OpenRaiser/Intern-Atlas huggingface dataset:
GTMA: Dynamic Representation Optimization for OOD Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-language models (VLMs) struggle in open-world applications, where out-of-distribution (OOD) concepts often trigger cross-modal alignment collapse and severely degrade zero-shot performance. We identify the root cause as modal asymmetry: while the visual encoder can extract discriminative features from unseen images, the text encoder is constrained by a fixed discrete vocabulary and cannot synthesize new semantic anchors. Existing approaches such as CoOp or LoRA provide only partial remedies, as they remain confined to the pre-trained semantic space. To overcome this bottleneck, we propose dynamic representation optimization, realized through the Guided Target-Matching Adaptation (GTMA) framework. At inference time, GTMA constructs a continuous pseudo-word embedding that best aligns with an OOD image's visual anchor, effectively bypassing vocabulary limitations. The optimization is driven by an adaptive gradient-based representation policy optimization algorithm, which incorporates semantic regularization to preserve plausibility and compatibility with the model's prior knowledge. Experiments on ImageNet-R and the VISTA-Beyond benchmark demonstrate that GTMA improves zero-shot and few-shot OOD accuracy by up to 15-20 percent over the base VLM while maintaining performance on in-distribution concepts. Ablation studies further confirm the necessity of pseudo-word optimization.
Prediction and Forecast of Short-Term Drought Impacts Using Machine Learning to Support Mitigation and Adaptation Efforts
Venue: arXiv cs.LG 2025
Year: 2025

Drought is a complex natural hazard that affects ecological and human systems, often resulting in substantial environmental and economic losses. Recent increases in drought severity, frequency, and duration underscore the need for effective monitoring and mitigation strategies. Predicting drought impacts rather than drought conditions alone offers opportunities to support early warning systems and proactive decision-making. This study applies machine learning techniques to link drought indices with historical drought impact records (2005:2024) to generate short-term impact forecasts. By addressing key conceptual and data-driven challenges regarding temporal scale and impact quantification, the study aims to improve the predictability of drought impacts at actionable lead times. The Drought Severity and Coverage Index (DSCI) and the Evaporative Stress Index (ESI) were combined with impact data from the Drought Impact Reporter (DIR) to model and forecast weekly drought impacts. Results indicate that Fire and Relief impacts were predicted with the highest accuracy, followed by Agriculture and Water, while forecasts for Plants and Society impacts showed greater variability. County and state level forecasts for New Mexico were produced using an eXtreme Gradient Boosting (XGBoost) model that incorporated both DSCI and ESI. The model successfully generated forecasts up to eight weeks in advance using the preceding eight weeks of data for most impact categories. This work supports the development of an Ecological Drought Information Communication System (EcoDri) for New Mexico and demonstrates the potential for broader application in similar drought-prone regions. The findings can aid stakeholders, land managers, and decision-makers in developing and implementing more effective drought mitigation and adaptation strategies.
Detection of AI Generated Images Using Combined Uncertainty Measures and Particle Swarm Optimised Rejection Mechanism
Venue: arXiv cs.CV 2025
Year: 2025

As AI-generated images become increasingly photorealistic, distinguishing them from natural images poses a growing challenge. This paper presents a robust detection framework that leverages multiple uncertainty measures to decide whether to trust or reject a model's predictions. We focus on three complementary techniques: Fisher Information, which captures the sensitivity of model parameters to input variations; entropy-based uncertainty from Monte Carlo Dropout, which reflects predictive variability; and predictive variance from a Deep Kernel Learning framework using a Gaussian Process classifier. To integrate these diverse uncertainty signals, Particle Swarm Optimisation is used to learn optimal weightings and determine an adaptive rejection threshold. The model is trained on Stable Diffusion-generated images and evaluated on GLIDE, VQDM, Midjourney, BigGAN, and StyleGAN3, each introducing significant distribution shifts. While standard metrics such as prediction probability and Fisher-based measures perform well in distribution, their effectiveness degrades under shift. In contrast, the Combined Uncertainty measure consistently achieves an incorrect rejection rate of approximately 70 percent on unseen generators, successfully filtering most misclassified AI samples. Although the system occasionally rejects correct predictions from newer generators, this conservative behaviour is acceptable, as rejected samples can support retraining. The framework maintains high acceptance of accurate predictions for natural images and in-domain AI data. Under adversarial attacks using FGSM and PGD, the Combined Uncertainty method rejects around 61 percent of successful attacks, while GP-based uncertainty alone achieves up to 80 percent. Overall, the results demonstrate that multi-source uncertainty fusion provides a resilient and adaptive solution for AI-generated image detection.
The MEVIR 2 Framework: A Virtue-Informed Moral-Epistemic Model of Human Trust Decisions
Venue: arXiv cs.CY 2025
Year: 2025

