mstyslavity/prisma_v3 huggingface dataset:
Prompting has emerged as a practical way to adapt frozen vision-language models (VLMs) for video anomaly detection (VAD). Yet, existing prompts are often overly abstract, overlooking the fine-grained human-object interactions or action semantics that define complex anomalies in surveillance videos. We propose ASK-Hint, a structured prompting framework that leverages action-centric knowledge to elicit more accurate and interpretable reasoning from frozen VLMs. Our approach organizes prompts into semantically coherent groups (e.g. violence, property crimes, public safety) and formulates fine-grained guiding questions that align model predictions with discriminative visual cues. Extensive experiments on UCF-Crime and XD-Violence show that ASK-Hint consistently improves AUC over prior baselines, achieving state-of-the-art performance compared to both fine-tuned and training-free methods. Beyond accuracy, our framework provides interpretable reasoning traces towards anomaly and demonstrates strong generalization across datasets and VLM backbones. These results highlight the critical role of prompt granularity and establish ASK-Hint as a new training-free and generalizable solution for explainable video anomaly detection.
OpenAI's o3-preview reasoning model exceeded human accuracy on the ARC-AGI benchmark, but does that mean state-of-the-art models recognize and reason with the abstractions that the task creators intended? We investigate models' abstraction abilities on ConceptARC. We evaluate models under settings that vary the input modality (textual vs. visual), whether the model is permitted to use external Python tools, and, for reasoning models, the amount of reasoning effort. In addition to measuring output accuracy, we perform fine-grained evaluation of the natural-language rules that models generate to explain their solutions. This dual evaluation lets us assess whether models solve tasks using the abstractions ConceptARC was designed to elicit, rather than relying on surface-level patterns. Our results show that, while some models using text-based representations match human output accuracy, the best models' rules are often based on surface-level ``shortcuts'' and capture intended abstractions far less often than humans. Thus their capabilities for general abstract reasoning may be overestimated by evaluations based on accuracy alone. In the visual modality, AI models' output accuracy drops sharply, yet our rule-level analysis reveals that models might be underestimated, as they still exhibit a substantial share of rules that capture intended abstractions, but are often unable to correctly apply these rules. In short, our results show that models still lag humans in abstract reasoning, and that using accuracy alone to evaluate abstract reasoning on ARC-like tasks may overestimate abstract-reasoning capabilities in textual modalities and underestimate it in visual modalities. We believe that our evaluation framework offers a more faithful picture of multimodal models' abstract reasoning abilities and a more principled way to track progress toward human-like, abstraction-centered intelligence.
Pose estimation refers to tracking a human's full body posture, including their head, torso, arms, and legs. The problem is challenging in practical settings where the number of body sensors are limited. Past work has shown promising results using conditional diffusion models, where the pose prediction is conditioned on both <location, rotation> measurements from the sensors. Unfortunately, nearly all these approaches generalize poorly across users, primarly because location measurements are highly influenced by the body size of the user. In this paper, we formulate pose estimation as an inverse problem and design an algorithm capable of zero-shot generalization. Our idea utilizes a pre-trained diffusion model and conditions it on rotational measurements alone; the priors from this model are then guided by a likelihood term, derived from the measured locations. Thus, given any user, our proposed InPose method generatively estimates the highly likely sequence of poses that best explains the sparse on-body measurements.
Public funding processes demand fairness, learning, and outcomes that participants can understand. We introduce Komitee Equal Shares, a priceable virtual-budget allocation framework that integrates two signals: in voter mode, participants cast point votes  in evaluator mode, small groups assess proposals against collectively defined impact fields. The framework extends the Method of Equal Shares by translating both signals into virtual spending power and producing voting receipts. We deployed the framework in the 2025 Kultur Komitee in Winterthur, Switzerland. Our contributions are: (1) a clear separation of decision modes, addressing a gap in social choice that typically treats participatory budgeting as preference aggregation while citizens also see themselves as evaluators  and (2) the design of voting receipts that operationalise priceability into participant-facing explanations, making proportional allocations legible and traceable. The framework generalises to participatory grant-making and budgeting, offering a model where citizens act as voters and evaluators within one proportional, explainable allocation.
