This work introduces a diffusion model as a generative prior to produce high-quality global deployment plans, effectively avoiding the local-optima problem common in conventional reinforcement learning.
The Synergistic Policy network for Edge Resource Control (SPERC) is presented, a novel deep reinforcement learning framework designed to solve this joint problem of ensuring performance through appropriate computational resource allocation and minimizing storage costs through image layer sharing.
A cooperative multi-agent orchestration framework, in which each edge node is equipped with a scheduling agent to route tasks to local ASPs or neighboring edge nodes, and an attention-guided centralized critic to estimate per-agent values from cross-agent states under GPU memory heterogeneity is proposed.
Experiments on six real-world social networks under the Independent Cascade model show that Multiple-roles GWO achieves competitive or superior influence spread compared with state-of-the-art heuristic baselines, with comparable computational efficiency.
Action Generation with Topology Awareness (AGTA), a topology-aware sequential decision-making framework in MARL that integrates inter-agent correlation modeling with topology-guided decision-order optimization, and outperforms the state-of-the-art counterparts.
Experimental results demonstrate that the proposed method consistently outperforms nine state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) in terms of convergence, feasibility, and diversity, thereby confirming its effectiveness and strong general applicability.
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Education Innovation
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
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