Multi-Agent

SwarmWorld: LLM Agents That Cooperate Without Talking, and Technology That Outlives Them

SwarmWorld: LLM Agents That Cooperate Without Talking, and Technology That Outlives Them

In SwarmWorld, agents can continue one another’s work without talking by observing persistent devices and changes to their shared environment. That shared world helped them build collections of technologies serving several functions, but did not guarantee the best individual device. The MIT researchers’ paper demonstrates collaboration through accumulating functional artifacts inside a specified simulator. It does not establish the invention of real materials or a universal advantage for real-world multi-agent systems.

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Hermes Agent v0.18: When a Self-Improving Agent Gets MoA

Hermes Agent v0.18: When a Self-Improving Agent Gets MoA

Nous Research’s open-source agent Hermes Agent shipped v0.18.0 on July 1, 2026. The release notes call it “The Judgment Release.” Reading this as a routine feature update misses the point. The official docs describe Hermes Agent as “the self-improving AI agent,” and the project is built around a loop that accumulates memory and skills the more it gets used. This post covers how that loop was refined in v0.18, and what role Mixture of Agents (MoA), now a first-class model choice in the same release, plays inside it.

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TradingAgents: Reading the Paper and Code Behind an LLM Trading Desk

TradingAgents: Reading the Paper and Code Behind an LLM Trading Desk

TradingAgents is a research framework in which several LLM agents analyze evidence, challenge a proposal, and produce a judgment about one instrument with supporting reports. In the current code, the risk team reviews the Trader’s proposal and the Portfolio Manager assigns the final rating. The paper’s high backtest returns do not establish that these ratings translate into live profits. The current evaluation code explicitly excludes order fills and an account cash ledger. Final decision, evaluation scope

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Mixture of Agents: How Layering Open-Source LLMs Beat GPT-4 Omni

Mixture of Agents: How Layering Open-Source LLMs Beat GPT-4 Omni

Instead of scaling a single model up, you can stack multiple models in layers and have each one refine the previous layer’s output. Together AI’s research team formalized that approach in June 2024 as Mixture of Agents (MoA) in arXiv:2406.04692. Using only open-source models, their MoA configuration scored 65.1% on AlpacaEval 2.0, versus 57.5% for GPT-4 Omni.

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