arXiv:2512.07795v2 Announce Type: replace-cross Abstract: Benchmark scores for LLM reasoning systems are reported as single numbers, yet the same model, strategy, and task can produce meaningfully different answers and costs across repeated executions, even under greedy decoding (T = 0). This variance is not a statistical nuisance: the highest-performing strategy wins only 77% of head-to-head runs against its nearest competitor, meaning a single observed score can silently misrank systems. We introduce ReasonBench, a benchmark suite recording 30 independent trials across 10 reasoning strategie
The proliferation of LLMs and their increasing deployment in critical applications necessitates more robust and reliable benchmarking methodologies to understand their true capabilities and limitations.
A strategic reader should care because the instability in LLM reasoning, even under controlled conditions, indicates a critical vulnerability in current AI systems, fundamentally impacting their trustworthiness and deployment readiness for complex tasks.
The understanding of LLM reasoning performance shifts from single, potentially misleading scores to a more nuanced view acknowledging significant variance, requiring developers and evaluators to adopt more rigorous testing protocols.
- · AI researchers focused on stability and reliability
- · Companies developing robust AI evaluation platforms
- · Enterprises needing trustworthy AI deployments
- · LLM developers relying on single-score benchmarks
- · Users making critical decisions based on superficial LLM evaluations
- · AI projects with insufficient variability testing
Benchmark scores for LLM reasoning will become more comprehensive, incorporating metrics for variability and stability rather than just raw performance.
This shift will drive development towards more intrinsically stable LLM architectures and reasoning strategies, prioritizing consistency alongside accuracy.
The increased focus on AI stability could become a competitive differentiator, leading to a 'reliability premium' for LLM providers who can demonstrate consistent performance across diverse conditions.
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Read at arXiv cs.CL