Visual token pruning in MLLMs fails on complex reasoning due to Relevant Visual Information Shift during decoding, but the DSTP framework fixes it training-free across models.
Visiontrim: Unified vision token compression for training-free mllm acceleration
7 Pith papers cite this work. Polarity classification is still indexing.
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DualComp uses a lightweight router to split visual token compression into a semantic stream with size-adaptive clustering and a geometric stream with path-tracing recovery, enabling low-cost high-fidelity UHR remote sensing interpretation.
CLSE prunes tokens in MLLMs by quantifying cross-layer spectral redistribution in the frequency domain to preserve semantically active tokens and reduce compute.
PriorTR estimates model-induced prior attention via a null token in one forward pass and contrasts it with task-conditioned attention to improve visual token pruning accuracy-efficiency trade-offs in MLLMs.
Stream3D-VLM adds autoregressive streaming control, VSFI geometry integration, GAVC compression, and a 1M-pair benchmark to enable real-time 3D VLM performance that beats prior models on 29 online and offline tasks.
CIVIC is a path-consistent compact visual inference framework that reduces KV-cache memory to approximately one-third and end-to-end latency in VLMs while preserving accuracy via text-aligned KL distillation and adaptive spatial retention.
DepthVLM converts a standard VLM into a dense metric depth predictor by attaching a lightweight head and training under unified vision-text supervision, outperforming prior VLMs and some pure vision models on a new indoor-outdoor benchmark.
citing papers explorer
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Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding
Visual token pruning in MLLMs fails on complex reasoning due to Relevant Visual Information Shift during decoding, but the DSTP framework fixes it training-free across models.
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Semantic-Geometric Dual Compression: Training-Free Visual Token Reduction for Ultra-High-Resolution Remote Sensing Understanding
DualComp uses a lightweight router to split visual token compression into a semantic stream with size-adaptive clustering and a geometric stream with path-tracing recovery, enabling low-cost high-fidelity UHR remote sensing interpretation.
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Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models
CLSE prunes tokens in MLLMs by quantifying cross-layer spectral redistribution in the frequency domain to preserve semantically active tokens and reduce compute.
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Accelerating Multimodal Large Language Models with Prior-Corrected Token Reduction
PriorTR estimates model-induced prior attention via a null token in one forward pass and contrasts it with task-conditioned attention to improve visual token pruning accuracy-efficiency trade-offs in MLLMs.
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Stream3D-VLM: Online 3D Spatial Understanding with Incremental Geometry Priors
Stream3D-VLM adds autoregressive streaming control, VSFI geometry integration, GAVC compression, and a 1M-pair benchmark to enable real-time 3D VLM performance that beats prior models on 29 online and offline tasks.
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CIVIC: End-to-End Sequence Compactness for Efficient Vision-Language Models
CIVIC is a path-consistent compact visual inference framework that reduces KV-cache memory to approximately one-third and end-to-end latency in VLMs while preserving accuracy via text-aligned KL distillation and adaptive spatial retention.
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Unlocking Dense Metric Depth Estimation in VLMs
DepthVLM converts a standard VLM into a dense metric depth predictor by attaching a lightweight head and training under unified vision-text supervision, outperforming prior VLMs and some pure vision models on a new indoor-outdoor benchmark.