Large Language Models (LLMs) achieve strong results on code generation, but single model inference remains brittle on tasks that require iterative refinement. Existing multi agent frameworks improve reliability, yet they often incur substantial token and latency overhead. We introduce PairCoder, a framework that brings pair programming to autonomous LLM collaboration. PairCoder assigns one model to code generation and the other to review, and switches roles when repeated errors suggest that the current interaction has stalled. Across 13 LLMs on HumanEval, PairCoder consistently improves over single model inference. On eight representative backbones, it reaches 91.0% pass@1 and improves over single model inference by up to 20.3% while reducing token usage by 40% to 70% relative to multi agent baselines. Many heterogeneous pairings also outperform both constituent models, suggesting that the framework generalizes across model families. These results position PairCoder as an effective and deployment conscious alternative to heavier multi agent systems.Code is available at https://github.com/yisuanwang/PairCoder

Multimodal named entity recognition (MNER) for tweets has received increasing attention recently. Most of the multimodal methods used attention mechanisms to capture the text-related visual information. However, unrelated or weakly related text-image pairs account for a large proportion in tweets. Visual clues unrelated to the text would incur uncertain or even negative effects for multimodal model learning. In this paper, we propose a novel pre-trained multimodal model based on Relationship Inference and Visual Attention (RIVA) for tweets. The RIVA model controls the attention-based visual clues with a gate regarding the role of image to the semantics of text. We use a teacher-student semi-supervised paradigm to leverage a large unlabeled multimodal tweet corpus with a labeled data set for text-image relation classification. In the multimodal NER task, the experimental results show the significance of text-related visual features for the visual-linguistic model and our approach achieves SOTA performance on the MNER datasets.