OpenRaiser/Intern-Atlas huggingface dataset:
ISTD-YOLO: A Multi-Scale Lightweight High-Performance Infrared Small Target Detection Algorithm
Venue: arXiv cs.CV 2025
Year: 2025

Aiming at the detection difficulties of infrared images such as complex background, low signal-to-noise ratio, small target size and weak brightness, a lightweight infrared small target detection algorithm ISTD-YOLO based on improved YOLOv7 was proposed. Firstly, the YOLOv7 network structure was lightweight reconstructed, and a three-scale lightweight network architecture was designed. Then, the ELAN-W module of the model neck network is replaced by VoV-GSCSP to reduce the computational cost and the complexity of the network structure. Secondly, a parameter-free attention mechanism was introduced into the neck network to enhance the relevance of local con-text information. Finally, the Normalized Wasserstein Distance (NWD) was used to optimize the commonly used IoU index to enhance the localization and detection accuracy of small targets. Experimental results show that compared with YOLOv7 and the current mainstream algorithms, ISTD-YOLO can effectively improve the detection effect, and all indicators are effectively improved, which can achieve high-quality detection of infrared small targets.
Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection
Venue: arXiv cs.LG 2025
Year: 2025

The scarcity of high-quality residential load data can pose obstacles for decarbonizing the residential sector as well as effective grid planning and operation. The above challenges have motivated research into generating synthetic load data, but existing methods faced limitations in terms of scalability, diversity, and similarity. This paper proposes a Generative Adversarial Network-based Synthetic Residential Load Pattern (RLP-GAN) generation model, a novel weakly-supervised GAN framework, leveraging an over-complete autoencoder to capture dependencies within complex and diverse load patterns and learn household-level data distribution at scale. We incorporate a model weight selection method to address the mode collapse problem and generate load patterns with high diversity. We develop a holistic evaluation method to validate the effectiveness of RLP-GAN using real-world data of 417 households. The results demonstrate that RLP-GAN outperforms state-of-the-art models in capturing temporal dependencies and generating load patterns with higher similarity to real data. Furthermore, we have publicly released the RLP-GAN generated synthetic dataset, which comprises one million synthetic residential load pattern profiles.
Learning to Score
Venue: arXiv cs.LG 2025
Year: 2025

Common machine learning settings range from supervised tasks, where accurately labeled data is accessible, through semi-supervised and weakly-supervised tasks, where target labels are scant or noisy, to unsupervised tasks where labels are unobtainable. In this paper we study a scenario where the target labels are not available but additional related information is at hand. This information, referred to as Side Information, is either correlated with the unknown labels or imposes constraints on the feature space. We formulate the problem as an ensemble of three semantic components: representation learning, side information and metric learning. The proposed scoring model is advantageous for multiple use-cases. For example, in the healthcare domain it can be used to create a severity score for diseases where the symptoms are known but the criteria for the disease progression are not well defined. We demonstrate the utility of the suggested scoring system on well-known benchmark data-sets and bio-medical patient records.
Expanding the Generative AI Design Space through Structured Prompting and Multimodal Interfaces
Venue: arXiv cs.HC 2025
Year: 2025

Text-based prompting remains the predominant interaction paradigm in generative AI, yet it often introduces friction for novice users such as small business owners (SBOs), who struggle to articulate creative goals in domain-specific contexts like advertising. Through a formative study with six SBOs in the United Kingdom, we identify three key challenges: difficulties in expressing brand intuition through prompts, limited opportunities for fine-grained adjustment and refinement during and after content generation, and the frequent production of generic content that lacks brand specificity. In response, we present ACAI (AI Co-Creation for Advertising and Inspiration), a multimodal generative AI tool designed to support novice designers by moving beyond traditional prompt interfaces. ACAI features a structured input system composed of three panels: Branding, Audience and Goals, and the Inspiration Board. These inputs allow users to convey brand-relevant context and visual preferences. This work contributes to HCI research on generative systems by showing how structured interfaces can foreground user-defined context, improve alignment, and enhance co-creative control in novice creative workflows.
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
Venue: arXiv cs.LG 2025
Year: 2025

