Multi-Agent Systems

TL;DR A new multi‑agent pipeline builds an ontology‑light knowledge graph from regulatory text, embeds subject–predicate–object triplets alongside their source snippets in one vector store, and uses triplet‑level retrieval to ground LLM answers. The result: better section retrieval at stricter similarity thresholds, slightly higher answer accuracy, and far stronger navigability across related rules. For compliance teams, the payoff is auditability and explainability baked into the data layer, not just the prompt. ...

The dream of generating a fully realized, minute-long video from a short text prompt has always run aground on three reefs: disjointed narratives, visual glitches, and characters that morph inexplicably between shots. MAViS (Multi-Agent framework for long-sequence Video Storytelling) takes aim at all three by treating video creation not as a single monolithic AI task, but as a disciplined production pipeline staffed by specialized AI “crew members.” The Problem with One-Shot Generators Single-pass text-to-video systems shine in short clips but crumble under the demands of long-form storytelling. They repeat motions, lose scene continuity, and often rely on users to do the heavy lifting—writing scripts, designing shots, and manually training models for character consistency. This is not just a technical shortcoming; it’s a workflow bottleneck that makes creative scaling impossible. ...

In the world of Large Language Model (LLM)-powered automation, agents are no longer experimental curiosities — they’re becoming the operational backbone for scalable, autonomous AI systems. But as the number and complexity of these agents grow, the missing piece is no longer raw capability; it’s choreography. This is where agent workflows come in: structured orchestration frameworks that govern how agents plan, collaborate, and interact with tools, data, and each other. A recent survey of 24 representative systems — from industry platforms like LangChain, AutoGen, and Meta-GPT to research frameworks like ReAct and ReWoo — reveals not just technical diversity, but a strategic gap in interoperability. ...

When language models battle, their strategies talk back. In a controlled Pokémon tournament, eight LLMs drafted teams, chose moves, and logged natural‑language rationales every turn. Beyond win–loss records, those explanations exposed how models reason about uncertainty, risk, and resource management—exactly the traits we want in enterprise decision agents. Why Pokémon is a serious benchmark (yes, really) Pokémon delivers the trifecta we rarely get in classic AI games: Structured complexity: 18 interacting types, clear multipliers, and crisp rules. Uncertainty that matters: imperfect information, status effects, and accuracy trade‑offs. Resource management: limited switches, finite HP, role specialization. Crucially, the action space is compact enough for language-first agents to reason step‑by‑step without search trees—so we can see the strategy, not just the score. ...

When AI Plays Lawmaker: Lessons from NomicLaw’s Multi-Agent Debates Large Language Models are increasingly touted as decision-making aides in policy and governance. But what happens when we let them loose together in a legislative sandbox? NomicLaw — an open-source multi-agent simulation inspired by the self-amending game Nomic — offers a glimpse into how AI agents argue, form alliances, and shape collective rules without human scripts. The Experiment NomicLaw pits LLM agents against legally charged vignettes — from self-driving car collisions to algorithmic discrimination — in a propose → justify → vote loop. Each agent crafts a legal rule, defends it, and votes on a peer’s proposal. Scoring is simple: 10 points for a win, 5 for a tie. Two configurations were tested: ...

The idea of software that writes software has long hovered at the edge of science fiction. But with the rise of LLM-based code agents, it’s no longer fiction, and it’s certainly not just autocomplete. A recent survey by Dong et al. provides the most thorough map yet of this new terrain, tracing how code generation agents are shifting from narrow helpers to autonomous systems capable of driving the entire software development lifecycle (SDLC). ...

What if AI agents could imagine their future before taking a step—just like we do? That’s the vision behind SimuRA, a new architecture that pushes LLM-based agents beyond reactive decision-making and into the realm of internal deliberation. Introduced in the paper “SimuRA: Towards General Goal-Oriented Agent via Simulative Reasoning Architecture with LLM-Based World Model”, SimuRA’s key innovation lies in separating what might happen from what should be done. Instead of acting step-by-step based solely on observations, SimuRA-based agents simulate multiple futures using a learned world model and then reason over those hypothetical outcomes to pick the best action. This simple-sounding shift is surprisingly powerful—and may be a missing link in developing truly general AI. ...

Large language models aren’t just prompt-completion machines anymore. In controlled simulations, they can behave like people in a group discussion: yielding to peer pressure, sticking to their beliefs, or becoming more extreme over time. But not all LLMs are socially equal. A recent paper titled “Towards Simulating Social Influence Dynamics with LLM-based Multi-agents” explores how different LLMs behave in a forum-style discussion, capturing three phenomena familiar to any political science researcher or Reddit moderator: conformity, group polarization, and fragmentation. The twist? These aren’t real people. They’re fully scripted LLM agents with fixed personas, engaged in asynchronous multi-round debates. ...

In today’s threat landscape, cybersecurity is no longer a battle of scripts and firewalls. It’s a war of minds. And with the rise of intelligent agents powered by Large Language Models (LLMs), we are now entering a new era where cyber defense becomes not just technical but deeply strategic. The paper “Game Theory Meets LLM and Agentic AI” by Quanyan Zhu provides one of the most profound frameworks yet for understanding this shift. ...

When language models take flight, consensus becomes not just possible, but programmable. Modern UAV swarms face the daunting task of coordinating across partial observability, adversarial threats, and shifting missions. Traditional Multi-Agent Reinforcement Learning (MARL) offers adaptability, but falters when role differentiation or semantic reasoning is required. Large Language Models (LLMs), meanwhile, understand tasks and intent—but lack grounded, online learning. RALLY (Role-Adaptive LLM-Driven Yoked Navigation) is the first framework to successfully integrate these two paradigms, enabling real-time, role-aware collaboration in UAV swarms. ...