aiqbench analysis
This study reveals a critical limitation in frontier LLMs regarding their ability to predict computational outcomes for algorithmic tasks. The research demonstrates that even top-tier models exhibit "algorithmic blindness," often performing worse than random guessing when estimating performance metrics like runtime or memory usage, relying instead on benchmark memorization rather than genuine reasoning. For practitioners deploying AI-driven tools for system optimization, this finding underscores the current unreliability of using LLMs to guide algorithm selection without rigorous validation against ground truth data.
Algorithmic Blindness in Large Language Models: A Calibration Study of Performance Prediction
arXiv:2602.21947v5 Announce Type: replace Abstract: Large language models (LLMs) demonstrate remarkable breadth of knowledge, yet their ability to reason about computational processes remains poorly understood. Closing this gap matters for practitioners who rely on LLMs to guide algorithm selection and deployment. We address this limitation using causal discovery as a testbed and evaluate eight frontier LLMs against ground truth derived from algorithm executions. We find systematic, near-total failure across models. The predicted ranges are far wider than true confidence intervals yet still fail to contain the true algorithmic mean in most cases. Most models perform worse than random guessing. The best model's marginal improvement points to benchmark memorization rather than principled reasoning. We term this failure algorithmic blindness and argue it reflects a fundamental gap between declarative knowledge about algorithms and calibrated procedural prediction.
Abstract:Large language models (LLMs) demonstrate remarkable breadth of knowledge, yet their ability to reason about computational processes remains poorly understood. Closing this gap matters for practitioners who rely on LLMs to guide algorithm selection and deployment. We address this limitation using causal discovery as a testbed and evaluate eight frontier LLMs against ground truth derived from algorithm executions. We find systematic, near-total failure across models. The predicted ranges are far wider than true confidence intervals yet still fail to contain the true algorithmic mean in most cases. Most models perform worse than random guessing. The best model's marginal improvement points to benchmark memorization rather than principled reasoning. We term this failure algorithmic blindness and argue it reflects a fundamental gap between declarative knowledge about algorithms and calibrated procedural prediction.
Computation and Language (cs.CL)
arXiv:2602.21947 [cs.CL]
(or arXiv:2602.21947v5 [cs.CL] for this version)
Full text under the publisher's open licence · 141 words
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