arXiv:2605.07053v2 Announce Type: replace Abstract: Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed test sets. Most robustness variants apply surface-level perturbations (paraphrases, renamings, number swaps, distractors) that largely preserve the underlying facts, and static releases can themselves become memorization targets over time. We introduce GSM-SEM, a reusable and stochastic framework for generating semantically diverse benchmark variants with substantially higher semantic variance
The proliferation of mathematical reasoning benchmarks like GSM8K is leading to memorization, necessitating new methods for robust evaluation of AI models. This work addresses the current limitations in assessing true AI capabilities beyond surface-level perturbations by introducing a framework for semantically variant augmentations.
The development of more robust, semantically diverse AI benchmarks like GSM-SEM will ensure that progress in AI is based on genuine algorithmic improvement rather than test set memorization. This is crucial for evaluating the true reasoning capabilities of AI, particularly in sensitive areas such as scientific and mathematical problem-solving.
The standard for evaluating AI's mathematical reasoning and problem-solving abilities will evolve, forcing AI developers to create more genuinely capable models rather than those optimized for specific, static datasets. This shifts the focus from superficial robustness to deeper semantic understanding and variance.
- · AI research labs focused on foundational reasoning
- · Developers of robust and generalizable AI models
- · Users of AI systems requiring reliable reasoning
- · AI models reliant on test set memorization
- · Developers using static, easily memorized benchmarks
- · Benchmarking organizations with less sophisticated variant generation
AI models will be pushed to develop more sophisticated and generalized reasoning capabilities to perform well on new, semantically varied benchmarks.
This improved evaluation could accelerate the development of truly intelligent agents, as the 'goalposts' for success become more challenging and reflective of real-world complexity.
The methodology could be extended to other AI evaluation domains, leading to a broader paradigm shift in how AI capabilities are assessed across various tasks, impacting the commercial viability of 'agentic systems' capable of navigating ambiguity.
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Read at arXiv cs.CL