StreamPro introduces a benchmark and training method using CB-Stream Loss and GRPO to enable proactive decision-making in streaming videos, achieving 41.5 on StreamPro-Bench compared to 10.4 previously.
Efficient memory management for large language model serving with pagedattention
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Quantitative benchmarks across recent AI accelerators reveal that optimal hardware choice varies with workload parameters and that several platforms incur substantially higher idle power than GPUs.
Q-Zoom achieves up to 4.39x inference speedup in high-resolution MLLM scenarios via query-aware gating and region localization, matching or exceeding baseline accuracy on document and high-res benchmarks.
EyeVLM benchmark finds that current VLMs underperform specialized visual models on gaze following and social gaze prediction, with fine-tuning narrowing but not closing the gap.
citing papers explorer
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StreamPro: From Reactive Perception to Proactive Decision-Making in Streaming Video
StreamPro introduces a benchmark and training method using CB-Stream Loss and GRPO to enable proactive decision-making in streaming videos, achieving 41.5 on StreamPro-Bench compared to 10.4 previously.
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The xPU-athalon: Quantifying the Competition of AI Acceleration
Quantitative benchmarks across recent AI accelerators reveal that optimal hardware choice varies with workload parameters and that several platforms incur substantially higher idle power than GPUs.
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Q-Zoom: Query-Aware Adaptive Perception for Efficient Multimodal Large Language Models
Q-Zoom achieves up to 4.39x inference speedup in high-resolution MLLM scenarios via query-aware gating and region localization, matching or exceeding baseline accuracy on document and high-res benchmarks.
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Eyes on VLM: Benchmarking Gaze Following and Social Gaze Prediction in Vision Language Models
EyeVLM benchmark finds that current VLMs underperform specialized visual models on gaze following and social gaze prediction, with fine-tuning narrowing but not closing the gap.