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Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by downstream execution. Existing prompt-adaptation methods infer compression errors by comparing full-context and compressed trajectories. Such comparisons cannot isolate indi...
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque, making it difficult to understand, predict, or control their behavior. As LLMs are increasingly deployed in high-stakes settings, this lack of transparency raises ser...
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks, from text summarization to question answering. Despite these capabilities, their black-box nature obscures internal decision-making processes. Mechanistic interpretability (MI) aims to address this by reverse-engineering neural...
Across four reasoning benchmarks and four models, \textsc{MIST} consistently outperforms baseline methods, suggesting that model-internal saliency provides an effective proxy for reasoning-token importance.
This tutorial provides a comprehensive and up-to-date overview of LLM mechanism discovery, validation, and editing, and surveys mechanistic editing techniques that leverage MI insights to modify behavior at varying granularity.
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