Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems
为了解决大规模RAG系统中检索器评估问题，本文提出了Q2D-Web基准，包含1.9亿文档和7万由用户查询改写的搜索请求，并提供了多种相关性判断方法。
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Representative Papers
Diffusion-Pretrained Dense and Contextual Embeddings
This work addresses the challenge of balancing global semantic modeling and computational efficiency in multilingual long-document retrieval by proposing a novel embedding method based on diffusion-based pretrained language models. The approach integrates document-level global context into paragraph representations through a late-chunking strategy and a context-aware bidirectional attention mechanism. High-quality dense vectors are further refined via multi-stage contrastive learning and mean pooling. The resulting model, pplx-embed-v1, achieves strong performance across multilingual and code retrieval benchmarks, including MTEB and MIRACL. Its contextual variant, pplx-embed-context-v1, sets a new state-of-the-art on ConTEB and demonstrates both efficiency and practicality in large-scale production environments with tens of millions of documents.
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RDMA Point-to-Point Communication for LLM Systems
Existing LLM systems—including decoupled inference, MoE routing, and asynchronous reinforcement learning fine-tuning—rely on flexible point-to-point communication, yet mainstream RDMA implementations are tightly coupled to vendor-specific NICs, hindering portability and integration into inference engines. This work proposes TransferEngine, a universal RDMA communication framework that introduces a unified abstraction layer and novel primitives—WriteImm and ImmCounter—to enable precise, out-of-order transmission completion notification. TransferEngine achieves transparent, hardware-agnostic management across heterogeneous NICs (e.g., NVIDIA ConnectX-7, AWS EFA). Evaluation demonstrates: (1) efficient and reliable dynamic KvCache migration; (2) trillion-parameter RL weight updates completed in just 1.3 seconds; and (3) MoE inference latency lower than DeepEP with peak throughput reaching 400 Gbps.
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Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems
为了解决大规模RAG系统中检索器评估问题，本文提出了Q2D-Web基准，包含1.9亿文档和7万由用户查询改写的搜索请求，并提供了多种相关性判断方法。
0 citationsRead paper
Diffusion-Pretrained Dense and Contextual Embeddings
This work addresses the challenge of balancing global semantic modeling and computational efficiency in multilingual long-document retrieval by proposing a novel embedding method based on diffusion-based pretrained language models. The approach integrates document-level global context into paragraph representations through a late-chunking strategy and a context-aware bidirectional attention mechanism. High-quality dense vectors are further refined via multi-stage contrastive learning and mean pooling. The resulting model, pplx-embed-v1, achieves strong performance across multilingual and code retrieval benchmarks, including MTEB and MIRACL. Its contextual variant, pplx-embed-context-v1, sets a new state-of-the-art on ConTEB and demonstrates both efficiency and practicality in large-scale production environments with tens of millions of documents.
0 citationsRead paper
RDMA Point-to-Point Communication for LLM Systems
Existing LLM systems—including decoupled inference, MoE routing, and asynchronous reinforcement learning fine-tuning—rely on flexible point-to-point communication, yet mainstream RDMA implementations are tightly coupled to vendor-specific NICs, hindering portability and integration into inference engines. This work proposes TransferEngine, a universal RDMA communication framework that introduces a unified abstraction layer and novel primitives—WriteImm and ImmCounter—to enable precise, out-of-order transmission completion notification. TransferEngine achieves transparent, hardware-agnostic management across heterogeneous NICs (e.g., NVIDIA ConnectX-7, AWS EFA). Evaluation demonstrates: (1) efficient and reliable dynamic KvCache migration; (2) trillion-parameter RL weight updates completed in just 1.3 seconds; and (3) MoE inference latency lower than DeepEP with peak throughput reaching 400 Gbps.
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