⚡ VLM Efficiency

🤖 AAAI2026 · 5 paper notes

📌 Same area in other venues: 📷 CVPR2026 (62) · 🧪 ICML2026 (4) · 💬 ACL2026 (5) · 🔬 ICLR2026 (5) · 🧠 NeurIPS2025 (8) · 📹 ICCV2025 (11)

🔥 Top topics: Multimodal/VLM ×3 · Compression ×2

EM-KD: Distilling Efficient Multimodal Large Language Model with Unbalanced Vision Tokens

This paper proposes EM-KD, a distillation framework that leverages the Hungarian algorithm to address the vision token count imbalance between teacher and student models. By combining Vision Semantic Distillation (VSD) and Vision-Language Affinity Distillation (VLAD), EM-KD transfers knowledge from a vanilla teacher to an efficient student MLLM, achieving an average score of 50.4 across 11 benchmarks at 144 tokens/patch — surpassing LLaVA-NeXT with 576 tokens (49.4) while delivering nearly 2× inference speedup.

Filter, Correlate, Compress: Training-Free Token Reduction for MLLM Acceleration

This paper proposes FiCoCo, a three-stage framework (Filter–Correlate–Compress) that identifies redundant tokens via integrated vision-aware and semantic-aware redundancy metrics, adaptively recycles information from discarded tokens via inter-token correlation, and achieves training-free MLLM acceleration. On LLaVA-NeXT, FiCoCo achieves a 14.7× FLOPs reduction while retaining 93.6% of performance, and consistently outperforms FastV, SparseVLM, and other state-of-the-art methods across five MLLM architectures.

Global Compression Commander: Plug-and-Play Inference Acceleration for High-Resolution Large Vision-Language Models

This paper proposes GlobalCom², a plug-and-play, training-free token compression framework tailored for high-resolution VLMs with dynamic cropping architectures. It leverages the global thumbnail as a "commander" to guide differentiated compression across local crop regions, achieving >90% of original performance while compressing 90% of visual tokens.

Rethinking Visual Token Reduction in LVLMs under Cross-Modal Misalignment

This paper identifies three forms of cross-modal misalignment (causal, semantic, and spatial) in text-guided visual token importance estimation within LVLMs, and proposes VisionDrop—a training-free progressive token pruning framework that relies exclusively on visual self-attention. The framework performs multi-stage compression across both the visual encoder and LLM decoder, retaining over 91% of original performance while keeping only 5.6% of tokens.

TinyChemVL: Advancing Chemical Vision-Language Models via Efficient Visual Token Reduction and Complex Reaction Tasks

TinyChemVL is a chemistry-domain VLM with only 4B parameters. It compresses visual tokens to 1/16 of the original count via an adaptive token merging and pruning strategy, introduces reaction-level tasks and the ChemRxn-V benchmark, and achieves state-of-the-art performance on both molecular- and reaction-level visual chemistry tasks while significantly improving inference and training speed.