Patchscopes

Patch-and-decode: Takes a hidden state from one forward pass, patches it into a second LLM call with an auxiliary prompt, and reads back natural-language descriptions of what that state encodes.

Unifies prior methods: Subsumes a wide range of existing interpretability techniques (logit lens, activation patching, probing) as special cases of its patching/decoding pattern.

Answers computational questions: Can answer questions about the role of specific layers, attribute representations, or the flow of information within the model.

Fixes latent reasoning: Demonstrates that patching in corrected intermediate representations can actually fix latent multi-hop reasoning errors at inference time - interpretability moving into a practical intervention.