# multi-agent LLM framework

ElecLoad reads weather alerts, grid notices and news event descriptions alongside historical load, judging each event's relevance to the target load, the timing of its effect, and whether events reinforce or offset one another, then decides whether to forecast the whole horizon at once or split it where event effects change. On the segmented path, sub-agents draft each segment independently and the main agent reviews them and sends feedback to selected segments only. Across hourly load data including GEFCom2014, PJM and LCL it ranks near the top in most evaluation scenarios; under multiple events on PJM its WAPE is 3.69% versus 4.31% for a direct-only variant using the same model and inputs.

ElecLoad reads weather alerts, grid notices and news event descriptions alongside historical load, judging each event's relevance to the target load, the timing of its effect, and whether events reinforce or offset one another, then decides whether to forecast the whole horizon at once or split it where event effects change. On the segmented path, sub-agents draft each segment independently and the main agent reviews them and sends feedback to selected segments only. Across hourly load data including GEFCom2014, PJM and LCL it ranks near the top in most evaluation scenarios; under multiple events on PJM its WAPE is 3.69% versus 4.31% for a direct-only variant using the same model and inputs.

TradingAgents 0.7.0 lets users run the framework on their own ChatGPT Plus or Pro subscription and scores backtests from the first close after each decision. Earlier versions let backtests see only data published by each analysis date, so the numbers stopped at the analysis day. The framework splits a trading firm's roles across specialized LLM-driven agents, from fundamental, sentiment and technical analysts to a trader and a risk management team; 0.7.0 also notes at start-up when a newer release is out and ships a wheel with each GitHub release.

TradingAgents 0.7.0 lets users run the framework on their own ChatGPT Plus or Pro subscription and scores backtests from the first close after each decision. Earlier versions let backtests see only data published by each analysis date, so the numbers stopped at the analysis day. The framework splits a trading firm's roles across specialized LLM-driven agents, from fundamental, sentiment and technical analysts to a trader and a risk management team; 0.7.0 also notes at start-up when a newer release is out and ships a wheel with each GitHub release.

A multi-agent large language model framework confines neural network tuning to designated functional blocks while the overall network structure stays fixed. Such key components were previously chosen by hand, and existing agent systems either optimize the entire machine learning script or build a network from a text description. With analyst, coder, and recorder roles plus a sandboxed environment and an experiment log, the framework beats its baselines on five tasks: MNIST classification accuracy rises 13 percentage points, digits 6 points, the relative error on the single-soliton generalized nonlinear Schrödinger equation drops about 2491-fold, the conservation error drops about 325-fold in the selected run, and mass drift on the Kuramoto–Sivashinsky problem drops 14-fold.

A multi-agent large language model framework confines neural network tuning to designated functional blocks while the overall network structure stays fixed. Such key components were previously chosen by hand, and existing agent systems either optimize the entire machine learning script or build a network from a text description. With analyst, coder, and recorder roles plus a sandboxed environment and an experiment log, the framework beats its baselines on five tasks: MNIST classification accuracy rises 13 percentage points, digits 6 points, the relative error on the single-soliton generalized nonlinear Schrödinger equation drops about 2491-fold, the conservation error drops about 325-fold in the selected run, and mass drift on the Kuramoto–Sivashinsky problem drops 14-fold.