## 3 Self-Correction Reflection  
To address the limitation of the LLM's deterministic tokenization process being vulnerable to input perturbations (e.g., spelling errors or irregular token combinations), we proposed self-correction reflection (Figure 2). It is inspired by the intrinsic ability of the human brain to correct errors during language processing [7] and leverages the consistency principle of [8], where LLMs exhibit an inherent tendency to maintain coherence across their responses. This method enables the model to detect and rectify perturbations using its existing capabilities autonomously. Unlike traditional approaches that rely on external correction tools or additional training, our method preserves semantic integrity without modifying the model architecture or requiring external resources, ensuring high practicality and scalability. The design of the self-correction reflection framework comprises four key stages: Input Reception and Tokenization, Self-Correction Phase, Historical Enhancement through the Consistency Principle, and Inference with Corrected Context (Figure 3). Initially, the perturbed input undergoes tokenization and hidden-state extraction, establishing the foundation for subsequent analysis. The second stage activates the model's self-diagnosis mechanism, where variational inference and temperature-guided sampling iteratively refine the perturbed input into a corrected version. Building on this, the third stage integrates the corrected text into the dialogue history while preserving the original perturbed context, enabling cross-attention between distorted and rectified representations to mitigate error propagation. Finally, the model performs inference using the corrected embeddings, ensuring alignment with the intended semantic space. These stages collectively mimic the brain's error-checking loop while leveraging the LLM's intrinsic coherence to balance correction rigor with computational efficiency. The self-correction reflection process operates as follows:  
(1) Input Reception and Tokenization: When a user submits a perturbed input text \(T^{\prime}\), the model processes it through a tokenizer to convert it into a sequence of tokens:  
\[t^{\prime}=\text{Tokenizer}_{k}(T^{\prime}). \tag{1}\]  
These tokens are passed through the model to extract hidden states from the final embedding layer, yielding vector representations:  
\[h^{\prime}=\text{Model}_{k}(t^{\prime}), \tag{2}\]  
Figure 2: Illustration of the self-correction reflection for improving LLM performance by simulating the brain’s error correction ability and leveraging relevant research findings. where \(h^{\prime}\) are the hidden states of the final embedding layer of \(\text{Model}_{k}\). Perturbations in \(T^{\prime}\) may cause \(h^{\prime}\) to deviate from the intended semantic representation, potentially resulting in misinterpretation. (2) Self-Correction Phase: The model analyzes the tokenized input \(t^{\prime}\) and its hidden state \(h^{\prime}\) to generate a corrected text \(T\). Using a system prompt (see Table 2), the model rectifies errors. This correction is modeled as a variational distribution:  
\[q(T\mid T^{\prime})=\text{LLM}_{\text{correct}}(T^{\prime}\to T), \tag{3}\]  
which approximates the true posterior \(P(T\mid T^{\prime})\). The model outputs a probability distribution over the generated tokens, and the logarithmic probability \(\log q(T\mid T^{\prime})\) is calculated as the sum of the logarithmic probabilities of each token in \(T\) conditioned on \(T^{\prime}\). Similarly, \(P(T\mid T^{\prime})\) is approximated by the model's output probability distribution for the corrected text, computed as  
\[\log P(T\mid T^{\prime})\approx\sum_{i=1}^{n}\log P(t_{i}\mid t_{<i},T^{ \prime}), \tag{4}\]  
where \(t_{i}\) are the tokens in \(T\), and \(P(t_{i}\mid t_{<i},T^{\prime})\) is the model's predicted probability for each token given the preceding tokens and the perturbed input.