SPEC CPU2026 increases instruction volume and memory footprint while shifting pressure to instruction-cache bottlenecks; 4-5 workload subsets per group preserve 96.4-99.9% of full-suite behavior and show complementary traits to DCPerf and MLPerf.
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A scalable training-free pipeline using video segmentation, filtering, and off-the-shelf multimodal models creates DenseStep2M, a dataset of 100K videos and 2M detailed instructional steps that improves dense captioning, step grounding, and cross-modal retrieval.
FaaSMoE treats MoE experts as on-demand FaaS functions with configurable granularity, using under one-third the resources of a full-model baseline under multi-tenant workloads.
Geometric Reward Credit Assignment disentangles rewards to geometric tokens and adds reprojection consistency to boost 3D keypoint accuracy from 0.64 to 0.93 and bounding box IoU to 0.686 on a ShapeNetCore benchmark while preserving 2D performance.
citing papers explorer
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SPEC CPU2026: Characterization, Representativeness, and Cross-Suite Comparison
SPEC CPU2026 increases instruction volume and memory footprint while shifting pressure to instruction-cache bottlenecks; 4-5 workload subsets per group preserve 96.4-99.9% of full-suite behavior and show complementary traits to DCPerf and MLPerf.
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DenseStep2M: A Scalable, Training-Free Pipeline for Dense Instructional Video Annotation
A scalable training-free pipeline using video segmentation, filtering, and off-the-shelf multimodal models creates DenseStep2M, a dataset of 100K videos and 2M detailed instructional steps that improves dense captioning, step grounding, and cross-modal retrieval.
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FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving
FaaSMoE treats MoE experts as on-demand FaaS functions with configurable granularity, using under one-third the resources of a full-model baseline under multi-tenant workloads.
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Reinforcing 3D Understanding in Point-VLMs via Geometric Reward Credit Assignment
Geometric Reward Credit Assignment disentangles rewards to geometric tokens and adds reprojection consistency to boost 3D keypoint accuracy from 0.64 to 0.93 and bounding box IoU to 0.686 on a ShapeNetCore benchmark while preserving 2D performance.