LLM-as-its-judge: A compute paradigm for self-aware and self-directing reasoning machines

Large language models (LLMs) can perform multi-step reasoning to divide complex problems into a sequence of conquerable parts. These reasoning steps empowered LLMs for mathematical, logical, and algorithmic feats beyond one-step generation. Yet, with token generation being strictly autoregressive, early mistakes compound and subtle missteps can derail the entire reasoning trajectory. We demonstrate that LLMs can sense when these reasoning trajectories approach a tipping point to go off road. Then, by injecting specialized control tokens, LLMs can learn to push their own reasoning trajectories back on track. To bring “self-awareness” to LLM inference, we introduce *reflexive reasoning*, a novel reasoning paradigm complementary to current reasoning approaches. Across multiple standard benchmarks, reflexive reasoning improves the task performance of LLMs spanning a range of architectures and parameter counts, with no modification of LLM weights and no added inference-time compute. Through self-reasoning, we can also enable *reflexive tuning*, a fine-tuning framework to enforce tight policy and persona adherence at inference time, with fixed model weights. By repurposing the model’s internal representations to serve as its own observer and overseer, our approach delivers robust-by-design inference for complex, high-risk reasoning applications, with minimal computational overhead.