arXiv:2605.19099v1 Announce Type: new Abstract: We introduce DecisionBench, a benchmark substrate for emergent delegation in long-horizon agentic workflows. The substrate fixes a task suite (GAIA, tau-bench, BFCL multi-turn), a peer-model pool (11 models, 7 vendor families), a delegation interface (call_model plus an optional read_profile channel), a deterministic skill-annotation layer, and a multi-axis metric suite covering quality, cost, latency, delegation rate, routing fidelity-at-k, vendor self-preference, and a counterfactual-delegation ceiling. The substrate is agnostic to how peer inf
The proliferation of advanced AI models necessitates robust benchmarking for emergent capabilities like delegation in complex agentic workflows, moving beyond simpler task evaluations.
A strategic reader should care because this benchmark directly measures the efficacy and risks of autonomous AI agents in real-world scenarios, impacting future AI development and deployment strategies.
The introduction of DecisionBench offers a standardized, multi-faceted evaluation framework for emergent delegation, enabling more rigorous comparison and development of agentic systems.
- · AI Agent developers
- · AI model providers
- · Organizations deploying AI agents
- · AI safety researchers
- · AI agent models performing poorly
- · Proprietary benchmarks lacking comprehensiveness
DecisionBench will accelerate the development of more capable and reliable AI agents by providing clear performance metrics.
Improved agentic systems will lead to increased automation of complex white-collar tasks, impacting industries relying on human decision-making.
The widespread adoption of highly autonomous agents could necessitate new regulatory frameworks for AI delegation and accountability.
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Read at arXiv cs.AI