HALO distills VLM priors via question-answering objectives and applies sparse attention to enable reliable memory retrieval from up to eight minutes of history in imitation-learned visuomotor policies.
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arXiv preprint arXiv:2507.20198 , year=
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ZipRerank delivers state-of-the-art multimodal listwise reranking accuracy for long documents at up to 10x lower latency via early interaction and single-pass scoring.
AVOC is a retrieval-inspired token compression framework that improves long-form audio-video understanding in multimodal LLMs by selecting informative tokens based on classical IR principles.
Learnable sparsification framework compresses WSI visual tokens to 32 (0.78% of original) via SparseLearn, achieving 73.32% accuracy on SlideBench (TCGA) and outperforming baselines.
dMoE aggregates token expert distributions to block level in dLLMs, cutting unique experts from 69.5 to 14.6, memory by 76-80%, and latency by 1.14-1.66x while retaining 99.11% performance.
EarlyTom is a training-free early token compression method inside the vision encoder with decoupled spatial selection that reduces TTFT up to 2.65x and FLOPs 61% on LLaVA-OneVision-7B while keeping accuracy comparable to full tokens.
O-MARC is a compression distillation framework that lets compact omnimodal models maintain or exceed full-token performance on video QA while cutting latency and memory by about 35%.
OmniZip introduces an audio-guided dynamic token compression framework that achieves 3.42X inference speedup and 1.4X memory reduction for omnimodal LLMs without any training.
MVPruner is a two-stage adaptive token pruning technique for multi-view VLMs that achieves 87.3% FLOPs reduction and 4.97x prefilling speedup while retaining 98.5% accuracy on DriveLM.
StateKV is an inference-time technique that replaces quadratic self-attention prefill in video VLMs with a fixed-capacity importance-based recurrent state, keeping accuracy near full attention on long-video benchmarks without retraining.
TAPE applies temporal-aware token pruning with smoothing, reselection, and timestep scheduling to speed up video diffusion models while preserving visual fidelity and coherence.
OmniRefine introduces alignment-aware chunk refinement via similarity and dynamic programming followed by modality-cooperative token compression, achieving near-baseline accuracy at 44% token retention on WorldSense.
Fre-Res compresses video tokens by preserving spatial anchors and representing temporal dynamics with low-frequency residual tokens derived from 1D-DCT on inter-frame residuals, plus a Spatial-Guided Absorber to reinject the information.
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
citing papers explorer
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Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control
HALO distills VLM priors via question-answering objectives and applies sparse attention to enable reliable memory retrieval from up to eight minutes of history in imitation-learned visuomotor policies.
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Very Efficient Listwise Multimodal Reranking for Long Documents
ZipRerank delivers state-of-the-art multimodal listwise reranking accuracy for long documents at up to 10x lower latency via early interaction and single-pass scoring.
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AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token Compression
AVOC is a retrieval-inspired token compression framework that improves long-form audio-video understanding in multimodal LLMs by selecting informative tokens based on classical IR principles.
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Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning
Learnable sparsification framework compresses WSI visual tokens to 32 (0.78% of original) via SparseLearn, achieving 73.32% accuracy on SlideBench (TCGA) and outperforming baselines.
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dMoE: dLLMs with Learnable Block Experts
dMoE aggregates token expert distributions to block level in dLLMs, cutting unique experts from 69.5 to 14.6, memory by 76-80%, and latency by 1.14-1.66x while retaining 99.11% performance.
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EarlyTom: Early Token Compression Completes Fast Video Understanding
EarlyTom is a training-free early token compression method inside the vision encoder with decoupled spatial selection that reduces TTFT up to 2.65x and FLOPs 61% on LLaVA-OneVision-7B while keeping accuracy comparable to full tokens.
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O-MARC: Omni Memory-Augmented Compression Distillation for Efficient Video Understanding
O-MARC is a compression distillation framework that lets compact omnimodal models maintain or exceed full-token performance on video QA while cutting latency and memory by about 35%.
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OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models
OmniZip introduces an audio-guided dynamic token compression framework that achieves 3.42X inference speedup and 1.4X memory reduction for omnimodal LLMs without any training.
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MVPruner: Dynamic Token Pruning for Accelerating Multi-view Vision-Language Models in Autonomous Driving
MVPruner is a two-stage adaptive token pruning technique for multi-view VLMs that achieves 87.3% FLOPs reduction and 4.97x prefilling speedup while retaining 98.5% accuracy on DriveLM.
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Linear Scaling Video VLMs for Long Video Understanding
StateKV is an inference-time technique that replaces quadratic self-attention prefill in video VLMs with a fixed-capacity importance-based recurrent state, keeping accuracy near full attention on long-video benchmarks without retraining.
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Temporal Aware Pruning for Efficient Diffusion-based Video Generation
TAPE applies temporal-aware token pruning with smoothing, reselection, and timestep scheduling to speed up video diffusion models while preserving visual fidelity and coherence.
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OmniRefine: Alignment-Aware Cooperative Compression for Efficient Omnimodal Large Language Models
OmniRefine introduces alignment-aware chunk refinement via similarity and dynamic programming followed by modality-cooperative token compression, achieving near-baseline accuracy at 44% token retention on WorldSense.
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Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs
Fre-Res compresses video tokens by preserving spatial anchors and representing temporal dynamics with low-frequency residual tokens derived from 1D-DCT on inter-frame residuals, plus a Spatial-Guided Absorber to reinject the information.
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Toward Native Multimodal Modeling: A Roadmap
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.