7826 papers
This paper proposes a self-supervised evolutionary learning framework that extracts individualized neurodynamic progressions and identity manifolds from unlabeled EEG data during safety-critical decision-making, enabling robust user authentication, anomaly detection, and improved generalization without relying on external labels or predefined cognitive models.
This paper introduces VisualScratchpad, an interactive inference-time analysis tool that leverages sparse autoencoders and attention mechanisms to visualize and debug vision language models by linking visual concepts to text tokens, thereby revealing previously underexplored failure modes such as limited cross-modal alignment and misleading visual concepts.
This paper proposes a lightweight, digital-twin-based framework for edge-assisted vehicle tracking and collision prediction that achieves approximately 88% accuracy by relying on object detection and K-D tree-indexed path maps instead of computationally intensive trajectory prediction models.
The paper introduces Agora, an AI-powered platform that leverages LLMs to simulate diverse human perspectives on policy issues, enabling users to practice consensus-building and demonstrating through a preliminary study that access to authentic voice explanations significantly enhances problem-solving skills and the quality of collective decisions compared to viewing aggregate data alone.
Uber's Failover Architecture (UFA) replaces its costly uniform 2x capacity model with a differentiated, criticality-based approach that opportunistically shares resources and preempts non-critical services during peak failovers, thereby reducing steady-state provisioning from 2x to 1.3x and eliminating over one million CPU cores while maintaining 99.97% availability.
This paper introduces VRxBioRelax, a real-time virtual reality biofeedback system that utilizes sEMG data to drive an immersive relaxation environment, demonstrating through extensive pre-clinical testing that its average end-to-end latency of 25.34 ms significantly meets both VR comfort and clinical benchmarks for effective muscle relaxation training.
This study argues that while federally mandated shared data systems like HMIS facilitate coordination and accountability in U.S. homelessness services, they simultaneously reinforce power asymmetries by shifting resource-constrained providers toward compliance roles and concentrating decision-making authority.
This paper demonstrates that environmental pressure in multi-agent LLM simulations follows a Yerkes-Dodson inverted-U relationship, where medium stress optimizes emergent cooperative trade while extreme pressure causes behavioral collapse, and suggests that calibrating such pressure serves as an effective curriculum design strategy for agent development.
This paper presents a novel in silico neuromechanical framework that integrates forward musculoskeletal simulation, reinforcement learning, and online EMG synthesis to create a flexible, closed-loop virtual participant capable of generating synchronized neural and kinematic data for evaluating neural controllers and augmenting training datasets.
By tracking macaque V4 neural responses during visual discrimination learning, the study demonstrates that task learning increases information redundancy rather than reducing it, supporting a Bayesian generative inference model where redundancy enhances the information carried by individual neurons.
This paper introduces Spectra, a three-step scheduling algorithm that efficiently manages time-varying AI traffic across parallel optical circuit switches by decomposing traffic matrices, assigning permutations load-awarely, and balancing loads through controlled splitting, thereby significantly reducing makespan compared to state-of-the-art baselines.
This paper proposes a Multi-Agentic AI framework utilizing specialized LLM agents and retrieval-augmented generation to automate conflict-aware rApp policy orchestration in Open RAN, achieving significant improvements in deployment accuracy and cost reduction while enabling zero-shot generalization.
This paper presents a comprehensive systematization of transaction processing security across four evolutionary generations, introducing a new taxonomy, mapping vulnerabilities to CWE identifiers, and proposing the RANCID property set to address the insufficiencies of the classical ACID model in modern, real-time, multi-context systems.
This paper presents a holistic resiliency framework for Apache Pinot at LinkedIn, featuring Query Workload Isolation, Impact-Free Rebalancing, Maintenance Zone Awareness, and Adaptive Server Selection, which collectively ensure stable subsecond query latency and high availability for petabyte-scale OLAP workloads under failures and load spikes.
This paper introduces the first sketching method capable of approximating arbitrary tensor network contractions, including cyclic ones, and proposes a second method for acyclic networks that achieves polynomial time and space complexity, overcoming the exponential limitations of existing approaches.
This paper argues that the current fragmentation in high-performance interconnects stems from a fundamental "semantic crisis" caused by implicit, vendor-specific assumptions about time and ordering, and proposes that adopting explicit, testable link semantics through the Open Atomic Ethernet (OAE) standard is essential to restore correctness and enable convergence.
This review paper proposes a constraint-coupled perspective on underwater embodied intelligence, arguing that reliable autonomy requires integrating sensing, communication, and resource limitations into planning and control to address the interdependent challenges of real ocean environments.
This paper proposes an interpretable 3D Concept Bottleneck Model that integrates morphological and hemodynamic clinical features to classify intracranial aneurysms with high accuracy (up to 93.33%) while ensuring clinical transparency and regulatory compliance.
This paper proposes a perceptive mixed-integer model predictive control framework that enables humanoid robots to dynamically plan foot placement and step timing on disconnected, obstacle-filled terrain by fusing depth data into convex foothold regions and enforcing safety via DCM capturability bounds.
This paper introduces VIVECaption, a systematic two-sided approach that combines a comprehensive evaluation taxonomy with a gold-standard dataset creation methodology and model alignment strategy to significantly improve image-caption alignment quality for training text-to-image and text-to-video generative models using copyright-safe "vegan" data.