New benchmarks evaluate LLMs on end-to-end business intelligence, active scientific experimentation and professional workflows. They consistently reveal that frontier models remain below human-level reliability without orchestration or guidance.
End-to-end BI exposes a structural limit of LLMs: below 50 percent accuracy without human guidance. The real gains come from architectures that orchestrate tools and post-training on proprietary data. For on-premise and self-hosted deployments, TCO s...
The BI-Bench benchmark, built from real projects and public dashboards, shows that even frontier LLMs remain below 50% accuracy in end-to-end business intelligence. BI-Agent, a tool-augmented agent that breaks down search, join, and transformation an...
A systematic mapping of 14,767 arXiv preprints published between January 2022 and August 2026 shows how LLM benchmarks are shifting toward action, interaction, and professional applications. Model participation is asymmetric: LLM-based scoring grows,...
PhysMent evaluates LLMs on 105 classical mechanics scenes in the MuJoCo simulator: models must apply forces, query states, advance time, and modify geometry before answering. On quantitative multi-step tasks, most drop below 30 per cent: the bottlene...
