reinforcement learning

An Offline approach for training Reinforcement-Learning Models for the 6G Cloud Edge Continuum

We demonstrate a system that replaces the default Kubernetes scheduler with an inference and topology-aware Reinforcement Learning policy. The system time-slices each GPU, consumes live Prometheus telemetry and places inference workloads across Far-Edge, Edge and Cloud tiers using an action masked agent (MaskablePPO, M...

A multilevel meta-analysis of visual reasoning in multimodal large language models

Abstract Multimodal Large Language Models (MLLMs) have become a leading approach to visual reasoning, yet the field still lacks a quantitative synthesis that rigorously identifies the drivers of their performance. In this paper, we evaluate 21 models across 7 standardised benchmarks, yielding 102 model–benchmark observ...

Reinforcement learning-assisted time-to-boundary mobility control with selective DAPS for QoS-aware handover in dense HetNets

Results indicate a favorable overall QoS–energy–signaling trade-off for selective risk-aware DAPS activation with hybrid rule-constrained and RL-assisted control within the controlled single-UE 1-D boundary-crossing model.

AI Driven Jamming in 6G Networks

This paper investigates AI-driven jamming as an emerging security threat to sixth-generation (6G) wireless networks. Unlike conventional jamming techniques that rely on relatively fixed and predictable interference patterns, AI-driven jamming can use machine learning to sense network conditions, adapt attack strategies...

Dual Pattern Equilibrium Framework (W+X=1): A Robust Reward Optimization Architecture for Autonomous Local Coding Agents - Hardened V2

chabira abderraouf Autonomous developer agents operating on commodity hardware face a critical trade-off between code correctness and resource stability. Traditional reinforcement learning from human feedback (RLHF) and PPO-based code optimization rely on binary success/failure rewards, which are vulnerable to reward h...

Corporate AI Training Program Template

A template for corporate AI training with role groups, learning objectives, governance practice, session design and post-training reinforcement.

Robotic Garment Folding via VLA Policy: No Pure Math Content Found — E8 Intelligence Research

FINDING: The search results are video titles and abstracts, not actual mathematical solutions. No equations, constants, or derivations are present. The only concrete mathematical content is a reference to an arXiv paper on robotic garment folding (LeHome Challenge 2026), which is applied robotics, not pure competition...

Structured Prediction Meets Neural Geometry: How Compositionality, Representational Manifolds, Criticality, and Hierarchical Coding Jointly Constrain What Neural Circuits Compute

This version (2026-09-26) corrects a citation error found by an automated check and confirmed by hand against the arXiv abstracts. Version 2 cited the identifier 2606.04428, an unrelated astrophysics paper on fast-spinning black holes, for the theoretical framework in which chaotic recurrent dynamics produce local roug...

Residual-Gated Diffusion Augmentation for Multi-Agent Reinforcement Learning in Energy Management

Sub-second dopaminergic reinforcement orchestrates juvenile social play and is disrupted in Shank3 deficiency. Chen et al. (Enriched version)

This dataset accompanies a study testing whether nucleus accumbens dopamine (DA) acts as an action-contingent reinforcement signal shaping juvenile social play in a sex-divergent manner, and whether Shank3 deficiency disrupts this signal. We collected multi-modal data from wild‑type (WT) and Shank3+/- (PND 35-85) durin...

Marwa-Chouhdary/MEC_IIoT_E_DDPG_LS_MCOR_TD3_Code: MEC IIoT E-DDPG-LS and MCOR-TD3 Code v1.0.0

MCOR-TD3 for MEC-Enabled IIoT — v1.0.1 This release contains Python code associated with the paper "Adaptive Task Caching and Resource Allocation in MEC-Enabled IIoT Using Deep Reinforcement Learning." It supports simulation of cooperative task offloading and computing-resource allocation using MCOR-TD3 to minimize max...

Hierarchical graph reinforcement learning for coordinated dispatch and reconfiguration of active distribution networks

Traditional studies on active distribution networks often optimize operational dispatch and network topology reconfiguration in a decoupled manner, although the two decisions are strongly coupled through power-flow redistribution, controllable-device outputs, and network constraints. This paper develops a problem-speci...