Emergent Lexical Synthesis Through Contextual Feedback Mechanisms in Large Language Models

Adaptive mechanisms capable of refining intermediate latent states during text generation are essential for addressing limitations in current model architectures, particularly with respect to contextual drift, linguistic coherence, and output diversity. Contextual Feedback Mechanisms (CFMs) introduce internal feedback loops that recondition latent representations dynamically during runtime, allowing large language models to adjust predictions iteratively without requiring architectural retraining. Through precise integration of gating functions, softmax-based reweighting, and auxiliary loss constraints, CFMs achieve significant improvements in token-level prediction accuracy, sequence-level coherence, and adaptability to ambiguous or incomplete inputs. Empirical evaluations across multiple large-scale datasets, including WikiText-103, BookCorpus, and Reddit Corpus, demonstrate a marked reduction in perplexity and token repetition, particularly for extended sequences exceeding several hundred tokens. Output diversity, as quantified through unique token ratios, showed consistent gains across both conversational and formal text inputs, indicating that CFMs enhance lexical variability without sacrificing fluency. Comparative analysis revealed that the proposed mechanisms surpass existing techniques, such as nucleus sampling and iterative decoding, in balancing computational efficiency and real-time adaptability. Experimental benchmarks further highlight the scalability of CFMs, with latency overhead remaining within acceptable thresholds across varying batch sizes and input lengths. Additionally, CFMs demonstrate a robust ability to resolve ambiguity in challenging prompts, offering new opportunities for improving model performance in dynamic and evolving contexts. The framework maintains practical feasibility through computational optimizations, ensuring that feedback integration imposes minimal resource demands while delivering measurable gains.