Large vision-language models (LVLMs) excel at multimodal tasks but often generate instance-level object hallucinations, describing nonexistent objects. Yet most mitigation strategies rely on output-level or coarse-grained interventions, limiting their ability to identify and selectively regulate attention heads associated with faithful versus hallucinatory generation, often at the cost of descriptive coverage. To address this limitation, we propose HACP, a training-free framework that integrates head-level semantic attribution with instance-adaptive counteractive pruning. HACP introduces InfoSpectralScore to summarize spectral changes in head-perturbed representations and identify heads associated with hallucination or grounded generation, which are then selectively suppressed or reinforced during decoding. Across multiple LVLMs and benchmarks, HACP consistently reduces hallucinations while preserving descriptive coverage; on a 1000-image multi-label COCO subset, it lowers CHAIR-s/i from 66.7/18.0 to 43.4/8.6 and improves F1 from 73.0 to 77.0. These findings suggest that head-level attribution provides an actionable signal for fine-grained inference-time hallucination control in LVLMs, while helping preserve descriptive coverage. The official project repository for HACP is available at https://github.com/S1yang/HACP.

Post-training quantization (PTQ) for multimodal large language models (MLLMs) faces a distinct optimization challenge: text and vision tokens can induce conflicting gradients when learning a shared channel-wise transformation. We identify this issue as modality gradient conflict, characterized by opposing update directions and severe magnitude imbalance between modality-specific gradients, and propose Modality-Coordinated Transformation Quantization (MCTQ) to address it. MCTQ combines whitening-based preconditioning, modality-decomposed reconstruction, and modality-coordinated optimization to learn a single shared transformation that serves both modalities without introducing modality-specific parameters or inference overhead. Across multiple MLLMs, benchmarks, and precision settings, MCTQ consistently outperforms existing channel-wise PTQ methods, with the largest gains under aggressive low-bit quantization. These results show that coordinating modality-specific gradients is critical for learning effective shared transformations in multimodal PTQ. Code and reproduction materials are available at https://github.com/jtsdw/UniMQ.