AI Research Techniques

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Benchmarks, evaluation, scaling laws, interpretability, and research methods.

The AI Research Techniques section provides a rigorous investigation into the methodologies, evaluation frameworks, and mechanistic analyses driving modern artificial intelligence. As models scale from pattern recognizers to autonomous agents, understanding their internal architectures, optimizing their scaling trajectories, and validating their real-world capabilities are critical challenges for researchers and practitioners alike.

A primary focus of our research is mechanistic interpretability—the science of reverse-engineering neural networks to identify internal causal pathways. We explore how neural networks represent complex concepts through high-dimensional vector geometry, examining the superposition hypothesis and polysemanticity, and how tools like sparse autoencoders, activation patching, and representation probing help disentangle internal activations. This extends to analyzing how models process reasoning internally, tracking hidden states using the logit lens, and evaluating model beliefs through formal logic, Theory of Mind benchmarks, and false belief tasks.

We also examine the structural laws governing AI progress and capability forecasting. This includes analyzing empirical scaling laws across biological and artificial systems, understanding the limits of hardware and training budgets, and assessing how models transition toward test-time compute and deliberative inference to overcome data constraints.

Evaluating these systems requires robust benchmarking. We dissect current evaluation methodologies, analyzing benchmark vulnerabilities such as data contamination, metric saturation, and the implications of Goodhart’s Law on measuring true fluid intelligence versus simple memory retrieval. Additionally, we cover model calibration, discussing how reinforcement learning affects confidence and why uncertainty quantification is essential for preventing failures.

Finally, we evaluate these research techniques in specialized domains. This includes assessing time-series foundation models and explainable AI in algorithmic trading—addressing issues like look-ahead bias and semantic entropy—while tracking how frontier models impact scientific discovery, cybersecurity, and document authentication.

34 published articles