The MEVIR 2 framework innovates and improves how we understand trust decisions in our polarized information landscape. Unlike classical models assuming ideal rationality, MEVIR 2 recognizes that human trust emerges from three interacting foundations: how we process evidence procedurally, our character as epistemic agents virtue theory, and our moral intuitions shaped by both evolutionary cooperation MAC model and cultural values Extended Moral Foundations Theory. This explains why different people find different authorities, facts, and tradeoffs compelling. MEVIR 2's key innovation introduces "Truth Tribes" TTs-stable communities sharing aligned procedural, virtue, and moral epistemic profiles. These arent mere ideological groups but emergent clusters with internally coherent "trust lattices" that remain mutually unintelligible across tribal boundaries. The framework incorporates distinctions between Truth Bearers and Truth Makers, showing disagreements often stem from fundamentally different views about what aspects of reality can make propositions true. Case studies on vaccination mandates and climate policy demonstrate how different moral configurations lead people to select different authorities, evidential standards, and trust anchors-constructing separate moral epistemic worlds. The framework reinterprets cognitive biases as failures of epistemic virtue and provides foundations for designing decision support systems that could enhance metacognition, make trust processes transparent, and foster more conscientious reasoning across divided communities. MEVIR 2 thus offers both descriptive power for understanding polarization and normative guidance for bridging epistemic divides.
ShibuyaSocial: Multi-scale Model of Pedestrian Flows in Scramble Crossing
Venue: arXiv cs.MA 2025
Year: 2025

This paper presents a learning-based model of pedestrian flows that integrates multi scale behaviors such as global route selection and local collision avoidance in urban spaces, particularly focusing on pedestrian movements at Shibuya scramble crossing. Since too much congestion of pedestrian flows can cause serious accidents, mathematically modeling and predicting pedestrian behaviors is important for preventing such accidents and providing a safe and comfortable environment. Although numerous studies have investigated learning-based modeling methods, most of them focus only on the local behavior of pedestrians, such as collision avoidance with neighbors and environmental objects. In an actual environment, pedestrian behavior involves more complicated decision making including global route selection. Moreover, a state transition from stopping to walking at a traffic light should be considered simultaneously. In this study, the proposed model integrates local behaviors with global route selection, using an Attention mechanism to ensure consistent global and local behavior predictions. We recorded video data of pedestrians at Shibuya scramble crossing and trained the proposed model using pedestrian walking trajectory data obtained from the video. Simulations of pedestrian behaviors based on the trained model qualitatively and quantitatively validated that the proposed model can appropriately predict pedestrian behaviors.
Neologism Learning as a Parameter-Efficient Alternative to Fine-Tuning for Model Steering
Venue: arXiv cs.CL 2025
Year: 2025

In language modeling, neologisms are new tokens trained to represent a concept not already included in a given model's vocabulary. Neologisms can be used to encourage specific behavior in models, for example by appending prompts with "Give me a neologism answer." Behavioral steering can also be achieved through fine-tuning, albeit with more compute and less flexibility: learning a neologism only trains d parameters and allows the user to still access the model's default behavior. We compare the performance of neologism learning against low-rank adaptation (LoRA) fine-tuning, finding that neologisms outperform fine-tuned models under a matched training setup (same data and hyperparameters). We also investigate self-verbalizations of neologisms, and observe that the model will occasionally make up its own new words when asked about a neologism.
Proof of Authenticity of General IoT Information with Tamper-Evident Sensors and Blockchain
Venue: arXiv cs.CR 2025
Year: 2025

Sensor data in IoT (Internet of Things) systems is vulnerable to tampering or falsification when transmitted through untrusted services. This is critical because such data increasingly underpins real-world decisions in domains such as logistics, healthcare, and other critical infrastructure. We propose a general method for secure sensor-data logging in which tamper-evident devices periodically sign readouts, link data using redundant hash chains, and submit cryptographic evidence to a blockchain-based service via Merkle trees to ensure verifiability even under data loss. Our approach enables reliable and cost-effective validation of sensor data across diverse IoT systems, including disaster response and other humanitarian applications, without relying on the integrity of intermediate systems.
ESearch-R1: Learning Cost-Aware MLLM Agents for Interactive Embodied Search via Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have empowered embodied agents with remarkable capabilities in planning and reasoning. However, when facing ambiguous natural language instructions (e.g., "fetch the tool" in a cluttered room), current agents often fail to balance the high cost of physical exploration against the cognitive cost of human interaction. They typically treat disambiguation as a passive perception problem, lacking the strategic reasoning to minimize total task execution costs. To bridge this gap, we propose ESearch-R1, a cost-aware embodied reasoning framework that unifies interactive dialogue (Ask), episodic memory retrieval (GetMemory), and physical navigation (Navigate) into a single decision process. We introduce HC-GRPO (Heterogeneous Cost-Aware Group Relative Policy Optimization). Unlike traditional PPO which relies on a separate value critic, HC-GRPO optimizes the MLLM by sampling groups of reasoning trajectories and reinforcing those that achieve the optimal trade-off between information gain and heterogeneous costs (e.g., navigate time, and human attention). Extensive experiments in AI2-THOR demonstrate that ESearch-R1 significantly outperforms standard ReAct-based agents. It improves task success rates while reducing total operational costs by approximately 50\%, validating the effectiveness of GRPO in aligning MLLM agents with physical world constraints.
Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model
Venue: arXiv cs.CV 2025
Year: 2025

