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Natan Vidra

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#artificial intelligence Preprint Jul 2026

Adversarial Course-of-Action Generation: Game-Theoretic Multi-Agent Algorithms for COA matching&COA generation

COA-Bench is presented, a small offline benchmark and reproducibility artifact for comparing COA generation policies through self-play, which represents COAs as typed action chains with conditional branches, assigns a synthetic COA quality score, and scores doctrinal coherence with an FM 3-0-inspired heuristic rubric.

Natan Vidra, Alina Kapanova, Arun Kanhai et al. · 0 citations
Preprint Aug 2026

OrchestraBench: Evaluating Multi-Agent Orchestration Failure Modes, Recovery, and Decomposition Quality

Multi-agent orchestration frameworks are moving from demos to production, yet benchmarks typically report task accuracy without diagnosing why a pipeline failed, where a cascade began, or which routing decision caused the breakdown. OrchestraBench evaluates failure, recovery, and decomposition through a controlled, see...

Yidian Chen, Ying Gu, Natan Vidra et al. · 2 citations
Preprint Jul 2026

Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark

This work introduces an executable benchmark and a budget-aware meta-router that composes heterogeneous operations from raw task text that establishes a reproducible testbed and a bounded proof of concept, not evidence of live-LLM performance.

Natan Vidra, Alina Kapanova, Arun Kanhai et al. · 0 citations
Preprint Jul 2026

MetaRoute-Bench: Evaluating Meta-Decision Policies for Agentic Workflow Routing

This work presents MetaRoute-Bench, an open, inspectable framework for comparing meta-decision policies under a shared execution model, and releases task generation, policies, traces, tests, and analysis artifacts to support live-system validation.

Natan Vidra, Alina Kapanova, Arun Kanhai et al. · 0 citations
Preprint Aug 2026

Beyond Pass@k: Measuring Reliability and Security of Agentic Code Generation

Security-adjusted reliability@k is proposed, which counts only rollouts that are both functionally correct and free of high-severity insecure patterns, which counts only rollouts that are both functionally correct and free of high-severity insecure patterns.

Jia-Jun Jiang, Sharon Zheng, Natan Vidra et al. · 1 citation · ⚡1

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