Self-Agent System for Reflective Reasoning

Reasoning reliability is critical as LLM agents become increasingly long-horizon, since local errors can silently accumulate and propagate across steps, yet the reliability of evolving reasoning trajectories remains underexplored. To address this, we introduce the self-agent perspective: an LLM-based agent treats its own evolving reasoning trajectory as the object of perception, deliberation, and action. Specifically, we realize this perspective by internalizing the agent loop over the model's own reasoning process. A tiny learned monitor acts as perception, identifying prefixes that are likely to derail later reasoning from past model-generated conversations and final-answer feedback. The frozen LLM itself then serves as deliberation, re-examining the suspect reasoning in a separate context rather than blindly continuing the flawed trajectory. A conservative commit gate provides action, writing a repair back only when it is specific and reliable. In this sense, self-agent turns a fixed LLM from a one-way generator into an online controller of its own reasoning trajectory, without retraining the generator, annotating intermediate steps, or relying on stronger planner/controller models or specialized expert agents. Comprehensive experiments on mainstream mathematical reasoning benchmarks show that Self-Agent provides plug-and-play gains across base models, improving long-horizon reasoning and recovering failing trajectories with only modest additional computation.