Differentially private (DP) machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretraining. While public data assumptions may be reasonable in text and image domains, they are less likely to hold for tabular data due to tabular data heterogeneity across domains. We propose leveraging powerful priors to address this limitation; specifically, we synthesize realistic tabular data directly from schema-level specifications - such as variable names, types, and permissible ranges - without ever accessing sensitive records. To that end, this work introduces the notion of "surrogate" public data - datasets generated independently of sensitive data, which consume no privacy loss budget and are constructed solely from publicly available schema or metadata. Surrogate public data are intended to encode plausible statistical assumptions (informed by publicly available information) into a dataset with many downstream uses in private mechanisms. We automate the process of generating surrogate public data with large language models (LLMs); in particular, we propose two methods: direct record generation as CSV files, and automated structural causal model (SCM) construction for sampling records. Through extensive experiments, we demonstrate that surrogate public tabular data can effectively replace traditional public data when pretraining differentially private tabular classifiers. To a lesser extent, surrogate public data are also useful for hyperparameter tuning of DP synthetic data generators, and for estimating the privacy-utility tradeoff.
A fast MPI-based Distributed Hash-Table as Surrogate Model demonstrated in a coupled reactive transport HPC simulation
Venue: arXiv cs.DC 2025
Year: 2025

Surrogate models can play a pivotal role in enhancing performance in contemporary High-Performance Computing applications. Cache-based surrogates use already calculated simulation results to interpolate or extrapolate further simulation output values. But this approach only pays off if the access time to retrieve the needed values is much faster than the actual simulation. While the most existing key-value stores use a Client-Server architecture with dedicated storage nodes, this is not the most suitable architecture for HPC applications. Instead, we propose a distributed architecture where the parallel processes offer a part of their available memory to build a shared distributed hash table based on MPI. This paper presents three DHT approaches with the special requirements of HPC applications in mind. The presented lock-free design outperforms both DHT versions which use explicit synchronization by coarse-grained resp. fine-grained locking. The lock-free DHT shows very good scaling regarding read and write performance. The runtime of a coupled reactive transport simulation was improved between 14% and 42% using the lock-free DHT as a surrogate model.
The Geometry of Self-Verification in a Task-Specific Reasoning Model
Venue: arXiv cs.AI 2025
Year: 2025

How do reasoning models verify their own answers? We study this question by training a model using DeepSeek R1's recipe on the CountDown task. We leverage the fact that preference tuning leads to mode collapse, yielding a model that always produces highly structured chain-of-thought sequences. With this setup, we do top-down and bottom-up analyses to reverse-engineer how the model verifies its outputs. Top-down, we find Gated Linear Unit (GLU) weights encoding verification-related tokens, such as ``success'' or ``incorrect''. Bottom-up, we find that ``previous-token heads'' are mainly responsible for self-verification in our setup. Our analyses meet in the middle: drawing inspiration from inter-layer communication channels, we use the identified GLU weights to localize as few as three attention heads that can disable self-verification, pointing to a necessary component of a potentially larger verification circuit. Finally, we verify that similar verification components exist in our base model and a general reasoning DeepSeek-R1 model.
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

