Implicit Reasoning in Language Models

The field asks how hidden states, recurrent or looped computation, latent tokens, and memory can support multi-step inference without generating full rationales. Approaches include distilling from explicit reasoning traces, adaptively allocating test-time computation, and using mechanistic tools such as activation patching and probes; evaluations span math, symbolic and multi-hop tasks, retrieval, commonsense, and hidden constraints. These methods can reduce token use or latency and sometimes match explicit reasoning, but results depend on supervision, model, and task, while extra computation can cause shortcuts or overthinking. A central open question is whether latent states carry causally useful, generalizable reasoning—and how to verify, interpret, and secure it when visible answers or rationales may be misleading.