OpenRaiser/Intern-Atlas huggingface dataset:
mmMirror: Device Free mmWave Indoor NLoS Localization Using Van-Atta-Array IRS
Venue: arXiv cs.NI 2025
Year: 2025

Industry 4.0 is transforming manufacturing and logistics by integrating robots into shared human environments, such as factories, warehouses, and healthcare facilities. However, the risk of human-robot collisions, especially in Non-Line-of-Sight (NLoS) scenarios like around corners, remains a critical challenge. Existing solutions, such as vision-based and LiDAR systems, often fail under occlusion, lighting constraints, or privacy concerns, while RF-based systems are limited by range and accuracy. To address these limitations, we propose mmMirror, a novel system leveraging a Van Atta Array-based millimeter-wave (mmWave) reconfigurable intelligent reflecting surface (IRS) for precise, device-free NLoS localization. mmMirror integrates seamlessly with existing frequency-modulated continuous-wave (FMCW) radars and offers: (i) robust NLoS localization with centimeter-level accuracy at ranges up to 3 m, (ii) seamless uplink and downlink communication between radar and IRS, (iii) support for multi-radar and multi-target scenarios via dynamic beam steering, and (iv) reduced scanning latency through adaptive time slot allocation. Implemented using commodity 24 GHz radars and a PCB-based IRS prototype, mmMirror demonstrates its potential in enabling safe human-robot interactions in dynamic and complex environments.
From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification
Venue: arXiv cs.CV 2025
Year: 2025

Foundation models provide robust embeddings for diverse tasks, including medical imaging. We evaluate embeddings from seven general and medical-specific foundation models (e.g., DenseNet121, BiomedCLIP, MedImageInsight, Rad-DINO, CXR-Foundation) for training lightweight adapters in multi-class radiography classification. Using a dataset of 8,842 radiographs across seven classes, we trained adapters with algorithms like K-Nearest Neighbors, logistic regression, SVM, random forest, and MLP. The combination of MedImageInsight embeddings with an SVM or MLP adapter achieved the highest mean area under the curve (mAUC) of 93.1%. This performance was statistically superior to other models, including MedSigLIP with an MLP (91.0%), Rad-DINO with an SVM (90.7%), and CXR-Foundation with logistic regression (88.6%). In contrast, models like BiomedCLIP (82.8%) and Med-Flamingo (78.5%) showed lower performance. Crucially, these lightweight adapters are computationally efficient, training in minutes and performing inference in seconds on a CPU, making them practical for clinical use. A fairness analysis of the top-performing MedImageInsight adapter revealed minimal performance disparities across patient gender (within 1.8%) and age groups (std. dev < 1.4%), with no significant statistical differences. These findings confirm that embeddings from specialized foundation models, particularly MedImageInsight, can power accurate, efficient, and equitable diagnostic tools using simple, lightweight adapters.
Robust 2D lidar-based SLAM in arboreal environments without IMU/GNSS
Venue: arXiv cs.RO 2025
Year: 2025

Simultaneous localization and mapping (SLAM) approaches for mobile robots remains challenging in forest or arboreal fruit farming environments, where tree canopies obstruct Global Navigation Satellite Systems (GNSS) signals. Unlike indoor settings, these agricultural environments possess additional challenges due to outdoor variables such as foliage motion and illumination variability. This paper proposes a solution based on 2D lidar measurements, which requires less processing and storage, and is more cost-effective, than approaches that employ 3D lidars. Utilizing the modified Hausdorff distance (MHD) metric, the method can solve the scan matching robustly and with high accuracy without needing sophisticated feature extraction. The method's robustness was validated using public datasets and considering various metrics, facilitating meaningful comparisons for future research. Comparative evaluations against state-of-the-art algorithms, particularly A-LOAM, show that the proposed approach achieves lower positional and angular errors while maintaining higher accuracy and resilience in GNSS-denied settings. This work contributes to the advancement of precision agriculture by enabling reliable and autonomous navigation in challenging outdoor environments.
BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset
Venue: arXiv cs.SD 2025
Year: 2025

