A Meta-Analysis of Cloud-Based Llms in Implicit Multi-Step Reasoning

Implicit multi-step reasoning has emerged as a crucial challenge in evaluating large language models (LLMs), requiring them to infer intermediate steps without explicit decomposition. While chain-of-thought prompting and retrievalaugmented techniques have improved reasoning capabilities, LLMs still exhibit brittleness, over-reliance on statistical patterns, and difficulties in verifying intermediate logical coherence. We systematically categorizes and evaluates state-of-the-art approaches, analyzing their methodologies, effectiveness, and inherent trade-offs. By synthesizing key insights, we identify critical research gaps and propose future directions, aiming to advance LLMs toward more adaptive, self-correcting, and cognitively inspired reasoning frameworks.