This work presents VeraGrid-Agent, a tool-augmented LLM that autonomously writes the simulator input, executes the open-source VeraGrid solver, and reads the solver output before answering, and does a failure-mode analysis to show that the few remaining errors arise from wrong interpretations during multi-step reasoning, rather than any failure in the simulators execution.
It is argued that evaluations of scientific agents should report not only accuracy, but also item-level retention, output-access sensitivity, trajectory failures, and where the computation chain breaks.
Ke Zhang, Sahchit Chundur, M. J. Qomi et al.· 0 citations
Backtrader-Bench, a framework with two complementary pipelines that generates questions from backtest configurations across five trading strategies, 33 templates, and three difficulty tiers, and is designed to produce a training corpus for reinforcement learning, with the ultimate goal of building a specialized agent for quantitative trading workflows.
This work uses a full $2^3$ factorial design to decompose three recurring interventions in formalization pipelines: parametric expert drafting, Mathlib/context search, and Lean elaboration feedback, suggesting that formal validity, proof-oriented Lean competence, and faithful statement generation should be reported separately.
Ke Zhang, P. Gallardo, S. Murthy et al.· arXiv.org· 1 citation