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Abstract
Full-duplex spoken dialogue systems significantly surpass traditional turn-based dialogue systems, as they allow simultaneous bidirectional communication, closely mirroring human-human interactions
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https://arxiv.org/html/2410.17799v2
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Abstract
Full-duplex spoken dialogue systems significantly surpass traditional turn-based dialogue systems, as they allow simultaneous bidirectional communication, closely mirroring human-human interactions
https://arxiv.org/html/2608.02609v1
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https://arxiv.org/html/2608.03494v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2510.03215v2
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https://arxiv.org/html/2508.02739v1
2 Preliminary
3 Methodology
K-line Tokenization
Hierarchical Autoregressive Modeling
Model Pre-training
Dataset
Model Training
Inference
4 Experiments
Experimental Setup
Main Results
Prediction Tasks
Generative Tasks
Investment
https://arxiv.org/html/2608.03599v1
We argue that the community should move toward a byte-level interface as a shared standard: if the hypothesis holds, then once byte-level models are the norm, transferring capabilities and reshaping boundaries between them become cheap and routine, free of the per-model tokenizer that blocks them today
https://arxiv.org/html/2608.02613v1
Then it must make an even harder judgment: even if the answer is known, is it actually appropriate to tell Bob
https://arxiv.org/list/cs.AR/new
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[1] arXiv:2608
https://arxiv.org/abs/2608.02244
Abstract:This paper studies a resource-allocation inefficiency in batched large language model (LLM) serving: heterogeneous requests that share a decode batch impose max-driven computational costs on one another
https://arxiv.org/abs/2609.16338
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Computer Science > Artificial Intelligence
arXiv:2609
https://arxiv.org/list/cs.MM/current
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Multimedia
Authors and titles for August 2026
Total of 90 entries : 1-50 51-90
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https://arxiv.org/list/cs.GT/new
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https://arxiv.org/list/cs.OS/new
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[1] arXiv:2608
https://arxiv.org/list/cs.PF/new
CoRun employs isolated prefill and fixed-shape batched decode to handle the two stages of LLM inference, respectively, leveraging CUDA graphs for efficient execution and simplified implementation
https://arxiv.org/list/cs.PF/current
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Performance
Authors and titles for August 2026
Total of 47 entries
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[1] arXiv:
https://arxiv.org/abs/2608.01651
72\times$ the offline decode throughput of autoregressive decoding and up to $2
https://arxiv.org/list/cs.DC/recent
Simulating Prefill--Decode--Attention--FFN Specialization for Agentic LLM Inference
Przemyslaw Forys, Haoran Wu, Can Xiao, Jiayi Nie, Tony Liu, Rika Antonova, Timothy Jones, Robert Mullins, Wayne Luk, Aaron Zhao, George A
https://arxiv.org/list/cs.IR/new
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Information Retrieval
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https://arxiv.org/html/2608.02942v1
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[1] arXiv:2608
https://arxiv.org/html/2608.03452v1
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Several components are the subject of pending United States patent applications by the first author and are presented here as candidate open architectural principles
[2] arXiv:2608
https://arxiv.org/html/2608.00648v1
Distributed Multi-Agent Systems
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https://arxiv.org/html/2407.13076v1
Transmission Parameter Allocation in Multi-Gateway LoRa Networks
I INTRODUCTION
II RELATED WORK
III SYSTEM MODEL
III-A The Traffic Pattern of LoRa Networks
III-B Capture Effect and Quasi-Orthogonality between SFs
III-C Packet Delivery
https://arxiv.org/list/eess/new
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Electrical Engineering and Systems Science
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https://arxiv.org/pdf/2210.00045
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Computation and Language
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Project Page: this https URL
[2] arXiv:2608
https://arxiv.org/pdf/2006.02318
3/ pdfx PDF/X Schema external URL to an online version or preprint AuthoritativeDomain Text http://www
https://arxiv.org/pdf/2508.11152
com three Proceedings of ACM Conference (Conference'17) book 1 1 9 9 Tianjiao ZhaoJingrao LyuStokes JonesHarrison GarberStefano PasqualiDhagash Mehtahttp://arxiv