Our goal is to enable social robots to interact autonomously with humans in a realistic, engaging, and expressive manner. The 12 Principles of Animation are a well-established framework animators use to create movements that make characters appear convincing, dynamic, and emotionally expressive. This paper proposes a novel approach that leverages Dynamic Movement Primitives (DMPs) to implement key animation principles, providing a learnable, explainable, modulable, online adaptable and composable model for automatic expressive motion generation. DMPs, originally developed for general imitation learning in robotics and grounded in a spring-damper system design, offer mathematical properties that make them particularly suitable for this task. Specifically, they enable modulation of the intensities of individual principles and facilitate the decomposition of complex, expressive motion sequences into learnable and parametrizable primitives. We present the mathematical formulation of the parameterized animation principles and demonstrate the effectiveness of our framework through experiments and application on three robotic platforms with different kinematic configurations, in simulation, on actual robots and in a 12 users study. Our results show that the approach allows for creating diverse and nuanced expressions using a single base model.
Deep learning has achieved remarkable success in medical image analysis, however its adoption in clinical practice is limited by a lack of interpretability. These models often make correct predictions without explaining their reasoning. They may also rely on image regions unrelated to the disease or visual cues, such as annotations, that are not present in real-world conditions. This can reduce trust and increase the risk of misleading diagnoses. We introduce the Guided Focus via Segment-Wise Relevance Network (GFSR-Net), an approach designed to improve interpretability and reliability in medical imaging. GFSR-Net uses a small number of human annotations to approximate where a person would focus within an image intuitively, without requiring precise boundaries or exhaustive markings, making the process fast and practical. During training, the model learns to align its focus with these areas, progressively emphasizing features that carry diagnostic meaning. This guidance works across different types of natural and medical images, including chest X-rays, retinal scans, and dermatological images. Our experiments demonstrate that GFSR achieves comparable or superior accuracy while producing saliency maps that better reflect human expectations. This reduces the reliance on irrelevant patterns and increases confidence in automated diagnostic tools.
Understanding how subjective experience arises from information processing remains a central challenge in neuroscience, cognitive science, and AI research. The Modular Consciousness Theory (MCT) proposes a biologically grounded and computationally explicit framework in which consciousness is a discrete sequence of Integrated Informational States (IISs). Each IIS is a packet of integrated information tagged with a multidimensional density vector that quantifies informational richness. Its magnitude correlates with subjective intensity, shaping memory, behavior, and continuity of experience. Inputs from body and environment are adaptively filtered, processed by modules (abstraction, narration, evaluation, self-evaluation), and integrated into an IIS. The resulting packet, tagged with its density vector, is transmitted to behavioral readiness, memory, and decision-making modules, closing the loop. This explains why strongly tagged states exert greater influence on long-term memory and action. Unlike Global Workspace Theory, Integrated Information Theory, or Higher-Order Thought, MCT specifies a full computational pipeline producing discrete informational units with quantifiable internal structure. Subjectivity is reframed as a correlate of the density-tagging signal with functional consequences. MCT generates testable predictions, such as stress enhancing memory encoding, and provides a naturalistic blueprint for both biological and artificial architectures. Consciousness, in this view, is not an irreducible essence but an evolvable, quantifiable, and constructible feature of complex information processing.
Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of providing or seeking explanations is to achieve (better) 'understanding' on the part of the explainee. However, what it means to 'understand' is still not clearly defined, and the concept itself is rarely the subject of scientific investigation. This conceptual article aims to present a model of forms of understanding for XAI-explanations and beyond. From an interdisciplinary perspective bringing together computer science, linguistics, sociology, philosophy and psychology, a definition of understanding and its forms, assessment, and dynamics during the process of giving everyday explanations are explored. Two types of understanding are considered as possible outcomes of explanations, namely enabledness, 'knowing how' to do or decide something, and comprehension, 'knowing that' -- both in different degrees (from shallow to deep). Explanations regularly start with shallow understanding in a specific domain and can lead to deep comprehension and enabledness of the explanandum, which we see as a prerequisite for human users to gain agency. In this process, the increase of comprehension and enabledness are highly interdependent. Against the background of this systematization, special challenges of understanding in XAI are discussed.
We present BiasLab, a dataset of 300 political news articles annotated for perceived ideological bias. These articles were selected from a curated 900-document pool covering diverse political events and source biases. Each article is labeled by crowdworkers along two independent scales, assessing sentiment toward the Democratic and Republican parties, and enriched with rationale indicators. The annotation pipeline incorporates targeted worker qualification and was refined through pilot-phase analysis. We quantify inter-annotator agreement, analyze misalignment with source-level outlet bias, and organize the resulting labels into interpretable subsets. Additionally, we simulate annotation using schema-constrained GPT-4o, enabling direct comparison to human labels and revealing mirrored asymmetries, especially in misclassifying subtly right-leaning content. We define two modeling tasks: perception drift prediction and rationale type classification, and report baseline performance to illustrate the challenge of explainable bias detection. BiasLab's rich rationale annotations provide actionable interpretations that facilitate explainable modeling of political bias, supporting the development of transparent, socially aware NLP systems. We release the dataset, annotation schema, and modeling code to encourage research on human-in-the-loop interpretability and the evaluation of explanation effectiveness in real-world settings.
Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by capturing user preferences through interactive dialogues. Explainability in CRSs is crucial as it enables users to understand the reasoning behind recommendations, increasing system transparency and trustworthiness. However, current CRSs often leverage knowledge graphs (KGs) or language models to extract and represent user preferences as latent vectors, which limits their explainability. Large language models (LLMs) offer powerful reasoning capabilities that can bridge this gap by generating human-understandable preference summaries. However, effectively reasoning over user preferences in CRSs remains challenging as LLMs pre-trained on large-scale corpora may not be well-suited for analyzing user preferences. While KGs provide rich domain knowledge, integrating them with LLMs encounters a significant modality gap between structured KG information and unstructured conversations. In this paper, we propose COMPASS, a plug-and-play framework that synergizes LLMs and KGs to reason over user preferences, enhancing the performance and explainability of existing CRSs. COMPASS employs a two-stage training approach: first, it bridges the gap between the structured KG and natural language through novel graph entity captioning pre-training. Next, COMPASS optimizes user preference reasoning via knowledge-aware instruction fine-tuning, where the LLM learns to reason and summarize user preferences from dialogue histories and KG-augmented context. This enables COMPASS to perform knowledge-aware reasoning and generate interpretable user preferences that can seamlessly integrate with existing CRS models for improving recommendation performance and explainability. Our experiments on benchmark datasets demonstrate the effectiveness of COMPASS in improving various CRS models.
The safe use of pharmaceuticals in food-producing animals is vital to protect\nanimal welfare and human food safety. Adverse events (AEs) may signal\nunexpected pharmacokinetic or toxicokinetic effects, increasing the risk of\nviolative residues in the food chain. This study introduces a predictive\nframework for classifying outcomes (Death vs. Recovery) using ~1.28 million\nreports (1987-2025 Q1) from the U.S. FDA's OpenFDA Center for Veterinary\nMedicine. A preprocessing pipeline merged relational tables and standardized\nAEs through VeDDRA ontologies. Data were normalized, missing values imputed,\nand high-cardinality features reduced; physicochemical drug properties were\nintegrated to capture chemical-residue links. We evaluated supervised models,\nincluding Random Forest, CatBoost, XGBoost, ExcelFormer, and large language\nmodels (Gemma 3-27B, Phi 3-12B). Class imbalance was addressed, such as\nundersampling and oversampling, with a focus on prioritizing recall for fatal\noutcomes. Ensemble methods(Voting, Stacking) and CatBoost performed best,\nachieving precision, recall, and F1-scores of 0.95. Incorporating Average\nUncertainty Margin (AUM)-based pseudo-labeling of uncertain cases improved\nminority-class detection, particularly in ExcelFormer and XGBoost.\nInterpretability via SHAP identified biologically plausible predictors,\nincluding lung, heart, and bronchial disorders, animal demographics, and drug\nphysicochemical properties. These features were strongly linked to fatal\noutcomes. Overall, the framework shows that combining rigorous data\nengineering, advanced machine learning, and explainable AI enables accurate,\ninterpretable predictions of veterinary safety outcomes. The approach supports\nFARAD's mission by enabling early detection of high-risk drug-event profiles,\nstrengthening residue risk assessment, and informing regulatory and clinical\ndecision-making.."
Humans intuitively perceive complex social signals in visual scenes, yet it remains unclear whether state-of-the-art AI models encode the same similarity structure. We study (Q1) whether modern video and language models capture human-perceived similarity in social videos, and (Q2) how to instill this structure into models using human behavioral data. To address this, we introduce a new benchmark of over 49,000 odd-one-out similarity judgments on 250 three-second video clips of social interactions, and discover a modality gap: despite the task being visual, caption-based language embeddings align better with human similarity than any pretrained video model. We close this gap by fine-tuning a TimeSformer video model on these human judgments with our novel hybrid triplet-RSA objective using low-rank adaptation (LoRA), aligning pairwise distances to human similarity. This fine-tuning protocol yields significantly improved alignment with human perceptions on held-out videos in terms of both explained variance and odd-one-out triplet accuracy. Variance partitioning shows that the fine-tuned video model increases shared variance with language embeddings and explains additional unique variance not captured by the language model. Finally, we test transfer via linear probes and find that human-similarity fine-tuning strengthens the encoding of social-affective attributes (intimacy, valence, dominance, communication) relative to the pretrained baseline. Overall, our findings highlight a gap in pretrained video models' social recognition and demonstrate that behavior-guided fine-tuning shapes video representations toward human social perception.
