Permissionless consensus protocols require a scarce resource to regulate leader election and provide Sybil resistance. Existing paradigms such as Proof of Work ...
Neural-Symbolic (NeSy) Artificial Intelligence has emerged as a promising approach for combining the learning capabilities of neural networks with the interpret...
This work presents DCIM 3.0, a unified framework integrating semantic reasoning, predictive analytics, autonomous orchestration, and unified connectivity for ne...
Deep learning has transformed visual data analysis, with Convolutional Neural Networks (CNNs) becoming highly effective in learning meaningful feature represent...
The pervasive 'memory wall' bottleneck is significantly amplified in modern large-scale Mixture-of-Experts (MoE) architectures. MoE's inherent architectural spa...
Graph Neural Networks (GNNs) are powerful tools for learning graph-structured data, but their scalability is hindered by inefficient mini-batch generation, data...
This paper introduces DDMIN-LOC, a technique that combines Delta Debugging Minimization (DDMIN) with Spectrum-Based Fault Localization (SBFL). It can be applied...
Resource autoscaling mechanisms in cloud environments depend on accurate performance metrics to make optimal provisioning decisions. When infrastructure faults ...
Mechanism design is pivotal to federated learning (FL) for maximizing social welfare by coordinating self-interested clients. Existing mechanisms, however, ofte...
We deployed an LLM agent with ReAct reasoning and full data access. It executed flawlessly, yet when asked 'Why is completion rate 80%?', it returned metrics in...
Collaborative perception (CP) is a critical technology in applications like autonomous driving and smart cities. It involves the sharing and fusion of informati...
Recent advancements in large language models (LLMs) have automated various software engineering tasks, with benchmarks emerging to evaluate their capabilities. ...
In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability an...
Recent advances in language models (LMs) have driven significant progress in various software engineering tasks. However, existing LMs still struggle with compl...
We present a new blocking linearizable stack implementation which utilizes sharding and fetch&increment to achieve significantly better performance than all...
Phasor Agents are dynamical systems whose internal state is a Phasor Graph: a weighted graph of coupled Stuart-Landau oscillators. A Stuart-Landau oscillator is...
Dynamic objects in our physical 4D (3D + time) world are constantly evolving, deforming, and interacting with other objects, leading to diverse 4D scene dynamic...
Many embedded devices operate under resource constraints and in dynamic environments, requiring local decision-making capabilities. Enabling devices to make ind...
Existing visual localization methods are typically either 2D image-based, which are easy to build and maintain but limited in effective geometric reasoning, or ...
Reliable long-term decoding of surface electromyography (EMG) is hindered by signal drift caused by electrode shifts, muscle fatigue, and posture changes. While...
We demonstrate a deep learning framework capable of recovering physical parameters from the Nonlinear Schrodinger Equation (NLSE) under severe noise conditions....
Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scali...
Multi-agent Large Language Model (LLM) systems have emerged as powerful architectures for complex task decomposition and collaborative problem-solving. However,...
The application of machine learning on healthcare data is often hindered by the lack of standardized and semantically explicit representation, leading to limite...
