4492 papers
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.
This paper addresses the lack of specialized dental datasets by proposing a framework that uses Vision-Language Models with guided prompts to generate high-quality, holistic captions for single-tooth RGB images, thereby enabling more comprehensive dental image analysis.
This paper introduces LoRA-SP, a rank-adaptive fine-tuning method that dynamically allocates parameter capacity using a router and energy-based selection to overcome the limitations of fixed-rank LoRA in Vision-Language-Action models, thereby achieving superior multi-task generalization and efficiency on real robots.
The paper introduces UnSCAR, a scalable and controllable universal image restoration framework that utilizes a multi-branch mixture-of-experts architecture to overcome the limitations of catastrophic forgetting and performance degradation in existing all-in-one models when handling multiple real-world degradations.
This paper presents the first physical testbed evaluation of the Internet of Mirrors (IoM), demonstrating that while offloading computation to higher-tier nodes reduces local latency and resource load, the optimal task placement strategy depends on a dynamic trade-off between network conditions, payload size, and concurrent user load.
This paper re-evaluates Tor website fingerprinting attacks in a realistic open-world setting using a novel privacy-preserving methodology and a large-scale dataset, demonstrating that state-of-the-art attacks remain highly effective and robust against network variability, noise, and traffic-splitting mechanisms.
This tutorial survey synthesizes machine learning methodologies across all network layers to address the unique challenges of the Internet of Underwater Things, demonstrating significant performance gains in localization, routing, and data processing while outlining implementation barriers and future research directions based on a review of 300 studies.
This paper introduces QdaVPR, a novel query-based visual place recognition model that achieves state-of-the-art domain-agnostic performance across diverse environmental conditions by employing a dual-level adversarial learning framework and query-based triplet supervision trained on augmented synthetic domains.