Discrete Stochastic Localization for Non-autoregressive Generation
arXiv:2602.16169v1 Announce Type: new Abstract: Non-autoregressive (NAR) generation reduces decoding latency by predicting many tokens in parallel, but iterative refinement often suffers from error accumulation and distribution shift under self-generated drafts. Masked diffusion language models (MDLMs) and their remasking samplers...
Lawsuit: ChatGPT told student he was "meant for greatness"—then came psychosis
"AI Injury Attorneys" target the chatbot design itself.
ViTaB-A: Evaluating Multimodal Large Language Models on Visual Table Attribution
arXiv:2602.15769v1 Announce Type: new Abstract: Multimodal Large Language Models (mLLMs) are often used to answer questions in structured data such as tables in Markdown, JSON, and images. While these models can often give correct answers, users also need to know...
Avey-B
arXiv:2602.15814v1 Announce Type: new Abstract: Compact pretrained bidirectional encoders remain the backbone of industrial NLP under tight compute and memory budgets. Their effectiveness stems from self-attention's ability to deliver high-quality bidirectional contextualization with sequence-level parallelism, as popularized by BERT-style architectures....
FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health
arXiv:2602.15273v1 Announce Type: cross Abstract: Information ecosystems increasingly shape how people internalize exposure to adverse digital experiences, raising concerns about the long-term consequences for information health. In modern search and recommendation systems, ranking and personalization policies play a central role...
MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
arXiv:2602.15206v1 Announce Type: new Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as...
Automatically Finding Reward Model Biases
arXiv:2602.15222v1 Announce Type: new Abstract: Reward models are central to large language model (LLM) post-training. However, past work has shown that they can reward spurious or undesirable attributes such as length, format, hallucinations, and sycophancy. In this work, we introduce...
Size Transferability of Graph Transformers with Convolutional Positional Encodings
arXiv:2602.15239v1 Announce Type: new Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key design choice in GTs is the use of Graph Neural Network (GNN)-based positional...
Directional Reasoning Trajectory Change (DRTC): Identifying Critical Trace Segments in Reasoning Models
arXiv:2602.15332v1 Announce Type: new Abstract: Understanding how language models carry out long-horizon reasoning remains an open challenge. Existing interpretability methods often highlight tokens or spans correlated with an answer, but they rarely reveal where the model makes consequential reasoning turns,...
Benchmarking IoT Time-Series AD with Event-Level Augmentations
arXiv:2602.15457v1 Announce Type: new Abstract: Anomaly detection (AD) for safety-critical IoT time series should be judged at the event level: reliability and earliness under realistic perturbations. Yet many studies still emphasize point-level results on curated base datasets, limiting value for...
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
arXiv:2602.15546v1 Announce Type: new Abstract: The ability to accurately perform counterfactual inference on time series is crucial for decision-making in fields like finance, healthcare, and marketing, as it allows us to understand the impact of events or treatments on outcomes...
Multi-Objective Coverage via Constraint Active Search
arXiv:2602.15595v1 Announce Type: new Abstract: In this paper, we formulate the new multi-objective coverage (MOC) problem where our goal is to identify a small set of representative samples whose predicted outcomes broadly cover the feasible multi-objective space. This problem is...
Certified Per-Instance Unlearning Using Individual Sensitivity Bounds
arXiv:2602.15602v1 Announce Type: new Abstract: Certified machine unlearning can be achieved via noise injection leading to differential privacy guarantees, where noise is calibrated to worst-case sensitivity. Such conservative calibration often results in performance degradation, limiting practical applicability. In this work,...
The Computer Vision Foundation – A non-profit organization that fosters and supports research in all aspects of computer vision
Join the Largest Global Community in Computing
IEEE Computer Society is the top source for information, inspiration, and collaboration in computer science and engineering, empowering technologist worldwide
CVPR 2026 Compute Reporting Form - Author Guidelines
Lawsuit: EPA revoking greenhouse gas finding risks “thousands of avoidable deaths”
EPA sued for abandoning its mission to protect public health.
Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans
arXiv:2602.14100v1 Announce Type: new Abstract: Whether neural networks can serve as cognitive models of morphological learning remains an open question. Recent work has shown that encoder-decoder models can acquire irregular patterns, but evidence that they generalize these patterns like humans...
A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing
arXiv:2602.14158v1 Announce Type: new Abstract: Large language models (LLMs) show promise for healthcare question answering, but clinical use is limited by weak verification, insufficient evidence grounding, and unreliable confidence signalling. We propose a multi-agent medical QA framework that combines complementary...
Knowing When Not to Answer: Abstention-Aware Scientific Reasoning
arXiv:2602.14189v1 Announce Type: new Abstract: Large language models are increasingly used to answer and verify scientific claims, yet existing evaluations typically assume that a model must always produce a definitive answer. In scientific settings, however, unsupported or uncertain conclusions can...
Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization
arXiv:2602.13398v1 Announce Type: new Abstract: Designing cryoprotectant agent (CPA) cocktails for vitrification is challenging because formulations must be concentrated enough to suppress ice formation yet non-toxic enough to preserve cell viability. This tradeoff creates a large, multi-objective design space in...
Comparing Classifiers: A Case Study Using PyCM
arXiv:2602.13482v1 Announce Type: new Abstract: Selecting an optimal classification model requires a robust and comprehensive understanding of the performance of the model. This paper provides a tutorial on the PyCM library, demonstrating its utility in conducting deep-dive evaluations of multi-class...
Finding Highly Interpretable Prompt-Specific Circuits in Language Models
arXiv:2602.13483v1 Announce Type: new Abstract: Understanding the internal circuits that language models use to solve tasks remains a central challenge in mechanistic interpretability. Most prior work identifies circuits at the task level by averaging across many prompts, implicitly assuming a...
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
arXiv:2602.13485v1 Announce Type: new Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal...
Singular Vectors of Attention Heads Align with Features
arXiv:2602.13524v1 Announce Type: new Abstract: Identifying feature representations in language models is a central task in mechanistic interpretability. Several recent studies have made an implicit assumption that feature representations can be inferred in some cases from singular vectors of attention...
Data-driven Bi-level Optimization of Thermal Power Systems with embedded Artificial Neural Networks
arXiv:2602.13746v1 Announce Type: new Abstract: Industrial thermal power systems have coupled performance variables with hierarchical order of importance, making their simultaneous optimization computationally challenging or infeasible. This barrier limits the integrated and computationally scaleable operation optimization of industrial thermal power...