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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
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https://arxiv.org/html/2608.02613v1
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2608.03048v1
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https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction
2.1 Episodes
2.2 Semantic Entities and Facts
2.2.1 Entities
2.2.2 Facts
2.2.3 Temporal Extraction and Edge Invalidation
2.3 Communities
3 Memory Retrieval
3.1 Search
3.2 Reranker
4 Experiments
4.1 Choice
https://arxiv.org/html/2609.03027v1
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https://arxiv.org/html/2608.03086v1
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https://arxiv.org/html/2608.02918v1
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https://arxiv.org/html/2608.03480v1
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https://arxiv.org/html/2608.00458v1
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https://arxiv.org/html/2608.03529v1
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https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2608.03545v1
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https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2608.01085v1
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https://arxiv.org/pdf/2608.02122
) /Title (DEFT: Joint Task Placement and DVFS for Energy-Efficient Multi-GPU Runtimes) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/src/2506.01844
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https://arxiv.org/list/cs.PL/current
Andrew Uderian, Sridhar Gopinath, Jan Szczepaniec, Daniel Neilson, Blaine Burton Rister, Jordan Fix, Jazlyn Li, Zejun Huang, Lite Ye, Nan Zhang, Xinchen Guo, Andiry Xu, Michael Roberts, Kunming Ho, Site Cao, Suryadev Sahadevan Rajesh, Tristan Trouwen, Mike Tsai, Jake Lee, Wayne Su, Yuhan Chen, Xiaolong Xie
https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2608.02414
unit (GPU) plays a crucial role in boosting application performance and enhancing computational tasks
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/abs/2608.13759
Drehwald, Marcelo Domínguez, Kevin Sala, Alán Aspuru-Guzik, Johannes Doerfert
View a PDF of the paper titled GPU Offload in Rust: Portable, Safe, and Fast, by Manuel S
https://arxiv.org/abs/2006.02602
Roy
View a PDF of the paper titled Multi-GPU Performance Optimization of a CFD Code using OpenACC on Different Platforms, by Weicheng Xue and Christopher J
https://arxiv.org/abs/2608.02122
Although Dynamic Voltage and Frequency Scaling (DVFS) provides an effective mechanism for reducing GPU energy consumption, existing runtime systems decouple DVFS from task placement and inter-GPU communication, focus on single-GPU execution, or cannot adapt frequency to task granularity and runtime contention in multi
https://arxiv.org/abs/2608.13122
of a 250,000+ Line Legacy Weather Simulation Code, by Tetsuya Hoshino and 5 other authors
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Abstract:Recent advances in large language models have made CLI-based AI agents a practical tool for accelerating GPU porting of large legacy scientific applications
https://arxiv.org/abs/2608.13122?context=cs
of a 250,000+ Line Legacy Weather Simulation Code, by Tetsuya Hoshino and 5 other authors
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Abstract:Recent advances in large language models have made CLI-based AI agents a practical tool for accelerating GPU porting of large legacy scientific applications
https://arxiv.org/abs/2608.01785
Requests with different output modalities may share an initial multimodal backbone and then diverge into downstream generation stages, creating heterogeneous first-response metrics and service-level objective (SLO) targets on the same GPU
https://arxiv.org/abs/2608.12700
for the Gated-Linear-Recurrence Family, by Rishi Shah and 1 other authors
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Abstract:Systems that generate GPU kernels with language models report high correctness rates