Multi-bit watermarking for large language models (LLMs) enables richer provenance information, but existing schemes remain unreliable for short texts because they rely on local or pre-specified allocation strategies. To address this limitation, we propose a Closed-Loop Error Feedback (CLEF) Multi-Bit Watermarking framework. By treating the LLM as a noisy channel, CLEF tracks the global feature state of generated tokens and dynamically measures its deviation from an ideal multidimensional target trajectory. The resulting dynamic error vector is then used to modulate the logits of candidate tokens via an inner-product scoring mechanism, biasing generation toward tokens that compensate for underrepresented payload dimensions. This closed-loop control enables robust multi-bit embedding with a simple, fully linear decoding process. Experiments on short-text generation show that CLEF improves bit extraction accuracy and complete message recovery rate over the evaluated multi-bit watermarking baselines. Furthermore, CLEF achieves higher bit extraction accuracy under different text perturbations while maintaining acceptable generation quality across downstream tasks such as text summarization and machine translation.