Deepfake audio detection is challenging for low-resource languages like Bengali due to limited datasets and subtle acoustic features. To address this, we introduce BangalFake, a Bengali Deepfake Audio Dataset with 12,260 real and 13,260 deepfake utterances. Synthetic speech is generated using SOTA Text-to-Speech (TTS) models, ensuring high naturalness and quality. We evaluate the dataset through both qualitative and quantitative analyses. Mean Opinion Score (MOS) from 30 native speakers shows Robust-MOS of 3.40 (naturalness) and 4.01 (intelligibility). t-SNE visualization of MFCCs highlights real vs. fake differentiation challenges. This dataset serves as a crucial resource for advancing deepfake detection in Bengali, addressing the limitations of low-resource language research.
Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
Venue: arXiv cs.LG 2025
Year: 2025

Post-training of LLMs with RLHF, and subsequently preference optimization algorithms such as DPO, IPO, etc., made a big difference in improving human alignment. However, all such techniques can only work with a single (human) objective. In practice, human users have multiple objectives, such as helpfulness and harmlessness, and there is no natural way to aggregate them into a single objective. In this paper, we address the multi-objective preference-alignment problem, where a policy must optimize several, potentially conflicting, objectives. We introduce the Multi-Objective Preference Optimization (MOPO) algorithm, which frames alignment as a constrained KL-regularized optimization: the primary objective is maximized while secondary objectives are lower-bounded by tunable safety thresholds. Unlike prior work, MOPO operates directly on pairwise preference data, requires no point-wise reward assumption, and avoids heuristic prompt-context engineering. The method recovers policies on the Pareto front whenever the front is attainable; practically, it reduces to simple closed-form iterative updates suitable for large-scale training. On synthetic benchmarks with diverse canonical preference structures, we show that MOPO approximates the Pareto front. When fine-tuning a 1.3B-parameter language model on real-world human-preference datasets, MOPO attains higher rewards and yields policies that Pareto-dominate baselines; ablation studies confirm optimization stability and robustness to hyperparameters.
Vaiage: A Multi-Agent Solution to Personalized Travel Planning
Venue: arXiv cs.MA 2025
Year: 2025

Planning trips is a cognitively intensive task involving conflicting user preferences, dynamic external information, and multi-step temporal-spatial optimization. Traditional platforms often fall short - they provide static results, lack contextual adaptation, and fail to support real-time interaction or intent refinement. Our approach, Vaiage, addresses these challenges through a graph-structured multi-agent framework built around large language models (LLMs) that serve as both goal-conditioned recommenders and sequential planners. LLMs infer user intent, suggest personalized destinations and activities, and synthesize itineraries that align with contextual constraints such as budget, timing, group size, and weather. Through natural language interaction, structured tool use, and map-based feedback loops, Vaiage enables adaptive, explainable, and end-to-end travel planning grounded in both symbolic reasoning and conversational understanding. To evaluate Vaiage, we conducted human-in-the-loop experiments using rubric-based GPT-4 assessments and qualitative feedback. The full system achieved an average score of 8.5 out of 10, outperforming the no-strategy (7.2) and no-external-API (6.8) variants, particularly in feasibility. Qualitative analysis indicated that agent coordination - especially the Strategy and Information Agents - significantly improved itinerary quality by optimizing time use and integrating real-time context. These results demonstrate the effectiveness of combining LLM reasoning with symbolic agent coordination in open-ended, real-world planning tasks.
A Dataset for Spatiotemporal-Sensitive POI Question Answering
Venue: arXiv cs.LG 2025
Year: 2025

