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🔹 Title:
FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation

🔹 Publication Date: Published on Jun 23

🔹 Abstract:
FilMaster is an AI system that integrates cinematic principles to generate professional-grade films, featuring camera language design and cinematic rhythm control using real-world film data and generative AI models. AI-generated summary AI-driven content creation has shown potential in film production. However, existing film generation systems struggle to implement cinematic principles and thus fail to generate professional-quality films, particularly lacking diverse camera language and cinematic rhythm. This results in templated visuals and unengaging narratives. To address this, we introduce FilMaster, an end-to-end AI system that integrates real-world cinematic principles for professional-grade film generation, yielding editable, industry-standard outputs. FilMaster is built on two key principles: (1) learning cinematography from extensive real-world film data and (2) emulating professional, audience-centric post-production workflows. Inspired by these principles, FilMaster incorporates two stages: a Reference-Guided Generation Stage which transforms user input to video clips, and a Generative Post-Production Stage which transforms raw footage into audiovisual outputs by orchestrating visual and auditory elements for cinematic rhythm. Our generation stage highlights a Multi-shot Synergized RAG Camera Language Design module to guide the AI in generating professional camera language by retrieving reference clips from a vast corpus of 440,000 film clips. Our post-production stage emulates professional workflows by designing an Audience-Centric Cinematic Rhythm Control module, including Rough Cut and Fine Cut processes informed by simulated audience feedback, for effective integration of audiovisual elements to achieve engaging content. The system is empowered by generative AI models like (M)LLMs and video generation models . Furthermore, we introduce FilmEval , a comprehensive benchmark for evaluating AI-generated films. Extensive experiments show FilMaster's superior performance in camera language design and cinematic rhythm control, advancing generative AI in professional filmmaking.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.18899
• PDF: https://arxiv.org/pdf/2506.18899
• Github: https://filmaster-ai.github.io

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Article Title:
Hyperbolic Dataset Distillation

Article Date: 30 May 2025

Article Description:
To address the computational and storage challenges posed by large-scale datasets in deep learning, dataset distillation has been proposed to synthesize a compact dataset that replaces the original while maintaining comparable model performance. Unlike optimization-based approaches that require costly bi-level optimization, distribution matching (DM) methods improve efficiency by aligning the distributions of synthetic and original data, thereby eliminating nested optimization. DM achieves high computational efficiency and has emerged as a promising solution. However, existing DM methods, constrained to Euclidean space, treat data as independent and identically distributed points, overlooking complex geometric and hierarchical relationships. To overcome this limitation, we propose a novel hyperbolic dataset distillation method, termed HDD. Hyperbolic space, characterized by negative curvature and exponential volume growth with distance, naturally models hierarchical and tree-like structures. HDD embeds features extracted by a shallow network into the Lorentz hyperbolic space, where the discrepancy between synthetic and original data is measured by the hyperbolic (geodesic) distance between their centroids. By optimizing this distance, the hierarchical structure is explicitly integrated into the distillation process, guiding synthetic samples to gravitate towards the root-centric regions of the original data distribution while preserving their underlying geometric characteristics. Furthermore, we find that pruning in hyperbolic space requires only 20% of the distilled core set to retain model performance, while significantly improving training stability. Notably, HDD is seamlessly compatible with most existing DM methods, and extensive experiments on different datasets validate its effectiveness.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2505.24623v1.pdf

GitHub:
• https://github.com/Guang000/Awesome-Dataset-Distillation

Datasets:
• CIFAR-100
• Fashion-MNIST
• Tiny ImageNet
==================================

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🔹 Title:
Align Your Flow: Scaling Continuous-Time Flow Map Distillation

