The field of artificial intelligence is rapidly advancing, with significant developments in large language models (LLMs) and agentic AI. Recent research has focused on applying these technologies to cybersecurity and finance, with a particular emphasis on game theory and multi-agent systems. One of the key areas of innovation is the use of LLMs to model and analyze complex systems, such as financial markets and cyber threat environments. This has led to the development of new frameworks and tools for predicting and mitigating risks, as well as improving decision-making in these domains. Notable papers in this area include 'A Dynamic Stackelberg Game Framework for Agentic AI Defense Against LLM Jailbreaking', which presents a novel approach to modeling the interactions between attackers and defenders in the context of LLM jailbreaking. Another notable paper is 'Hide-and-Shill: A Reinforcement Learning Framework for Market Manipulation Detection in Symphony-a Decentralized Multi-Agent System', which proposes a multi-agent reinforcement learning framework for detecting market manipulation in decentralized finance systems.
Advances in Large Language Models and Agentic AI for Cybersecurity and Finance
Sources
A Dynamic Stackelberg Game Framework for Agentic AI Defense Against LLM Jailbreaking
Reasoning and Behavioral Equilibria in LLM-Nash Games: From Mindsets to Actions
Learning from Synthetic Labs: Language Models as Auction Participants
Hide-and-Shill: A Reinforcement Learning Framework for Market Manipulation Detection in Symphony-a Decentralized Multi-Agent System
StockSim: A Dual-Mode Order-Level Simulator for Evaluating Multi-Agent LLMs in Financial Markets
LLM-Stackelberg Games: Conjectural Reasoning Equilibria and Their Applications to Spearphishing
A Coincidence of Wants Mechanism for Swap Trade Execution in Decentralized Exchanges
Devanagari Handwritten Character Recognition using Convolutional Neural Network
FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios
AutoRAG-LoRA: Hallucination-Triggered Knowledge Retuning via Lightweight Adapters
Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats
AI Agent Architecture for Decentralized Trading of Alternative Assets
An Empirical Study of Multi-Agent RAG for Real-World University Admissions Counseling
Evasion Under Blockchain Sanctions
Strategy Adaptation in Large Language Model Werewolf Agents
Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest