arXiv:2603.06657v1 Announce Type: new Abstract: Aging interventions frequently improve function and healthspan without arresting long-term deterioration, indicating that existing frameworks do not fully specify the control conditions required for bounded organismal aging. A compact control-theoretic formulation is developed in which total organismal burden is decomposed into two lesion classes with distinct controllability properties: regulatable damage, […]
Continuous-Flow Data-Rate-Aware CNN Inference on FPGA
arXiv:2601.19940v2 Announce Type: replace-cross Abstract: Among hardware accelerators for deep-learning inference, data flow implementations offer low latency and high throughput capabilities. In these architectures, each neuron is mapped to a dedicated hardware unit, making them well-suited for field-programmable gate array (FPGA) implementation. Previous unrolled implementations mostly focus on fully connected networks because of their simplicity, […]
NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
arXiv:2603.06977v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) is increasingly used to design learning-enabled agents that interact in shared environments. However, training MARL algorithms in general-sum games remains challenging: learning dynamics can become unstable, and convergence guarantees typically hold only in restricted settings such as two-player zero-sum or fully cooperative games. Moreover, when agents […]
aCAPTCHA: Verifying That an Entity Is a Capable Agent via Asymmetric Hardness
arXiv:2603.07116v1 Announce Type: cross Abstract: As autonomous AI agents increasingly populate the Internet, a novel security challenge arises: “Is this entity an AI agent?” It is a new entity-type verification problem with no established solution. We formalize the problem through a three-class entity taxonomy (Human, Script, Agent) based on a verifiable agentic capability vector (action, […]
Improved Constrained Generation by Bridging Pretrained Generative Models
arXiv:2603.06742v1 Announce Type: cross Abstract: Constrained generative modeling is fundamental to applications such as robotic control and autonomous driving, where models must respect physical laws and safety-critical constraints. In real-world settings, these constraints rarely take the form of simple linear inequalities, but instead complex feasible regions that resemble road maps or other structured spatial domains. […]
Optimistic Policy Regularization
arXiv:2603.06793v1 Announce Type: cross Abstract: Deep reinforcement learning agents frequently suffer from premature convergence, where early entropy collapse causes the policy to discard exploratory behaviors before discovering globally optimal strategies. We introduce Optimistic Policy Regularization (OPR), a lightweight mechanism designed to preserve and reinforce historically successful trajectories during policy optimization. OPR maintains a dynamic buffer […]
TDM-R1: Reinforcing Few-Step Diffusion Models with Non-Differentiable Reward
arXiv:2603.07700v1 Announce Type: cross Abstract: While few-step generative models have enabled powerful image and video generation at significantly lower cost, generic reinforcement learning (RL) paradigms for few-step models remain an unsolved problem. Existing RL approaches for few-step diffusion models strongly rely on back-propagating through differentiable reward models, thereby excluding the majority of important real-world reward […]
Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System
arXiv:2603.07449v1 Announce Type: cross Abstract: Enterprises commonly deploy heterogeneous database systems, each of which owns a distinct SQL dialect with different syntax rules, built-in functions, and execution constraints. However, most existing NL2SQL methods assume a single dialect (e.g., SQLite) and struggle to produce queries that are both semantically correct and executable on target engines. Prompt-based […]
SketchGraphNet: A Memory-Efficient Hybrid Graph Transformer for Large-Scale Sketch Corpora Recognition
arXiv:2603.07521v1 Announce Type: cross Abstract: This work investigates large-scale sketch recognition from a graph-native perspective, where free-hand sketches are directly modeled as structured graphs rather than raster images or stroke sequences. We propose SketchGraphNet, a hybrid graph neural architecture that integrates local message passing with a memory-efficient global attention mechanism, without relying on auxiliary positional […]
Slumbering to Precision: Enhancing Artificial Neural Network Calibration Through Sleep-like Processes
arXiv:2603.07867v1 Announce Type: cross Abstract: Artificial neural networks are often overconfident, undermining trust because their predicted probabilities do not match actual accuracy. Inspired by biological sleep and the role of spontaneous replay in memory and learning, we introduce Sleep Replay Consolidation (SRC), a novel calibration approach. SRC is a post-training, sleep-like phase that selectively replays […]
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
arXiv:2603.06726v1 Announce Type: cross Abstract: Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series foundation models (TSFMs) effectively capture temporal dependencies, they typically underutilize cross-variate correlations and non-periodic patterns that are essential for price forecasting. Conversely, regression models excel at capturing feature interactions but are […]
Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
arXiv:2603.06758v1 Announce Type: cross Abstract: Alzheimer disease (AD) diagnosis and prognosis increasingly rely on machine learning (ML) models. Although these models provide good results, clinical adoption is limited by the need for technical expertise and the lack of trustworthy and consistent model explanations. SHAP (SHapley Additive exPlanations) is com-monly used to interpret AD models, but […]