The proliferation of visual data under tight storage and bandwidth budgets makes extremely low–bitrate generative image compression increasingly important. Vector quantization (VQ) is compelling in this regime because codebooks encode cross-channel correlations and dataset-level semantics, enabling perceptually faithful reconstructions when bits are scarce. We propose RDVQ, a vector-quantization (VQ) based generative image compression method designed for extremely low bitrates. While end-to-end learned image codecs rely on a differentiable rate term for rate–distortion (RD) optimization, however, a key challenge is that naïvely integrating VQ introduces non-differentiability and is not directly compatible with entropy modeling, forcing prior work to regulate bitrate only indirectly. We resolve this by defining a distance-aware soft posterior over codebook indices and training a conditional autoregressive entropy model to predict it. Therefore the cross-entropy between the approximate and predicted posteriors yields a differentiable rate loss, restoring a gradient pathway from rate to the encoder via codeword distances. Such predicted codebook index distribution enables prefix-only transmission at inference, with the model imputing the rest of the indices, delivering retraining-free bitrate control over a practical range. Our end-to-end RD optimized RDVQ outperforms all baseline methods in terms of DISTS and CLIPIQA, which reflect superior structural restoration and better alignment with human visual perception on the Kodak, DIV2K and CLIC2020 datasets.

Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image-related questions. We introduce FIne-grained NEgative queRies (FINER), alongside two benchmarks: FINER-CompreCap and FINER-DOCCI. Using FINER, we analyze hallucinations across four settings: multi-object, multi-attribute, multi-relation, and “what” questions. Our benchmarks reveal that MLLMs hallucinate when fine-grained mismatches co-occur with genuinely present elements in the image. To address this, we propose FINER-Tuning, leveraging Direct Preference Optimization (DPO) on FINER-inspired data. Finetuning four frontier MLLMs with FINER-Tuning yields up to 24.2% gains (InternVL3.5-14B) on hallucinations from our benchmarks, while simultaneously improving performance on eight existing hallucination suites, and enhancing general multimodal capabilities across six benchmarks. Benchmarks, training data, code and model checkpoints will be released.

Multi-modal learning approaches that integrate pathological images with genomic profiles have significantly enhanced the accuracy of survival prediction tasks. However, previous methods often struggle to effectively process long-range gigapixel whole slide images (WSIs) and sparse genomic profiles due to the limitations of conventional scanning strategies to serialize data and the complex and heterogeneous nature of the modalities. Inspired by recent advancements in Mamba and mixture of experts (MoE), we propose a novel multi-directional composite scanning strategy with mixture of attention and Mamba experts (MDCS-MoAME) for cancer survival prediction. Specifically, we introduce a multi-directional composite scanning (MDCS) strategy to both WSIs and genomic profiles, and use the Mamba encoder to process intra-modal representations at the region, patch, and gene level, ensuring sufficient utilization of the intrinsic information within each modality. To further capture heterogeneous inter-modal representations, we introduce mixture of attention and Mamba experts (MoAME), which dynamically selects tailored experts to model complex inter-modal correlations, flexibly focusing on the interactions between modalities. Finally, we introduce alignment constraints to recalibrate inter-modal interactions and reduce intra- and inter-modal representation redundancy, enhancing its discriminative power for comprehensive survival analysis. Experimental results on five publicly available datasets demonstrate that our method outperforms existing approaches, achieving state-of-the-art performance. Our code is included in the supplementary material.

Video LLMs suffer from temporal inconsistency: small shifts in frame timing can flip attention and suppress relevant frames. We trace this instability to the common extension of Rotary Position Embeddings to video through multimodal RoPE. The induced inverse Fourier time kernel exhibits frame-scale ripples that multiply adjacent frames by different factors, which perturbs attention that should otherwise be governed by the raw query key inner product. We present Phase Aggregated Smoothing (PAS), a simple, training-free mechanism that applies small opposed phase offsets across heads and then aggregates their outputs. PAS preserves the per-head spectrum magnitude, while the aggregation effectively smooths the temporal kernel and reduces phase sensitivity without changing the positional encoding structure. Our analysis shows that the RoPE rotated logit can be approximated as a content dot product scaled by a time kernel; smoothing this kernel yields Lipschitz stability of attention to small temporal shifts; multi phase averaging attenuates high frequency ripples while preserving per-head spectra under Nyquist-valid sampling. Experiments on multiple video understanding benchmarks under matched token budgets show consistent improvements with negligible computational overhead. PAS provides a plug and play upgrade for robust temporal encoding in Video LLMs.

Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing visual-acoustic correlations without considering the physical factors that shape real-world sounds. We present Physics-Aware Video-to-Audio Synthesis (PAVAS), a method that incorporates physical reasoning into a latent diffusion-based V2A generation through the Physics-Driven Audio Adapter (Phy-Adapter). The adapter receives object-level physical parameters estimated by the Physical Parameter Estimator (PPE), which uses a Vision Language Model (VLM) to infer the moving-object mass and a segmentation-based dynamic 3D reconstruction module to recover its motion trajectory for velocity computation. These physical cues enable the model to synthesize sounds that reflect underlying physical factors. To assess physical realism, we curate VGG-Impact, a benchmark focusing on object–object interactions, and introduce Audio-Physics Correlation Coefficient (APCC), an evaluation metric that measures consistency between physical and auditory attributes. Comprehensive experiments show that PAVAS produces physically plausible and perceptually coherent audio, outperforming existing V2A models in both quantitative and qualitative evaluations.

Recent advances in video generation have shown remarkable potential for constructing world simulators. However, current models still struggle to produce physically consistent results, particularly when handling large-scale or complex dynamics. This limitation arises primarily because existing approaches respond isotropically to physical prompts and neglect the fine-grained alignment between generated content and localized physical cues. To address these challenges, we propose ProPhy, a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation. ProPhy employs a two-stage Mixture-of-Physics-Experts (MoPE) mechanism for discriminative physical prior extraction, where Semantic Experts infer semantic-level physical principles from textual descriptions, and Refinement Experts capture token-level physical dynamics. This mechanism allows the model to learn fine-grained, physics-aware video representations that better reflect underlying physical laws. Furthermore, we introduce a physical alignment strategy that transfers the physical reasoning capabilities of vision-language models (VLMs) into the Refinement Experts, facilitating a more accurate representation of dynamic physical phenomena. Extensive experiments on physics-aware video generation benchmarks demonstrate that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.