Implicit Reasoning in LLMs

Three execution paradigms. Latent optimization adjusts internal representations directly: token-level inserts or learns special latent tokens; trajectory-level compresses or refines whole chains of thought for semantic fidelity, adaptive efficiency, progressive refinement, or exploratory diversification; internal-state-level distills or steers hidden activations to carry the reasoning signal. Signal-guided control uses lightweight controls to modulate compute without emitting text, from thinking or pause tokens to instance-level latent adjustment. Layer-recurrent execution reuses shared blocks in loops to simulate deeper chains internally, with models like ITT, looped Transformers, CoTFormer, Huginn, and RELAY.

Evidence that the latent process is real. Structural signals show layer-wise decomposition and shortcutting; behavioral signatures include step-skipping and grokking-driven phase transitions; representation studies recover intermediate facts from hidden states or induce reasoning via activation steering.

How it is evaluated. Metrics cover final answer correctness (accuracy, Pass@k, EM), efficiency (latency, output length, FLOPs, ACU), perplexity, and probing accuracy. Benchmarks span commonsense, math and code, reading comprehension, multi-hop QA, and multimodal reasoning.

Why is it not solved yet. Key gaps include limited interpretability, weak control and reliability, an accuracy gap to explicit CoT on hard tasks, uneven evaluation, architectural constraints, and dependence on explicit supervision.

Big picture. Implicit reasoning promises faster, cheaper inference and richer internal computation. The survey argues for hybrid designs that keep compute latent yet auditable, standardized evaluations that probe internal trajectories, and architectures that generalize beyond bespoke tokens or loops.