LLM agents are being deployed in finance, trading, Bayesian calibration, and autonomous agriculture, with control layers to avoid failure modes and cut inference costs. Retrieval-based agent factories and closed-loop optimization show practical enterprise value.
Backtrader-Bench introduces two pipelines to evaluate LLM agents in algorithmic trading: one generates multiple-choice questions from backtest configurations with independent verification, the other mines harder questions that require code execution....
Distribird is an agentic application that automates the construction of Bayesian priors from the literature, running entirely locally on open-weight models. Evaluated on 24 parameters across 10 domains, the multi-agent pipeline matches a single-promp...
Two LLM agents with opposing goals don't compete—they collapse. Experience Orchestrator, tested across 60,000 financial services simulations, adds a control layer using a Contextual Bandit, PID, and POMDP. High-intent advisor contact rate rises from ...
LLM Agents Factory offers a pragmatic approach to agent management: instead of generating agents per request, it retrieves them from a structured archive. This reduces inference costs, latency, and GPU pressure, with direct implications for those bui...
A retrieval-based framework with distillation builds specialized LLM agents without on-the-fly generation, sharply cutting compute costs and improving stability. Tests match AutoGen’s accuracy with a 120B backbone at far lower inference cost, marking...
A closed-loop system driven by an LLM that processes data from 49 phytosensors and directly controls lighting and microclimate autonomously optimized a vertical farm. The model cut the production cycle by 35% and uncovered an unlikely strategy – dark...