Deep neural networks (DNNs) have achieved remarkable success across domains but remain difficult to interpret, limiting their trustworthiness in high-stakes applications. This paper focuses on deep vision models, for which a dominant line of explainability methods are Class Activation Mapping (CAM) and its variants working by highlighting spatial regions that drive predictions. We figure out that CAM provides little semantic insight into what attributes underlie these activations. To address this limitation, we propose TextCAM, a novel explanation framework that enriches CAM with natural languages. TextCAM combines the precise spatial localization of CAM with the semantic alignment of vision-language models (VLMs). Specifically, we derive channel-level semantic representations using CLIP embeddings and linear discriminant analysis, and aggregate them with CAM weights to produce textual descriptions of salient visual evidence. This yields explanations that jointly specify where the model attends and what visual attributes likely support its decision. We further extend TextCAM to generate feature channels into semantically coherent groups, enabling more fine-grained visual-textual explanations. Experiments on ImageNet, CLEVR, and CUB demonstrate that TextCAM produces faithful and interpretable rationales that improve human understanding, detect spurious correlations, and preserve model fidelity.
The US Decennial Census provides valuable data for both research and policy purposes. Census data are subject to a variety of disclosure avoidance techniques prior to release in order to preserve respondent confidentiality. While many are interested in studying the impacts of disclosure avoidance methods on downstream analyses, particularly with the introduction of differential privacy in the 2020 Decennial Census, these efforts are limited by a critical lack of data: The underlying "microdata," which serve as necessary input to disclosure avoidance methods, are kept confidential. In this work, we aim to address this limitation by providing tools to generate synthetic microdata solely from published Census statistics, which can then be used as input to any number of disclosure avoidance algorithms for the sake of evaluation and carrying out comparisons. We define a principled distribution over microdata given published Census statistics and design algorithms to sample from this distribution. We formulate synthetic data generation in this context as a knapsack-style combinatorial optimization problem and develop novel algorithms for this setting. While the problem we study is provably hard, we show empirically that our methods work well in practice, and we offer theoretical arguments to explain our performance. Finally, we verify that the data we produce are "close" to the desired ground truth.
At HVEI-2012, I presented a neurobiologically-based model for trichromatic color sensations in humans, mapping the neural substrate for color sensations to V1-L4: the thalamic recipient layer of the primary visual cortex. In this paper, I propose that V1-L4 itself consists of three distinct sub-layers that directly correspond to the three primary color sensations: blue, red, and green. Furthermore, I apply this model to three aspects of color vision: the three-dimensional (3D) color solid, dichromatism, and ocular agnosticism. Regarding these aspects further: (1) 3D color solid: V1-L4 is known to exhibit a gradient of cell densities from its outermost layer (i.e., its pia side) to its innermost layer (i.e., its white matter side). Taken together with the proposition that the population size of a cell assembly directly corresponds with the magnitude of a color sensation, it can be inferred that the neurobiologically-based color solid is a tilted cuboid. (2) Chromatic color blindness: Using deuteranopia as an example, at the retinal level, M-cones are lost and replaced by L-cones. However, at the cortical level, deuteranopia manifests as a fusion of the two bottom layers of V1-L4. (3) Ocular agnosticism: Although color sensation is monocular, we normally are not aware of which eye we are seeing with. This visual phenomenon can be explained by the nature of ocular integration within V1-L4. A neurobiologically-based model for human color sensations could significantly contribute to future engineering efforts aimed at enhancing human color experiences.
We investigate whether large language models (LLMs) can generate effective, user-facing explanations from a mathematically interpretable recommendation model. The model is based on constrained matrix factorization, where user types are explicitly represented and predicted item scores share the same scale as observed ratings, making the model's internal representations and predicted scores directly interpretable. This structure is translated into natural language explanations using carefully designed LLM prompts. Many works in explainable AI rely on automatic evaluation metrics, which often fail to capture users' actual needs and perceptions. In contrast, we adopt a user-centered approach: we conduct a study with 326 participants who assessed the quality of the explanations across five key dimensions-transparency, effectiveness, persuasion, trust, and satisfaction-as well as the recommendations themselves. To evaluate how different explanation strategies are perceived, we generate multiple explanation types from the same underlying model, varying the input information provided to the LLM. Our analysis reveals that all explanation types are generally well received, with moderate statistical differences between strategies. User comments further underscore how participants react to each type of explanation, offering complementary insights beyond the quantitative results.