LookWhen factorizes video recognition into learning when, where, and what to compute via uniqueness-based token selection and dual-teacher distillation, achieving better accuracy-FLOPs trade-offs than baselines on multiple datasets.
Llava-mini: Efficient image and video large mul- timodal models with one vision token
9 Pith papers cite this work. Polarity classification is still indexing.
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SPpruner reduces visual tokens in VLMs via focus identification followed by context-aware scanning, retaining 22.2% tokens for 2.53x speedup on Qwen2.5-VL with negligible accuracy loss.
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
VisMMoE exploits visual-expert affinity via token pruning to achieve up to 2.68x faster VL-MoE inference on memory-constrained hardware while keeping accuracy competitive.
Geo3DPruner uses geometry-aware global attention and two-stage voxel pruning to remove 90% of visual tokens from spatial videos while keeping over 90% of original performance on 3D scene benchmarks.
POINTS-Long is a dual-mode multimodal large language model that uses dynamic visual token scaling to retain 97.7-99.7% accuracy on long-form tasks with 1/40 to 1/10th the tokens and supports streaming via detachable KV-cache.
SVD-Prune selects vision tokens via SVD leverage scores to outperform attention-based pruning at extreme budgets of 32 or 16 tokens.
Fourier Compressor uses FFT to remove frequency-domain redundancy from visual tokens in VLMs, retaining over 96% accuracy with up to 83.8% FLOP reduction.
A modular multimodal generative AI framework produces synthetic residential building data from public sources, with reported overlaps exceeding 65% against a national reference dataset.
citing papers explorer
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LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute
LookWhen factorizes video recognition into learning when, where, and what to compute via uniqueness-based token selection and dual-teacher distillation, achieving better accuracy-FLOPs trade-offs than baselines on multiple datasets.
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Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models
SPpruner reduces visual tokens in VLMs via focus identification followed by context-aware scanning, retaining 22.2% tokens for 2.53x speedup on Qwen2.5-VL with negligible accuracy loss.
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OProver: A Unified Framework for Agentic Formal Theorem Proving
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
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VisMMOE: Exploiting Visual-Expert Affinity for Efficient Visual-Language MoE Offloading
VisMMoE exploits visual-expert affinity via token pruning to achieve up to 2.68x faster VL-MoE inference on memory-constrained hardware while keeping accuracy competitive.
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Geometry-Guided 3D Visual Token Pruning for Video-Language Models
Geo3DPruner uses geometry-aware global attention and two-stage voxel pruning to remove 90% of visual tokens from spatial videos while keeping over 90% of original performance on 3D scene benchmarks.
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POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs
POINTS-Long is a dual-mode multimodal large language model that uses dynamic visual token scaling to retain 97.7-99.7% accuracy on long-form tasks with 1/40 to 1/10th the tokens and supports streaming via detachable KV-cache.
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Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models
SVD-Prune selects vision tokens via SVD leverage scores to outperform attention-based pruning at extreme budgets of 32 or 16 tokens.
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Fourier Compressor: Frequency-Domain Visual Token Compression for Vision-Language Models
Fourier Compressor uses FFT to remove frequency-domain redundancy from visual tokens in VLMs, retaining over 96% accuracy with up to 83.8% FLOP reduction.
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Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity
A modular multimodal generative AI framework produces synthetic residential building data from public sources, with reported overlaps exceeding 65% against a national reference dataset.