Spatiotemporal relationships are critical in data science, as many prediction and reasoning tasks require analysis across both spatial and temporal dimensions--for instance, navigating an unfamiliar city involves planning itineraries that sequence locations and timing cultural experiences. However, existing Question-Answering (QA) datasets lack sufficient spatiotemporal-sensitive questions, making them inadequate benchmarks for evaluating models' spatiotemporal reasoning capabilities. To address this gap, we introduce POI-QA, a novel spatiotemporal-sensitive QA dataset centered on Point of Interest (POI), constructed through three key steps: mining and aligning open-source vehicle trajectory data from GAIA with high-precision geographic POI data, rigorous manual validation of noisy spatiotemporal facts, and generating bilingual (Chinese/English) QA pairs that reflect human-understandable spatiotemporal reasoning tasks. Our dataset challenges models to parse complex spatiotemporal dependencies, and evaluations of state-of-the-art multilingual LLMs (e.g., Qwen2.5-7B, Llama3.1-8B) reveal stark limitations: even the top-performing model (Qwen2.5-7B fine-tuned with RAG+LoRA) achieves a top 10 Hit Ratio (HR@10) of only 0.41 on the easiest task, far below human performance at 0.56. This underscores persistent weaknesses in LLMs' ability to perform consistent spatiotemporal reasoning, while highlighting POI-QA as a robust benchmark to advance algorithms sensitive to spatiotemporal dynamics. The dataset is publicly available at https://www.kaggle.com/ds/7394666.
Enhanced Multiuser CSI-Based Physical Layer Authentication Based on Information Reconciliation
Venue: arXiv cs.CR 2025
Year: 2025

This paper presents a physical layer authentication (PLA) technique using information reconciliation in multiuser communication systems. A cost-effective solution for low-end Internet of Things networks can be provided by PLA. In this work, we develop an information reconciliation scheme using Polar codes along with a quantization strategy that employs an arbitrary number of bits to enhance the performance of PLA. We employ the principle of Slepian-Wolf coding to reconcile channel measurements spread in time. Numerical results show that our approach works very well and outperforms competing approaches, achieving more than 99.80% increase in detection probability for false alarm probabilities close to 0.
ToDMA: Large Model-Driven Token-Domain Multiple Access for Semantic Communications
Venue: arXiv cs.IT 2025
Year: 2025

Token communications (TokCom) is an emerging generative semantic communication concept that reduces transmission rates by using context and multimodal large language model (MLLM)-based token processing, with tokens serving as universal semantic units across modalities. In this paper, we propose a semantic multiple access scheme in the token domain, referred to as token domain multiple access (ToDMA), where a large number of devices share a token codebook and a modulation codebook for source and channel coding, respectively. Specifically, each transmitter first tokenizes its source signal and modulate each token to a codeword. At the receiver, compressed sensing is employed first to detect active tokens and the corresponding channel state information (CSI) from the superposed signals. Then, the source token sequences are reconstructed by clustering the token-associated CSI across multiple time slots. In case of token collisions, some active tokens cannot be assigned and some positions in the reconstructed token sequences are empty. We propose to use pre-trained MLLMs to leverage the context, predict masked tokens, and thus mitigate token collisions. Simulation results demonstrate the effectiveness of the proposed ToDMA framework for both text and image transmission tasks, achieving significantly lower latency compared to context-unaware orthogonal communication schemes, while also delivering superior distortion and perceptual quality compared to state-of-the-art context-unaware non-orthogonal communication methods.
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
Venue: arXiv cs.CV 2025
Year: 2025

The practical deployment of Visual Anomaly Detection (VAD) systems is hindered by their sensitivity to real-world imaging variations, particularly the complex interplay between viewpoint and illumination which drastically alters defect visibility. Current benchmarks largely overlook this critical challenge. We introduce Multi-View Multi-Illumination Anomaly Detection (M2AD), a new large-scale benchmark comprising 119,880 high-resolution images designed explicitly to probe VAD robustness under such interacting conditions. By systematically capturing 999 specimens across 10 categories using 12 synchronized views and 10 illumination settings (120 configurations total), M2AD enables rigorous evaluation. We establish two evaluation protocols: M2AD-Synergy tests the ability to fuse information across diverse configurations, and M2AD-Invariant measures single-image robustness against realistic view-illumination effects. Our extensive benchmarking shows that state-of-the-art VAD methods struggle significantly on M2AD, demonstrating the profound challenge posed by view-illumination interplay. This benchmark serves as an essential tool for developing and validating VAD methods capable of overcoming real-world complexities. Our full dataset and test suite will be released at https://hustcyq.github.io/M2AD to facilitate the field.
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning
Venue: arXiv cs.CV 2025
Year: 2025

