DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows

[2605.19099] DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows

[Submitted on 18 May 2026]

Title:DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows

View a PDF of the paper titled DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows, by Yuxuan Gao and 4 other authors

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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 information is generated or delivered, so learned routers, richer peer memories, adaptive profile construction, and multi-step delegation can all be evaluated against it. We characterize the substrate with a five-condition reference sweep on the full pool (n=23,375 task instances). Three benchmark-level findings emerge: (i) mean end-task quality is statistically indistinguishable across the four awareness conditions (|beta| = 0.21), so quality-only evaluation would miss the orchestration signal; (ii) routing fidelity-at-1 ranges from 7.5% to 29.5% across conditions at near-equal mean quality, with delivery channel (on-demand tool vs. preloaded description) dominating description content; (iii) a counterfactual ceiling places perfect delegation 15-31 percentage points above measured performance on every suite, locating large unrealized headroom for future orchestration methods. We release the substrate, annotation layer, reference intervention suite, analysis pipeline, and 220 per-condition run archives.

Comments: 28 pages, 9 figures, 11 tables. Code and data: this https URL

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)

ACM classes: I.2.7; I.2.6

Cite as: arXiv:2605.19099 [cs.AI]

(or arXiv:2605.19099v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.19099

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Megan Wang [view email] [v1] Mon, 18 May 2026 20:37:14 UTC (2,230 KB)

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