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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/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/2608.01024v1
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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/list/cs.SY/new
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[1] arXiv:2608
https://arxiv.org/html/1802.00048v2
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https://arxiv.org/list/cs.SE/new
96 points behind on SpreadsheetBench; Sequential Refinement recovers neither gain
https://arxiv.org/html/2608.02942v1
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arXiv:2608
https://arxiv.org/html/1802.00050v1
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https://arxiv.org/html/2608.03577v1
org/standards/popular/iso-9000-family specifically ISO 9001:2015 Applying the same logic to translation production workflows means that MT and AI must be incorporated sensibly to achieve manufacturing-grade reliability, risk guarantees, and robustness
https://arxiv.org/html/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2608.03077v1
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arXiv:2608
https://arxiv.org/html/2607.26327v3
and Why Liability Sustains Markets for Expert Services
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https://arxiv.org/html/2608.03573v1
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arXiv:2608
https://arxiv.org/html/2407.04889v1
behavior of other learners
1 Introduction
Setting.
Zero-sum games.
General-sum games.
1.1 Preliminaries
1.2 Algorithms for the learner
1.3 Our results
1.3.1 Zero sum games
Continuous-time games.
Discrete-time games.
1.3.2 A lower bound