While diffusion models excel at image synthesis, useful representations have been shown to emerge from generative pre-training, suggesting a path towards unified generative and discriminative learning. However, suboptimal semantic flow within current architectures can hinder this potential: features encoding the richest high-level semantics are underutilized and diluted when propagating through decoding layers, impeding the formation of an explicit semantic bottleneck layer. To address this, we introduce self-conditioning, a lightweight mechanism that reshapes the model's layer-wise semantic hierarchy without external guidance. By aggregating and rerouting intermediate features to guide subsequent decoding layers, our method concentrates more high-level semantics, concurrently strengthening global generative guidance and forming more discriminative representations. This simple approach yields a dual-improvement trend across pixel-space UNet, UViT and latent-space DiT models with minimal overhead. Crucially, it creates an architectural semantic bridge that propagates discriminative improvements into generation and accommodates further techniques such as contrastive self-distillation. Experiments show that our enhanced models, especially self-conditioned DiT, are powerful dual learners that yield strong and transferable representations on image and dense classification tasks, surpassing various generative self-supervised models in linear probing while also improving or maintaining high generation quality.
Dual Teacher-Student Learning for Semi-supervised Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Semi-supervised learning reduces the costly manual annotation burden in medical image segmentation. A popular approach is the mean teacher (MT) strategy, which applies consistency regularization using a temporally averaged teacher model. In this work, the MT strategy is reinterpreted as a form of self-paced learning in the context of supervised learning, where agreement between the teacher's predictions and the ground truth implicitly guides the model from easy to hard. Extending this insight to semi-supervised learning, we propose dual teacher-student learning (DTSL). It regulates the learning pace on unlabeled data using two signals: a temporally averaged signal from an in-group teacher and a cross-architectural signal from a student in a second, distinct model group. Specifically, a novel consensus label generator (CLG) creates the pseudo-labels from the agreement between these two signals, establishing an effective learning curriculum. Extensive experiments on four benchmark datasets demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches. Remarkably, on three of the four datasets, our semi-supervised method with limited labeled data surpasses its fully supervised counterparts, validating the effectiveness of our self-paced learning design.
The heteronomy of algorithms: Traditional knowledge and computational knowledge
Venue: arXiv cs.CY 2025
Year: 2025

If an active citizen should increasingly be a computationally enlightened one, replacing the autonomy of reason with the heteronomy of algorithms, then I argue in this article that we must begin teaching the principles of critiquing the computal through new notions of what we might call digital Bildung. Indeed, if civil society itself is mediated by computational systems and media, the public use of reason must also be complemented by skills for negotiating and using these computal forms to articulate such critique. Not only is there a need to raise the intellectual tone regarding computation and its related softwarization processes, but there is an urgent need to attend to the likely epistemic challenges from computation which, as presently constituted, tends towards justification through a philosophy of utility rather than through a philosophy of care for the territory of the intellect. We therefore need to develop an approach to this field that uses concepts and methods drawn from philosophy, politics, history, anthropology, sociology, media studies, computer science, and the humanities more generally, to try to understand these issues - particularly the way in which software and data increasingly penetrate our everyday life and the pressures and fissures that are created. We must, in other words, move to undertake a critical interdisciplinary research program to understand the way in which these systems are created, instantiated, and normatively engendered in both specific and general contexts.
DEMO:Diffusion-based Evolutionary Optimization for 3D Multi-Objective Molecular Generation
Venue: arXiv cs.NE 2025
Year: 2025

