7632 papers
This paper adopts a survivor-centered approach to expose how a "malicious technical ecosystem" of accessible tools enables the creation of AI-generated non-consensual intimate images, while demonstrating that current governance frameworks, such as the NIST AI 100-4 report, fail to effectively regulate this landscape due to flawed underlying assumptions.
This survey provides a comprehensive overview of Federated Learning, detailing its architecture and lifecycle while addressing key challenges like data heterogeneity and privacy, exploring emerging trends and applications, and outlining future research directions for scalable and trustworthy collaborative intelligence.
This paper introduces FourierSpecNet, a hybrid deep learning framework that integrates the Fourier spectral method to efficiently approximate the Boltzmann collision operator, achieving resolution-invariant learning, zero-shot super-resolution, and significant computational savings while maintaining accuracy across elastic and inelastic collision regimes.
This paper proposes a scalable Heterogeneous Graph Neural Network (HGNN) that employs a multi-task learning paradigm to simultaneously perform particle vertex association and graph pruning, thereby significantly improving beauty hadron reconstruction performance and inference efficiency for complex particle collision events at the Large Hadron Collider.
The paper introduces Reasoning Reward Models (ReasRMs), specifically the RM-R1 family, which reformulate reward modeling as a reasoning task using a chain-of-rubrics mechanism and a two-stage training pipeline to achieve superior interpretability and performance compared to existing large-scale models.
This paper introduces a variant of Polyak's stepsizes for entropic mirror descent to solve linear systems without restrictive domain assumptions, establishing sublinear and linear convergence rates, strengthening -norm implicit bias bounds, and generalizing results to arbitrary convex -smooth functions while proposing an exponentiation-free alternative method.
This paper introduces EVA, a novel event-by-event asynchronous-to-synchronous (A2S) framework inspired by language modeling that generates highly expressive features, outperforming prior methods in recognition tasks and achieving state-of-the-art results in detection for event-based vision.
The paper introduces ESGenius, the first comprehensive benchmark comprising a curated corpus of authoritative ESG documents and a rigorously validated question-answer dataset, which reveals that while large language models exhibit moderate zero-shot performance in sustainability domains, their accuracy significantly improves when grounded in retrieval-augmented generation (RAG) using the provided source materials.
This paper introduces ContextBench, a benchmark for evaluating methods that generate fluent inputs to trigger specific latent features in language models, and demonstrates that enhanced Evolutionary Prompt Optimization variants achieve state-of-the-art performance in balancing elicitation strength with linguistic fluency.
The paper introduces Sysformer, a novel approach that safeguards frozen large language models by learning to adapt system prompts in the embedding space to significantly improve safety robustness against harmful inputs and jailbreaking attacks without requiring costly model fine-tuning.
The paper introduces SPoT, a novel tokenization strategy that positions Vision Transformer tokens continuously within images via an oracle-guided search to overcome grid-based limitations, significantly reducing the required token count while improving performance and interpretability.
This paper introduces Quantifying Cross-Attention Interaction (QCAI), a novel post-hoc explainable AI method that interprets cross-attention mechanisms in encoder-decoder transformers to improve the understanding of TCR-pMHC binding, achieving state-of-the-art performance on the newly established TCR-XAI benchmark of 274 experimentally determined structures.
This paper introduces DejaVu, a novel attack that exploits in-vehicular network vulnerabilities to induce subtle temporal misalignments between camera and LiDAR streams, thereby severely degrading multimodal fusion-based perception tasks like object detection and tracking in autonomous driving systems.
This paper proposes a novel student-teacher framework for autonomous driving that utilizes a graph-based multi-agent RL teacher to automatically generate diverse, adaptive traffic curricula, enabling a student agent to achieve superior robustness and balanced driving performance compared to traditional rule-based approaches.
The paper proposes the State Space Neural Operator (SS-NO), a parameter-efficient architecture that extends structured state space models with adaptive damping and learnable frequency modulation to achieve state-of-the-art performance in learning solution operators for diverse time-dependent partial differential equations.
This paper introduces Multivariate Fields of Experts, a new image prior framework that generalizes existing methods using multivariate potential functions to achieve fast, interpretable, and theoretically guaranteed convergence in various inverse problems, outperforming univariate models while approaching deep learning performance with significantly fewer parameters and data.
This paper provides a theoretical characterization of the Expectation-Maximization algorithm's behavior in overspecified two-component mixed linear regression, establishing that unbalanced initial mixing weights yield linear convergence and optimal statistical accuracy, whereas balanced initial weights result in sublinear convergence and degraded accuracy.
This paper introduces Kernel VICReg, a novel self-supervised learning framework that extends the VICReg objective into a Reproducing Kernel Hilbert Space to capture nonlinear dependencies and improve representation learning performance on datasets with complex geometric structures.
The paper introduces ScaleZero, a multi-task planning model that combines a Mixture-of-Experts architecture to mitigate gradient conflicts and an online Dynamic Parameter Scaling strategy to efficiently allocate capacity, achieving performance comparable to specialized single-task agents with significantly reduced sample complexity.
This paper demonstrates that deep neural network ensembles enable accurate, real-time quantum parameter estimation with well-calibrated uncertainty quantification and drift detection, offering a faster alternative to traditional Bayesian inference methods without sacrificing accuracy.