Llm-Agents

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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Don't Ship Agent Skills Without Evals: Philipp Schmid's Testing Method and SkillsBench

Don't Ship Agent Skills Without Evals: Philipp Schmid's Testing Method and SkillsBench

If you use a coding agent like Claude Code, Gemini CLI, or Codex, you eventually end up writing skills: Markdown files that hand the agent your team’s coding conventions, a specific SDK’s usage patterns, or a deployment procedure. The problem is that almost nobody tests them. We would never ship code without tests, yet skills, which directly change how an agent behaves, get shipped after a few manual runs and a gut-level “looks fine.”

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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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