Optimizing multiple objective properties while satisfying structural constraints is a major challenge in 3D molecular discovery. This difficulty arises because optimization objectives can be non-differentiable and the structure-property relationship is often unknown. Evolutionary algorithms (EAs) are widely used for multi-objective optimization to find Pareto fronts and can naturally handle structural constraints without any explicit modelling; however, in the 3D molecular space they lack mechanisms to guarantee chemical validity and are therefore prone to producing invalid structures. Conversely, diffusion models excel at generating chemically valid 3D molecules but typically require modifying the model and retraining to incorporate structural constraints. Moreover, diffusion models are not inherently designed for direct multi-objective optimization and struggle to explore the Pareto front of the learned property distribution - a critical capability for discovering novel, high-performing molecules. To bridge this gap, we propose a novel 3D molecular multi-objective evolutionary algorithm that leverages the generative power of a pretrained diffusion model. Instead of manipulating molecules directly in the complex chemical space, our method performs crossover operations in the noise space defined by the diffusion model's forward process, thereby enabling parental features or desired fragments to be fused into offspring. The pretrained model's denoising process then restores structural validity. The approach is highly composable and, requiring no retraining, can be readily integrated with existing guidance methods to improve discovery. Experimental results demonstrate strong performance on single-objective, multi-objective, and structurally constrained optimization tasks.
Exploration by Random Distribution Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Exploration remains a critical challenge in online reinforcement learning, as an agent must effectively explore unknown environments to achieve high returns. Currently, the main exploration algorithms are primarily count-based methods and curiosity-based methods, with prediction-error methods being a prominent example. In this paper, we propose a novel method called \textbf{R}andom \textbf{D}istribution \textbf{D}istillation (RDD), which samples the output of a target network from a normal distribution. RDD facilitates a more extensive exploration by explicitly treating the difference between the prediction network and the target network as an intrinsic reward. Furthermore, by introducing randomness into the output of the target network for a given state and modeling it as a sample from a normal distribution, intrinsic rewards are bounded by two key components: a pseudo-count term ensuring proper exploration decay and a discrepancy term accounting for predictor convergence. We demonstrate that RDD effectively unifies both count-based and prediction-error approaches. It retains the advantages of prediction-error methods in high-dimensional spaces, while also implementing an intrinsic reward decay mode akin to the pseudo-count method. In the experimental section, RDD is compared with more advanced methods in a series of environments. Both theoretical analysis and experimental results confirm the effectiveness of our approach in improving online exploration for reinforcement learning tasks.
Artifacts of Idiosyncracy in Global Street View Data
Venue: arXiv cs.CV 2025
Year: 2025

Street view data is increasingly being used in computer vision applications in recent years. Machine learning datasets are collected for these applications using simple sampling techniques. These datasets are assumed to be a systematic representation of cities, especially when densely sampled. Prior works however, show that there are clear gaps in coverage, with certain cities or regions being covered poorly or not at all. Here we demonstrate that a cities' idiosyncracies, such as city layout, may lead to biases in street view data for 28 cities across the globe, even when they are densely covered. We quantitatively uncover biases in the distribution of coverage of street view data and propose a method for evaluation of such distributions to get better insight in idiosyncracies in a cities' coverage. In addition, we perform a case study of Amsterdam with semi-structured interviews, showing how idiosyncracies of the collection process impact representation of cities and regions and allowing us to address biases at their source.
Internal Effectful Forcing in System T
Venue: arXiv cs.LO 2025
Year: 2025

The effectful forcing technique allows one to show that the denotation of a closed System T term of type $(ι\to ι) \to ι$ in the set-theoretical model is a continuous function $(\mathbb{N} \to \mathbb{N}) \to \mathbb{N}$. For this purpose, an alternative dialogue-tree semantics is defined and related to the set-theoretical semantics by a logical relation. In this paper, we apply effectful forcing to show that the dialogue tree of a System T term is itself System T-definable, using the Church encoding of trees.