Although Large Vision-Language Models (LVLMs) excel at single-image understanding tasks, their performance often degrades significantly when processing multi-image inputs due to unintended cross-image information leakage, in which visual content from different images is erroneously fused and image-specific contextual information is lost. Current approaches attempt to mitigate this by inserting special vision delimiter tokens between images during input formatting to help models distinguish image boundaries and preserve context. However, our analysis reveals that such delimiters alone yield limited gains because models do not sufficiently perceive or respond to these boundary signals. To address this, we propose Vision-Delimiter-aware Attention Head Modulation (VDAHM), a training-free method that modulates the output of delimiter-sensitive attention heads, reorienting the model’s internal attention flow to strengthen its structural perception of boundary signals. Extensive evaluations across multiple benchmarks, including Mantis, BLINK, MuirBench, and Qbench2, demonstrate that VDAHM consistently improves LVLMs performance with virtually no additional computational cost. The source code is available at [https://github.com/ZeroNLP/VDAHM](https://github.com/ZeroNLP/VDAHM).

Lyrics generation has garnered increasing attention within the artificial intelligence community. Our task focuses on generating harmonious Cantonese lyrics. Unlike other languages, Cantonese has a unique system of nine contours and six tones, making it essential to satisfy the harmony rules that ensure the alignment between the melody and the tonal contours of the lyrics when composing lyrics. Current research has not yet addressed the challenge of generating lyrics that adhere to Cantonese harmony rules. To tackle this issue, we propose ToneCraft, a novel framework for generating Cantonese lyrics that ensures tonal and melodic harmony. It enables LLMs to generate lyrics with a fixed character count while aligning with tonal and melodic structures. We present an algorithm that combines character-level control, melodic guidance, and a task-specific loss to achieve tonal harmony without compromising generation flexibility and quality. By incorporating domain-specific expertise, we leverage pure lyric datasets to train our model, eliminating the need for aligned data. Both objective evaluations and subjective assessments show that our generated lyrics align with melodic contours significantly better than existing methods. All code and data are available at: https://github.com/purepasser-by/ToneCraft.