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VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
An Architectural Style for Self-Adaptive Multi-Agent Systems
Danny Weyns, Flavio Oquendo · 2019-09-08 · via cs.SE updates on arXiv.org

Modern distributed software systems often operate in dynamic environments in which operation conditions change continuously and subsystems may come and go at will, e.g. intelligent traffic management and multi-robot systems. To manage these dynamics, these systems have to self-adapt their structures and behaviors dynamically. While we have witnessed significant progress over the past decade in the manner in which such systems are designed, persistent challenges remain. In particular, dealing with distribution and decentralized control remains one of the major challenges in self-adaptive systems. This report presents an architecture style that supports software architects with designing architectures for a family of decentralized self-adaptive systems. The architecture style structures the software in a number of interacting autonomous entities (agents) that cooperatively realize the system tasks. Multi-agent systems derived from the architectural style realize flexibility (agents adapt their behavior and interactions to variable operating conditions) and openness (agents cope autonomously with other agents that enter and leave the system). The architectural style consists of five related patterns that distill domain-specific architectural knowledge derived from extensive experiences with developing various multi-agent systems. The architectural patterns are specified using pi-ADL, a formal architectural description language supporting specification of dynamic architectures. This specification provides architects with a rigorous description of the architecture elements of the patterns, their interactions and behavior. We illustrate how we have applied the architectural style with excerpts of two cases from our practice: an experimental system for anticipatory traffic routing and an industrial logistic system for automated transportation in warehouse environments.