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Pitwall: Faithful Natural-Language Race-Strategy Briefings from a Calibrated Real-Time Monte Carlo Engine

paperactiveprovisionalpitwall-faithful-natural-language-race-strategy-briefings-from-a-calibrated-real-time-monte-carlo-engine-f295c6d6·1 events·first seen 5d ago

Aliases: Pitwall: Faithful Natural-Language Race-Strategy Briefings from a Calibrated Real-Time Monte Carlo Engine

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Pitwall

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6arXiv · cs.CL·5d ago·source ↗

Pitwall: Production system for faithful LLM-generated F1 race briefings with Monte Carlo grounding

Pitwall is a production NLP system that generates real-time Formula 1 strategy briefings in three languages, using a calibrated Monte Carlo simulation engine (N=2,000 continuations, 126 races of training data) as a grounding substrate. Every generated sentence is decomposed into typed factual claims and verified against the probabilistic race state before publication; fine-tuning data is filtered to only state-supported targets (81.9% retention), preventing the model from ever training on ungrounded outputs. The system was validated at two live Grands Prix (Austria and Britain, 2026) and surfaces a generalizable finding: hallucination in sparse-context grounding traces to base-model instruction adherence rather than model scale. The paper contributes both a practical faithfulness-as-architecture approach and a real-world deployment case for grounded generation under strict latency constraints.

Evaluation and Benchmarking Agent and Tool Ecosystem Pitwall Pitwall: Faithful Natural-Language Race-Strategy Briefings from a Calibrated Real-Time Monte Carlo Engine