To develop trustworthy Vision-Language Models (VLMs), it is essential to address adversarial robustness and hallucination mitigation, both of which impact factual accuracy in high-stakes applications such as defense and healthcare. Existing methods primarily focus on either adversarial defense or hallucination post-hoc correction, leaving a gap in unified robustness strategies. We introduce \textbf{Hydra}, an adaptive agentic framework that enhances plug-in VLMs through iterative reasoning, structured critiques, and cross-model verification, improving both resilience to adversarial perturbations and intrinsic model errors. Hydra employs an Action-Critique Loop, where it retrieves and critiques visual information, leveraging Chain-of-Thought (CoT) and In-Context Learning (ICL) techniques to refine outputs dynamically. Unlike static post-hoc correction methods, Hydra adapts to both adversarial manipulations and intrinsic model errors, making it robust to malicious perturbations and hallucination-related inaccuracies. We evaluate Hydra on four VLMs, three hallucination benchmarks, two adversarial attack strategies, and two adversarial defense methods, assessing performance on both clean and adversarial inputs. Results show that Hydra surpasses plug-in VLMs and state-of-the-art (SOTA) dehallucination methods, even without explicit adversarial defenses, demonstrating enhanced robustness and factual consistency. By bridging adversarial resistance and hallucination mitigation, Hydra provides a scalable, training-free solution for improving the reliability of VLMs in real-world applications.
LLM-Driven Usefulness Judgment for Web Search Evaluation
Venue: arXiv cs.IR 2025
Year: 2025

Evaluation is fundamental in optimizing search experiences and supporting diverse user intents in Information Retrieval (IR). Traditional search evaluation methods primarily rely on relevance labels, which assess how well retrieved documents match a user's query. However, relevance alone fails to capture a search system's effectiveness in helping users achieve their search goals, making usefulness a critical evaluation criterion. In this paper, we explore an alternative approach: LLM-generated usefulness labels, which incorporate both implicit and explicit user behavior signals to evaluate document usefulness. We propose Task-aware Rubric-based Usefulness Evaluation (TRUE), a rubric-driven evaluation method that employs iterative sampling and reasoning to model complex search behavior patterns. Our findings show that (i) LLMs can generate moderate usefulness labels by leveraging comprehensive search session history incorporating personalization and contextual understanding, and (ii) fine-tuned LLMs improve usefulness judgments when provided with structured search session contexts. Additionally, we examine whether LLMs can distinguish between relevance and usefulness, particularly in cases where this divergence impacts search success. We also conduct an ablation study to identify key metrics for accurate usefulness label generation, optimizing for token efficiency and cost-effectiveness in real-world applications. This study advances LLM-based usefulness evaluation by refining key user metrics, exploring LLM-generated label reliability, and ensuring feasibility for large-scale search systems.
Algebraic Barriers to Halving Algorithmic Information Quantities in Correlated Strings
Venue: arXiv cs.IT 2025
Year: 2025

We study the possibility of scaling down algorithmic information quantities in tuples of correlated strings. In particular, we address a question raised by Alexander Shen: whether, for any triple of strings $(a, b, c)$, there exists a string $z$ such that each conditional Kolmogorov complexity $C(a|z), C(b|z), C(c|z)$ is approximately half of the corresponding unconditional Kolmogorov complexity. We give a negative answer to this question by constructing a triple $(a, b, c)$ for which no such string $z$ exists. Moreover, we construct a fully explicit example of such a tuple. Our construction is based on combinatorial properties of incidences in finite projective planes and relies on bounds for point-line incidences over prime fields. As an application, we show that this impossibility yields lower bounds on the communication complexity of secret key agreement protocols in certain settings. These results reveal algebraic obstructions to efficient information exchange and highlight a separation in information-theoretic behavior between fields with and without proper subfields.
How Do Mobile Applications Enhance Security? An Exploratory Analysis of Use Cases and Provided Information
Venue: arXiv cs.CR 2025
Year: 2025

The ubiquity of mobile applications has increased dramatically in recent years, opening up new opportunities for cyber attackers and heightening security concerns in the mobile ecosystem. As a result, researchers and practitioners have intensified their research into improving the security and privacy of mobile applications. At the same time, more and more mobile applications have appeared on the market that address the aforementioned security issues. However, both academia and industry currently lack a comprehensive overview of these mobile security applications for Android and iOS platforms, including their respective use cases and the security information they provide. To address this gap, we systematically collected a total of 410 mobile applications from both the App and Play Store. Then, we identified the 20 most widely utilized mobile security applications on both platforms that were analyzed and classified. Our results show six primary use cases and a wide range of security information provided by these applications, thus supporting the core functionalities for ensuring mobile security.
Optimizing SIA Development: A Case Study in User-Centered Design for Estuary, a Multimodal Socially Interactive Agent Framework
Venue: arXiv cs.HC 2025
Year: 2025

