Autonomous and multi-agent LLM systems require external orchestration, verification, and explicit process constraints to avoid failures in finance, clinical programming, and research. Studies highlight that reliable agentic behavior depends more on control loops and process graphs than raw model ability.
A multi-agent system turns regulatory process order into a directed acyclic graph and achieves 100% structural match in CDISC clinical dataset generation, while flat and single-agent approaches remain at zero. The CDISCPilot01 comparison shows that p...
A Princeton-led study put Claude Opus 4.8 agents to work on unpublished NeurIPS 2026 research questions. The agents handled engineering tasks but lacked the judgment and creativity needed for open-ended research, and both papers were rejected. The fi...
A controlled six-condition ablation challenges a common assumption: in LLM self-reflection for armed conflict forecasting, structured diagnostic questions and uncertainty taxonomies add no measurable value. Typed action routing drives consistent gain...
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 ...
