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https://arxiv.org/html/2608.02942v1
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arXiv:2608
https://arxiv.org/html/2608.03573v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02625v1
Causal ablations indicate that completed drafts are useful initializations: refinement from a fully masked span performs poorly, full global refinement provides a clear additional gain on GSM8K, and local refinement captures much of the gain on MBPP and MATH
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/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/html/1802.00050v1
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arXiv is now an independent nonprofit
https://arxiv.org/abs/2608.04003
Agents with the same headline gain can differ markedly in whether that gain is supported by evidence of the intended pathway
https://arxiv.org/html/2407.04889v1
We split our following discussion according to the study of zero-sum and general-sum games, as defined below
https://arxiv.org/list/cs.IR/recent
IR)
Tue, 4 Aug 2026 (showing first 34 of 45 entries )
[17] arXiv:2608
https://arxiv.org/html/2410.15608v2
2 Issues with fixed sequence length encoder
3 Approach
3.1 Model architecture
3.2 Training data collection & preprocessing
3.3 Training
4 Evaluations
4.1 Accuracy vs. Input Length
4.2 Robustness to Input Speech Signal Level
4.3 Robustness
https://arxiv.org/html/2509.02523v1
1 Introduction
2 Approach
2.1 Architecture
2.2 Data collection, preprocessing, & synthesis
2.3 Training
3 Evaluations
3.1 Results
4 Discussion & Conclusion
A Public Datasets
B Normalization Steps
C Complete Results
Flavors of Moonshine:
https://arxiv.org/list/cs.SY/new
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New submissions (showing 8 of 8 entries)
[1] arXiv:2608
https://arxiv.org/html/1802.00048v2
Description:
arXiv is now an independent nonprofit
https://arxiv.org/list/cs.DC/new
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/list/eess/new
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Electrical Engineering and Systems Science
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https://arxiv.org/html/2407.01757v1
Opportunities and Technology Feasibility
1 Introduction
1.1 What is a distributed instrument?
1.1.1 Distributed Instruments and Sensor Networks
1.1.2 Distributed Instruments and Swarms
1.2 Contribution and Organization
2 State of the Art