🔹 Publication Date: Published on Jun 17

🔹 Abstract:
Flow maps, introduced with new continuous-time objectives and training techniques, achieve state-of-the-art performance in few-step image and text-to-image generation. AI-generated summary Diffusion- and flow-based models have emerged as state-of-the-art generative modeling approaches, but they require many sampling steps. Consistency models can distill these models into efficient one-step generators; however, unlike flow- and diffusion-based methods, their performance inevitably degrades when increasing the number of steps, which we show both analytically and empirically. Flow maps generalize these approaches by connecting any two noise levels in a single step and remain effective across all step counts. In this paper, we introduce two new continuous-time objectives for training flow maps , along with additional novel training techniques, generalizing existing consistency and flow matching objectives. We further demonstrate that autoguidance can improve performance, using a low-quality model for guidance during distillation, and an additional boost can be achieved by adversarial finetuning, with minimal loss in sample diversity. We extensively validate our flow map models, called Align Your Flow , on challenging image generation benchmarks and achieve state-of-the-art few-step generation performance on both ImageNet 64x64 and 512x512, using small and efficient neural networks. Finally, we show text-to-image flow map models that outperform all existing non-adversarially trained few-step samplers in text-conditioned synthesis.

🔹 Links:
• arXiv Page: https://arxiv.org/abs/2506.14603
• PDF: https://arxiv.org/pdf/2506.14603
• Project Page: https://research.nvidia.com/labs/toronto-ai/AlignYourFlow/

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Article Title:
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

Article Date: 9 Oct 2023

Article Description:
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently. However, we often observe inconsistent or seemingly contradictory performance findings across different studies. This hinders our understanding of the merits of different approaches and slows down progress. We address the need for means of assessing MTS forecasting proposals reliably and fairly, in turn enabling better exploitation of MTS as seen in different applications. Specifically, we first propose BasicTS+, a benchmark designed to enable fair, comprehensive, and reproducible comparison of MTS forecasting solutions. BasicTS+ establishes a unified training pipeline and reasonable settings, enabling an unbiased evaluation. Second, we identify the heterogeneity across different MTS as an important consideration and enable classification of MTS based on their temporal and spatial characteristics. Disregarding this heterogeneity is a prime reason for difficulties in selecting the most promising technical directions. Third, we apply BasicTS+ along with rich datasets to assess the capabilities of more than 45 MTS forecasting solutions. This provides readers with an overall picture of the cutting-edge research on MTS forecasting. The code can be accessed at https://github.com/GestaltCogTeam/BasicTS.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2310.06119v2.pdf

GitHub:
• https://github.com/zezhishao/basicts
• https://github.com/gestaltcogteam/basicts
• https://github.com/zezhishao/step
• https://github.com/zezhishao/d2stgnn
• https://github.com/hitplz/dstrformer

Datasets:
• ETT
• Exchange
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Article Title:
Pseudo-Simulation for Autonomous Driving

Article Date: 4 Jun 2025

Article Description:
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can face insufficient realism or high computational costs. Open-loop evaluation, while being efficient and data-driven, relies on metrics that generally overlook compounding errors. In this paper, we propose pseudo-simulation, a novel paradigm that addresses these limitations. Pseudo-simulation operates on real datasets, similar to open-loop evaluation, but augments them with synthetic observations generated prior to evaluation using 3D Gaussian Splatting. Our key idea is to approximate potential future states the AV might encounter by generating a diverse set of observations that vary in position, heading, and speed. Our method then assigns a higher importance to synthetic observations that best match the AV's likely behavior using a novel proximity-based weighting scheme. This enables evaluating error recovery and the mitigation of causal confusion, as in closed-loop benchmarks, without requiring sequential interactive simulation. We show that pseudo-simulation is better correlated with closed-loop simulations (R^2=0.8) than the best existing open-loop approach (R^2=0.7). We also establish a public leaderboard for the community to benchmark new methodologies with pseudo-simulation. Our code is available at https://github.com/autonomousvision/navsim.PDFAbstract

PDF Download Link:
https://arxiv.org/pdf/2506.04218v1.pdf

GitHub:
• https://github.com/autonomousvision/navsim

Datasets:
• No datasets information available
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