Author

Alois Knoll

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#artificial intelligence Preprint Sep 2026

Porimon: An LLM-Based Pok\'emon Battle Agent Enhanced by Long/Short-Term Knowledge Augmented Generation

In this paper, we use Pok\'emon Battles as a case study to investigate how to improve the performance of LLM-based agents in tasks that require opponent-aware planning without additional fine-tuning. We propose Long/Short-Term Knowledge Augmented Generation (LSTKAG), a mechanism that enables LLM-based agents to leverag...

Dong-Yin Zhuo, Feng Pan, Nenad Petrovic et al. · 0 citations

#machine learning Preprint Sep 2026

MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrieval

MemoryLACE (MemLACE), a lightweight memory framework that explicitly models the lifecycle of textual evidence through sparse merge, supersession, and contradiction relations while preserving atomic natural-language memories and their provenance, is introduced.

M. Yacoubi, Pia Schmidt, Nenad Petrovic et al. · 1 citation

Jul 2026

Scaling LLM-Driven Multi-Agent Systems: Design Principles and Architectural Scalability Analysis

This paper distill four design principles for scalable MAS architectures from a structured analysis of prior work: simplicity, elastic feedback, sequential workflows with optional loops, and summary-based communication, and evaluates four configurations of increasing complexity on a standardized benchmark of terminal-b...

Linus Sander, Feng Pan, Vahid Zolfaghari et al. · arXiv.org · 0 citations

Preprint Aug 2026

Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152

RegulaRAG is presented, a Retrieval-Augmented Generation pipeline that couples SmartChunking, reference-aware enrichment of paragraphs and tables via graph traversal, with Smart Retrieve&Rerank over these enriched units, and maintains strong performance, remaining stable even as the number of regulatory sources grows.

Vahid Zolfaghari, Nenad Petrovic, André Schamschurko et al. · 0 citations

Preprint Aug 2026

Stateful Multi-Agent LLMs for Cross-View Interface Alignment in Automotive Model-Based Systems Engineering

This work proposes a stateful, multi-agent validation pipeline that eradicated cross-phase hallucinations and proves adversarial auditing enables LLMs to reliably synthesize zero-error MBSE architectures.

Aleksei Velsh, Nenad Petrovic, Alois Knoll · 0 citations

Preprint Aug 2026

CyberLLM: A Multi-Agent LLM Framework for Autonomous Detection and Guarded Response in Automotive Cybersecurity

CyberLLM is presented, a multi-agent, LLM-orchestrated framework that autonomously detects vulnerabilities and executes remediations under a formal, runtime safety guard, and indicates that LLM agents can perform useful autonomous cyber-defense when wrapped in a deterministic, auditable safety envelope.

Nenad Petrovic, Oussama Jeddou, Feres Ben Fraj et al. · 0 citations