Placenta Accreta Spectrum (PAS) is a serious obstetric condition that can be challenging to diagnose with Magnetic Resonance Imaging (MRI) due to variability in radiologists' interpretations. To overcome this challenge, a hybrid 3D deep learning model for automated PAS detection from volumetric MRI scans is proposed in this study. The model integrates a 3D DenseNet121 to capture local features and a 3D Vision Transformer (ViT) to model global spatial context. It was developed and evaluated on a retrospective dataset of 1,133 MRI volumes. Multiple 3D deep learning architectures were also evaluated for comparison. On an independent test set, the DenseNet121-ViT model achieved the highest performance with a five-run average accuracy of 84.3%. These results highlight the strength of hybrid CNN-Transformer models as a computer-aided diagnosis tool. The model's performance demonstrates a clear potential to assist radiologists by providing a robust decision support to improve diagnostic consistency across interpretations, and ultimately enhance the accuracy and timeliness of PAS diagnosis.
Modality-Dependent Memory Mechanisms in Cross-Modal Neuromorphic Computing
Venue: arXiv cs.LG 2025
Year: 2025

Memory-augmented spiking neural networks (SNNs) promise energy-efficient neuromorphic computing, yet their generalization across sensory modalities remains unexplored. We present the first comprehensive cross-modal ablation study of memory mechanisms in SNNs, evaluating Hopfield networks, Hierarchical Gated Recurrent Networks (HGRNs), and supervised contrastive learning (SCL) across visual (N-MNIST) and auditory (SHD) neuromorphic datasets. Our systematic evaluation of five architectures reveals striking modality-dependent performance patterns: Hopfield networks achieve 97.68% accuracy on visual tasks but only 76.15% on auditory tasks (21.53 point gap), revealing severe modality-specific specialization, while SCL demonstrates more balanced cross-modal performance (96.72% visual, 82.16% audio, 14.56 point gap). These findings establish that memory mechanisms exhibit task-specific benefits rather than universal applicability. Joint multi-modal training with HGRN achieves 94.41% visual and 79.37% audio accuracy (88.78% average), matching parallel HGRN performance through unified deployment. Quantitative engram analysis confirms weak cross-modal alignment (0.038 similarity), validating our parallel architecture design. Our work provides the first empirical evidence for modality-specific memory optimization in neuromorphic systems, achieving 603x energy efficiency over traditional neural networks.
On Finding Inconsistencies in Documents
Venue: arXiv cs.CL 2025
Year: 2025

Professionals in academia, law, and finance audit their documents because inconsistencies can result in monetary, reputational, and scientific costs. Language models (LMs) have the potential to dramatically speed up this auditing process. To understand their abilities, we introduce a benchmark, FIND (Finding INconsistencies in Documents), where each example is a document with an inconsistency inserted manually by a domain expert. Despite the documents being long, technical, and complex, the best-performing model (gpt-5) recovered 64% of the inserted inconsistencies. Surprisingly, gpt-5 also found undiscovered inconsistencies present in the original documents. For example, on 50 arXiv papers, we judged 136 out of 196 of the model's suggestions to be legitimate inconsistencies missed by the original authors. However, despite these findings, even the best models miss almost half of the inconsistencies in FIND, demonstrating that inconsistency detection is still a challenging task.
Trajectory Planning for UAV-Based Smart Farming Using Imitation-Based Triple Deep Q-Learning
Venue: arXiv cs.LG 2025
Year: 2025

Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-based smart agriculture is challenging due to the high uncertainty of the environment, partial observations, and limited battery capacity of UAVs. To address these issues, we formulate the trajectory planning problem as a Markov decision process (MDP) and leverage multi-agent reinforcement learning (MARL) to solve it. Furthermore, we propose a novel imitation-based triple deep Q-network (ITDQN) algorithm, which employs an elite imitation mechanism to reduce exploration costs and utilizes a mediator Q-network over a double deep Q-network (DDQN) to accelerate and stabilize training and improve performance. Experimental results in both simulated and real-world environments demonstrate the effectiveness of our solution. Moreover, our proposed ITDQN outperforms DDQN by 4.43\% in weed recognition rate and 6.94\% in data collection rate.
Uni-Neur2Img: Unified Neural Signal-Guided Image Generation, Editing, and Stylization via Diffusion Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Generating or editing images directly from Neural signals has immense potential at the intersection of neuroscience, vision, and Brain-computer interaction. In this paper, We present Uni-Neur2Img, a unified framework for neural signal-driven image generation and editing. The framework introduces a parameter-efficient LoRA-based neural signal injection module that independently processes each conditioning signal as a pluggable component, facilitating flexible multi-modal conditioning without altering base model parameters. Additionally, we employ a causal attention mechanism accommodate the long-sequence modeling demands of conditional generation tasks. Existing neural-driven generation research predominantly focuses on textual modalities as conditions or intermediate representations, resulting in limited exploration of visual modalities as direct conditioning signals. To bridge this research gap, we introduce the EEG-Style dataset. We conduct comprehensive evaluations across public benchmarks and self-collected neural signal datasets: (1) EEG-driven image generation on the public CVPR40 dataset; (2) neural signal-guided image editing on the public Loongx dataset for semantic-aware local modifications; and (3) EEG-driven style transfer on our self-collected EEG-Style dataset. Extensive experimental results demonstrate significant improvements in generation fidelity, editing consistency, and style transfer quality while maintaining low computational overhead and strong scalability to additional modalities. Thus, Uni-Neur2Img offers a unified, efficient, and extensible solution for bridging neural signals and visual content generation.
Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Deep Learning (Inference++)
Venue: arXiv cs.CR 2025
Year: 2025

Privacy-preserving inference of convolutional neural networks (CNNs) using homomorphic encryption has emerged as a promising approach for enabling secure machine learning in untrusted environments. In our previous work, we introduced a matrix-encoding strategy that allows convolution and matrix multiplication to be efficiently evaluated over encrypted data, enabling practical CNN inference without revealing either the input data or the model parameters. The core idea behind this strategy is to construct a three-dimensional representation within ciphertexts that preserves the intrinsic spatial structure of both input image data and model weights, rather than flattening them into conventional two-dimensional encodings. However, this approach can operate efficiently $only$ when the number of available plaintext slots within a ciphertext is sufficient to accommodate an entire input image, which becomes a critical bottleneck when processing high-resolution images. In this paper, we address this fundamental limitation by proposing an improved encoding and computation framework that removes the requirement that a single encrypted ciphertext must fully contain one input image. Our method reformulates the data layout and homomorphic operations to partition high-resolution inputs across multiple ciphertexts while preserving the algebraic structure required for efficient convolution and matrix multiplication. As a result, our approach enables privacy-preserving CNN inference to scale naturally beyond the slot-capacity constraints of prior methods, making homomorphic evaluation of CNNs practical for higher-resolution and more complex datasets.
Adversarial Robustness in Zero-Shot Learning:An Empirical Study on Class and Concept-Level Vulnerabilities
Venue: arXiv cs.CV 2025
Year: 2025

Zero-shot Learning (ZSL) aims to enable image classifiers to recognize images from unseen classes that were not included during training. Unlike traditional supervised classification, ZSL typically relies on learning a mapping from visual features to predefined, human-understandable class concepts. While ZSL models promise to improve generalization and interpretability, their robustness under systematic input perturbations remain unclear. In this study, we present an empirical analysis about the robustness of existing ZSL methods at both classlevel and concept-level. Specifically, we successfully disrupted their class prediction by the well-known non-target class attack (clsA). However, in the Generalized Zero-shot Learning (GZSL) setting, we observe that the success of clsA is only at the original best-calibrated point. After the attack, the optimal bestcalibration point shifts, and ZSL models maintain relatively strong performance at other calibration points, indicating that clsA results in a spurious attack success in the GZSL. To address this, we propose the Class-Bias Enhanced Attack (CBEA), which completely eliminates GZSL accuracy across all calibrated points by enhancing the gap between seen and unseen class probabilities.Next, at concept-level attack, we introduce two novel attack modes: Class-Preserving Concept Attack (CPconA) and NonClass-Preserving Concept Attack (NCPconA). Our extensive experiments evaluate three typical ZSL models across various architectures from the past three years and reveal that ZSL models are vulnerable not only to the traditional class attack but also to concept-based attacks. These attacks allow malicious actors to easily manipulate class predictions by erasing or introducing concepts. Our findings highlight a significant performance gap between existing approaches, emphasizing the need for improved adversarial robustness in current ZSL models.