This case study presents our user-centered design model for Socially Intelligent Agent (SIA) development frameworks through our experience developing Estuary, an open source multimodal framework for building low-latency real-time socially interactive agents. We leverage the Rapid Assessment Process (RAP) to collect the thoughts of leading researchers in the field of SIAs regarding the current state of the art for SIA development as well as their evaluation of how well Estuary may potentially address current research gaps. We achieve this through a series of end-user interviews conducted by a fellow researcher in the community. We hope that the findings of our work will not only assist the continued development of Estuary but also guide the development of other future frameworks and technologies for SIAs.
ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we introduce ResNetVLLM (ResNet Vision LLM), a novel cross-modal framework for zero-shot video understanding that integrates a ResNet-based visual encoder with a Large Language Model (LLM. ResNetVLLM addresses the challenges associated with zero-shot video models by avoiding reliance on pre-trained video understanding models and instead employing a non-pretrained ResNet to extract visual features. This design ensures the model learns visual and semantic representations within a unified architecture, enhancing its ability to generate accurate and contextually relevant textual descriptions from video inputs. Our experimental results demonstrate that ResNetVLLM achieves state-of-the-art performance in zero-shot video understanding (ZSVU) on several benchmarks, including MSRVTT-QA, MSVD-QA, TGIF-QA FrameQA, and ActivityNet-QA.
Deuteronomy 2.0: Record Caching and Latch Freedom
Venue: arXiv cs.DB 2025
Year: 2025

The Deuteronomy transactional key-value store is unique architecturally in providing separation between transaction functionality -- its Transactional Component (TC) and data management -- its Data Component (DC). It is unique in technology by (1) supporting record caching, a smaller unit than the traditional page; and (2) protecting resources during concurrent execution using a latch-free approach. Both technologies are enabled by delta updating. This paper explains how record caching improves cache cost/performance. It also shows how a new latch-free approach makes implementation easier and improves performance.
Information Diffusion and Preferential Attachment in a Network of Large Language Models
Venue: arXiv cs.SI 2025
Year: 2025

This paper models information diffusion in a network of Large Language Models (LLMs) that is designed to answer queries from distributed datasets, where the LLMs can hallucinate the answer. We introduce a two-time-scale dynamical model for the centrally administered network, where opinions evolve faster while the network's degree distribution changes more slowly. Using a mean-field approximation, we establish conditions for a locally asymptotically stable equilibrium where all LLMs remain truthful. We provide approximation guarantees for the mean-field approximation and a singularly perturbed approximation of the two-time-scale system. To mitigate hallucination and improve the influence of truthful nodes, we propose a reputation-based preferential attachment mechanism that reconfigures the network based on LLMs' evaluations of their neighbors. Numerical experiments on an open-source LLM (LLaMA-3.1-8B) validate the efficacy of our preferential attachment mechanism and demonstrate the optimization of a cost function for the two-time-scale system.
CoLoTa: A Dataset for Entity-based Commonsense Reasoning over Long-Tail Knowledge
Venue: arXiv cs.CL 2025
Year: 2025

