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All digests tagged SWE-bench
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The future of AI lies not just in bigger models, but in better harnesses. RLMs and multi-agent swarms show how programmatic wrappers can unlock compositional generalization, making the system architecture the most critical component.
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Learn how Applied Compute brings continual learning to enterprises using a 2x2 distillation spectrum. Discover how online hinting and per-step guidance can boost agent performance from 15% to 80% without golden answers.
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This lecture examines test-time compute scaling, showing how repeated sampling and advanced architectures like Arkon can boost model performance without retraining. It covers power laws and optimizing inference pipelines.
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Benchmarking often suffers from contamination, reward hacking, and misalignment between prompts and verifiers. To improve AI evaluation, domain expertise and rigorous QC are essential.
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The industry is shifting focus from training bigger models to optimizing cost and speed. Key techniques include the 'sidekick' agent architecture, which uses parallel processing of multiple LLMs for better price performance.
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