arXiv:2603.29875v1 Announce Type: cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector. These vector representations are then fundamentally treated as isolated, independent and self-sufficient, with no attempt to […]
Training deep learning based dynamic MR image reconstruction using synthetic fractals
arXiv:2603.29922v1 Announce Type: cross Abstract: Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets. Methods: A training dataset was generated using quaternion Julia fractals to produce 2D+time images. Multi-coil […]
Phyelds: A Pythonic Framework for Aggregate Computing
arXiv:2603.29999v1 Announce Type: cross Abstract: Aggregate programming is a field-based coordination paradigm with over a decade of exploration and successful applications across domains including sensor networks, robotics, and IoT, with implementations in various programming languages, such as Protelis, ScaFi (Scala), and FCPP (C++). A recent research direction integrates machine learning with aggregate computing, aiming to […]
Improving Execution Concurrency in Partial-Order Plans via Block-Substitution
arXiv:2406.18615v2 Announce Type: replace Abstract: Partial-order plans in AI planning facilitate execution flexibility and several other tasks, such as plan reuse, modification, and decomposition, due to their less constrained nature. A acrfull*pop specifies partial-order over actions, providing the flexibility of executing unordered actions in different sequences. This flexibility can be further extended by enabling parallel […]
AI and Consciousness
arXiv:2510.09858v4 Announce Type: replace Abstract: This is a skeptical overview of the literature on AI consciousness. We will soon create AI systems that are conscious according to some influential, mainstream theories of consciousness but are not conscious according to other influential, mainstream theories of consciousness. We will not be in a position to know which […]
The Future of AI is Many, Not One
arXiv:2603.29075v1 Announce Type: new Abstract: The way we’re thinking about generative AI right now is fundamentally individual. We see this not just in how users interact with models but also in how models are built, how they’re benchmarked, and how commercial and research strategies using AI are defined. We argue that we should abandon this […]
The Geometry of Thought: How Scale Restructures Reasoning In Large Language Models
arXiv:2601.13358v2 Announce Type: replace Abstract: Scale does not uniformly improve reasoning – it restructures it. Analyzing 25,000+ chain-of-thought trajectories across four domains (Law, Science, Code, Math) and two scales (8B, 70B parameters), we discover that neural scaling laws trigger domain-specific phase transitions rather than uniform capability gains. Legal reasoning undergoes Crystallization: 45% collapse in representational […]
Design and Development of an ML/DL Attack Resistance of RC-Based PUF for IoT Security
arXiv:2603.28798v1 Announce Type: cross Abstract: Physically Unclonable Functions (PUFs) provide promising hardware security for IoT authentication, leveraging inherent randomness suitable for resource constrained environments. However, ML/DL modeling attacks threaten PUF security by learning challenge-response patterns. This work introduces a custom resistor-capacitor (RC) based dynamically reconfigurable PUF using 32-bit challenge-response pairs (CRPs) designed to resist such […]
Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling
arXiv:2603.14841v2 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities. Existing prediction models predominantly produce binary outcomes, which offer limited actionable insights for real-time driver feedback. These approaches often lack continuous risk quantification, interpretability, and explicit consideration of vulnerable road users (VRUs), such as pedestrians and cyclists. This research introduces SafeDriver-IQ, […]
ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training
arXiv:2603.29871v1 Announce Type: new Abstract: In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often generate sets of candidate recommendations where the objective is to maximize the collective utility of the entire set rather than individual candidates independently. However, existing reinforcement learning post-training paradigms, such as Group Relative Policy […]
CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing
arXiv:2603.29755v1 Announce Type: new Abstract: Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often treat anomaly detection, causal inference, and root-cause analysis as isolated stages, limiting scalability and explainability. In this work, we present CausalPulse, an industry-grade multi-agent copilot that automates causal diagnostics in smart […]
Copy-Spread-Annihilate Dynamics in Degree-Assortative Networks
arXiv:2603.29833v1 Announce Type: new Abstract: In many systems, communication proceeds by broadcasting rather than single source-target routing, but network structures that maximize signal lifetime are not well understood. Degree correlations are known to influence robustness and spreading, yet their effect on signal persistence has remained unclear. Here we introduce Copy-Spread-Annihilate dynamics, a minimal synchronous broadcasting […]