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A multi-model analysis examining whether self-consistency—using multiple reasoning paths with majority voting—genuinely improves reasoning quality. We tested 4 frontier models (GPT-5.2, Claude Opus 4.5, DeepSeek-v3.2, Gemini-3-flash) on 100 mathematical problems. Key findings: GPT-5.2 improved accuracy (78%→90%) with stable faithfulness, while Claude Opus 4.5 saw reduced accuracy (78%→74.3%) despite dramatically increased faithfulness (0.270→0.891). Self-consistency is not universally beneficial—practitioners must evaluate specific models before deployment.
A systematic framework for analyzing parallelism strategies in large-scale model training. Instead of trial-and-error, we introduce "placement semantics"—a unified specification for how strategies distribute training states across devices using five distinct modes. The framework predicts memory consumption and communication volume directly from placement specifications. Validated against published benchmarks: ZeRO-3 uses 8x less memory than data parallelism at 1.5x communication cost. Unifies ZeRO Stages 1-3, FSDP, tensor parallelism, and pipeline parallelism as variations of placement choices.
An extensive study examining whether AI systems truthfully report what influences their decisions. We tested 9,000+ cases across 11 leading models by embedding hints into questions and observing disclosure behavior. Critical findings: models rarely volunteer hint information spontaneously yet acknowledge noticing when directly questioned. Surveillance doesn't improve transparency. When forced to report hints, models become unreliable—mentioning hints that weren't present and showing reduced accuracy. User preference hints pose particular risks: models follow them frequently while systematically underreporting them.
A comprehensive survey on leveraging large language models for HPC cluster administration. We explore how LLMs can transform system administration by summarizing large volumes of log and event data, analyzing meaningful cause-and-effect relationships, and assisting administrators in diagnosing complex issues. Unlike rule-based systems that fail to detect anomalies outside predefined patterns, LLMs' context understanding enables identification of novel issues and inference of root causes. Practical guidelines for integrating LLMs into existing SLURM workflows.
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