The rise of Large Language Models (LLMs) has redefined the AI landscape, particularly due to their ability to encode factual and commonsense knowledge, and their outstanding performance in tasks requiring reasoning. Despite these advances, hallucinations and reasoning errors remain a significant barrier to their deployment in high-stakes settings. In this work, we observe that even the most prominent LLMs, such as OpenAI-o1, suffer from high rates of reasoning errors and hallucinations on tasks requiring commonsense reasoning over obscure, long-tail entities. To investigate this limitation, we present a new dataset for Commonsense reasoning over Long-Tail entities (CoLoTa), that consists of 3,300 queries from question answering and claim verification tasks and covers a diverse range of commonsense reasoning skills. We remark that CoLoTa can also serve as a Knowledge Graph Question Answering (KGQA) dataset since the support of knowledge required to answer its queries is present in the Wikidata knowledge graph. However, as opposed to existing KGQA benchmarks that merely focus on factoid questions, our CoLoTa queries also require commonsense reasoning. Our experiments with strong LLM-based KGQA methodologies indicate their severe inability to answer queries involving commonsense reasoning. Hence, we propose CoLoTa as a novel benchmark for assessing both (i) LLM commonsense reasoning capabilities and their robustness to hallucinations on long-tail entities and (ii) the commonsense reasoning capabilities of KGQA methods.
A Bio-inspired Asymmetric Double-Gate Ferroelectric FET for Emulating Astrocyte and Dendrite Dynamics in Neuromorphic Systems
Venue: arXiv cs.ET 2025
Year: 2025

Neuromorphic systems seek to replicate the functionalities of biological neural networks to attain significant improvements in performance and efficiency of AI computing platforms. However, these systems have generally remained limited to emulation of simple neurons and synapses; and ignored higher order functionalities enabled by other components of the brain like astrocytes and dendrites. In this work, drawing inspiration from biology, we introduce a compact Double-Gate Ferroelectric Field Effect Transistor (DG-FeFET) cell that can emulate the dynamics of both astrocytes and dendrites within neuromorphic architectures. We demonstrate that with a ferroelectric top gate for synaptic weight programming as in conventional synapses and a non-ferroelectric back gate, the DG-FeFET realizes a synapse with a dynamic gain modulation mechanism. This can be leveraged as an analog for a compact astrocyte-tripartite synapse, as well as enabling dendrite-like gain modulation operations. By employing a fully-depleted silicon-on-insulator (FDSOI) FeFET as our double-gate device, we validate the linear control of the synaptic weight via the back gate terminal (i.e., the gate underneath the buried oxide (BOX) layer) through comprehensive theoretical and experimental studies. We showcase the promise such a tripartite synaptic device holds for numerous important neuromorphic applications, including autonomous self-repair of faulty neuromorphic hardware mediated by astrocytic functionality. Coordinate transformations based on dragonfly prey-interception circuitry models are also demonstrated based on dendritic function emulation by the device. This work paves the way forward for developing truly "brain-like" neuromorphic hardware that go beyond the current dogma focusing only on neurons and synapses.
ApexNav: An Adaptive Exploration Strategy for Zero-Shot Object Navigation with Target-centric Semantic Fusion
Venue: arXiv cs.RO 2025
Year: 2025

Navigating unknown environments to find a target object is a significant challenge. While semantic information is crucial for navigation, relying solely on it for decision-making may not always be efficient, especially in environments with weak semantic cues. Additionally, many methods are susceptible to misdetections, especially in environments with visually similar objects. To address these limitations, we propose ApexNav, a zero-shot object navigation framework that is both more efficient and reliable. For efficiency, ApexNav adaptively utilizes semantic information by analyzing its distribution in the environment, guiding exploration through semantic reasoning when cues are strong, and switching to geometry-based exploration when they are weak. For reliability, we propose a target-centric semantic fusion method that preserves long-term memory of the target and similar objects, enabling robust object identification even under noisy detections. We evaluate ApexNav on the HM3Dv1, HM3Dv2, and MP3D datasets, where it outperforms state-of-the-art methods in both SR and SPL metrics. Comprehensive ablation studies further demonstrate the effectiveness of each module. Furthermore, real-world experiments validate the practicality of ApexNav in physical environments. The code will be released at https://github.com/Robotics-STAR-Lab/ApexNav.